standardization and optimization of index for 28 day...
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Standardization and optimization of index for 28 day strength for cement made from standard clinker By Anton Hermansson
Supervisor: Tina Hjellström, Cementa
Proposer: Patrick Thålin, Cementa
Examiner: Associate Professor Matthäus Bäbler, KTH
Summary This project regards the prediction of 28 day compressive strengths of cement. Using traditional
multivariate analysis in combination with Artificial Neural Networks indexes have been developed
which makes these predictions possible. Compressive strength is highly dependent on the cement
hydration and clinker reactivity and literature on these topics have been studied followed by statistical
analyses in Unscrambler X by Camo AS and the Neural Networks model created by Dr. Jan Skocek at
Heidelberg Technology Center (HTC).
The report and this project is initiated by a description of the cement production process as it is at
Cementas Slite plant. Following this, the theory behind cement hydration and current state of research
into this area is described along with description of the various tools that is used.
After studying the theory behind compressive strength, some key parameters are identified including
the Alite content, particle sizes as well as a variety of other parameters. Following this, a data set has
been collected and formatted for the use in the project. Data on cement properties including
compressive strength has been compiled by the quality engineer in Slite making the data collection
simple. Having the data, the procedure includes a start with traditional multivariate analysis in
Unscrambler to identify significant parameters in an effort of reducing the number of variables in the
final model. In Unscrambler, Partial Least Squares regression has been used with uncertainty analysis
as a selected option for parameter selection.
Following the analysis in Unscrambler, the data set for each cement type is inserted into the neural
networks models and the significant parameters are selected to act as input data, predicting either 1d or
28d strength. Before insertion into the Neural Networks model, the parameters are manually vetted
with support of the literature and the accepted theories on cement hydration as correlations not
necessarily mean that there is a causal link.
Results are presented using the verification set of these indexes, indicating the prediction capacity of
the indexes. Scenarios have also been used to study the underlying correlations between various
properties and the compressive strengths.
The results have shown good performance of the indexes created, and the procedure has proven to be
fast and effective in creating these indexes. This opens up possibilities of using similar approaches to
other areas of the plant in the future efforts to improve environmental and financial sustainability.
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Included in the final section of the report are also a few recommendations that would simplify the
future work on this topic.
Acknowledgements The author would like to thank the project group for invaluable support during this project and the
willingness at which experiences and knowledge has been shared. The project group includes Senior
Project leaders at Cementa Slite, Tina Hjellström (main supervisor) and Johan Larsson whose
experience and initiatives made the project work possible. A big thanks also to Quality manager
Patrick Thålin who is proposer of this project.
Large thanks are also directed at Investigation Engineer Alexander Åkerlund and Process Engineer
José Aguirre Castillo who have been invaluable resources during this work, sharing the knowledge on
the areas this project involves. Dr. Jan Skocek has also provided valuable input on the subject of the
Neural Networks model that he created.
Outside the project group the author also wishes to thank Quality Engineer Tore Jönsson whose
compilation of quality data over the year formed the basis for the data set used in this project.
Laboratory Group leader Karin Larsson’s work on compressive strength prediction as well as support
with the neural networks model has also been a valuable resource in this project.
A large thanks to Associate Professor Matthäus Bäbler who has been examiner for this project and
been the contact point between the project group and KTH.
Finally, a large thanks to Cementa who provided the opportunity and the resources for this project.
Nomenclature HTC – Heidelberg Technology Center
LSF – Lime Saturation Factor
SR – Silica Ratio
AR – Alumina Ratio
DoS – Degree of Sulfatization
IP21 – Process overview tool used at Cementa
LOI – Loss on ignition
TGA – Thermogravimetric Analysis
LIMS – Laboratory Information Management System
XRF – X-ray fluorescence spectroscopy
XRD – X-ray diffraction (crystallography)
CR – Cementa Research (main laboratory)
ANN – Artificial Neural Network
Cement chemistry notations
The cement industry uses shorthand for some of the most common compounds in cement chemistry.
Chemical
formula
Cement industry
denomination
CaO C
Al2O3 A
SiO2 S
Fe2O3 F
SO3 s̅
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Na2O N
K2O K
Ca(OH)2 CH
H2O H Table N1 - Cement chemistry notation
Mineral Cement industry
denomination
General chemical
formula
Alite C3S Ca3SiO5 3CaO∙SiO2
Belite C2S Ca2SiO4 2CaO∙SiO2
Aluminate C3A Ca3Al2O6 3CaO∙Al2O3
Ferrite C4AF Ca4Al2Fe2O10 4CaO∙Al2O3∙Fe2O3 Table N2 – Most common clinker minerals
Gypsum CaSO4·2H2O
Hemi-hydrate CaSO4·½H2O
Anhydrite CaSO4 Table N3 – Calcium sulfates
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Contents Summary ................................................................................................................................................. 1
Acknowledgements ................................................................................................................................. 2
Nomenclature .......................................................................................................................................... 2
Cement chemistry notations ..................................................................................................................... 2
Figures ...................................................................................................................................................... 8
1. Purpose ............................................................................................................................................. 10
1.1 Compressive strength index ........................................................................................................ 10
1.1.1 HTC Neural Networks model ................................................................................................ 10
1.1.2 Optimization of the model ..................................................................................................... 10
1.2 Sustainability ................................................................................................................................ 10
1.2.1 Ecological sustainability ........................................................................................................ 10
1.2.2 Financial sustainability .......................................................................................................... 11
2. Background........................................................................................................................................ 11
2.1 Cement production ....................................................................................................................... 12
2.1.1 Raw material ......................................................................................................................... 12
2.1.2 Preheating and Precalcining ................................................................................................. 13
2.1.3 Kiln stage ............................................................................................................................... 13
2.1.4 Clinker components............................................................................................................... 15
2.1.5 Finish grinding/Cement mills ................................................................................................. 15
2.1.6 Fly-ash addition ..................................................................................................................... 15
2.1.7 Shipping ................................................................................................................................ 16
2.2 Cement types ............................................................................................................................... 16
2.3 Concrete....................................................................................................................................... 16
2.4 Setting properties ......................................................................................................................... 16
2.5 Laboratories at Cementa in Slite ................................................................................................. 17
2.5.1 Samples tested ...................................................................................................................... 17
2.6 Analyzed parameters ................................................................................................................... 17
2.6.1 Particle size distribution (PSD) .............................................................................................. 18
2.6.2 X-ray fluorescence (XRF) ...................................................................................................... 18
2.6.3 X-ray diffraction (XRD) .......................................................................................................... 19
2.6.4 Compressive strengths & Setting time .................................................................................. 20
2.6.5 Other ..................................................................................................................................... 21
2.7 Sustainability ................................................................................................................................ 22
2.7.1 Life cycle view ....................................................................................................................... 22
2.7.2 Alternative raw materials ....................................................................................................... 22
2.7.3 Additives ................................................................................................................................ 23
2.7.4 Fly-ash ................................................................................................................................... 23
2.7.5 Alternative fuels ..................................................................................................................... 23
2.8 Previous work .............................................................................................................................. 23
3. Theory ................................................................................................................................................ 23
3.1 Alite, Belite & Aluminates ............................................................................................................. 24
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3.1.1 Alite (C3S) .............................................................................................................................. 25
3.1.2 Belite (C2S) ............................................................................................................................ 26
3.1.3 Aluminates (C3A & C4AF) ...................................................................................................... 26
3.2 Minor constituents ........................................................................................................................ 28
3.2.1 Set accelerators .................................................................................................................... 28
3.2.2 Sulfur ..................................................................................................................................... 29
3.2.3 Portlandite ............................................................................................................................. 29
3.2.4 Zinc ........................................................................................................................................ 29
3.3 Gypsum, Limestone & Fly-ash ..................................................................................................... 29
3.3.1 Gypsum ................................................................................................................................. 29
3.3.2 Limestone .............................................................................................................................. 30
3.3.3 Fly-ash ................................................................................................................................... 30
3.4 Hydration ...................................................................................................................................... 30
3.4.1 Calcium-Silicates reactions ................................................................................................... 30
3.4.2 Aluminate reactions ............................................................................................................... 31
3.4.2 Water/cement ratio ................................................................................................................ 32
3.4.3 Progression ........................................................................................................................... 33
3.4.4 Pozzolan reaction .................................................................................................................. 33
3.5 Strength development & Setting time .......................................................................................... 33
3.5.1 Setting time ........................................................................................................................... 34
3.5.2 1d strength ............................................................................................................................ 34
3.5.3 28d strength .......................................................................................................................... 34
3.6 Multivariate analysis ..................................................................................................................... 34
3.6.1 Multivariate analysis & regression ......................................................................................... 34
3.6.2 Excel regression tool ............................................................................................................. 35
3.6.3 Partial Least Squares ............................................................................................................ 35
3.7 Neural network ............................................................................................................................. 36
3.8 Bias-Variance ............................................................................................................................... 36
3.8.1 Sample stratification .............................................................................................................. 37
3.9 Sustainability ................................................................................................................................ 38
3.9.1 Reduced clinker content in the cement ................................................................................. 38
3.9.2 Reduced amount of virgin limestone ..................................................................................... 38
3.9.3 Process optimization ............................................................................................................. 38
3.9.4 Concrete carbonation ............................................................................................................ 39
4. Procedure .......................................................................................................................................... 39
4.1 Literature study ............................................................................................................................ 40
4.2 Data collection and preparation ................................................................................................... 40
4.3 Traditional Multivariate analysis ................................................................................................... 40
4.3.1 Excel ...................................................................................................................................... 41
4.4 Choosing parameters ................................................................................................................... 41
4.4.1 Initial exclusion ...................................................................................................................... 41
4.4.2 Uncertainty test ..................................................................................................................... 41
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4.4.3 Manual selection ................................................................................................................... 42
4.4.4 Unknowns .............................................................................................................................. 43
4.5 Selecting samples ........................................................................................................................ 43
4.5.1 Variations in laboratory equipment ........................................................................................ 43
4.5.1 Bias ....................................................................................................................................... 44
4.6 HTC model ................................................................................................................................... 45
4.6.1 Functions & Procedure .......................................................................................................... 45
4.6.2 Separate indexes for 1d and 28d .......................................................................................... 46
4.6.3 SH-cement ............................................................................................................................ 46
4.6.4 BAS-cement .......................................................................................................................... 46
4.6.5 Verification/validation ............................................................................................................ 46
4.6.6 Bias issue identification ......................................................................................................... 47
4.6.7 Bias-Variance balance .......................................................................................................... 47
4.6.8 Model parameters ................................................................................................................. 47
4.7 Clinker minerals ........................................................................................................................... 48
4.7.1 Analyzing clinker mineral impact ........................................................................................... 48
4.7.2 Procedure .............................................................................................................................. 49
4.8 Additional work ............................................................................................................................. 49
4.8.1 Macros ................................................................................................................................... 49
4.8.2 Anläggningsindex .................................................................................................................. 50
5. Results and Discussion ..................................................................................................................... 50
5.1 Verification ................................................................................................................................... 50
5.2 Procedure .................................................................................................................................... 51
5.2.1 Including 1d strength as parameter for 28d strength prediction ........................................... 51
5.2.2 Target of 2 MPa ..................................................................................................................... 51
5.2.3 Parameter selection .............................................................................................................. 51
5.2.4 Separate indexes .................................................................................................................. 52
5.2.5 Unscrambler models ............................................................................................................. 53
5.2.6 Anläggningsindex .................................................................................................................. 53
5.3 SH-cement ................................................................................................................................... 53
5.3.1 1 day index ............................................................................................................................ 54
5.3.2 28 d index .............................................................................................................................. 55
5.3.3 SH-Cement indexes discussion ............................................................................................ 57
5.4 BAS-Cement ................................................................................................................................ 57
5.4.1 1d........................................................................................................................................... 58
5.4.2 28 d ....................................................................................................................................... 59
5.4.3 BAS-Cement index discussion .............................................................................................. 61
5.5 Bias .............................................................................................................................................. 61
5.5.1 Model parameter variations ................................................................................................... 61
5.5.2 Sample selection ................................................................................................................... 63
5.6 Clinker minerals ........................................................................................................................... 64
5.6.1 Inconsistency ......................................................................................................................... 64
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5.6.2 Results .................................................................................................................................. 65
5.6.3 Combinations ........................................................................................................................ 65
5.6.4 Fly-ash ................................................................................................................................... 66
5.7 Difficulties ..................................................................................................................................... 67
5.7.1 Available data ........................................................................................................................ 67
5.7.2 Unknowns .............................................................................................................................. 67
5.7.3 Neural Networks model functionality ..................................................................................... 67
5.7.4 Early and late strengths ........................................................................................................ 68
5.8 Excel regression tool .................................................................................................................... 68
5.9 Sustainability ................................................................................................................................ 70
5.9.1 Fly-ash ................................................................................................................................... 70
5.9.2 PSD ....................................................................................................................................... 70
5.10 Tool development ...................................................................................................................... 71
5.10.1 Extraction tool for sample selection .................................................................................... 71
5.10.2 “IP21-fetch” .......................................................................................................................... 71
5.10.3 Tool development discussion .............................................................................................. 72
6. Conclusions/Recommendations ........................................................................................................ 72
6.1 Indexes......................................................................................................................................... 72
6.1.1 Sustainability ......................................................................................................................... 73
6.1.2 Setting time ........................................................................................................................... 73
6.1.3 Different strengths ................................................................................................................. 73
6.1.4 Non-linearity of index............................................................................................................. 73
6.2 Bias .............................................................................................................................................. 74
6.3 Recommendations ....................................................................................................................... 74
6.3.1 Additional particle sizes reported .......................................................................................... 74
6.3.2 Analysis result compiling ....................................................................................................... 74
6.3.3 Increased control of fly-ash ................................................................................................... 75
6.3.4 XRD, Clinker reactivity and Indexes ...................................................................................... 75
7. Further work....................................................................................................................................... 75
7.1 Indexes......................................................................................................................................... 75
7.1.1 Index maintenance ................................................................................................................ 75
7.1.2 Other uses of the model ........................................................................................................ 76
7.2 Sustainability ................................................................................................................................ 76
7.3 Process optimization .................................................................................................................... 76
References ............................................................................................................................................ 77
Appendices ............................................................................................................................................ 78
A1 - Partial Least Square Regression ............................................................................................... 78
A1.1 NIPALS algorithm .................................................................................................................. 78
A2 – Neural Networks ........................................................................................................................ 80
A2.1 Radial Basis Function Network .............................................................................................. 80
A3 – Unscrambler results for Bas and SH ......................................................................................... 81
A3.1 SH-cement ............................................................................................................................. 81
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A3.2 BAS-cement ........................................................................................................................... 84
A3.3 Anläggningscement ............................................................................................................... 87
A4 – Clinker mineral correlations ....................................................................................................... 88
A4.1 Scenarios 28d SH .................................................................................................................. 88
A4.2 Scenarios 1d .......................................................................................................................... 93
A4.3 Flyash ........................................................................................................................................ 97
A4.3.1 Fly-ash content on 28d ....................................................................................................... 98
A4.3.2 Fly-ash on 1d strength ...................................................................................................... 100
A5 – Sample selection VBA ............................................................................................................. 101
A6 – IP21 fetch ................................................................................................................................ 106
Figures Figure 1 - Cement production process overview ................................................................................... 12 Figure 2 - Typical reaction process in a cement kiln from Heidelberg Cement World-of-Knowledge ... 14 Figure 3 - Factors contributing to clinker reactivity [12] ......................................................................... 24 Figure 4 - Phase diagram of CaO-SiO2 system during clinker burning [13] .......................................... 25 Figure 5 - Ettringite and calcium hydroxide [4] ...................................................................................... 27 Figure 6 - Impact and limits of Na2O and Fe2O3 on polymorphism [13] ................................................ 27 Figure 7 - 1: Hydration of C3S after 5 min. 2: Formation of needle shapes of CSH after 16 h. 3: Needles up to 900 nm in size 21 days [4] ............................................................................................. 31 Figure 8 - Water/Cement ratio [4] .......................................................................................................... 32 Figure 9 - Cement hydration progression [14] ....................................................................................... 33 Figure 10 - RBFN Neural Network explanation [18] .............................................................................. 36 Figure 11 - Bias vs. Variance [19] ......................................................................................................... 37 Figure 12 - Distribution of samples 28 day strength BAS-cement from Unscrambler ........................... 37 Figure 13 - Uncertainty analysis results from Unscrambler .................................................................. 41 Figure 14 - Na2O measured on the same samples (only SH-cement), sorted chronologically ............. 43 Figure 15 - Histogram for 1d strengths BAS-cement ............................................................................ 44 Figure 16 - Histogram for selected 1d samples for BAS-cement .......................................................... 45 Figure 17 - GUI for model parameter adjustment in the ANN model .................................................... 48 Figure 18 - The GUI for creating scenarios in the ANN model .............................................................. 49 Figure 19 - Verification sets for 2 model runs from ANN model ............................................................ 50 Figure 20 - Difference between Carbon-Sulfur determinator and XRF for SO3 .................................... 52 Figure 21 - Results of analysis on SO3 on XRF and Carbon-Sulfur determinator ................................ 52 Figure 22 - The difference between measured and predicted for 1d strength SH-cement from HTCs model ..................................................................................................................................................... 55 Figure 23 - The difference between measured and predicted for 28d strength (without 1d as input) SH-cement from HTCs model ...................................................................................................................... 56 Figure 24 - The difference between measured and predicted for 1d strength BAS-cement from HTCs model ..................................................................................................................................................... 59 Figure 25 - The difference between measured and predicted for 28d strength BAS-cement from HTCs model ..................................................................................................................................................... 60 Figure 26 - Histogram for 28d strength for SH-cement from Unscrambler ........................................... 63 Figure 27 - Error plotted against measured strength for selected samples 28d BAS ........................... 64 Figure 28 - Alite_sum plotted against Belite_beta in Unscrambler ....................................................... 66 Figure 29 - Error vs measured strength from Excel .............................................................................. 69 Figure 30 - X- and Y-loadings for 1d model for SH-cement .................................................................. 79 Figure 31 – The number of components impact on the degree of explained Y-variance ..................... 79 Figure 32 - Weighted coefficients for significant variables from Unscrambler for 1d SH ...................... 82 Figure 33 - PLS model performance for 28d for SH-cement from Unscrambler ................................... 83 Figure 34 - Weighted coefficients for SH 28d strength from Unscrambler (blue marked as insignificant) ............................................................................................................................................................... 83 Figure 35 - PLS model performance for 28d for SH-cement from Unscrambler ................................... 84 Figure 36 - Weighted coefficients for significant variables from Unscrambler for 1d BAS .................... 85
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Figure 37 - PLS model performance for 1d strength BAS from Unscrambler ....................................... 86 Figure 38 - Weighted coefficients for significant variables from Unscrambler for 28d BAS .................. 86 Figure 39 - PLS model performance for 1d strength BAS from Unscrambler ....................................... 87 Figure 40 - PLS model performance from Unscrambler for Anläggningscement ................................. 87 Figure 41 - Weighted coefficient (only significant) from Unscrambler ................................................... 88 Figure 42 - 28d strength as variation in Alite content ............................................................................ 89 Figure 43 - 28d strength as variation in fraction of M1 Alite to Alite sum .............................................. 89 Figure 44 - 28d strength depending on Belite content .......................................................................... 90 Figure 45 - 28d strength depending on variation in Aluminate content ................................................. 90 Figure 46 - 28d strength depending on cubic C3A ................................................................................ 91 Figure 47 - 28d strength depending on variations in orthorhombic C3A content .................................. 91 Figure 46 - 28d strength depending on variations in Aphthitalite content ............................................. 92 Figure 47 - 28d strength depending on variations in Arcanite content .................................................. 92 Figure 50 - 28d strength depending on variations in Langbeinite content ............................................ 93 Figure 51 - 1d strength depending on Alite content .............................................................................. 93 Figure 52 - 1d strength depending on fraction of M1 to total Alite content ........................................... 94 Figure 53 - 1d strength depending on Belite content ............................................................................ 94 Figure 54 - 1d strength depending on total Aluminate content ............................................................. 95 Figure 55 - 1d strength depending on cubic C3A content ..................................................................... 95 Figure 56 - 1d strength depending on orthorhombic C3A content ........................................................ 96 Figure 57 - 1d strength depending on the Aphthitalite content ............................................................. 96 Figure 58 - 1d strength depending on the Arcanite content .................................................................. 97 Figure 59 - 1d strength depending on the Langbeinite content ............................................................. 97 Figure 60 - 28d strength variations depending on fly-ash content ........................................................ 98 Figure 61 - 28d strength variations depending on fly-ash content ........................................................ 99 Figure 62 - 28d strength variations depending on fly-ash content ...................................................... 100 Figure 63 - Fly-ash impact on 1d strength from HTC model ............................................................... 100
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1. Purpose The objectives in this project is both optimizing and standardizing a tool which can be used for
prediction of compressive strengths in cement and also increased understanding of parameter
relationships in the cement. The main focus on understanding the parameter relationships is the
increased knowledge on the impacts of clinker minerals on the compressive strengths of cement.
1.1 Compressive strength index Having an index for prediction of compressive strengths of cement produced will benefit both
Cementa and the customer. Identifying issues with compressive strengths at an early stage will allow
for customers to adapt recipes to avoid failures. It can also possibly help Cementa identify issues
within the production process. This will strengthen the customer confidence in the product as well as
reducing the risks for impacts on Cementa.
1.1.1 HTC Neural Networks model HTCs neural networks model is already used to predict the 28-day compressive strength of one cement
type: Anläggningscement. However, indexes are specific for each cement type as the chemical
composition and physical properties varies between the different types.
A good target for the model is that it should predict the strength within ±2 MPa of the measured value.
However, with this target in mind it should be noted that the 95% confidence level (2 x total
measurement uncertainty) is 9% for 1d strength and 5% for 28d strength. For the cement types studied
in this project, this represents roughly 2 MPa for 1d strength and 3 MPa for 28d strength.
1.1.2 Optimization of the model The optimization in this project is finding a good balance between usability and precision. A model
using only the results from the process laboratory will have high usability as the results are quickly
gained, however the precision will be lower as the analyses there have lower accuracy.
A good model have to be precise and be based on independent and a limited number of variables. At
the process laboratory, samples are run quickly and both XRF, XRD and particle size distribution are
analyzed at that laboratory. To be able to quickly calculate and estimate the strength of the cement,
using only these results would be optimal.
1.2 Sustainability In the development of products as well as optimization of the process, knowledge on the clinker
impact is vital. Using an increased understanding of the clinker minerals impact on the compressive
strength of the cement can allow for a more sustainable production.
1.2.1 Ecological sustainability Clinker burning requires high temperature and large amounts of CO2 is emitted from this step in the
production process, not only from the fuel, but also from the calcination of the limestone. Several
ways to reduce the impact of this stage on the environmental performance of the finished product is
already being used. This includes optimizing the burning process, reducing the clinker content in the
cement and reducing the amount of limestone burned.
Replacing raw materials or lowering the clinker content of the cement has to be balanced against the
quality of the cement. Understanding of the clinker mineral impact on the compressive strengths can
for that reason benefit the further measures taken in this area.
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1.2.2 Financial sustainability While ecological and financial sustainability is connected to an increasing extent, the increased
understanding of the clinker mineral impacts (in combination with tools similar to the ones used in this
project) can lower production costs for the cement.
The costs and quality of raw materials fluctuate and using knowledge on clinker minerals can help
determine and adapt recipes to account for these fluctuations without risking compressive strength
issues. While the chemical composition as well as content of various alternative raw materials are
regulated by standards as well as agreements with customers, small changes in the amount of, for
example fly-ash, mixed into the cement can be made without violating these. This can therefore be
optimized to some extent to account for fluctuations.
2. Background The quality of the cement is crucial for the consumer of the cement. In case of deviations in quality the
consumers may need to adapt recipes, or they may experience failures which are costly to correct. To
ensure the quality of the cement, Cementa runs multiple analyses on the cement shipments and during
the production process.
Certain analyses takes longer to run than other though, an example of this is the 28 day compressive
strength. This analysis requires 28 days of strength development to take place, meaning that the results
for a specific sample is available no sooner than 28 days from the shipping date. A credible
model/index that can quickly predict the 28 d strength will benefit both Cementa and the consumer;
the consumer will be able to adapt their recipe to account for unsatisfactory quality and Cementa can
find and correct issues with the strength at an early stage.
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2.1 Cement production
Figure 1 - Cement production process overview
As can be seen in Figure 1, the cement production process involves multiple steps and different types
of processes. The production of cement is a highly automated process and is monitored and controlled
from a control room at the plant. The process is monitored through both sensors located at various
points in the process, scales and through analyses in the production laboratory where samples are
analyzed at set times throughout the day.
2.1.1 Raw material The main raw material for cement production is limestone. At Cementa in Slite two qualities of
limestone are used: Limestone and marl. Marl has a higher clay content and a lower content of calcite
compared to pure limestone. The raw material is mined in quarries located close to the plant by
blasting the quarry walls. Quality of the stone varies depending on the location of the blasting and later
mixing of the resulting rocks are planned by geologists to ensure a consistent quality.
The blasted rocks are mixed according to a recipe and crushed down to a diameter below 80 mm and
homogenized during storage in a large storage facility. Additional materials are also added at this point
to further enhance the properties.
From the storage facility, the rocks are transported on conveyors to the raw mills. At Cementa’s Slite
plant, there are 2 raw mills, each serving a separate kiln line. However, the transport from the quarry is
shared between the two kiln lines. At the raw mills, other additives are added mainly sand and iron
oxide. These materials are not mined at the plant but bought/imported from other suppliers.
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Sand is hard and requires a large amount energy to grind, and at Cementa Slite it is pre-ground in
separate mills and mixed with where water is added to reduce dust containing fine quartz-particles
form a slurry.
The raw mills are vertical roller mills and depend on the hot kiln gases to dry and carry the finer
material to a separator located above the mill. For that reason the raw mills cannot run without the
kilns. The resulting product from the raw mills is called raw meal and is stored in silos before moving
to the next stage.
2.1.2 Preheating and Precalcining The formation of clinker, the main constituent of cement, requires heating of the raw meal to high
temperatures. To increase the thermal efficiency of the production process the raw meal is preheated
using the hot gases from the kiln, this is done in large towers with several stages of heat exchangers in
the form of “cyclones”.
As previously mentioned, Cementas Slite plant has two kiln lines, where kiln line 7 is older and
smaller and kiln line 8 is the more modern and larger one. In the more modern kiln line, the material,
in addition to being preheated, is also precalcined. This is done in a calciner close to the kiln inlet.
The calciner further increases the energy efficiency of the process by calcining part of the material
before it enters the kiln. This is aided by the flow of tertiary air from the cooler. The calcination
reaction is the removal of carbon dioxide (CO2) from calcium carbonate (CaCO3) and is vital for the
formation of clinker.
CaCO3 → CaO + CO2
Formula 1 - Calcination reaction
The calcination reaction accounts for a large portion of the carbon dioxide released by the plant.
2.1.3 Kiln stage The kiln stage including the calcining is by far the most energy demanding step in the process as the
calcining step is endothermic and although the formation of clinker mineral is exothermic, it requires
the material to be heated to roughly 1450°C. The fuel used at the Slite plant is a mixture of fossil fuels
and “alternative” fuels. The alternative fuels are a mixture of waste plastics, biofuels and waste oils.
The share of the alternative fuels varies, but at the Slite plant the majority of the fuels used are
alternative fuels.
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Figure 2 - Typical reaction process in a cement kiln from Heidelberg Cement World-of-Knowledge
The temperature in kiln varies along the length of the kiln. Figure 2 shows the areas where formation
of the various minerals typically takes place. However, as previously mentioned, kiln 8 have a calciner
before the kiln meaning that the picture best resembles and represents kiln 7.
From the preheating to the cooler, the air streams are specified by the location of introduction into the
system. The primary and secondary air is introduced in the burner and the kiln stage. The tertiary air is
air originating at the cooler, and the heat energy of this air flow contributes to the calcining.
The burner is located at the end of the kiln, giving the highest temperature area (sinter zone) close to
the outlet of the kiln. The short section after the burner is the precooling zone which starts the cooling
process.
After the kiln, the clinker falls into a cooler to rapidly cool the material. This is necessary as the
clinker mineral Alite, the main constituent providing strength during the first 28d, is
thermodynamically unstable. Rapid cooling “freezes” these minerals in their configuration.
The reactions and formation temperatures of the main contributors to the strength of the cement are
[1]:
Belite: 2 CaO + SiO2 → Ca2SiO4 at 800 − 1,250℃
Alite: CaO + Ca2SiO4 → Ca3SiO5 at 1,350 − 1,450℃
Formula 2 - Formation of Alite and Belite in the kiln [1]
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After the cooler, the clinker is transported to silos for storage.
2.1.4 Clinker components Cement consists mainly of clinker which in turn consists mainly of different minerals [1]. The
minerals contribute to the properties of the cement in different ways. The main minerals that make up
clinker along with typical concentrations are shown in Table 1.
Name Formula Concentration (%)
Alite C3S 50-70
Belite C2S 5-25
Aluminate C3A 2-10
Ferrite C4AF 5-12
Free lime CaO 0.5-2
Periclase MgO 0-3
Alkali sulfates 0.8-2.5 Table 1 - Typical mineral content of clinker [1]
The different components provide the clinker with different properties. C3S and C2S are responsible
for the majority of the strength development of the cement, C3S reacts quickly with water and is
responsible for the majority of the strength development for the first 28d. C2S reacts slower and
contributes to the strength development mainly from 28d to up to 5 years. [1]
In industrially produced clinker, the phases also contain other components embedded in the lattice,
these stabilize different crystal configurations.
2.1.5 Finish grinding/Cement mills The last stage is the grinding of the clinker along with additives to form the cement. The amount of
clinker to other materials varies depending on the type of cement being produced and the expected
properties of the product. At this stage, gypsum is added along with limestone. The mills at Cementa
are ball mills, meaning that they use steel balls which crushes and grinds the material.
The general chemical composition of gypsum is CaSO4 in complex with H2O, and the sulfates are
important to regulate not only the setting time of the cement but it also contributes to the strength. In
the case of BAS-cement, gypsum slurry from the desulfurization scrubber in Slite is also added during
grinding. As this slurry contains both gypsum and water this functions both as a gypsum source as
well as cooling. [1]
The finished particle size of the cement after the mills depends on the type of cement being produced.
The particle size is controlled by a separator, where the coarser material is returned to the mill. For a
cement with smaller particle sizes the particles are recirculated more times, giving the particle longer
total time in the mills.
Other additives at this point are a ferrous sulfate (FeSO4) which acts as a reduction agent for
chromium. Chromium in the raw material is oxidized at the high temperatures in the kiln to form Cr6+
which is hazardous to human health. To reduce this back to an oxidation level where Chromium is less
dangerous at Cementa FeSO4·7H2O is added to the cement.
2.1.6 Fly-ash addition After the cement mills Cementa adds fly-ash to their main products. The fly-ash cements accounts for
roughly 45% of the sales of cement from Slite. Fly-ash is a waste material from coal combustion, it is
a pozzolan material and contains mainly silicon (Si) and alumina (Al). A puzzolan material like fly-
16
ash has the capability of reacting with calcium hydroxide Ca(OH)2 in the presence of water to behave
similarly to cement. [2]
Adding fly-ash reduces the clinker fraction of the cement improving the environmental performance of
the product.
2.1.7 Shipping All cement produced in Slite is shipped in bulk transport, most of it by ship but also a small portion by
truck. When a ship is loaded, a sample, in compliance with the demands of the standard, is taken from
the transport conveyor to the ship. The majority of the cement shipped from Slite is destined for
terminals around Sweden, however cement is also exported to various some countries.
2.2 Cement types In Europe, the standard EN197-1 is used to classify cement. The classification is done based on the
contents, the strength and the strength development. [3]
SH-cement is classified as CEM I 52.5 R. CEM I means that it contains 95-100% clinker, 52.5 is the
minimum strength after 28-days and the R indicates that it has a rapid strength development and that
the strength after 2 days should be above 30 MPa for this case.
BAS-cement is classified as CEM II/A-V 52.5 N. CEM II/A-V means that it can contain 6-20% silica-
aluminum containing fly-ash, N means that it has a normal strength development speed and in the
standard this is defined by a 2-day strength above 20 MPa. [3]
2.3 Concrete Concrete is the mixture of cement, sand, aggregates and water. As the most important component,
cement is the “glue” binding the mixture together. It is one of the most widely used construction
materials and has a wide variety of applications and benefits.
The hardening or setting of the concrete is determined by the properties of the cement which impact
the “hydration”. The hydration is a term used to describe the reactions that take place once the cement
is mixed with water. Cement forms C-S-H which binds together the aggregates by formation of
needle-like structures growing into each other.
Concrete structures often contain reinforcing steel bars as the concrete, while having good
compression strength, lacks tensile strength. These steel bars also limits the chemical composition of
the cement to minimize corrosion.
2.4 Setting properties Certain properties are expected from the customer on the cement as it sets. Firstly, the cement should
set within a certain timeframe. Too fast setting and hardening means that the cement is difficult to
work with for the customer, it cannot be below 60 min. A slow setting time (above 200 min) is
typically also viewed as negative by the customer as this leads to longer production times for the
customer, it also increases the risk of separation. [1]
As mentioned above, the strength development is also regulated depending on the cement type.
Other properties of the setting which need to be controlled is the heat development as the cement sets,
which is limited as heat development during the first 7 days of setting. This depends on the clinker
mineral content. [1] [4]
17
2.5 Laboratories at Cementa in Slite There are two laboratories at Cementa’s Slite plant, one located by the control room (process
laboratory) and a larger laboratory (Cementa Research) in a separate building. These have different
purposes and are for that reason set up differently to fit these.
The process laboratory is highly automated and typically analyzes samples from the process. Several
samples from the process are conducted every day at set times, and as the results aims at providing
guidance with regards to process control, quick results are necessary. Cement leaving the plant by ship
is also analyzed at the process lab on XRF, XRD and PSD as described below. These results form a
major part of the data set in this project.
Cementa Research (CR) is an accredited laboratory, meaning that the laboratory is approved by a
government agency and follows the regulations for quality control. The analyses at CR are typically
more complex or time consuming but has a higher accuracy than the process laboratory analyses.
2.5.1 Samples tested There are several types of samples tested on several different materials at the different laboratories.
2.5.1.1 Process samples
The bulk of the samples analyzed at the process laboratory are process samples. These are scheduled at
intervals and include various intermediate products from the different kiln lines as well as the finished
product from the mills. Process samples reflect the state of the process and aids the operators as well
as production engineers in optimizing the process as well as identifying issues. For cement, the
analyses run at the process laboratory is XRD, XRF and PSD.
Process samples are to a lesser extent tested at CR as the analyses takes longer meaning that the
process condition may have changed before a result is available.
2.5.1.2 Dispatch samples
Samples of the shipments of cement and in certain cases intermediary products are conducted at both
the process laboratory and CR. At the process laboratory the same analyses are conducted on the
dispatch samples as on the process samples.
At CR additional analyses are conducted on the products, including compressive strength, setting time
as well as various chemical and other physical testings. The analyses conducted as well as the limits
are regulated according to the standard EN197-1.
2.5.1.3 Additional samples
To identify issues, optimize the process and as parts pf projects, samples are collected from various
points in the process. The analyses run on these samples varies depending on the purpose for
collecting the sample and the types and amounts of samples also vary.
Notable cases for additional samples being collected are crash-stops of the mills, where several
samples are collected from the mills to check the performance of the mills. This is to conduct process
or product optimization, achieving the optimal quality and analyzing the performance of the mills.
2.6 Analyzed parameters The analyses run on the shipment samples provides the data set for this project.
Analyses are conducted on samples from steps in the production process including the raw materials,
“intermediate products” (like clinker), fuels and the finished product. These analyses are both quality
18
control as well as a means to control and optimize the process. Several of the analyses run are
specified and described in the standards regulating cement testing EN196-1 through EN196-9.
The analyses run at Cementa are also described in internal documents “methods” that describe the
procedures and use of the equipment at the laboratories. The majority of the methods run at CR are
accredited, meaning that the procedure, equipment, controls etc. are audited by a national agency to
ensure that these are in compliance with the regulations and standards regarding these.
2.6.1 Particle size distribution (PSD) The sizes of the particles in the cement impact the cement properties, as the particles become smaller,
the specific surface area increases. This increases the rate of the reaction. However, to achieve a finer
cement more energy has to be used in the grinding stage. [1]
Particle size distribution is analyzed at Cementa using laser diffraction, typically the result is reported
as a cumulative size distribution, giving the relative number of particles below a certain size.
However, d50 is also provided, which is the size where 50% of the particles are below this size, the
value is in µm. In the data set used in this project, 32 µm, 32-2 µm (the percentage of particles
between 32 µm and 2 µm) and d50 is given from the process laboratories PSD analysis. 32-2 µm
represents the most active particle size in terms of cement strength development and clinker reactivity.
2.6.1.1 Blaine Specific surface area
A common measurement on the specific surface area of the cement is the Blaine method of air
permeability through a known volume of sample. The surface area is highly dependent on the PSD of
the cement and the methods used for measurement of PSD are robust and provides a lot of information
on the cement. However, the Blaine method is still used for certain cement types.
Regarding BAS-cement, where the fly-ash addition results in it being essentially a mixture of two
components of different surface area and where the particles have different shapes and properties,
Blaine is not currently used.
For SH-cement where the setting time and early strength development is key, Blaine is analyzed at
CR.
2.6.2 X-ray fluorescence (XRF) The XRF analysis provides the content of the most important elements in the cement namely:
SiO2
Al2O3
Fe2O3
CaO
MgO
SO3
K2O
Na2O
Cl
ZnO
Quantitative analysis of these elements are mainly done at the process laboratory, however, certain
samples are run at CR as well. The analysis is conducted in different states at the main laboratory
compared to at the process laboratory.
19
CR runs the analysis on glass disks prepared by melting the sample with a lithium borate substance.
Before the sample is melted the sample is heated to high temperature allowing for loss-on-ignition
analysis to be conducted if requested, this involves heating the sample to 950°C until constant weight.
As the sample is melted at high temperature, any weight loss at this stage would interfere with the
sample concentration and test results. A consequence of the heating is also that SO3 cannot be
analyzed accurately as some of the sulfur will be released into the exhaust during the melting phase.
However, the fused disks provide a homogenous sample and good accuracy on the analysis. Fusing
temperature must be kept stable as a variation in this may lead to various amounts of loss of sulfur
from the sample.
At the process laboratory, XRF is done on pressed powder disks without heating the sample before.
While being quicker and allowing for measuring of SO3, the sample is less homogenous and the
precision of the analysis may be lower.
The importance of the chemical content will be discussed in the following parts of this report.
2.6.3 X-ray diffraction (XRD) The XRD analysis provides information on the mineralogical content of the product. The main
minerals and compounds reported are:
Alite Crystal size
Alite M1
Alite M3
Alite sum
Cubic Aluminate (C3A)
Orthorhombic Aluminate (C3A)
Aluminate sum
Anhydrite
Aphthitalite
Arcanite
β-Belite
Calcite
CO2 (calculated)
Free lime
Fly-ash sum
Fraction M1
Dihydrate gypsum
Hemi-hydrate gypsum
Langbeinite
Lime
Periclase
Portlandite
Quartz
SO3 (calculated)
The minerals will be further discussed in chapter 3. Theory.
In addition to these parameters which are reported in the document, the Ferrite (C4AF) content is also
analyzed along with the content of the minerals Dolomite, Hematite, Magnetite and Mullite.
20
2.6.3.1 Rietveld
The XRD analysis result in a spectrogram with peaks of varying height, width and location along the
x-axis (measuring 2θ angels). The minerals have known patterns of peaks at various 2θ-angels, and
using this allows for quantification of the minerals. The Rietveld method for analyzing the XRD data
allows for fast and automated quantification of the various minerals. The method works by fitting a
theoretical spectrogram to a measured spectrogram using a least squares approach. [5]
While XRD/Rietveld measures several important parameters impacting the cement properties, it also
has drawbacks. XRD only measures the crystalline components of the sample and the Rietveld method
normalizes the results to 100 wt%, meaning that the assumption that only crystalline compounds are
present in the sample or that the amorphous components impacts the spectrogram.
The XRD/Rietveld method is also sensitive to the fineness of the material being analyzed. In the
method used at the process laboratory the sample is prepared and ground, however, for the fine SH-
cement overgrinding can easily happen reducing measurement precision.
Residual from the Rietveld determination is indicated by the R_wp parameter, a higher R_wp value
means a worse fit for the curve.
2.6.4 Compressive strengths & Setting time The compressive strengths and setting times are key features for the produced cement, these properties
are characterized by the following quantities:
1 day compressive strength
2 day compressive strength
7 day (for SH only) compressive strength
28 day compressive strength
Setting time (Vicat_EN)
H2O
Soundness
Knowing that the cement will be strong enough to carry other components during construction or
maintain its shape when casting in forms is important to the consumer.
2.6.4.1 Setting time
The setting time is measured by mixing with water to a set consistency. The water demand is
measured and the sample is placed in a machine which drops a needle into the sample at set intervals.
When the needle no longer penetrates the paste, the sample has set.
The H2O demand to reach the required consistency also varies depending on the chemical and physical
properties of the cement.
2.6.4.2 Compressive strengths
The compressive strength testing at CR is done on mortar, a mixture of cement, sand and water. This
is cast in molds to form “prisms”, compacted and allowed to set for the amount of time specified by
the analysis. A standardized type of sand is used and every step of the procedure is specified in the
method, including the mixing times and compacting settings. The number of prisms cast varies
depending on the number of ages at which compressive strengths shall be tested, 3 prisms are cast for
each age.
21
During the first day of the setting the prisms are stored in a cabinet with 90% relative humidity and at
a temperature of 20°C. After the first day, the 1d strength analysis is done on three of the prisms cast,
the rest of the prisms are allowed to age in a water bath. Before testing, the prisms are split and a total
of 6 tests are conducted on 3 prisms from each age.
2.6.4.3 Soundness
Soundness is also tested at CR in regards to the expansion of the cement during setting. Uncontrolled
expansion may cause adverse effects for the customers and needs to be kept within reasonable limits.
2.6.5 Other Other quantities used to characterize the produced cement at Cementa are:
SO3 – Carbon-Sulfur determinator
Limestone – Calculation based on CO2 content, not on BAS-cement.
Insoluble residue
Loss on ignition (LOI) – Thermogravimetric Analysis
Limestone content – Weight loss method for BAS-cement
Free Lime (wet chemical method)
Chloride content
Cr6+ reduction capability
Gypsum hydration through (STA-TG)
2.6.5.1 Carbon-sulfur determination
While sulfur is analyzed in the XRF in the process laboratory, a more accurate result is obtained from
the carbon-sulfur determinator at CR. Heating the sample through induction, the resulting gases are
analyzed by infrared detectors and the portion of both carbon and sulfur in the sample is determined.
As the high temperature will calcinate the limestone to produce CO2, the limestone content can for
most cement types be calculated to some degree of certainty using the results of this analysis. As BAS-
cement contains fly-ash which may contain other sources for carbon, this is not done on BAS-cement.
2.6.5.2 Insoluble residue
The insoluble residue of the cement is analyzed by subjecting the cement to acids and bases. It is one
of the regulated parameters of the cement, and must not exceed given levels for certain cement types.
2.6.5.3 Loss on ignition (LOI)
Loss on ignition is measured through a thermogravimetric analysis (TGA), which heats the sample to
950°C and weighing the sample at intervals until constant weight. While being an important stage in
the preparation for measurement on the XRF at CR, the LOI is also a parameter providing some
information on the cement.
2.6.5.4 Free lime
The content of free lime is measured through a titration analysis at CR. Free lime is important to
control as high levels can cause cracks in the cement as it sets. It is also analyzed through the XRD,
although the titration method is more accurate.
2.6.5.5 Chloride content
While chloride content can be measured through the XRF, the water soluble chloride content is also
important to control. This is measured through ion-chromatography at CR after first mixing the
cement with water, boiling the liquid and filtering out the solids. Chloride can have many effects in the
finished product and the level is limited to 0.1% in the cement because of corrosion. [6]
22
2.6.5.6 Cr6+ reduction capability
Cr6+ is hazardous to human health and can cause allergic inflammation, skin irritation etc. Chromium
is present in the refractory in the kiln, steel balls in the mills and steel plates in the clinker cooler and
is transferred into the material during the manufacturing process. Chromium introduced before or
during the clinker burning may oxidize to Cr6+. Addition of iron sulfate reduces the chromium to Cr3+,
a less harmful state. The reduction capability is regulated and when encountering samples with low
reduction capability the total amount of soluble Cr6+ is measured in addition to the reduction capability
at CR. [2] [6]
2.6.5.7 Gypsum dehydration
The gypsum content and the water content of the gypsum is measured at CR through thermal analysis
(STA) where the weight loss is measured through thermogravimetric analysis simultaneously as DSC
(differential scanning calorimetry) measures the heat demand for a temperature increase. Syngenite
and Portlandite content is also measured through this method for certain samples. [7]
The different hydrates vary in solubility during the hydration depending on temperature, hemi-hydrate
dissolves quicker than gypsum (di-hydrate).The SO42- in the solution will facilitate the formation of
ettringite AFt which in turn slows down the reactions of alite and the aluminates to improve the
handling properties of the paste. [8] [6]
2.7 Sustainability Concrete accounts for roughly 95% of the construction materials used worldwide and the cement
manufacturing accounts for roughly 5% of the total global CO2 emissions. Among Swedish industries,
Cementas plant in Slite was the second largest emitter of CO2 in 2016. [9] [10]
Cementa has an ambitious project of achieving climate neutral cement by 2030. Both the fuels used
and the calcination reaction are sources of CO2 emissions and today roughly 40% of Cementa’s
emissions are from fuels, the other 60% is from the raw material: Limestone. Cementa will use a
combination of actions and developments to counter these emissions: Increased energy efficiency,
biofuels, new cement types with additional materials, Carbon Capture and Storage (CCS) and
carbonation (absorption of CO2 into the finished concrete structure).
Environmental and financial sustainability are also highly connected. Innovative products and
production techniques can lower both costs and emissions, especially when the cost of CO2 emissions
increase. Consumers may also demand or desire more sustainable products in the future.
Cementa currently works to improve sustainability in several areas, including alternative materials
added to the production process and using alternative fuels.
2.7.1 Life cycle view The sustainability work at Cementa and the target of climate neutral cement in 2030 is centered around
the entire life cycle of the product.
2.7.2 Alternative raw materials Using alternative materials in the process can benefit the sustainability of the final product. The
materials can be added at different points in the process depending on the properties of the material
and the need for processing.
Today, mixing slag into the raw materials are tested at the plant. Reducing the amount of virgin
limestone and decreasing the calcination need, and by extension lowering the CO2 emissions from the
23
process. A reduction in the amount of virgin limestone can also lower the impact from the mining step
in the process.
2.7.3 Additives Waste materials from the cement production process itself are also used as filler in the final product.
The product called Cement Kiln Dust (CKD) are added to the cement, the amount possible to add
being regulated by the type of cement and the chemical composition.
CKD is high in chlorides and originates from the clinker burning stage. A cement type called
Multicem which is produced by Cementa for ground stabilization contains a high share of CKD,
however CKD is added to other products to a lesser degree as well.
2.7.4 Fly-ash Fly-ash is used in BAS-cement to reduce the share of clinker in the final product. It is a pozzolan
material, meaning that it contributes to the strength of the cement to some degree and acts as a reactive
additive rather than simply a filler.
2.7.5 Alternative fuels Cementa also focuses on reducing the amount of fossil fuels in the clinker burning process, and today
a majority of the fuels used are classified as “alternative fuels”. Roughly half of the alternative fuels
used are bio-based (including paper, wood and fabric) and the other half is fossil based (plastics, tires
and waste oils and solvents). Using waste as alternative fuels allows for extraction of the chemical
energy and prevents the waste from ending up in landfills.
2.8 Previous work Engineers at Cementa have worked on 28d compressive strength predictions in the past, however
mainly using traditional multivariate analysis in Excel. [11]
With the development of the Neural Networks model by Dr. Jan Skocek at Heidelberg Technology
Center a robust tool for modelling various parameters in cement production is available. The Artificial
Neural Networks (ANN) model provides a good and user-friendly way of making predictions on not
only cement performance but can be applied to other areas of the process as well.
3. Theory The compressive strength of the cement depends on several parameters. The strength is mainly formed
through the hydration of Alite and Belite to form C-S-H. It forms needle-like structures which grows
into each other. However, several other parameters impacts this reaction, both positively and
negatively.
24
3.1 Alite, Belite & Aluminates
Figure 3 - Factors contributing to clinker reactivity [12]
The strength development of the cement is highly dependent on the clinker reactivity, defined by Dr.
Marsicano as “The contribution of all clinker phases, when hydrated, towards the strength
development is defined as clinker reactivity.” Figure 3 indicates the number different factors that
contribute to clinker reactivity. [12]
Alite, Belite, Aluminate and Ferrite, the main minerals components of clinker, are formed in the kiln.
Figure 2 provides a simplistic perspective on the regions, temperatures and concentration of these
minerals in a typical cement process, however these are not the only minerals which can form. The
phase diagram in Figure 4 illustrates the large number of minerals that can be formed from CaO-SiO2
during the clinker burning depending on concentrations and temperature.
25
Figure 4 - Phase diagram of CaO-SiO2 system during clinker burning [13]
Depending on temperature and content of the chemical compounds which form the minerals, a large
number of minerals can potentially form. The mineral formation can also be impacted by other
substances than the main constituents, favoring specific minerals or conformations of the crystals.
3.1.1 Alite (C3S) Alite is as mentioned responsible for the majority of the strength development during the first 28-days.
It reacts quickly and generates a moderate amount of heat during the hydration. As can be seen in
Figure 2 it is formed in the late stages of the kiln and requires high temperature for the formation.
While typically written C3S, industrial Alite incorporates other compounds in the lattice. In industrial
clinker, typically 3-4% of Alite is substituent oxides, these include MgO, Al2O3, Fe2O3 and SO3 as
well as alkalis. [8]
3.1.1.1 Formation
Alite is formed in the kiln, when Belite (C2S) in the melted material reacts with CaO. The formation
takes place in the area of highest temperature in the kiln and the Alite is thermodynamically unstable
at lower temperatures, requiring fast cooling. [2]
Below 1,250°C during equilibrium cooling C3S decomposes into Belite+CaO, this decomposition is
slow and under 700°C imperceptible. [8]
3.1.1.2 Forms
There are different forms of Alite present in the cement, these are formed at various temperature and
different forms are stabilized by other chemical compounds. The T forms are triclinic and are formed
26
at the lowest temperatures, these are also the most hydraulically reactive. M means a monoclinic
structure and R is rhombohedral. [8] [12]
The reactivity of the different forms typically increases from T1 with R being the most reactive
according to [12]. However, there are some controversy on this topic.
𝑇1 ↔ 𝑇2 ↔ 𝑇3 ↔ 𝑀1 ↔ 𝑀2 ↔ 𝑀3 ↔ 𝑅
Incorporation of substitute ions as well as temperature determine the stabilization of the various
configurations. The forms in clinker typically approximates to a mixture of M1 and M3, and in this
project these are the configurations measured. [8]
M3 is stabilized by MgO and increasing SO3 stabilizes M1, and R is typically stabilized by F- or Sr2+.
However, as mentioned higher temperature is also required for formation of more reactive forms. [8]
[12] [13]
3.1.1.3 Size
Slow formation of Alite in the kiln yields larger crystals and more perfect crystals. This is not
beneficial for the reactivity of the clinker and larger crystals also require more energy for grinding. [1]
3.1.2 Belite (C2S) Belite reacts more slowly compared to Alite and is not a major contributor to the strength during the
first 28 days. However, Belite is important as an intermediary as it reacts with CaO to form Alite. For
that reason a high Belite content may have a negative impact on the 28-day compressive strength as
well as on the early strengths if it replaces Alite. [1]
3.1.2.1 Forms
Belite has several polymorphs: α, α’H, α’L, β and γ. In industrial clinker, β-Belite is the dominant form
and is similar to the α forms in configuration. γ-Belite differs significantly from the other
configurations, being less dense and causing cracking of β-Belite in a process known as dusting. [8]
Transformations between the forms take place at different temperatures according to the following
scheme.
𝛼 ⇆ 𝛼𝐻′ ⇆ 𝛼𝐿
′ ⇆ β → γ
The α and α’H transformation takes place at 1,425°C, α’H and α’L transformation takes place at
1,160°C, α’L to β takes place at 630 - 680°C and the reverse at 690°C. β to γ takes place below 500°C
and between 780 - 860°C γ is transformed to α’L. [8]
3.1.3 Aluminates (C3A & C4AF) The aluminates react similarly during hydration and contribute little to the compressive strength of the
cement. However, it speeds up the setting of the cement paste and can lead to “flash setting” where the
paste hardens decreasing the workability of the paste. When the cement is hydrated, C3A and C4AF
react with sulfate to form ettringite which turns into small needle-like structures in the liquid phase of
the mixture. The ettringite layer forms a cover and hinders the further hydration of the aluminates, thus
slowing the setting. [2]
27
Figure 5 - Ettringite and calcium hydroxide [4]
The structure of the ettringite (AFt) is shown in Figure 5 along with the structure of Calcium
Hydroxide (CH).
3.1.3.1 Aluminate (C3A)
Aluminates are formed in the kiln, depending on the Al2O3 content of the kiln feed. Pure C3A only has
one configuration: Cubic. However, through incorporation of other compounds into the lattice
orthorhombic and monoclinic configurations are possible. [8]
Figure 6 - Impact and limits of Na2O and Fe2O3 on polymorphism [13]
28
As with Alite and Belite other compounds are substituted into the mineral lattice impacting the crystal
forms, key ones being Fe2O3 and Na2O. Substitution of Aluminum by Na+ plays a key role in the
polymorphism of C3A, as shown in Figure 6. Fe3+ will only have a minor impact on the polymorphs
obtained. [13]
The main C3A forms present in the clinker is cubic and orthorhombic. Of these, orthorhombic C3A
reacts quicker than cubic C3A. [6]
3.1.3.2 Ferrite (C4AF)
The amount of Ferrite formed during the clinker burning depends on the level of Fe2O3 in the kiln
feed. Ferrite is not reported and compiled in the data set used for this project, however Ferrite reacts in
a similar manner to that of Aluminate on hydration. The Ferrite reaction is slower than that of
Aluminates but the end results of AFt and AFm (as long as sulfates are present) are the same, although
with higher iron content in the crystal lattices. [2]
3.2 Minor constituents In addition to the main minerals, several other minerals and compounds are found in the cement.
3.2.1 Set accelerators Several chemical components can act as set accelerators, including the alkali metal salts, chloride and
nitrides and nitrates. In addition to these there are organic compounds that can act as set accelerators,
however, these are not further explored in this project.
3.2.1.1 Alkali metals (K and Na)
Similar to the aluminates, alkali content in the cement can speed up the Alite reaction. This can make a
significant difference and improve the early strengths. However, alkali content typically has a negative
impact on 28-day strength. [2] [8]
The most notable Alkali salts are the alkali sulfates. Alkali sulfates have different impacts on the
clinker reactivity, Langbeinite most beneficial, Arcanite medium and Aphthitalite is undesirable in the
cement. K2O can also incorporate into the Alite lattice which negatively impact the clinker reactivity.
To control the amount of soluble alkali in the clinker, sulfate levels are controlled and Degree of
Sulfatization (DoS) is used as a measure. [12]
𝐷𝑜𝑆 (%) = 77.41 ∗ 𝑆𝑂3
𝑁𝑎2𝑂 + 0.658 ∗ 𝐾2𝑂
Formula 3 - Degree-of-Sulfatization
Na+ is also found in the C3A lattice substituting Aluminum, the increased content of this will impact
the aluminate forms found in the clinker as shown in 3.1.3.1 Aluminate (C3A). Increasing Na+ into the
lattice will increase the hydration rate as the C3A takes on orthorhombic or monoclinic configurations.
3.2.1.2 Chloride
Calcium chloride CaCl2 is commonly used as a set accelerator and increases both the rate of hydration
and the heat evolution. [2]
Cl- containing salts accelerate the setting differently depending on the cations, where Ca2+ is one of the
strongest accelerators. [2]
29
3.2.1.3 Free CaO
Free CaO (free lime) will decrease the setting time for the cement and is the CaO that has not reacted
with other compounds during the clinker burning. However, at high levels it also contributes to cracks
(unless lime saturation factor is low) and expansion of the concrete and should for that reason be
controlled. A low free lime content is also an indication of over-burning the clinker, meaning that an
unnecessary amount of energy has been used for the burning of the clinker. Optimal levels of free lime
are around 1.5% according to Truedsson. [1]
3.2.2 Sulfur The sulfur content in the cement is important, as can be seen it controls the amount of soluble alkalis
and is a controlling parameter for the aluminate reactions. Sulfur content needs to be balanced for
optimal cement performance.
Sulfur is introduced into the cement both through the clinker and from the gypsum which is discussed
later. For environmental reason, keeping the sulfur bound in the clinker reduces the gaseous sulfur
emissions from the plant and is therefore preferred to some extent. The sulfur content in the fuels
varies between fuels and can therefore be managed by controlling the fuel mixture to the kiln and
calciner as well as the temperature and oxygen levels in the kiln.
3.2.3 Portlandite While it forms naturally during the hydration of Alite and Belite, Portlandite present in the cement
before hydration is a sign of prehydration of the cement, where the cement has been subjected to water
during production or handling.
At Cementa the mills are cooled through water injection. Most of this water is evaporated before
coming into contact with the cement as the temperature in the mills are in excess of 100°C, however
depending on the effective spray of water and the mill temperature some water can come into contact
with the product creating Portlandite. [6]
3.2.4 Zinc Zinc have a positive impact on the clinkering process as it stabilizes the Alite phases. Although it has a
significant negative impact on the properties of the cement and reduces the cement 28 d strength even
at low levels. [8] [2]
3.3 Gypsum, Limestone & Fly-ash Added at the milling stage (into the mills for gypsum and limestone and after milling before storage
for the fly-ash) these materials all play various roles in the cement.
3.3.1 Gypsum During the milling process, the gypsum originally CaSO4·2H2O (di-hydrate) is to varying degrees
dehydrated to CaSO4·0.5H2O (hemi-hydrate). Compared to di-hydrate gypsum, hemi-hydrate
dissolves faster. The gypsum is quickly solved upon hydration to release Ca2+ and SO42- into the
liquid. SO42- reacts with the aluminates to form ettringite, controlling the rate of the setting of the
paste. The formation of ettringite also prevents a phenomenon known as flash setting, where
premature stiffening of the paste occurs as a result of formation of secondary gypsum in the case of
low C3A content or formation of calcium aluminate hydrates or monosulfate (AFm) if the dissolved
gypsum content is too low. [13]
30
3.3.2 Limestone Limestone is added as a way to reduce the clinker fraction of the cement. Limestone contains mainly
CaCO3 which has various impacts on the cement. During hydration, the CaCO3 dissolves slowly and
the main reactions occur at the interface between the liquid and solid. It reacts with C3A and also
speeds up the C3S reactions. The limestone particles also provide nucleation spots and fine particles
give a good and dense structure increasing the strength. [1] [2] [6]
3.3.3 Fly-ash Fly-ash is as mentioned a by-product from coal burning processes, and two main types of fly-ash are
available: Calcareous and siliceous (calcium rich or silica rich). It is a common material for use in the
cement industry as the pozzolan nature of the fly-ash means that it reacts during the cement hydration
and contributes to the strength. Fly-ash addition helps reduce the clinker content of the finished
cement. [2]
Fly-ash is added to the BAS-cement as well as other products. The type of fly-ash used in BAS-
cement is siliceous fly-ash which mainly contains reactive SiO2 and Al2O3. The fly-ash mainly
consists of amorphous compounds, however it does contain some crystalline components and the
reactivity of these are disputed. [8]
The quality of the fly-ash varies depending on the process it originates from and the type of and source
of the coal being burnt. Chemical composition and particle sizes will impact the reactivity of the fly-
ash and its eventual contribution to the strength development. Fly-ashes with significant quantities of
materials above 45 µm have exhibited poor quality in other studies. [2]
3.4 Hydration As the cement is hydrated (mixed with water), reactions in the mixture form different compounds. It is
a complex process with many parameters that impact the progression of the hydrations reactions.
3.4.1 Calcium-Silicates reactions The hydration of Alite and Belite forms C-S-H, calcium silicate hydrate (CaO ∙ SiO2 ∙ H2O) as well as
Portlandite (CH).
3CaO ∙ SiO2 + H2O → 1.7CSH + 1.3CH
Formula 4 - Alite hydration reaction
2CaO ∙ SiO2 + H2O → 1.7CSH + 0.3CH
Formula 5 – Belite hydration reaction
31
Figure 7 - 1: Hydration of C3S after 5 min. 2: Formation of needle shapes of CSH after 16 h. 3: Needles up to 900 nm in size 21 days [4]
Alite and Belite (C3S and C2S) reacts with water to form C-S-H (Calcium silicate hydrate) and
Portlandite (Ca(OH)2). The strength of the paste comes from the C-S-H which has a fiber-like
structure which grows into each other forming networks. The development of these needles can be
seen in Figure 7. C3S is responsible for most of the strength for the first 28d and C2S for the strength
developed after that, with C3S being responsible for 96% of the strength development for the first 7d,
78% of the first 28d and 45% after one year. [1] [4]
The aluminates (C3A and C4AF) react quickly as the cement is hydrated and gives generates heat. The
aluminates do not directly contribute to the strength of the cement and an uncontrolled reaction can
lead to flash setting of the cement. However, as mentioned, the reaction of C3A with water stimulates
the hydration reactions of C3S and C2S and reduces the setting time. C3A can increase the water
demand for the hydration. [1] [4]
Free lime and alkali sulfates also stimulate the early strength development of the cement. However,
high levels of free lime can lead to cracks in the concrete and while the alkali sulfate stimulates early
strength, it reduces later strength. Free lime is formed when there is a surplus of burned lime or
incomplete homogenization of the raw meal as burned lime CaO reacts with C2S to form C3S. [1]
3.4.2 Aluminate reactions Upon hydration the Aluminates react quickly and can form a multitude of compounds depending on
the presence of other components. The reactions of C3A and C4AF are similar, with C4AF introducing
Fe-ions into the mixture. Out of the C3A configurations, the cubic C3A will have the highest hydration
rate. [2]
The number of compounds that the Aluminates can form are numerous and the formations depend on
the presence of other compounds. Main phases formed from the Aluminates are AFm (Al2O3-Fe2O3-
mono) and AFt (Al2O3-Fe2O3-tri). The general formulas for these can be written
[Ca2(Al,Fe)(OH)6]·X·xH2O for mono and [Ca3(Al,Fe)(OH)6·12H2O]2·X3·xH2O for tri phase. [8]
Illustrating the differences between these two phases, the general formula for an AFm phase can be
written C3(A,F)·CX2·yH2O where the ‘mono’ refers to the single CaX2 unit. X denoting an anion and
the most common in cement hydration is OH-, SO42- and CO3
2-. Similarly for the tri phases the
C3(A,F)·3CX·yH2O and tri referring to the 3CX unit. [8]
Ettringite formed with SO42- from gypsum surrounds the C3A crystals, forming an impermeable layer
which hinders further reaction of C3A. [2]
32
3.4.2 Water/cement ratio A key parameter to control when hydrating the cement is the water-to-cement ratio or w/c-ratio.
Different cement types have different ideal w/c-ratio depending on many parameters like fineness and
chemical content. Higher w/c-ratios increases the workability of the paste but may lower the final
strength as pores of water can form leading to crack formation later. A low w/c-ratio may lead to un-
hydrated material and lower workability of the paste. This is illustrated schematically in Figure 8.
Figure 8 - Water/Cement ratio [4]
33
3.4.3 Progression
Figure 9 - Cement hydration progression [14]
By following the rate of heat evolution the progression of the hydration can be followed. As can be
seen in Figure 9, the aluminates react quickly and form ettringite (AFt). The AFt forms needle like
structures in the liquid phase, impacting the rheological properties of the paste. [4]
As cement is mixed with water, a solid and a liquid phase is formed. The gypsum is dissolved into the
water (giving Ca2+ and SO42-) as well as the leaching of other compounds such as alkalis into the liquid
phase from the cement. [8]
Small particles (<3 µm) also dissolve quickly and typically only give rise to temperature change,
however, most of the particles below 3 µm is the gypsum and limestone addition. [1]
The sulfate concentration decreases as the ettringite formation continues, the formation of
monosulfates (AFm) takes place. This is seen as a second “peak” in heat evolution. [13]
3.4.4 Pozzolan reaction The fly-ash as a pozzolan material, reacts with Ca(OH)2 during the cement hydration to form C-S-H.
The reactivity of the fly-ash is affected by several of the physical and chemical properties of the fly-
ash. Studies have used lime consumption tests for analyzing the fly-ash reactivity, these show that
finer fly-ash reacts quicker and that the fly-ash mainly reacts during the first 8-15 days after hydration.
[15]
The pozzolan reaction involves the attack of OH- ions on SiO2 or SiO2-Al2O3 compounds in the fly-
ash to form C-S-H. The added SiO2 from the fly-ash will decrease the ratio of Ca/Si content in the C-
S-H. The aluminum released will also be incorporated in the C-S-H as well as possibly form AFm. [8]
3.5 Strength development & Setting time The strength development and the setting time is determined by the hydration process. However, the
various properties of the cement will impact and contribute to the strength development in different
34
ways over time. In general the properties of the various impacting parameters have been discussed
above.
3.5.1 Setting time The setting time of cement paste is important to control with regards to the handling qualities of the
paste. Setting time is below 200 min for the cement types studied in this report with setting time for
SH significantly shorter than for BAS.
Normal setting of cement is mainly caused by C3S reacting to form C-S-H and C3A reacting to form
AFt. It also depends on the gypsum content, as the dissolution of CaSO4 to Ca2+ and SO42- is required
for the formation of AFt. [13]
3.5.2 1d strength The first compressive strength is measured after 1 day, the strength at this point depends mainly on the
hydration of C3S to form C-S-H. However, indications of C4AF possibly contributing to strength
development is also present in the literature. [13]
The amount of formed C-S-H will be impacted by other factors in addition to the C3S content. A finer
cement gives a higher reaction surface area for interactions between the C3S leading to the increase in
the amount of C3S that has reacted. The amount of liquid and the content of other chemical
compounds like aluminates will also impact the early strengths. Aluminates accelerate the strength
development to a degree, however can have adverse effects if the sulfate content is inadequate. [13]
[2] [1]
3.5.3 28d strength After 28 days the formation of C-S-H from Alite is still the main contributor to the strength. However,
at this point the fly-ash has also reacted to form C-S-H, and some of the Belite have also reacted.
3.6 Multivariate analysis Modelling and prediction aims at formulating a model explaining the variance in the output parameters
(Y) using input parameters (X). In this project, in addition to the Neural Networks model, classical
multivariate analysis software and tools have been used.
Some variations may exist between different software platforms and the theory below is all based on
the version of these methods used in Excel and Unscrambler.
3.6.1 Multivariate analysis & regression The purpose for using traditional multivariate analysis in this project is the information and increased
understanding of a data set which can be extracted using these methods. Principal Component
Analysis & Regression (PCA), Partial Least Square (PLS) and Multiple Linear Regression (MLR) are
all available in the software tools used in this project. These methods are further explained in
Appendix x.
The tools used in the traditional multivariate analysis in this project all create linear models. A
coefficient is found for each input parameter X and the sum of these will give the output parameters.
Additionally, in many cases a constant (β) is also found, representing the level around which the (Y)
parameter varies.
𝑌 = 𝛽 + 𝑐1𝑥1 + 𝑐2𝑥2 + ⋯ + 𝑐𝑛𝑥𝑛
Equation 1 - Linear multivariate model
35
The coefficients for each parameter acts as weights for a parameter, regulating and matching the
degree to which it impacts Y.
3.6.2 Excel regression tool Excel has a built-in function for multiple regression analysis with several variables. It has some major
limitations, it can only account for 16 variables and produce 1 output for each run. It produces a linear
model and has previously been used at Cementa for predictions of 28-day strength. [11]
The benefits of the tool in Excel is the availability, however it has serious limitations. It can only
handle 16 parameters, and while this is plenty for the final model, it reduces the possibility of using
this for parameter selection as the number of variables for each sample is well above 16.
Parameter selection could be done by running several iterations using the tool and the results include
values indicating the importance of each variable. However, this is a less robust way compared to the
methods in Unscrambler.
3.6.3 Partial Least Squares Partial Least Squares is a multivariate analysis method used for connecting an input X to an output Y.
In this project Partial Least Squares regression is used, creating a model for connecting X to Y. The
PLS regression also provides additional information on the data set. PLS regression is a common tool
in chemo metrics. PLS regression is described in more detail in Appendix x. [16]
The parameters used in this project are measured in different units and vary wildly, for that reason
when running PLS the input data can be scaled. The variables are scaled to “unit variance” by dividing
the variable with the standard deviation of that variable. This allows for evaluation of the importance
of the variables. [16]
As mentioned, from the PLS analysis, in addition to the model, other information on the data set can
be found. This information include insight on the samples and the variables in the data set, including
variable and sample impacts as well as residuals.
The variable importance is in the PLS regression in Unscrambler given through Uncertainty Analysis.
In essence, this identifies the variables whose impact cannot be attributed to chance by analyzing the
uncertainty in the estimations of the variable impacts. This is a major benefit to the work of reducing
the number of variables used in prediction in future steps.
The influence of samples as well as residuals for samples are also found using the PLS method.
Samples with large influence as well as samples with large residuals should be reviewed: Large
residuals mean that the model to a higher degree fails to explain the output for this sample and it may
be an outlier or a result of some error in the data set.
Samples with high influence on the models also may impact the model negatively. While having only
one or a few samples at the extreme ends of the interval means the increased influence of these, it may
not benefit the model.
In Unscrambler, cross validation can be used to check the performance of the formulated model. This
involves dividing the data into several sets removing one set of samples from the training set, using the
model to predict the Y for these samples and comparing it to the measured Y. This is then repeated
until every set has been tested. Several methods of selecting the samples used for validation is
available, however, allowing Unscrambler to select samples randomly out of the set is sufficient for
this project as this model will not be used for prediction. [17]
36
3.7 Neural network
Figure 10 - RBFN Neural Network explanation [18]
An Artificial Neural Network (ANN) as used in the model developed by HTC works by forming
networks of “neurons”. Simulating the learning processes found in biology, the ANN is trained using a
training set and the trained ANN can then be used for prediction. The neurons are in essence
mathematical weights for each of the input parameters. The amount of layers of neurons varies
between the various models and applications of ANNs.
The process of teaching the neural network models is also reminiscent of the process of learning found
in nature. Teaching the model is done by giving it known data. In the case of this project, the known
data would include the parameters which affect the 28d strength and the experimentally determined
value of the 28d strength. The model “adapts” to this data and when it is later given only the
parameters it can predict the 28d strength. [18]
Radial Basis Functional Network is the type of ANN most used in this project. Each neuron has a
weight and the impact of each neuron depends on the distance of the input from the neuron. As can be
seen in the “Basis function” in Figure 10, larger distance between the “neural center” and the input x
will reduce the influence of this neuron on the output.
RBFN has a hidden layer containing the neural weights as well as the basis functions and the training
is not monitored by the user. While robust and easy to use, the model for this reason provides limited
information regarding variable and sample impacts.
In the model used in this project, several of the model parameters such as number of neurons and
kernel width can be adjusted by the user. This allows the user to achieve better performance of the
model without adding samples to or changing the data set.
3.8 Bias-Variance The possibility of accurately predicting samples above or below set boundaries are key to identifying
issues with the cement. However, ANN accuracy tend to decrease towards the extremes of the interval
due to the bias of the models.
This bias needs to be balanced with the variance, a high bias tends to mean a low variance and this can
be described as a tradeoff between the degrees to which the model learns the training set versus the
degree of accuracy for the predictions.
37
Figure 11 - Bias vs. Variance [19]
High bias means that the model fails to account for variations in the data. While Figure 11 provides a
highly simplified view of the issue, it demonstrates the key balance between overfitting and
underfitting.
A high variance (overfitting) model will include the noise of the data, it also reduces the bias of the
model: This will provide good correlation for the training set, however the accuracy of the prediction
will worsen.
The issue of bias and the ANN tendency to underestimate high values and overestimate low values
have been discussed in the medical field along with possible solutions to this issue. [20]
3.8.1 Sample stratification
Figure 12 - Distribution of samples 28 day strength BAS-cement from Unscrambler
As can be seen in Figure 12, the distribution of samples over the interval is not even over the entire
span. The majority of samples are in the mid-range of the span which can lead to problems with bias.
38
Sample stratification involves dividing the samples into ranges and compiling a set with a more even
distribution of data points between the ranges. Duplication has been tried where the samples at the end
points of the interval are replicated to artificially even out the distribution. Unfortunately this reduced
the precision in the center of the interval without sufficiently addressing the issue. [20]
In the case of sample selection, picking out samples equally spaced over the interval will also remove
the large focus on the center samples. However, an approach where sample selection is used requires
the data set to be sufficiently large and include sufficient number of samples at the extreme ends of the
interval.
3.9 Sustainability As mentioned, Cementa has a target of the cement being carbon neutral by the year 2030. While
several different measures are taken to achieve this, this project mainly touches on the cement strength
and quality. This combined with the production costs are limiting factors in the work towards a more
sustainable production.
3.9.1 Reduced clinker content in the cement As the clinker burning stage is both costly and the source for most emissions from the process,
reducing the clinker share of the cement is one of the actions already taken. BAS-cement with the fly-
ash addition as well as the limestone addition to the products are such examples. Depending on the
material, the addition can take place in various locations in the process. While a fine material such as
fly-ash can be added after the mill, limestone is added to the cement mills and milled with the cement.
Reducing the clinker share in the cement can also provide a financial benefit, however this depends on
the cost of the material used. An added difficulty regarding this is the fluctuations of the supply,
demand and in the end the cost of the material. As regulations and agreements define the limits for
variations in the addition of these materials, quality also determine the possible variations.
Addition of these materials will impact the cement properties, and for that reason the addition has to
be balanced against other properties of the cement.
3.9.2 Reduced amount of virgin limestone Alternative materials can also be added at the start of the process, adding materials that does not
require calcination reduces the CO2 emissions from this step. These additions will impact the clinker
quality and similarly to the addition of materials to the cement mills or to the finished product the
addition has to be balanced against the final product quality.
Addition of slag products containing calcined limestone is done to some extent today at Cementa,
reducing the amount of CO2 released per unit of cement.
3.9.3 Process optimization Over-grinding and over-burning present significant challenges in the cement production. Working
towards sustainability, decreasing the thermal and electrical energy demands per unit of produced
cement will be a step in the correct direction.
3.9.3.1 Alternative fuel usage
Even though overall thermal energy demand per unit of produced clinker has decreased, the amount of
fuels needed are still a major source of the emissions from the cement production process. [21]
39
Working with the current process at Cementa, work on increasing the share of alternative fuels,
particularly the bio-based alternative fuels, is currently under way. As mentioned, the current
alternative fuels are RDF (refuse derived fuels), tires and waste oils.
The amount of alternative fuels which can be used depends on the quality of the fuel, and the
alternative fuel varies in quality depending on the source and over time. RDF typically have a lower
energy content and a higher content of chlorides and alkalis compared to the fossil fuels.
3.9.3.2 Over-grinding
A finer product generally means a product with higher strength and a reduced setting time as discussed
previously in this chapter. While these are desirable qualities, grinding the materials requires large
amounts of electrical energy. A finer product with a large fraction below 3 µm will also be highly
reactive but contribute little to the final compressive strength. [1]
Balancing the fineness of the cement against variations in clinker reactivity or in other ways reduce the
fraction of material ground to unnecessary fineness while still maintaining good compressive strength
may be beneficial from a sustainability point-of-view.
3.9.4 Concrete carbonation Concrete carbonation, the absorption of CO2 into the finished concrete structure is another method for
decreasing the carbon footprint over the lifespan of the cement.
The amount of CO2 that can be absorbed by the concrete structure depends on several factors. The
physical properties of the concrete such as pore sizes will have a major impact as well as the CO2
concentration in the air. This means that the w/c ratio will impact the amount of carbonation taking
place as a higher w/c ratio will lead to increased number of pores. A lower pH-value will also benefit
the carbonation rate, which in turn means that the addition of fly-ash which consumes a part of the
formed CH will benefit the carbonation. However, for fly-ash, the formation of new C-S-H can block
capillary pores which in turn negatively impacts the carbonation rate. [22]
4. Procedure The work flow in this project is cyclic. The first step involves studying and gathering information on
the theoretical aspects of this project, including cement hydration and clinker mineral composition etc.
After this step, data has been collected using files compiled by the quality engineer in Slite (i.e. Tore
Jönsson). Preparation of data involves formatting and looking for typos etc. in the data. Data analysis
is done through the traditional multivariate analysis methods and the results are later used in the ANN
model to create an index. The index and the results from this is validated through different methods.
After this, depending on the results from the previous steps, return to the theory, data collection etc. to
both explain the results and possibly improve the index.
40
4.1 Literature study At the start of this project, a literature study was produced with the aim to increase the knowledge
about the theory behind cement performance as well as compiling this information for later use in the
project. Several parts of the literature study were used in the background and theory sections of this
report and the literature study in its complete form can be found in Appendix.
4.2 Data collection and preparation Data for this project is already available in an excel file containing the analysis results for all
shipments of the various cement types. This file is updated regularly and the data forms the basis for
the further work.
Before it can be used in the project, the data needed to be prepared. Unscrambler as well as the ANN
model requires data formatted in a certain way, and the data from the excel file is therefore stored and
formatted in separate files. At this stage a rudimentary error analysis is conducted, however, excel
sheets provide limited possibilities for identifying issues.
4.3 Traditional Multivariate analysis Traditional multivariate analysis provides, in contrast to the Neural Networks model, a large amount
of information regarding the data set. As described in section 3.6 Multivariate analysis, information on
the impacts of samples and variables can easily be extracted using these methods.
In Unscrambler the Partial Least Squares tool is used for the analysis of the data. This creates a PLS
regression model which can be used for prediction, however, only the additional information on the
data is used. Cross-validation is also used to check the models performance as well as a foundation for
the uncertainty analysis.
Theory
Data Collection
Data preparation
Data Analysis
Neural Networks
model
Validation
41
The most significant benefit with using the PLS method in Unscrambler is the uncertainty analysis.
This uses the different sub-models produced during the cross-validation and the determined
coefficients for each of these sub-models to create a variable which represent the variance of the
coefficient between the sub-models. A t-test is used to determine the significance of each coefficient
and uncertainty limits (2 std. deviations) gives the “uncertainty limits” for each parameter. If the
uncertainty limits (represented in Figure 13 as error bars) do not cross the zero-line, the parameter is
determined to be significant. [17]
Figure 13 - Uncertainty analysis results from Unscrambler
The uncertainty analysis uses the uncertainty of the coefficient determination to determine if the
variable has an impact that cannot be explained by chance.
4.3.1 Excel Multivariate regression in Excel was used at the start of the project, however as it can only handle 16
parameters it could not be used for parameter selection without running several iterations and picking
the most significant variables each time. The results give the coefficients for a linear model which can
be used for prediction of future value.
4.4 Choosing parameters Overall in excess of 40 parameters are analyzed for each cement type, however for a robust and usable
model the amount of variables used needs to reduced. A target of roughly 10 parameters for each
index is used in this project. The process of choosing parameters has been done in steps.
4.4.1 Initial exclusion To the highest degree only the analysis results from the process laboratory were used and for that
reason most of the parameters analyzed at CR were excluded. While CR generally has better precision
of the analysis results, their exclusion will increase the usefulness of the indexes as it can be used not
only on samples from shipments but also for samples collected during production.
In cases where the same parameter is analyzed several times through different methods at the process
laboratory, the most reliable has been selected. This mainly includes SO3 which is analyzed at the
process laboratory both through XRF and XRD and in this case the XRF data has been used.
4.4.2 Uncertainty test One method to narrow it down to the most significant parameters which can provide a strong model is
Unscrambler’s “Uncertainty analysis” which has been used during the PLS regression analysis runs.
However, the identified parameters from the uncertainty test does not necessarily provide the best
model as the identification is based on the data used in the PLS analysis. There are several factors to
take into account when evaluating the parameters identified, for example the accuracy of the analysis,
42
the variance of the parameter as well as the theory on strength development. For that reason, the next
step has been manual selection.
4.4.3 Manual selection The uncertainty test is purely mathematical and the results will not necessarily include all the
important parameters or exclude all unimportant. Results from the uncertainty test may also indicate
more or less parameters than is desirable. For these reasons, the theoretical impacts of the parameters
has been used for manual selection of the final parameters with the results from the Uncertainty test as
a starting point.
During the manual selection, the target has been to include the variables that supply the most amount
of useful information to the model. A few of the following issues also have been identified during this
step
4.4.3.1 Connected and correlated parameters
There are several dependencies and connections between the parameters in the data set, this needs to
be accounted for when selecting the parameters for the model. Having the same property of the cement
used twice in the model is not only redundant but also put an increased amount of weight on that
property for the model. It can also increase the amount of noise and decrease the robustness of the
model.
Clear cases for this would be the configurations of Alite, M1 and M3. While the content of these
phases will impact the strength, the content of these phases will increase with the total content of
Alite. For that reason, Fraction_M1 which describes the share of the Alite in the sample that is in M1
configuration provides more useful information.
4.4.3.2 Not identified critical parameters
While the analysis in Unscrambler generally identified most of the parameters that can be considered
critical for cement strength, some additions have been made to guarantee the index performance over
time. Possible additions have been considered carefully and while the uncertainty test managed to
include these parameters in the majority of cases this still needs consideration.
Examples of this is the content of Alite in the sample. Alite is as mentioned responsible for the
majority of the strength development during the first 28d, and not including this as a variable could
reduce the index’s performance in the future.
4.4.3.3 Identified unimportant parameters
To some degree captured through the connected and correlated parameters, in some cases the analysis
in Unscrambler identified parameters that should not be included in the models. This is for example
true in cases where parameters with small variations or where the content was generally zero apart
from a few samples.
Examples of this is Langbeinite and Anhydrite. While these can impact the strength, the content of
these are in many cases zero. The small number of samples containing these will therefore get unfair
importance, and if the samples containing these also have high or low strength these could be
considered by the uncertainty analysis as more important than is the case.
43
4.4.3.4 Final selection
As mentioned, including the variables which provides the most amount of useful information and the
largest theoretical impact has been the target. For that reason, a final check was made using both the
correlation coefficients achieved in the indexes in the ANN model as well as manually.
4.4.4 Unknowns In addition to the parameters measured, unknown parameters will also impact the model. While
4.5 Selecting samples The sample selection phase, similarly to the data preparation, aims to provide good and useful data for
the model. However, going further than simple formatting and removing obvious errors, the sample
selection utilizes Unscrambler, the HTC model and Excel to identify issues that can impact the model
negatively. The goal is to achieve a low bias meaning a high accuracy over the entire interval.
4.5.1 Variations in laboratory equipment A key issue regarding sample selection is variations in the analysis equipment needs to be accounted
for. As calibrations and other possible sources for variations in the equipment does not occur at the
same time in both laboratories, comparing results between the laboratories may provide insight into
when major changes have occurred.
While calibrations are logged, the impact of these calibrations varies. For XRF, the impact not only
varies between calibrations but also varies between the analyzed elements. Comparing the XRF results
from CR and the process laboratory provides the following graph for Na2O in SH-cement.
Figure 14 - Na2O measured on the same samples (only SH-cement), sorted chronologically
In Figure 14 it can be seen that there is a large difference between the results for Na2O content before
the spring of 2017. The period studied is chosen as XRF results from both laboratories are available
and compiled for this period.
0,12
0,17
0,22
0,27
0,32
0,37
2016-11-26 2017-03-06 2017-06-14 2017-09-22 2017-12-31 2018-04-10 2018-07-19 2018-10-27 2019-02-04
Na2O
Na2O Na2O CR
44
This information suggests that a calibration was conducted in the spring of 2017 with a large impact
on the results. Using samples both before and after this calibration can impact the index and provide
conflicting results on the statistical impact of Na2O on the strength development.
Such clear impact of that calibration cannot be seen on other elements. Although for SO3, comparing
the results from carbon-sulfur determinator with the results from the process laboratory, a consistent
difference between the results can be seen. Consistent/constant errors have less impact on the model,
as the error becomes accounted for in the process of constructing the model.
4.5.1 Bias As most samples are in the center of the interval with occurrences decreasing towards the extremes of
the interval sample selection was tested. In this case the sample selection was conducted in
Unscrambler, picking equally spaced samples over the entire interval. It was only possible to do this
for BAS-cement due to the small data set for SH-cement.
Figure 15 - Histogram for 1d strengths BAS-cement
Figure 15 shows the histogram for all 1d strengths for BAS-cement in the data set used in this project.
45
Figure 16 - Histogram for selected 1d samples for BAS-cement
Figure 16 shows the histogram for the selected samples, demonstrating a more even distribution. The
reason for the still low portion of the samples at the extreme ends of the interval is simply due to the
lack of samples at these points. Removing enough sample for a completely even distribution would
reduce the number of samples too far for achieving a good index.
4.6 HTC model The final indexes will be in HTCs Neural Networks model as this is an easy to use and robust model.
Information from Unscrambler is for this reason used as a basis to form the model in HTCs model.
This includes the selection of samples and parameters.
Significant variables identified through Unscrambler for the prediction provides a starting point for the
model. As the functionality and method varies significantly between the two platforms, additional
work must be done to assure that the best combination of parameters is chosen for the final model.
To help identify good models, when the model is created HTCs model provides certain data regarding
the model, the main indication being the correlation coefficient. A correlation coefficient close to one
means a model which explains the variances in the output parameters. While only an indication, it is
helpful towards selection of parameters where the target is to maximize the correlation coefficient.
4.6.1 Functions & Procedure The model software developed by Dr. Jan Skocek at Heidelberg Technology Center contains several
different neural networks models. Most developed is the RBFN model while the 3LNN (three layer
neural network) model is still under development. For that reason, the RBFN model has been used. An
additional functionality is the multiple linear regression, this is more similar to the traditional
multivariate approach and has not been used in this project.
The model runs in excel and the data is entered into a sheet called “Data Example”. The number of
parameters and their use in the model can be specified in this sheet. The data is formatted, removing
46
samples without data on all parameters used in the modelling and this formatted data is then pasted
into a separate sheet. From here, the model is run and the graphical user interface (GUI) is opened.
When running the model, it identifies samples which are potential outliers in the sense that they do not
correspond to the behavior of other samples. The user can choose to deactivate these samples or
simply deactivate the samples with the highest relative difference from the measured value for the
output parameter. A correlation coefficient as well as information on the errors between measured
values and approximated values are shown to the user in the GUI.
Approving the model the next step allows for verification of the model, the model can pick samples
randomly from the data set, removing them from the training set and then predicting the values of the
output parameters for a fairer evaluation of the model performance. The model reports on the errors as
well as correlation coefficients for both the training and verification set when the verification is run.
And in case of large errors and/or low correlation, samples can be moved from the verification set to
the training set.
The model also allows for adjustments on several parameters of the model, including the number of
neurons per sample, punishments for large neural weights etc. Changing these parameters allow for
balancing the variance and bias. The model can also optimize these parameters automatically, however
this is a slow process when having large data sets.
4.6.2 Separate indexes for 1d and 28d As the variables used in the model vary between the 1d and 28d indexes for the cement types, the
decision of using separate indexes for prediction of the different strengths is made. While this
decreases the user-friendliness, the prediction accuracy would suffer from having both strengths
predicted in one index.
As the model uses all the variables specified by the user, for example including all variables that may
be significant for both strengths would lead to the model including the insignificant variables for
predictions of the separate strengths. This risks decreasing the index performance and robustness as it
would mean including far more variables than necessary.
4.6.3 SH-cement While the analyses from CR was included in the Unscrambler runs, the focus is on the process
laboratory analyses. However, while the data only contains 32 µm, 32-2 µm and d50 from the PSD
analysis at the process laboratory, recently 15 µm was added. As SH is a finely ground cement, the 15
µm passing percentage will provide a more interesting measure regarding the particle size. As this was
only recently added to the reports from the process laboratory, 15 µm analysis from CR can be used in
the model.
4.6.4 BAS-cement For BAS, the analyses run at the process laboratory forms the basis for the model. A coarser cement,
reported particle sizes analyzed at the process laboratory better reflect the variations in the particle
sizes between samples. The number of analyses run on BAS-cement at CR is also lower compared to
the SH-cement, further motivating limiting the variables used to those from the process laboratory.
4.6.5 Verification/validation HTCs model has a built in verification function, removing a number of random samples from the
training set and using them for verifying the performance of the model. The number of samples used
47
for verification can be chosen and the choice is made by stating “on average every nth sample will be
used” where 3 will lead to that on average every 3rd sample is used for verification.
The sample selection is random, and when the selection is done samples can be returned to the training
set if the correlation is impacted to a high degree. As the selection is random, the verification is run 3
times for each of the indexes.
4.6.6 Bias issue identification The issue with bias is identified as the ANN model showed a clear tendency to overestimate the low
samples and overestimate the high samples during the verification step.
4.6.7 Bias-Variance balance Working with Bias-Variance balance is mainly done using the functionality of the model itself and the
balance is dictated by the desired functionality of the index. While in many models the target is to
achieve the lowest possible total error of prediction, in this case the ability to identify samples outside
acceptable boundaries (at the extremes of the interval) is more useful.
Looking at a typical verification set for the model, the error can be plotted against the measured
strength. This gives an indication regarding how the error will change over the interval. Plotting a
trendline in excel and using the slope and the R-squared value for the trendline provides information
on the bias of the model. A model with low bias will have a low slope and low R-squared value,
meaning that there is low correlation between the strength and the error.
To investigate the impact of the model parameters on the bias-variance balance several indexes with
identical training and verification sets are created and the error plotted against the measured strength.
The correlation coefficients of each index is also considered in addition to the slope and the R2-value
of the trendline in the figure.
A “Bias value” is calculated by multiplying the slope and the R2-value. A higher slope indicates a
higher bias and the R2-value indicates the fit to the trend line. As the R2-value decreases, the fit of the
trend line decreases and the errors are more randomly distributed over the interval. A random
distribution of the errors is desirable for this model.
While Bias-Variance balancing typically aim at the precision of the model throughout the entire
interval, in this project the samples at the extreme ends of the interval are more important to identify.
For that reason, an increased focus has been on reducing the bias of the model. Several ways of
approaching this have been used, mainly sample selection and model parameter variations.
4.6.8 Model parameters For RBFN the model parameters can be varied in the GUI, as shown in Figure 17.
48
Figure 17 - GUI for model parameter adjustment in the ANN model
The theory behind the various model parameters impact on the model is described in A2.1 Radial
Basis Function Network. Option to optimize the parameters is available, however this will be time
consuming for larger data sets as it varies the model parameters and creates new models until the
optimum is reached. For SH-cement this has proven possible within minutes but for BAS-cement and
the larger data set it is impractical.
4.7 Clinker minerals The clinker minerals are also studied separately with the aim to further increase the knowledge around
the clinker mineral impact on the strength development of the cement. While the clinker is the same,
the clinker content and several other parameters are different between the cement types. As the content
of the various clinker minerals is reported as share of the total sample, actual content of clinker rather
than the quality of the clinker will have a large impact if studying the different cement types together.
As each cement type needs to be studied separately, SH-cement should be chosen. While a small
number of samples are available, the variations in the quality of the fly-ash introduces an added
unknown into the BAS-cement. However, XRD analysis on SH-cement is currently less accurate than
on BAS-cement due to the fineness of the cement.
4.7.1 Analyzing clinker mineral impact The final properties and composition of the cement is not only the result of the clinker content and
quality but also depends on the cement milling and the mixing of materials at that stage. While an
ideal scenario would include constant quality and quantity of the additives and large variations in the
clinker quantity and quality, these types of analyses has not been done on a scale where the data set
size would support using the methods used in this project. These types of investigations are time
consuming and costly as the cement has to be milled in the laboratory in sufficient quantity for
analysis.
49
Certain analyzed parameters of the cement are theoretically however, only a result of the clinker.
Specifically the contents of minerals formed during the clinker burning like Alite, Belite and
Aluminates.
4.7.2 Procedure
Figure 18 - The GUI for creating scenarios in the ANN model
The impact of the clinker minerals can be found by using the scenarios function in the ANN model.
This allows for variation of only selected parameters throughout a range specified by the user, fixing
the other parameters to set values. The user can also specify the number of levels for which scenarios
will be created, the default number of levels is three. A screenshot showing this functionality is shown
in Figure 18.
Multiple parameters can be varied at the time, however, the model will in those cases calculate every
possible combination of these parameters depending on the number of levels selected. This number of
scenarios calculated will for that reason be the number of levels to the power of the number of
parameters selected.
4.8 Additional work Not part of the main procedure, the following work has been conducted to either simplify the project
work or to provide new tools for future work in the area.
4.8.1 Macros In Excel, Visual Basic for Applications (VBA) can be used for coding additional functionality to
Excel. This has been done for two tools that added some functionality that was needed for this project.
Basic knowledge of programming as well as online resources (forums etc.) was used in the
programming of these macros. For the function “IsFileOpen” this is available on Microsoft Support
forum and is copied from that location.
50
4.8.2 Anläggningsindex The current index for Anläggningscement has been evaluated using the insights gained during this
project, and a new index produced using the procedure in this project has been proposed to Cementa.
5. Results and Discussion Overall, within this project four indexes for estimating the compressive strengths of BAS and SH-
cement has been produced. A key result in this project is also the procedure used to develop the
indexes, as will be shown in this section, the indexes produced show results deemed adequate.
The results regarding clinker minerals show that the Alite, as expected, has a major positive impact on
the cement strength development. It also shows that the procedure used in this project can be used in
the future to develop indexes for other cement types and other parameters in the cement manufacturing
process.
The main issues include the bias of the indexes, data set sizes and the model functionality.
Mentioned as an objective with lower priority, prediction of setting time has been tested. However, the
setting time varies over a large interval and the setting time data is not continuous over the interval,
rather being reported as 5 minute steps.
5.1 Verification Verification has mainly been done using the built-in feature in the ANN model, where the software
selects a random set of samples (the number of which can be controlled by the user) and removes these
from the training set.
Verification is done through the built-in verification feature in the ANN model which selects a random
set of samples, removes these from the training set and predicts the strengths of these and comparing
to the known value. As the sample selection is random, the distribution of these varies between runs
and with that the verification set can indicate different index performances between different runs.
Figure 19 - Verification sets for 2 model runs from ANN model
y = 0,4639x - 27,097R² = 0,3857
y = 0,3627x - 21,302R² = 0,2246
-6
-4
-2
0
2
4
6
53 55 57 59 61 63 65
Erro
r (M
Pa)
Measured strength (MPa)
Bas 28d RBFN
Error run1
Error run2
Linjär (Error run1)
Linjär (Error run2)
51
Figure 19 shows the verification sets of 2 different runs of, in every other way, identical models. For
this reason the verification has been run 3 times on each of the indexes as shown in the verification
graphs for the indexes for BAS and SH.
The randomness and the differences between runs means that a roll-out time for the indexes should be
implemented where the real-world performance is evaluated. Verification sets and performances for
these runs could later be compared over time to get a better insight into the index performance.
5.2 Procedure The procedure was developed as functionality of the tools and the needs was identified. As parameter
selection must be done manually in the ANN model, using Unscrambler’s uncertainty analysis to
identify the important parameters was used.
5.2.1 Including 1d strength as parameter for 28d strength prediction The early strengths and the later strengths are highly correlated and including the 1d strength for
prediction of the 28d strength would benefit the index. However, including 1d strength means that the
index cannot be run using only data from the process laboratory.
Using predicted 1d for prediction of 28d is bad practice during modelling due to error propagation.
5.2.2 Target of 2 MPa The target of 2 MPa has been used in previous modelling work, and for good reason: It allows for
catching relatively small variations in the compressive strength. However, the uncertainty of the
compressive strength analysis needs to be considered as well.
Method ER9227, the accredited method for compression strength testing used at CR, states that the
95% confidence level (2 x total measurement uncertainty) is 9% for 1d strength and 5% for the 28d
strength. This corresponds to roughly ±2MPa for 1d strength and roughly ±3MPa for 28d strength for
these cement types. With this knowledge, the target of 2MPa should be used carefully and 3 MPa was
chosen as a target for 28d strength in this project.
5.2.3 Parameter selection The target of the parameter selection is to identify the parameters which have the highest impact on the
compressive strength. Some things has been considered during the parameter selection in addition to
what has been reported from the Uncertainty analysis.
Having parameters that are connected and to a degree include the same data puts unfair weight on the
connected quality. The model does not understand the connection between parameters and will for that
reason treat these as separate.
In addition to connections between parameters, the quality of the analysis has also been weighed into
the selection process. SO3 is analyzed both through XRF and XRD, however the general view is that
the XRF analysis provides a more accurate value and for that reason this has been selected.
On the same note, the SO3 reading from the XRF at the process laboratory is less reliable than the SO3
reading from the carbon-sulfur determinator at CR. However, for the analysis at CR fewer data points
are available and the analysis is slower, and to enhance the usefulness of the model, XRF value has
been used. In this case, the SO3 from the carbon-sulfur determinator and the XRF was compared
before and the error was to a degree systematic rather than random, and systematic errors is handled
well by the model. The comparison is seen in Figure 20 & Figure 21
52
Figure 20 - Difference between Carbon-Sulfur determinator and XRF for SO3
Figure 21 - Results of analysis on SO3 on XRF and Carbon-Sulfur determinator
5.2.4 Separate indexes While the user would benefit of having the same index for prediction 1d and 28d strength of the
cement types, there are some issues with this approach. The significant variables are not the same for
both 1d and 28d as the parameters will have different impact at 1d and 28d. For that reason, separate
indexes must be made.
Predicting both strengths in one index would risk significantly reduce the performance of the indexes
and as the number of variables would need to be increased the bias may worsen.
y = -0,0002x - 0,1667R² = 0,0097
-0,5
-0,4
-0,3
-0,2
-0,1
0
0,1
0,2
0,3
0 20 40 60 80 100 120 140 160
Dif
fere
nce
(%
-po
ints
)
Sample number
Difference between XRF and CS
Difference
Linjär (Difference)
20 per. glid. med. (Difference)
3,2
3,3
3,4
3,5
3,6
3,7
3,8
3,9
4
4,1
0 20 40 60 80 100 120 140 160
Rea
din
g (%
)
Sample number
SO3 from XRF and CS
SO3_CS SO3_XRF
53
5.2.5 Unscrambler models As seen in the figures from Unscrambler, the correlations coefficients for the PLS models are low in
comparison to the correlations for the ANN indexes. This demonstrates that using the PLS model for
prediction would not perform at the same level as the ANN indexes created. However, using the
additional information generated through the PLS runs have proven to give good results in the ANN
indexes.
5.2.6 Anläggningsindex While not part of this project, using the procedure described in this report a new index was proposed
to Cementa for their index for 28 d strength on Anläggningscement. Cementa has used an index for
Anläggningscement since before this project started, however, using the procedure and the lessons
learned from this project some improvements could be proposed to this index. The index currently
used at the plant is the proposed index.
As this is not part of the project, the development and evaluation of this index was conducted outside
this project and the results are excluded from this report. However, the use of this index will allow for
evaluation of possible deterioration of indexes developed in this manner and is a basis for further
study.
5.3 SH-cement For SH-cement, runs in Unscrambler indicate statistical importance of a number of variables. A full
list of variables used and additional graphs are available in appendix.
1 d 28 d
SO3_XRF 0,283 0,7105
K2O 0,1793 -0,3245
Cl 0,245 0,3676
d50 -0,3192 0
2 µm 0,203 0
3 µm 0,2087 0
15 µm 0,3888 0,4786
Alite_M3 0,2213 0
Alite_Sum 0,2327 0
Alum_cubic -0,1916 -0,1166
Alum_Sum -0,2038 -0,1588
Aphthitalite 0 -0,2983
Arcanite 0,1523 0
Belite_beta -0,2043 0
Calcite -0,1655 -0,0754
CO2_XRD -0,1664 -0,0742
Hemi-
hydrate 0,1314 0
Table 2 - Variables indicated by Unscrambler for SH-cement
Most of the variables indicated have a foundation in literature which is discussed in section 3. Theory.
However, while these provide a good starting point, further limiting of the number of variables as well
as checking for redundancy has to be made.
54
5.3.1 1 day index While most of the variables identified by Unscrambler, additional reduction of variables is done.
As the variables d50, 2 µm, 3 µm and 15 µm all represent particle sizes 3 µm are not included in the
final model. 2 µm, d50 and 15 µm provides a good representation of both the amount of fines that
theoretically does not contribute to the strength development as well as the variation of the general
particle size distribution difference between different samples.
The reasoning for choosing 15 µm rather than 32 µm for SH-cement is due to this cement type being a
finer cement type. For that reason the amount of material passing 32 µm is generally high for all
samples and for that reason 15 µm provides a better indication of variations in grinding between
different samples. The variable d50 (size where 50% of particles are below this size) also provides
valuable input into the overall particle size distribution. All being correlated to some degree, however,
as the particle curve can be uneven, they all provide valuable input.
While Alite M3 is identified as significant variable as well as Alite Sum, the level of Alite M3 is
highly correlated to the total amount of Alite. In this case, to capture the potential difference which is
caused by the amount of M3 the variable Fraction M1 is included in its place. This variable indicates
the portion of Alite that is of M1 configuration and for that reason describes the variations in M1
versus M3 without depending on the total amount of Alite, which is included in a separate variable.
For aluminates, the total amount of aluminates is deemed more important than the different
configurations and to limit the amount of variables, only Aluminate Sum is included.
CO2 is excluded as this may be correlated to the amount of Calcite in the sample.
SO3 (XRF)
K2O
Cl
d50
2 µm
15 µm
Alite Sum
Aluminate Sum
Arcanite
Belite_beta
Calcite
Fraction M1
Hemi-hydrate gypsum
These variables are used in the ANN model to produce an index. This results in a correlation
coefficient of 0.941 before verification, and using the built-in verification function of the model with
the results demonstrated below. While the total data set is constant between runs, the data set is
selected randomly by the model and for that reason three runs is done for each index with the aim to
demonstrate the variations between runs.
Run 1 Run 2 Run 3
Correlation coefficient: 0.941 0.941 0.941
Number of samples in verification set: 28 27 27
Samples with error above 2 MPa: 2 0 1
Percentage of samples with error above 2 MPa: 7.14% 0 3.70%
55
Table 3 - Results for produced index for SH 1 day compressive strength
Figure 22 - The difference between measured and predicted for 1d strength SH-cement from HTCs model
Table 3 shows that in all three runs, the number of samples in the verification set with a difference
between measured and predicted above the set threshold at 2 MPa are below 10 %. The errors are
visualized in Figure 22 (measured value minus predicted). In Figure 22 the trendlines demonstrate the
indexes tendency to overestimate the strengths of samples with low compressive strength and
underestimate the strengths of the samples with high compressive strength (bias).
However, in the case of SH-cement the data set is small, this means that the verification set is also
smaller than desired.
5.3.2 28 d index For the 28 d strength index, the results from Unscrambler yielded very few variables as significant
with the method used. For that reason, the procedure was stopped after a few runs where a reasonable
number of variables where left. While all are not indicated as significant in the final run before the
procedure was aborted, all the variables left were indicated as significant in previous runs.
It also failed to indicate the significance of certain variables that have a large impact on the 28 d
strength. One example of this is Alite, which is the largest contributor to strength development during
the first 28 d as is discussed in previous chapters.
In the end the selected variables are similar to those used in the 1 d strength index, although Arcanite
being replaced with Aphthitalite. While both are present in small quantities they can have a negative
impact on the 28d strength and possibly a positive impact on 1d strength. For that reason the one
indicated by Unscrambler was used.
SO3 (XRF)
K2O
y = 0,1872x - 5,9968R² = 0,14
y = 0,1342x - 4,5059R² = 0,1169
y = 0,3529x - 11,076R² = 0,4297
-4
-3
-2
-1
0
1
2
3
27 29 31 33 35 37
Erro
r (M
Pa)
Measured strength (MPa)
SH 1d index
Error run 1
Error run 2
Error run 3
Linjär (Error run 1)
Linjär (Error run 2)
Linjär (Error run 3)
56
Cl
d50
2 µm
15 µm
Alite Sum
Aluminate Sum
Aphthitalite
Belite_beta
Calcite
Fraction M1
Hemi-hydrate
In the end, as shown below, the index performed adequately.
Run 1 Run 2 Run 3
Correlation coefficient: 0.891 0.891 0.891
Number of samples in verification set: 27 27 27
Samples with error above 3 MPa: 2 0 0
Percentage of samples with error above 3 MPa: 7.41% 0% 0%
Table 4 - Results for produced index for SH 28 day compressive strength
Figure 23 - The difference between measured and predicted for 28d strength (without 1d as input) SH-cement from HTCs model
For 28 day the threshold for error is 3 MPa rather than 2 MPa and for all three runs the amount of
errors above this value was below 10 % as can be seen in Table 4. However, similarly to the 1 d, the
data set is small and for that reason the verification set is also smaller than desired.
Comparing this case to that of the 1 day index, Figure 23 indicates that bias is a larger issue in this
case. A random distribution of errors over the entire span is desirable to indicate low bias, and while
y = 0,4242x - 26,614R² = 0,3643
y = 0,3838x - 24,075R² = 0,6326
y = 0,4647x - 29,254R² = 0,5344
-4
-3
-2
-1
0
1
2
3
4
58,5 59,5 60,5 61,5 62,5 63,5 64,5 65,5 66,5
Erro
r (M
Pa)
Measured strength (MPa)
SH 28d index
Error run 1
Error run 2
Error run 3
Linjär (Error run 1)
Linjär (Error run 2)
Linjär (Error run 3)
57
trendlines demonstrated an issue with bias for 1 d, the value describing the fit of the trendline to the
errors (R2) is higher in this case. This indicates a less random distribution and higher bias.
5.3.3 SH-Cement indexes discussion For SH-cement the increased importance of the PSD is apparent in the results. The final indexes show
good promise for prediction. Overall, 1d proved to provide better correlation than 28d as the impact of
unknowns will increase during the hydration. Using 1d strength as a parameter would increase the
precision of 28d indexes but reduces the overall usability of the model.
5.3.3.1 Parameter selection
As can be seen the variables used as input for the model varies from the variables identified by the
uncertainty analysis in Unscrambler. The results of the uncertainty analysis provides a good start
however, as mentioned, a theoretical understanding of the hydration reaction and the impacts of the
parameters must also be used.
In the case of 1d strength index for SH, the number of parameters needed to be reduced further from
the uncertainty analysis. In this case, the sum of Alite and Aluminate was chosen and the content of
the mineral forms where excluded. This is due to the fact that the total content of the minerals can be
assumed to have a higher impact compared to the form which they are present in.
The smaller data set also include some risks when the analysis is done in Unscrambler as random
occurrences can impact the parameter impacts. For example, if the Portlandite content happens to be
high for a sample with high strength this could mean that Portlandite is indicated as positively
impacting the strength. This is also one reasons for the need of having a theoretical understanding as a
background when selecting the parameters.
5.3.3.2 Including parameters from CR
While the focus as mentioned has been on the results from the process laboratory, 15 µm and 2 µm are
included from CR. SH-cement is a fine cement and the 32 µm size may not provide the necessary
variations for a robust model as it is typically around 90% with the deviations from this being
relatively small.
When including 15 µm, adding the 2 µm was also done. While this can be deduced from the 32-2 µm
and 32 µm parameter, having it separate reduced the number of parameters.
Adding this parameters was not taken lightly, as the PSD analysis at CR is not reported on all samples
in the data collection used. Comparing to the approximately 130 samples with data from the process
laboratory, only approx. 105 samples include the PSD analysis conducted at CR.
5.4 BAS-Cement As for SH-cement, runs in Unscrambler was used indicate statistical importance of a number of
variables. A full list of variables used and additional graphs are available in appendix. For BAS-
cement a larger data set is available compared to that for SH-cement. The indicated significant
variables are listed in Table 5.
1 d 28 d
SiO2 -0,2789 -0,274
Al2O3 -0,0771 -0,1817
CaO 0 0,1007
MgO 0,1847 0
58
SO3 0,1727 0,7095
K2O 0,3204 0
Zn 0,1449 0
d50 -0,1799 -0,1461
Alite_M1 0,1339 0,3131
Alite_Sum 0,1029 0,144
Anhydrite -0,1147 0
Aphthitalite 0,3066 0
Arcanite 0,1753 0
Belite_beta -0,1732 0
Calcite -0,0689 0
CO2_XRD -0,0678 0
FA_Sum -0,0642 0
Fraction_M1 0,0979 0
Gypsum -0,4199 0
Hemi-
hydrate 0,4602 0,327
Lime 0,2442 0
Portlandite 0 -0,3229
Quartz 0,0833 0
Table 5 - Significant variables indicated by Unscrambler for BAS-cement
As shown in Table 5 a large number of variables was indicated as statistically significant for 1 d
strength and the number of variables had to be reduced to produce the index. It also did not indicate
aluminate as important while indicating aluminum as important. For 28 d a smaller number of
variables was identified, however, not all theoretically important variables was identified by
Unscrambler.
5.4.1 1d In the list below is the selected variables for the 1d index for BAS-cement.
SiO2
MgO
SO3
K2O
d50
Alite_sum
Alum_sum
Belite_beta
Calcite
FA_Sum
Fraction_M1
Gypsum
Hemi-hydrate
Several variables are removed for different reasons and Aluminate is added. In this case Anhydrite is
removed as a variable even though it is indicated as significant by Unscrambler and impact the setting
of the cement. However, the levels of Anhydrite over the period was typically 0% with few
exceptions.
59
In terms of Aluminum, this is highly correlated to the content of flyash in the cement represented by
FA_sum.
The variables shown above yield the following results.
Run 1 Run 2 Run 3
Correlation coefficient: 0.928 0.928 0.928
Number of samples in verification set: 163 165 173
Samples with error above 2 MPa: 13 17 8
Percentage of samples with error above 2 MPa: 7.98% 10.30% 4.62%
Table 6 - Results for produced index for BAS 1 day compressive strength
Figure 24 - The difference between measured and predicted for 1d strength BAS-cement from HTCs model
In the case of BAS-cement a larger data set is available. However, the results shown in Table 6 shows
that one of the runs yielded results where over 10 % of the verification set differs from the measured
value. As shown in Figure 24, the bias is also greater in this run.
For BAS-cement, and particularly the 1 d strength, an unknown factor is the gypsum slurry from the
flue gas desulfurization scrubber in Slite. This is injected into the mills during BAS-grinding but not
during SH-grinding. The slurry solids contains mainly gypsum (CaSO4∙2H2O) but the quality of this
varies greatly. The impact of this will be discussed in section 5.4.3.1 Gypsum slurry5.4.3.1 Gypsum
slurry.
5.4.2 28 d SiO2
Al2O3
CaO
SO3
d50
Alite_sum
Fraction_M1
y = 0,2588x - 5,4841R² = 0,1211
y = 0,3378x - 7,3209R² = 0,233
y = 0,1921x - 4,1629R² = 0,0773
-5
-4
-3
-2
-1
0
1
2
3
4
5
17 18 19 20 21 22 23 24 25 26
Erro
r (M
Pa)
Measured strength (MPa)
BAS 1d index
Error run 1
Error run 2
Error run 3
Linjär (Error run 1)
Linjär (Error run 2)
Linjär (Error run 3)
60
Hemi-hydrate
Portlandite
In the case of BAS-cement there are more significant differences between the variables used for 1 d
and 28 d. Unscrambler did not indicate that Aluminates was important in the case of both 1 d and 28 d
but is included in the 1 d index anyway as they have a significant impact on early strengths. However,
as the hydration of Aluminates is quick, the impact on the 28 d strength should be less significant.
In this case Portlandite is also added as a variable, this is indicated as significant by Unscrambler and
this is also supported by theory. Portlandite is an indication of prehydration of the cement and has a
significant negative impact on the cement strength.
Compared to 1 d, in this case CaO is added and MgO is removed. While MgO has an impact on the
cement properties through Periclase as well as stabilization of different Alite configurations, the
inclusion of Alite_Sum and Fraction_M1 will to some degree cover this. And as MgO is not indicated
as significant by Unscrambler for 28 d it can be removed.
While the Aluminates is not included, Al2O3 as well CaO is included. These, along with SiO2 will
correlate to the contents of both Aluminates and Flyash. As Unscrambler indicates these as significant
these are included instead of Aluminates and FA_sum.
Run 1 Run 2 Run 3
Correlation coefficient: 0.874 0.874 0.874
Number of samples in verification set: 166 164 180
Samples with error above 3 MPa: 11 11 8
Percentage of samples with error above 3 MPa: 6.27% 6.71% 4.44%
Table 7 - Results for produced index for BAS 28 day compressive strength
Figure 25 - The difference between measured and predicted for 28d strength BAS-cement from HTCs model
Table 7 indicates a slightly lower correlation coefficient than for other indexes, however still in at an
adequate level. And as the amount of verification samples with an error greater than the 3 MPa level
y = 0,4639x - 27,097R² = 0,3857
y = 0,3627x - 21,302R² = 0,2246
y = 0,3584x - 21,116R² = 0,1779
-6
-4
-2
0
2
4
6
53 55 57 59 61 63 65
Erro
r (M
Pa)
Measured strength (MPa)
Bas 28d index
Error run 1
Error run 2
Error run 3
Linjär (Error run 1)
Linjär (Error run 2)
Linjär (Error run 3)
61
are below 10 % in all cases, the index can be said to perform adequately. Figure 25 in turn
demonstrates a slightly larger issue with bias in this instance compared to the 1 d index. However, the
errors are still sufficiently random (poor fit of the trendlines).
5.4.3 BAS-Cement index discussion In the case of BAS-cement additional unknowns are present compared to for SH-cement. SH-cement
is a “pure” product containing only clinker and small quantities of additives of known qualities, BAS-
cement on the other hand contains both fly-ash and gypsum slurry.
The amount of fly-ash can be estimated with the XRD-analysis however, the quality of the fly-ash in
the cement samples is not known. The fly-ash is shipped to Slite and stored in 2 silos in which
different qualities are mixed.
5.4.3.1 Gypsum slurry
A key unknown in the BAS-cement is the gypsum slurry, particularly for 1 d strength. The quality of
the slurry varies over time as it is a byproduct from the production of the clinker. Recently it has been
seen in Slite that slurry containing CaSO3∙½H2O can significantly delay setting time and impact the
short term strength.
BAS-cement has proven more difficult to accurately predict, this is possibly due to the fly-ash
addition. However, the results from the verifications of the indexes indicates that the majority of the
samples are within the boundaries (only around 7% above). Similarly to SH-cement the PLS models
created in Unscrambler has a significantly lower correlation coefficient compared to what is achieved
in the ANN model.
5.4.3.2 Parameter selection
Selecting parameters was done in the same way as the selection for the SH-cement. For the 28d
strength the number of identified parameters is very small.
As the data set is larger for BAS-cement, the parameter selection in Unscrambler should be more
robust. The risk of random occurrences giving erroneous results is reduced.
5.4.3.3 Fly-ash
The variations in fly-ash quality provides an unknown variable for the modelling. While the fly-ash is
tested as it arrives at the plant, the quality of the fly-ash used at any given time is not known and not
included in the data set for this project.
Further, the fly-ash used in the process varies in quality and fly-ash from some sources are coarser
than desired. As with clinker particles, coarser material will react slower and
5.5 Bias Reducing the bias has proven a key difficulty in this project. Although an important challenge, it has
not been possible to solve it during the course of this project.
5.5.1 Model parameter variations As mentioned, the model has built in functionality allowing to vary the model parameters. Using the
28 d index with the parameters presented in previous section, the model parameters was varied with
the aim of reducing bias without losing overall performance of the model.
The samples used for verification is not changed between the runs and the correlation coefficients
shown below are with the verification samples removed from the training set and the verification set
62
computed. For that reason, the correlation coefficient shown in this section differs from the correlation
coefficient of the finished model, even with the default parameters used in the index.
As the verification set is not changed, the changes in correlation coefficients and graphs between runs
is for that reason only the impact of changes in model parameters.
To evaluate the bias, a “bias value” is calculated by multiplying the slope of the trendline with the R2
(fit) of the trendline. The setup of each case along with results are shown in Table 8. The graphs are
available in appendix x.
Case 1
(Reference)
Case 2 Case 3 Case 4
Model Parameters:
Kernel width (dimensionless) 0.1 0.1 0.1 0.25
Penalty for large weights/smoothening 0.000001 0.000001 0.000001 1E-09
Max. No. of data points per neuron 3 2 1 1
Correlation coefficients:
Training set 0.854 0.905 0.949 1
Verification set 0.675 0.563 0.468 0.213
Bias value: 0.1789 0.1172 0.0961 0.0185 Table 8 - Results from model parameter variations
Comparing the cases above, it is obvious that the bias reduces between with increasing number of
neurons. This means that the errors are significantly more randomly distributed over the entire range
compared to the reference case. However, looking at the correlation coefficient the issue of variance
can be seen to increase. In essence, the model is able to fully learn the training set and in Case 4
provides perfect correlation in the training set. The issue with this is seen in the verification set
correlation coefficients which demonstrates that while the model has learned the training set fully, it is
not able to apply this to predict unknowns.
In essence this means that the errors, while being more randomly distributed, are larger and in case 4
errors up to 20 MPa is recorded.
A near infinite number of possible combinations of the parameters are available, however, these cases
demonstrates the difficulty with decreasing the bias without losing overall performance. Overall, Case
1 provided the highest correlation coefficient for the verification set meaning that of these four cases,
this is the most capable configuration for prediction even though the bias is higher.
5.5.1.1 Automatic Optimization
The model features the functionality to optimize the model parameters automatically and these can
after this be saved for future use. This will balance the correlations of the training and the verification
set with the target to achieving highest possible correlation for both. However, this might not be
desired for this case as this can increase the bias of the model and trade-off the precision at the
endpoints of the interval for better overall precision.
This function for automatic optimization of the model parameters is also a slow process. While
possible within a reasonable amount of time for SH-cement where the number of data points is low,
the process is too for BAS-cement to be useful in this case. As mentioned, the possibility to store the
optimized parameters is available, however as the index will vary over time it will require updating
occasionally.
63
5.5.2 Sample selection Sample selection was also tried in an effort to control the bias of the index. This was not possible for
SH-cement as the data set was small from the beginning, the histogram shown in Figure 26 also
indicate a less unbalanced distribution compared to that of BAS-cement.
Figure 26 - Histogram for 28d strength for SH-cement from Unscrambler
For the 1d prediction on BAS-cement, the variables identified as significant by the uncertainty
analysis are numerous and an index using these variables shows significantly worse bias compared to
the first 1d index for BAS. Using the same variables as is used in the first index yields a better result,
however this is still not any significant improvement over the original index.
5.52.1 Issues with sample selection
While Unscrambler provides a good way to select evenly distributed samples, it does not account for
all possible variations. The selected sample set must include the entire intervals for all variables used
in the final index, otherwise the model will extrapolate with potential for large errors.
64
Figure 27 - Error plotted against measured strength for selected samples 28d BAS
Figure 27 shows an extreme example found during this project. In this case, the selected samples did
not include some samples with high SiO2 content. As the model found that a higher SiO2 content
meant a lower strength, in this case it extrapolated and approximated the strength to negative levels (-
31.53 MPa in the most extreme case). The small variations in SiO2 in the training set and the
correlations of these with lower strength meant that the model vastly overestimated the impact of SiO2.
To avoid similar situations, sample selections should be done carefully to ensure that the samples
selected represent the entire interval of the variables. It also means that predictions of samples where
one variable is outside the interval for which it is trained will not be accurate and must be controlled.
These samples should be added to the training set once the analyses are done, increasing the interval of
this/these variables and making accurate predictions possible in the future.
5.6 Clinker minerals To analyze and estimate the impacts of the clinker minerals on the strength, the scenarios function in
the ANN model was used. Only one parameter was allowed to vary at the time and the number of
levels where set to 25 in all cases. Creating scenarios with more than one parameter varying means, as
mentioned, that the model will create a scenario for every possible combination and the number of
scenarios will increase exponentially. For this reason, only one parameter was varied at the time.
5.6.1 Inconsistency Mathematical models using software such as these will always try to explain the output parameter
using the data given. For that reason, adding or removing parameters will change the impact of any
given variable.
y = -0,1421x + 10,423R² = 0,0014
-20,00
0,00
20,00
40,00
60,00
80,00
100,00
53 55 57 59 61 63 65
Erro
r (M
Pa)
Measured strength (MPa)
BAS 28d selected samples
Error
Linjär (Error)
65
The samples also vary slightly between each run as the model automatically places samples in the
verification set which are randomly selected.
5.6.2 Results All figures demonstrating the impact of variation of the variables is available in Appendix A4 –
Clinker mineral correlations. In these figures it is seen that a higher Alite content correlates to a higher
strength both at 1 day and at 28 days. This is expected as hydration of Alite is responsible for the
majority of the strength development during this period.
The total Alite content measured is the sum of Alite in M1 and M3 forms. Fraction M1 as a parameter
indicates the share of Alite that is in the M1 form and it is a good parameter for the use in this case.
While the content of M1 is also reported, the amount of M1 will increase as the Alite levels increase
and this will obscure some impact of the various forms.
The results in this indicate the overall positive correlation for fraction of M1 for the 1 day strength.
However, the variation in strength depending on the M1 fraction is small. The fraction level varies
over an interval from roughly 36% to roughly 49% and the strength only varies by less than 1 MPa
over this interval. This parameter has also not been identified by the analysis in Unscrambler to be
significant.
Total Aluminate content is shown to be overall negative for the compressive strengths at both 1 day
and 28 days. However, interestingly a positive correlation between Cubic C3A content and
compressive strength is found at 1 day. However, again the impact is mild and the compressive
strength varies less than 1 MPa depending on the content of cubic C3A.
Regarding the alkali-sulfates these do not contribute to the compressive strength other than to similarly
to aluminates stimulate the setting of the cement. While mentioned in the guidance paper on clinker
reactivity that Langbeinite is preferred, Arcanite medium and Aphthitalite is undesired the correlations
found for 28-day strength would to some degree support this. The content levels of these are low
however and no conclusion can definitely be drawn from the correlations above.
5.6.3 Combinations An issue with this approach is that it fails to take effects of the combinations of the parameters into
account.
66
Figure 28 - Alite_sum plotted against Belite_beta in Unscrambler
Example of this is the calcium-silicates Alite and Belite. As high Belite levels typically correlates with
lower Alite levels as indicated in Figure 28, the indicated negative impact of Belite may actually be
the negative impact of lower Alite content. The Belite will contribute to the compressive strengths,
although the main contributor for the first 28 days is the Alite, and for that reason the negative impact
of the Belite is indicated for these times.
Illustrating this issue was when setting time for BAS was studied in Unscrambler (not within this
project) and the Belite content was indicated as shortening the setting time. Belite is slow and should
not contribute to this, however free CaO will shorten the setting time, and as CaO + Belite forms Alite,
the increased Belite content also correlated to a higher free CaO content.
5.6.4 Fly-ash In Unscrambler, the fly-ash content is indicated to have a negative impact on 1d strength. This is to be
expected as the main fly-ash reactions will take place after this point. In the ANN model, the negative
impact is also seen, however, the scenarios and the results will vary depending on the data in the
model as well as which variables are selected. For that reason, several scenarios are used for fly-ash.
These figures are available in Appendix A4.3 Flyash.
For 28d, in Unscrambler the fly-ash content is indicated to be insignificant for the 28d strength model.
As seen when run in the HTC model, the correlation varies depending on the parameters used in the
model. The clear negative impact shown in Figure 61 possibly provides the best insight into the
impacts of the chemical content in the fly-ash as this model assumes a constant particle size of the
finished cement. The fly-ashes used are generally coarser than the ground clinker, gypsum and
limestone in the cement and as it is added after the grinding stage the fly-ash addition will impact the
overall PSD of the cement.
A negative impact of the fly-ash addition is to be expected as the fly-ash contains a variety of
compounds where only some are clearly reactive and form the desired C-S-H.
67
The fly-ash is added to the cement after the cement mills and the fly-ash PSD will impact the PSD for
the final product. This needs to be considered and is seen in the scenarios from the indexes, including
or excluding the PSD variables will change the results of the scenarios and the indicated impact of the
fly-ash.
As mentioned, the quality of the fly-ash in each sample is also unknown, while the content of certain
minerals indicate the fly-ash content and can indicate the quality to some extent. However, the
majority of the fly-ash is amorphous and the individual chemical components of the amorphous part
cannot be analyzed through XRD. Regarding XRF analysis, as the fly-ash contains mainly silicon and
aluminum the variations and increases are to some degree masked by variations in the content of these
components in the clinker.
5.7 Difficulties Several obstacles where discovered during the process of this project. The number of samples
available for SH-cement from after May 2017 are fairly limited, limiting the potential for rectifying
actions of the bias. It also limits the interval over which the SH indexes operate.
5.7.1 Available data The amount of data available for each sample is large with many parameters being reported. A neural
network model can only be as good as the data though. Some key parameters are not added to the file
compiling all analyses for the samples, including the ferrite (C4AF) content. While this to some extent
is captured with the Fe2O3, adding this in the future would be beneficial as it impacts the strength and
the strength development of the cement.
As mentioned, the small data set for SH-cement also means that validation has been difficult and that
sample selection cannot be applied.
5.7.1.1 Measurement precisions
In this project, the XRD measurements form key parts of the data set. While XRD provide plenty of
valuable information the XRD results have several sources of error. Firstly, the Rietveld method is
based on PLS modelling and it normalizes the measured content to 100% meaning that while an
increase in Alite may mean a decrease in another mineral content. It can also mean that while the Alite
content is indicated as 70%, this does not account for an unmeasured minerals content.
5.7.2 Unknowns While the amount of data on the known and measured parameters are a key limitation, the unknown
parameters will also influence the performance of any model. As the cement production, subsequent
hydration and testing is complex procedures, there are several areas which can influence the actual
compressive strength and the measured compressive strength.
5.7.2.1 Fly-ash quality
Fly-ash varies plenty depending on the source, and today it is not kept track on which fly-ash is used at
which time. As the 28d strength for BAS-cement depends to some extent on the amount of fly-ash that
has reacted, this introduces an unknown making the prediction of 28d strength less reliable.
5.7.3 Neural Networks model functionality The latest version of the model stores the preference regarding number of samples used for training
set. This means that in the case that the user has its own specified verification set that can be used, the
model will still randomly select a verification set, reducing the number of samples used for training.
68
In the case of using sample selection, the samples not marked for training could be used for
verification. These are roughly equally spaced over the interval and will not vary between runs making
them more ideal for this task than the randomly selected sample set that the model uses. A randomly
selected sample set may not be evenly distributed throughout the interval and for that reason this
method for verification may be less robust and indicate a model performance above or below the
actual performance.
The sample set used for verification will also vary between model runs and the GUI can only be
reopened by creating a new model. This means that in the case the user closes the GUI the verification
set and training set may vary compared to previous run. Adding or removing variables requires, by
definition, a new index to be created and this feature makes comparisons difficult.
5.7.4 Early and late strengths Prediction of the late strength proved more difficult and the correlation coefficients of the indexes
demonstrate this to some extent. For the early strength, a few parameters has a major impact on the
strength, mainly PSD and the accelerators as well as Alite content. The strength development during
this period is highly dependent on the amount of Alite that has been hydrated and the variables that
impact this therefore has a major impact.
A key difficulty in predictions are the unknown combination effects of the compounds as well as
content of unknown compounds.
5.8 Excel regression tool In addition to using Unscrambler and the Neural Networks model Excels built-in feature allowing
modelling was also tested. This tool is included with excel and is for that reason widely available, both
at the Slite plant but also other plants.
The following errors was found using the Excel based regression tool. However, these samples are
included in the model itself (included in the training set) and is therefore not an accurate reflection the
performance of the model for prediction. Given the lacking performance of the model demonstrated by
this graph and that the model will perform equal or worse for any unknown sample, the decision to not
further investigate this path is motivated.
69
Figure 29 - Error vs measured strength from Excel
Coefficients Standard
error
t-quota p-value
Constant 24,37761 8,517882 2,861934 0,004327
SO3 2,369015 0,363368 6,519596 1,29E-10
H2O 0,634644 0,079615 7,971435 5,78E-15
SiO2 0,278519 0,144633 1,925693 0,054517
Al2O3 -0,89639 0,156434 -5,73015 1,45E-08
Fe2O3 -1,82537 0,474921 -3,84352 0,000131
CaO 0,026768 0,085681 0,312413 0,754813
Alite_Sum 0,202242 0,028771 7,029359 4,64E-12
Fraction_M1 0,035418 0,019427 1,823124 0,068679
Gypsum 1,484012 0,259115 5,727231 1,47E-08
Hemi-hydrate 1,456558 0,16978 8,579102 5,39E-17
Langbeinite 4,554151 1,018777 4,470216 9,01E-06
Lime -0,26386 0,116764 -2,25974 0,024121
Quartz 1,12334 0,301281 3,728543 0,000207
R_wp -0,43184 0,117267 -3,68256 0,000247
K2O -3,76614 1,1574 -3,25396 0,001189
Table 9 - Results from Excel regression analysis
The excel tool is a very basic tool, allowing only the use of a limited number of variables for
prediction of a y-variable. Identifying insignificant variables are also significantly more difficult and
requires interpreting the results.
Seen in Figure 29 and Table 9 is the results from the Excel tool. In this case, interpreting the results
led to R_wp, Langbeinite and H2O was identified as significant variables. R_wp is simply a residual
from the Rietveld method, Langbeinite content may impact the 28-day strength however many of the
samples in the data set has a Langbeinite content of 0 % with a maximum of 0.55%. H2O is a measure
of the water demand when doing the setting time analysis, this varies depending on the content of
y = 0,5718x - 33,31R² = 0,5718
-8
-6
-4
-2
0
2
4
6
8
51 53 55 57 59 61 63 65
Erro
r (M
Pa)
Measured strength (MPa)
Excel regression BAS 28d
Error
Linjär (Error)
70
several important minerals meaning that it may be interesting to include. However, it varies little
between samples and as the minerals that impact H2O needed impacts the strength in different ways, it
is better to include this separately.
Overall, the Excel tool provides a linear model with comparatively high bias. As the samples used for
verification is the same as is used for training the model, the correlation between the models prediction
and measured values are similar to what in the ANN model is the correlation coefficient for the
training set. However, as this method showed little promise and the development of the index was
significantly more complicated than using Unscrambler followed by the ANN model, this method was
abandoned.
As is seen in the study on the clinker minerals, the impact of these on 28 day strength is not linear. It
varies depending on the content of the specific mineral. Several of the compounds found in cement are
not either good or bad, but rather have an optimum which linear models fail to account for in a
sufficient way.
However, for a user with experience and detailed knowledge in the theory of cement hydration the
Excel tool can be used to provide estimations and crude predictions of compressive strengths. If this is
desired, the user should first limit the number of variables by selecting the theoretically most
important variables. The number of variables should be below the number that the Excel tool can
handle to speed up the process.
5.9 Sustainability Looking at the sustainability aspect of production of the cement, replacing more clinker with fly-ash
and optimizing the PSD is identified as potential uses. However, while the index will allow for
optimization of these variables with regards to the final quality, further problems may arise.
5.9.1 Fly-ash As mentioned, the impact of flyash content has been studied demonstrating a generally negative
impact on the cements 1 day compressive strength. This is to be expected as the pozzolanic reactions
will take place after this point. For 28 day strength the impact is not indicated as significant. The
approach for increasing the fly-ash addition would, based on these results, mean increasing content of
the compounds that benefit the 1 d strength. However, looking at Aluminates as an example of this,
this negatively impact the 28 day strength meaning that to maintain the same 1 day strength with
increasing fly-ash addition by increasing Aluminate content will overall impact the 28 day strength
negatively.
As mentioned in previous parts of this report, flyash is also a byproduct of coal firing power plants and
as the number of these plants decrease in favor of more sustainable alternatives, the flyash supply will
also decrease and the cost of this may rise.
5.9.2 PSD Particle size and strength is clearly correlated as shown above and a finer cement will, to an extent,
increase both the early and late strengths of the cement. However, optimizing the grinding stage to
achieve approved cement qualities without unnecessary grinding can be an interesting use for the
indexes.
71
5.10 Tool development During the course of the project, tools have been developed to assist in the work. These are mainly
simple programs written in Visual Basic for Applications (VBA) and used as macros in excel. The
tools are to some extent specific for the combination of software’s used in this project (Unscrambler,
Excel and the HTC model) but have the possibility to be used for a wider range of applications.
5.10.1 Extraction tool for sample selection While Unscrambler can assist with marking evenly distributed samples over the interval, it lacks the
functionality to extract the complete samples selected only providing the sample numbers and
approximated and measured Y for these samples. For that reason, a macro for these selections where
developed.
To use this macro, the sample-id’s which should be extracted is pasted into the first sheet and the
complete data set into another sheet. When run, the program goes from top to bottom in the list of
sample-id’s and looks for these samples in the data set, copying and pasting the selected samples into a
third sheet. Two versions of this macro was created, one where it copies and pastes leaving the
original data set complete (including the extracted samples) and one where the macro deletes the row
after copying it to create two data sets. Creating the two data sets allows for using one for training and
one for verification, in this case it also creates a new sheet and pastes the original data set into this.
The full VBA-code for these macros are presented in Appendix A5 – Sample selection VBA and A6 –
IP21 fetch.
5.10.2 “IP21-fetch” Results from analyses of samples from the production process at the process laboratory is available in
the process monitoring system used at the plant: Aspen InfoPlus21 or IP21 for short. This software has
an add-on for Excel allowing for gathering of both historic and current data into an Excel-file.
However, this add-on has historically proven to interfere with the HTC model software.
To be able to use the indexes for prediction of strengths for process samples, a tool is needed to be
able to gather, format and insert the process data into the model. While the add-on can achieve this by
simply using the add-on in the model file, this will be both time consuming as well as work intensive
for the user.
Using the add-on to gather the results of analyses conducted on samples from the cement mills at a
time specified by the user the macro performs several checks and tasks in the background. When
initiating the macro, the user is also required to specify the current model for the selected cement
types, as this can shift over time. After this, the macro runs completely in the background.
Firstly, as the values for the parameters at a specific time is always the results from the latest samples
and samples are only collected from running mills, the macro checks the status of the mill at the
specified time. This is to avoid cases where one mill may have been stationary for weeks for repairs
and where the values therefore are old. However, the macro can also be run without the condition of
the mill being online.
After this, the macro checks the type of product being produced by looking at which silo the product is
transported to after milling. The macro then transfer the sample results at the specified time into the
selected version of the index and strength can be predicted for this.
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5.10.3 Tool development discussion While not part of the main objective, to enhance the usefulness of the finished indexes and to simplify
future work in this area at the plant the additional tools where developed.
5.10.3.1 Sample selection and sorting
As mentioned, Unscrambler does not sort out the entire samples when equally spaced samples are
marked. For that reason the macros where developed. While useful in this project, it can also be useful
as a simple way to sort out a list of samples using the sample-id:s or other variables. As this has been
developed as a part of this project fully within the Excel VBA interface, the macros are both “free”
and can be readily made available to anyone within the group without need for additional software.
These macros are not unique and other methods and add-ons to Excel can to some extent provide the
same functionality. However, the built-in functions in Excel is less flexible and the use of these are
time consuming and are less user-friendly. Also, installing add-ons or additional software to
computers owned by HeidelbergCement Group to fulfil the need requires the approval and assistance
of the IT department to guarantee that no harmful software enters the group’s networks.
The code for these macros are found in Appendix and while not elegant, it fulfils the tasks.
5.10.3.2 “IP21-fetch”
The Excel-add-on for the Process monitoring tool (“IP21”) provides the possibility to get historic and
current values for a variety of different process parameters. Interesting in this project is the data from
the process laboratory where the analysis results for every sample from the production process is
available through the process monitoring tool.
However, issues regarding having the IP21 add-on installed while running the ANN model have been
found at the plant previously. For that reason the work of creating a tool which can easily transfer
values at a specified time was initiated. While possible to manually, this tool reduces the risk of user
error as well as speeds up the process by formatting and checking the cement types etc. before
transferring the data.
Currently, the program is limited to having a single index for each of the cement types SH and BAS
and it requires the format in the index to match the format used in the file. However, as this format
matches to some extent the order of variables used in the shipping file, any changes will be minor.
Option of having several indexes for each cement type can also be added with ease using the code
already available.
The tool itself is described in the results chapter of this report and the code is available in Appendix.
The function IsFileOpen is available on Microsoft support: (https://support.microsoft.com/en-
us/help/291295/macro-code-to-check-whether-a-file-is-already-open).
6. Conclusions/Recommendations The indexes produced in this project has shown encouraging results on the abilities for predictions of
the cement strength. In addition to this, the procedure used in this project can simplify the generation
of new indexes for other cement types or other properties in the process.
6.1 Indexes The indexes have proven to be able to predict the compressive strength with relatively low error,
however consistent work on updating these must be done to both maintain and improve the current
73
performance. The indexes can also be used for scenario evaluations as seen regarding fly-ash, however
this is highly sensitive to changes in the index.
6.1.1 Sustainability The indexes themselves can be used in several ways beyond the strength predictions. While a
consistent high quality of the cement is always the target, this needs to be balanced against the
production costs and sustainability.
A clear example of application where the indexes could benefit the work towards sustainability is
regarding the particle sizes. While a smaller particle size of the cement will increase the strengths
(within boundaries), the electrical energy demand for the grinding step is big cost for the company.
Particle sizes are to some extent regulated in the agreements and standards, but optimizing the
grinding step to meet these regulations and the quality demands without unnecessary grinding can
possibly provide savings for the company.
Similarly, using indexes like these to provide additional insight into the impacts of various variables
regarding the clinker burning stage as well as the raw meal grinding can help optimize these areas. The
thermal energy demand for the kiln is responsible for the vast majority of the total energy demand of
the plant. This would however, demand additional work with adapting an index to this and it requires
additional testing to be done.
It should be noted that while the indexes can provide prediction they cannot replace testing.
6.1.1.1 Procedure
While the indexes can be used directly to optimize the cement milling step, using the procedure used
in this project may provide more benefits elsewhere in the cement production process. Producing
ANN-indexes for prediction of other process parameters may aid in overall process optimization.
6.1.2 Setting time Prediction of setting time was mentioned in the project description to be done with regards to available
time. However, there are some key issues with prediction of this. The setting time data set is not fluent
over the entire interval and it also has a large variance, making predictions difficult as neither PLS or
the ANN model is designed to cope with this.
6.1.3 Different strengths Predicting 1d strength proved easier for both cement types when compared to 28d strength. This may
be due to the unknowns having increasing impact on this strength. Using early strength in the
prediction of 28d strength would improve the accuracy and performance of the model, however as
these are not available at the same time as the process laboratory results this has not been included in
this project.
Including early strengths should not be done by simply adding these to the indexes described in this
project, rather, the procedure of analysis in Unscrambler followed by insertion in the ANN model
should be repeated. The reason for this is that the early strength will depend highly on certain variables
which for that reason already will be included in the model. Typically, including the early strengths
would mean a decrease in the number of other variables which needs to be included.
6.1.4 Non-linearity of index Linear models are not ideal for use in this type of scenarios, as the impact of chemical composition
will vary depending on the levels of the compounds. Example of this is the content of Aluminates on
74
the 1d compressive strength. While Aluminates stimulate the setting and the strength development, it
does not directly contribute to the compressive strength.
6.2 Bias Work on reducing the index bias needs to be continued. As the data set grows in size, new attempts at
sample selection can be made, however as seen this needs to be done carefully including all extreme
samples as well as a lare number of possible combinations.
Mentioned by Dr. Skocek at Heidelberg Technology Center, the creator of the model, is the wish to
have 2n samples, where n is the number of variables used in the index. For 10 variables, this means
1024 samples, and decreasing the number of variables used to a level significantly below 10 may also
have a significant impact on the prediction capabilities. For these reasons, parameter selection is a
balancing act between overall performance, bias and robustness.
Not including parameters that are significant according to theory but not indicated as significant for
the current data set may lead to a reduced performance over time.
6.3 Recommendations Some issues/weaknesses has been discovered during this project, and the following recommendations
will benefit future work in these matters. They may also benefit other projects and the daily operations
at the plant.
6.3.1 Additional particle sizes reported To better represent the variation in the PSD of finer cement types like SH-cement, additional sizes
should be added to the reports from the laser diffraction at the process laboratory. Typically, SH-
cement have a very low fraction of the cement larger than 32 µm and sizes such as 15 µm may better
represent the variations in the PSD of these fine cements.
Cementa is currently in the process of purchasing new laser diffraction equipment for both laboratories
which provides an opportunity for evaluation of which sizes should be reported from the process
laboratory.
6.3.2 Analysis result compiling There are several issues surrounding the way that analysis results for shipping samples are available
today. The Excel file containing this data has been a key asset in this work and this file is a valuable
asset to any project or investigation where this data is needed. Today however, this file is updated by
hand with data from multiple sources.
Gathering and inputting data by hand have several drawbacks: It is time consuming and involves the
risk of human error like spelling mistakes etc. Adding to this is the fact that CR and the process
laboratory does not store the analysis results in the same database and that samples from the same
shipment have different sample-id:s in the different databases. This makes checking possible errors
difficult and time consuming.
Several improvements therefore should be made in this area, including synchronizing the sample-id:s.
However an automated system for compiling results of all analyses for each shipment into a usable
format (for example an Excel file) should be prioritized.
75
6.3.3 Increased control of fly-ash There are large variations in the quality of the fly-ash depending on the source and over time. And
while all fly-ash are compliant with the quality requirements set by the plant, issues can still occur. An
example of this is the particle sizes of the fly-ash.
The current analyses on the fly-ash also lack analyses on the reactivity of the fly-ash, while the
chemical composition analysis through XRF and ICP may indicate this.
6.3.3.1 Storage
The plant has two silos for storing the fly-ash upon arrival to the plant which provides opportunities
for further control of the quality of fly-ash used in the production. Knowing the quality of the fly-ash
used can assist in determining the amount that can be added as well as identify sources for cement
quality issues.
6.3.4 XRD, Clinker reactivity and Indexes Cementa in collaboration with other companies within the group currently works on both XRD and
Clinker Reactivity with the aim to learn more and develop current methods. The progression of this
work is highly interesting to the future development of the indexes as these form a substantial part of
the index performances.
For that reason, it is suggested that the users of the indexes are involved in this work or is briefed
continuously on the progress of this.
7. Further work An obvious further work is the continued work on the results of this project. A model should be alive
and requires continuous updating and work to remain accurate for a longer period of time.
The tools used in this project is purely mathematical and not specifically adapted to the compressive
strengths in any way. As the procedure used where Unscrambler was used in combination with the
neural networks model has proven beneficial, this method can be applied to other subjects.
Increasing the ecological and financial sustainability of the cement production is a main focus for
Cementa and for that reason an investigation regarding application of the model for process
optimization regarding alternative fuels and raw materials should be conducted.
7.1 Indexes Indexes must be updated continuously and section 7.1.1 Index maintenance. The theoretical
knowledge as well as the procedure found during this project and presented in this report may also aid
in creation of new indexes for other cement types. This becomes increasingly important as new cement
types is invented to lower the ecological impact of the production of cement with potentially increased
variations in the cement.
7.1.1 Index maintenance As mentioned, the indexes must be kept alive and updated continuously. For that, the quality
department in Slite will need to not only use the index but also evaluate the results. The index predicts
using the data it is trained with, and if the training data is not comparable to the samples being
predicted the index will not be accurate.
With this in mind, a clear warning should be added to Excel when the samples being predicted have
one or several variables outside the trained range.
76
Checking the verification set of the index should also be done regularly depending on how often the
index is used. If the share of samples in the verification set that exceeds the defined error margin
exceeds 10 %, a new evaluation of the input data should be conducted to find potential changes in any
variable, both included and excluded in the model. The potential impact of these variables on the
cement strength can then be investigated in the current index after and if theory supports it, it can be
added or removed.
If this fails to enhance the performance of the index, the full procedure including runs in Unscrambler
should be conducted with the current data set to improve the index. Runs in Unscrambler should be
done regularly on the current data sets as these grows continuously, however, the frequency of these
runs depends on the number of samples and process variations meaning that this will be at the users
discretion.
7.1.2 Other uses of the model A vast number of things in the plant can also be predicted using indexes similar to this and using the
procedure presented in this project will help with the creation of these. This requires theoretical studies
of the topic however, as correlation does not necessarily mean causality. It also requires good ways of
extracting data from current databases, something that is already available for the process data stored
through IP21.
7.2 Sustainability The cement industry have massive challenges ahead with regards to sustainability. Both from a
financial and ecological standpoint, innovation must be prioritized. From a financial standpoint the
increasing cement production in developing countries means that the cement price and quality must
remain competitive.
Discussed during this project is also the fact that ecological sustainability and financial sustainability
will increasingly be connected. Today, carbon emission rights are a clear example of this, but this will
increase with increasingly strict regulation on the subject. This motivates large focus on research and
development of the process, and the tools used in this project may simplify this work as
approximations may be done before trials are conducted.
As the fly-ash is a result of coal burning in industries and power plants, fly-ash supply will decline
during coming years. Introducing a cost variable as well as an ecological impact variable in these tools
may help to optimize the fly-ash addition while maintaining proper quality.
7.3 Process optimization The plant in question logs plenty of process parameters continuously and using similar methodology to
that of this project, indexes can be developed for several areas. This will allow for possibility to search
for causes of errors as well as optimizing the process in itself.
There are plenty of limiting factors regarding product quality, cost and emissions and connecting these
and finding not only single, but multiple parameters, that can be changed to improve these areas will
benefit Cementa. However, for this, a larger project is required or several smaller projects working on
different areas.
77
References
[1] Å. Truedsson, Cementprocessen, Slite: Cementa Research, 2004.
[2] W. Kurdowski, Cement and Concrete Chemistry, Kraków: Springer, 2014.
[3] Techincal Commitee CEN/TC 51 "Cement and building limes", Cement - Part 1: Composition, specifications and conformity criteria for common cements, 2011.
[4] J. Aguirre Castillo, Cementtillverkning, Slite: Cementa AB, 2018.
[5] HeidelbergCement Group, Application of XRD/Rietveld Analysis for Production Control, 1 ed., 2016.
[6] T. Hjellström, Interviewee, Continuous discussions during the project regarding the process.
[Interview]. 2019.
[7] Netzsch, "Simultaneous Thermal Analysis," [Online]. Available: https://www.netzsch-thermal-analysis.com/en/commercial-testing/methods/simultaneous-thermal-analysis-sta/. [Accessed 29 May 2019].
[8] H. F. W. Taylor, Cement Chemistry, 2nd ed., London: Thomas Telford, 1997.
[9] The Guardian, "The Ultimate Climate Change FAQ: Which industries and activities emit the most carbon?," The Guardian, 28 April 2011. [Online]. Available: https://www.theguardian.com/environment/2011/apr/28/industries-sectors-carbon-emissions. [Accessed 14 April 2019].
[10] S. Hamnqvist, "De släpper ut mest koldioxid i Sverige," Sveriges Natur, 18 May 2017. [Online]. Available: http://www.sverigesnatur.org/aktuellt/de-slapper-ut-mest-koldioxid-sverige/. [Accessed 14 April 2019].
[11] K. Larsson, "Prediction of compressive strength 28 d - Excel model," Cementa, 2013.
[12] E. Marsicano, "Guidance Paper: Clinker Reactivity," Heidelberg Technology Center, 2014.
[13] P. C. Hewlett, Lea's Chemistry of Cement and Concrete, Oxford: Elsevier, 1998.
[14] K. Larsson, Cementkemi - Grundkurs presentation, Slite: Cementa Research, 2008.
[15] M. J. Y. H. S. N. &. C. L. McCarthy, "Evaluation of Fly Ash Reactivity Potential Using a Lime Consumption Test," Magazine of Concrete Research, vol. 69, no. 18, pp. 954-965, 2017.
[16] S. Wold, M. Sjöström and L. Eriksson, "PLS-regression: a basic tool for chemometrics," Chemometrics and Intelligent Laboratory Systems, no. 58, pp. 109-130, 2001.
[17] Camo Software AS, The Unscrambler Methods, 2006.
[18] D. J. Skocek, "Cement Strength Predictions using Neural Networks," Heidelberg Technology Center, 2018.
[19] S. Singh, "towardsdatascience.com," Towards Data Science, 21 May 2018. [Online]. Available: https://towardsdatascience.com/understanding-the-bias-variance-tradeoff-165e6942b229. [Accessed 29 July 2019].
[20] B. MacNamee, P. Cunningham, S. Byrne and O. I. Corrigan, "The problem of bias in training data in regression problems in medical decision support," Artificial Intelligence in Medicine, vol. 24, no. 1, pp. 51-70, 2002.
[21] A. Bourtsalas, J. Zhang, M.J.Castaldi and N.J.Themelis, "Use of non-recycled plastics and paper as alternative fuel in cement production," Journal of Cleaner Production, vol. 181, pp. 8-16, 2018.
[22] F. Pacheco-Torgal, S. Miraldo, J. A. Labrincha and J. D. Brito, "An eco-efficient approach to concrete carbonation," in Eco-efficient concrete, Woodhead Publishing, 2013, pp. 368-385.
[23] K.-L. Du and M. Swarmy, Neural Networks and Statistical Learning, London: Springer-Verlag, 2014.
[24] D. J. Skocek, "Neural Networks model: User Manual," Heidelberg Technology Center, Leimen, 2019.
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Appendices
A1 - Partial Least Square Regression Partial Least Square regression was introduced by Herman Wold in 1975 and provided a new
approach to multivariate modelling.
The data set used for the modelling is divided into two matrices X and Y. X contains the input
variables and Y contains the known values for the output variables. Different parameters are typically
stored as different columns in the matrices and different samples are stored in different rows. The
process of making the PLS model is then dependent on the algorithm used, however in this project
NIPALS (Non-linear Iterative Partial Least Squares) has been used.
A1.1 NIPALS algorithm NIPALS algorithm is iterative and is run in component. One component is run until the model
converges, after this, the data from this component is removed from the X and Y and the next
component will restart the process with the new X and Y.
The NIPALS algorithm works by first finding a starting vector u (Y-scores) which is one of the Y
columns, after this the X weights are calculated [16]:
𝑤 = 𝑋′𝑢/𝑢′𝑢
When weights have been found, the X-scores are calculated:
𝑡 = 𝑋𝑤
Y-weights are calculated:
𝑐 = 𝑌′𝑡/𝑡′𝑡
And after this, the Y-scores u are updated:
𝑢 = 𝑌′𝑐/𝑐′𝑐
When this is done, the algorithm checks the convergence by checking
‖𝑡𝑜𝑙𝑑 − 𝑡𝑛𝑒𝑤‖
‖𝑡𝑜𝑙𝑑‖< 𝜀
Where ε is small (<10-6). If not fulfilled, new X-weights are calculated using the new u value. If
fulfilled the model removes the current component data from X and Y and continues with the next
component:
𝑝 = 𝑋′𝑡/𝑡′𝑡
𝑋 = 𝑋 − 𝑡𝑝′
𝑌 = 𝑌 − 𝑡𝑐′
79
The p and c variables are called the X and Y-loadings and contains one value for each input parameter
for each component. The X-loadings when plotted with the Y-loadings show the impacts of each of
the parameters in the selected component. [16]
Figure 30 - X- and Y-loadings for 1d model for SH-cement
Figure 30 shows the first 2 components and the different parameter contributions. The X-axis show the
parameter impacts in the first component and Y-axis shows the impacts in the second component.
Further from 0 demonstrates a higher impact and an example from the graph above is 15 µm which
will positively correlate with 1d in both components while d50 will correlate negatively in both
components.
Figure 31 – The number of components impact on the degree of explained Y-variance
80
The explained variance will increase with the number of components until no more useful information
can be found in the X-data. However, the majority of the explained variance will be from the first few
components, and beyond these the risk of noise influencing the model increases.
A2 – Neural Networks Different Neural Network algorithms are available in the model, however, the RBFN is used for all
indexes as this is the most developed algorithm in the current version.
A2.1 Radial Basis Function Network Radial Basis Function Network (RBFN) is the model most used in this project. RBFN models take the
following form:
�̅�(𝑋) = ∑ 𝑤𝑖ℎ(𝑥𝑖)
The w is the weight and h(x) is the radial basis function. Together these form the neuron, and the
calculated value for the model is the sum of impacts from all neurons. The radial basis function will
vary depending on the distance of the point from the neural center. RBFNs typically only has one
hidden layer. [23]
RBFN learning is a 2 step procedure where the first step is placement of the neural centers and the
second step is learning the weights. Several methods for placement of the neural centers can be used,
which one is used in this project is not known as the model code is protected. The weight learning can
also be done using several methods, however when the neural centers are known this is reduced to a
linear optimization problem and a Least-squares approach is often used. Specifics on the weight
learning is also now known for the same reason as for the neural centers. [23]
Several functions can be used as the radial basis functions, however in the model used in this project
the Gaussian function is used.
ℎ𝑖(𝑥) = exp (−𝑟2
2𝜎2)
In this radial basis function r denotes the distance between the data point and the neural center and σ
denotes the kernel width of the neuron.
The algorithms of the RBFN shows that the neurons closer to the data point will contribute more to the
prediction.
Model parameters can be changed in the model, these are “Max. number of data points per Neuron
>1”, “Penalty for large weights” and “Dimensionless kernel width”.
Dimensionless kernel width in this model does not change the kernel width directly but varies the
automated algorithm that optimizes the kernel width. Kernel width needs to be adapted to the data set,
which the algorithm does and needs to vary for the data set as points are unevenly spaced over the
interval. Typically, the distance between points and the variations in the data set determine the optimal
kernel width. The algorithm used cannot be described further as the code is protected by the creator.
[24]
81
The maximum number of data points per neuron parameter allows the user to influence the number of
neurons used. A higher number of neurons will give a larger variance and smaller bias of the model,
and 3 is set as default value in this model. [24]
Penalty for large weights are local smoothening of the model, a higher penalty will reduce the
influence of high neural weights on the model. [24]
A3 – Unscrambler results for Bas and SH
A3.1 SH-cement Prediction of compressive strengths using the PLS method in Unscrambler with uncertainty analysis
identifies the following weighted coefficients (B_0 is the β coefficient as shown in Equation 1):
1 d 28 d
B_0 -4,0997 26,0514
SiO2 0,0000 0,0000
Al2O3 0,0000 0,0000
Fe2O3 0,0000 0,0000
CaO 0,0000 0,0000
MgO 0,0000 0,0000
SO3_XRF 0,2830 0,7105
K2O 0,1793 -0,3245
Na2O 0,0000 0,0000
Cl 0,2450 0,3676
ZnO 0,0000 0,0000
32 my 0,0000 0,0000
32-2 my 0,0000 0,0000
d50 -0,3192 0,0000
2 µm 0,2030 0,0000
3 µm 0,2087 0,0000
15 µm 0,3888 0,4786
Alite_CS 0,0000 0,0000
Alite_M1 0,0000 0,0000
Alite_M3 0,2213 0,0000
Alite_Sum 0,2327 0,0000
Alum_cubic -0,1916 -0,1166
Alum_ortho 0,0000 0,0000
Alum_Sum -0,2038 -0,1588
Anhydrite 0,0000 0,0000
Aphthitalite 0,0000 -0,2983
Arcanite 0,1523 0,0000
Belite_beta -0,2043 0,0000
Calcite -0,1655 -0,0754
CO2_XRD -0,1664 -0,0742
fCaO XRD 0,0000 0,0000
Fraction_M1 0,0000 0,0000
Gypsum 0,0000 0,0000
Hemi-
hydrate
0,1314 0,0000
82
Langbeinite 0,0000 0,0000
Lime 0,0000 0,0000
Periclase 0,0000 0,0000
Portlandite 0,0000 0,0000
Quartz 0,0000 0,0000
R_wp 0,0000 0,0000
Table 10 - Weighted coefficients for SH-cement from Unscrambler
The parameters identified as unimportant is down weighted between and in the table above have
coefficient of 0. For 28d strength, only 5 parameters are identified as important in the last PLS run and
therefore the important variables from the previous run is used.
Figure 32 & Figure 34 graphically represents the data shown in Table 10.
Figure 32 - Weighted coefficients for significant variables from Unscrambler for 1d SH
83
Figure 33 - PLS model performance for 28d for SH-cement from Unscrambler
Figure 34 - Weighted coefficients for SH 28d strength from Unscrambler (blue marked as insignificant)
84
Figure 35 - PLS model performance for 28d for SH-cement from Unscrambler
A3.2 BAS-cement Unlike the analysis for SH-cement, for BAS-cement the runs in Unscrambler is limited to the analyses
from the process laboratory but including the strengths (which are measured at CR). The reason for
this is the limited number of analyses run on BAS-cement at CR and that certain analyses has been
phased out as it was considered to have lower importance at this time. Below is the identified weighted
coefficients (B_0 is the β coefficient as shown in Equation 1):
28 d 1 d
B_0 39,4747 15,4737
SiO2 -0,2740 -0,2789
Al2O3 -0,1817 -0,0771
Fe2O3 0,0000 0,0000
CaO 0,1007 0,0000
MgO 0,0000 0,1847
SO3 0,7095 0,1727
K2O 0,0000 0,3204
Na2O 0,0000 0,0000
Cl 0,0000 0,0000
Zn 0,0000 0,1449
32 µm 0,0000 0,0000
32-2 my 0,0000 0,0000
d50 -0,1461 -0,1799
Alite_CS 0,0000 0,0000
Alite_M1 0,3131 0,1339
Alite_M3 0,0000 0,0000
Alite_Sum 0,1440 0,1029
Alum_cubic 0,0000 0,0000
Alum_ortho 0,0000 0,0000
85
Alum_Sum 0,0000 0,0000
Anhydrite 0,0000 -0,1147
Aphthitalite 0,0000 0,3066
Arcanite 0,0000 0,1753
Belite_beta 0,0000 -0,1732
Calcite 0,0000 -0,0689
CO2_XRD 0,0000 -0,0678
fCaO XRD 0,0000 0,0000
FA_Sum 0,0000 -0,0642
Fraction_M1 0,0000 0,0979
Gypsum 0,0000 -0,4199
Hemi-
hydrate
0,3270 0,4602
Langbeinite 0,0000 0,0000
Lime 0,0000 0,2442
Periclase 0,0000 0,0000
Portlandite -0,3229 0,0000
Quartz 0,0000 0,0833
R_wp 0,0000 -0,0838
Table 11 - Weighted coefficients for BAS-cement from Unscrambler
As with SH the identified significant variables are used as a starting point, however, the HTC model
varies from the PLS regression in Unscrambler. For this reason, as well as that some variables are
connected or dependent on other, the selection of variables are changed to some extent in the final
models.
Figure 36 - Weighted coefficients for significant variables from Unscrambler for 1d BAS
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Figure 37 - PLS model performance for 1d strength BAS from Unscrambler
Figure 38 - Weighted coefficients for significant variables from Unscrambler for 28d BAS
87
Figure 39 - PLS model performance for 1d strength BAS from Unscrambler
A3.3 Anläggningscement
Figure 40 - PLS model performance from Unscrambler for Anläggningscement
88
Figure 41 - Weighted coefficient (only significant) from Unscrambler
A4 – Clinker mineral correlations The indexes for 1d and 28d prediction for SH-cement is used as foundation and the additional clinker
minerals are added to evaluate the correlation of the variations in strength compared to the level of
these minerals. To identify the impact of clinker minerals on the strength, the scenarios tools in the
model is used. Fixing all other parameters at the average level, only clinker minerals are investigated.
A4.1 Scenarios 28d SH Following are scenarios where the variables are allowed to vary between the highest and lowest value
in the training set at 25 levels. Other variables are set to their mean value.
89
A4.1.1 Alite
Figure 42 - 28d strength as variation in Alite content
Figure 43 - 28d strength as variation in fraction of M1 Alite to Alite sum
y = 0,0064x2 - 0,6398x + 76,77R² = 1
60,5
61
61,5
62
62,5
63
63,5
64
56 58 60 62 64 66 68 70 72 74
28
d S
tren
gth
(M
Pa)
Alite Sum (%)
28d strength - Alite sum
28d strength
Poly. (28d strength)
y = 0,0011x3 - 0,1449x2 + 6,065x - 21,172R² = 1
61,8
61,9
62
62,1
62,2
62,3
62,4
62,5
62,6
36 38 40 42 44 46 48 50
28
d s
tren
gth
(M
Pa)
Fraction M1 (%)
28d strength - Fraction M1
28d strength
Poly. (28d strength)
90
A4.1.2 Belite
Figure 44 - 28d strength depending on Belite content
A4.1.3 Aluminate
Figure 45 - 28d strength depending on variation in Aluminate content
y = 0,0063x2 - 0,1527x + 63,148R² = 0,9973
62,1
62,2
62,3
62,4
62,5
62,6
62,7
62,8
0 5 10 15 20
28
d S
tren
gth
(%
)
Belite content (%)
28d strength - Belite
28d strength
Poly. (28d strength)
y = 0,0917x2 - 1,5241x + 68,271R² = 0,9994
61,8
62
62,2
62,4
62,6
62,8
63
63,2
63,4
63,6
4 5 6 7 8 9
28
d S
tren
gth
(M
Pa)
Aluminate Sum (%)
28d Strength - Aluminate sum
28d Strength
Poly. (28d Strength)
91
Figure 46 - 28d strength depending on cubic C3A
Figure 47 - 28d strength depending on variations in orthorhombic C3A content
y = 0,0436x2 - 0,6424x + 63,922R² = 0,9998
61
61,5
62
62,5
63
63,5
64
0 1 2 3 4 5 6 7 8
28
d s
tren
gth
Cubic C3A (%)
28d strength - Cubic C3A
28d strength
Poly. (28d strength)
y = -0,0233x3 + 0,1793x2 - 0,4954x + 62,239R² = 0,9979
61,7
61,75
61,8
61,85
61,9
61,95
62
0,5 1 1,5 2 2,5 3 3,5
28
d s
tren
gth
(M
Pa)
Orthorombic C3A (%)
28d strength - Orthorombic C3A
28d strength
Poly. (28d strength)
92
A4.1.4 Aphthitalite
Figure 48 - 28d strength depending on variations in Aphthitalite content
A4.1.5 Arcanite
Figure 49 - 28d strength depending on variations in Arcanite content
y = 0,2337x2 - 2,8412x + 64,251R² = 0,9995
61,4
61,6
61,8
62
62,2
62,4
62,6
62,8
63
0,45 0,55 0,65 0,75 0,85 0,95 1,05
28
d s
tren
gth
(M
Pa)
Aphthitalite (%)
28d strength - Aphthitalite
28d strength
Poly. (28d strength)
y = 6,9988x3 - 16,367x2 + 12,945x + 58,738R² = 0,9997
61,9
62
62,1
62,2
62,3
62,4
62,5
62,6
62,7
62,8
0,4 0,5 0,6 0,7 0,8 0,9 1 1,1 1,2 1,3
28
d s
tren
gth
(M
Pa)
Arcanite (%)
28d strength - Arcanite
28d strength
Poly. (28d strength)
93
A4.1.6 Langbeinite
Figure 50 - 28d strength depending on variations in Langbeinite content
A4.2 Scenarios 1d Following are scenarios where the variables are allowed to vary between the highest and lowest value
in the training set at 25 levels. Other variables are set to their mean value.
A4.2.1 Alite sum
Figure 51 - 1d strength depending on Alite content
y = 10,355x3 - 5,6197x2 + 1,6697x + 62,112R² = 0,9995
62
62,1
62,2
62,3
62,4
62,5
62,6
0 0,05 0,1 0,15 0,2 0,25 0,3 0,35 0,4 0,45
28
d s
tren
gth
(M
Pa)
Langbeinite (%)
28d strength - Langbeinite
28d strength
Poly. (28d strength)
y = -0,0078x2 + 1,1872x - 12,09R² = 0,9998
30
30,5
31
31,5
32
32,5
33
33,5
56 58 60 62 64 66 68 70 72 74
1d
str
engt
h (
MP
a)
Alite Sum (%)
1d strength - Alite Sum (C3S)
1d strength
Poly. (1d strength)
94
Figure 52 - 1d strength depending on fraction of M1 to total Alite content
A4.2.2 Belite
Figure 53 - 1d strength depending on Belite content
y = -0,0109x2 + 0,969x + 10,977R² = 0,9991
31,7
31,8
31,9
32
32,1
32,2
32,3
32,4
32,5
32,6
36 38 40 42 44 46 48 50
1d
str
engt
h (
MP
a)
Fraction M1 (%)
1d strength - Fraction M1
1d strength
Poly. (1d strength)
y = -0,0073x2 - 0,0234x + 33,11R² = 0,9999
30
30,5
31
31,5
32
32,5
33
33,5
0 5 10 15 20
1d
Str
engt
h (
MP
a)
Belite content (%)
1d strength - β-Belite (C2S)
1d strength
Poly. (1d strength)
95
A4.2.3 Aluminates
Figure 54 - 1d strength depending on total Aluminate content
Figure 55 - 1d strength depending on cubic C3A content
y = -0,0941x2 + 0,8309x + 30,746R² = 0,9993
30,8
31
31,2
31,4
31,6
31,8
32
32,2
32,4
32,6
32,8
4 5 6 7 8 9
1d
str
engt
h (
MP
a)
Aluminate sum (%)
1d strength - Aluminate Sum (C3A)
1d strength
Poly. (1d strength)
y = -0,0291x2 + 0,3327x + 31,029R² = 0,9995
31
31,1
31,2
31,3
31,4
31,5
31,6
31,7
31,8
31,9
32
32,1
0 1 2 3 4 5 6 7 8
1d
Str
engt
h (
MP
a)
Cubic C3A (%)
1d Strength - Cubic C3A
1d strength
Poly. (1d strength)
96
Figure 56 - 1d strength depending on orthorhombic C3A content
A4.2.4 Aphthitalite
Figure 57 - 1d strength depending on the Aphthitalite content
31,3
31,4
31,5
31,6
31,7
31,8
31,9
32
0,6 1,1 1,6 2,1 2,6 3,1
1d
Str
engt
h (
MP
a)
Orthorombic C3A (%)
1d strength - Orthorombic C3A
1d strength
Poly. (1d strength)
y = -5,2194x2 + 7,5867x + 29,436R² = 0,9958
31,6
31,7
31,8
31,9
32
32,1
32,2
32,3
0,45 0,55 0,65 0,75 0,85 0,95 1,05 1,15
1d
Str
engt
h (
MP
a)
Aphthitalite (%)
1d Strength - Aphthitalite
1d Strength
Poly. (1d Strength)
97
A4.2.5 Arcanite
Figure 58 - 1d strength depending on the Arcanite content
A4.2.5 Langbeinite
Figure 59 - 1d strength depending on the Langbeinite content
A4.3 Flyash Flyash is studied with various combinations of variables to increase the insight into the impact on the
combination of variables as well as possible correlations between flyash content and other variables.
y = -2,0165x2 + 6,559x + 27,803R² = 0,9977
30
30,5
31
31,5
32
32,5
33
0,45 0,55 0,65 0,75 0,85 0,95 1,05 1,15 1,25
1d
Str
engt
h
Arcanite (%)
1d strength - Arcanite
1d strength
Poly. (1d strength)
y = 19,637x3 - 17,252x2 + 2,5896x + 32,105R² = 0,9994
31,5
31,6
31,7
31,8
31,9
32
32,1
32,2
32,3
0 0,05 0,1 0,15 0,2 0,25 0,3 0,35 0,4 0,45
1d
str
engt
h (
MP
a)
Langbeinite (%)
1d strength - Langbeinite
1d strength
Poly. (1d strength)
98
A4.3.1 Fly-ash content on 28d The first run is done in a model containing the following parameters:
SiO2
Al2O3
CaO
SO3
d50
Alite_sum
Alum_sum
Belite_beta
Calcite
FA_Sum
Fraction_M1
Hemi-hydrate
Portlandite
Figure 60 - 28d strength variations depending on fly-ash content
In the second run 32-2 µm was added as a parameter.
SiO2
Al2O3
CaO
SO3
32-2 µm
d50
Alite_sum
Alum_sum
Belite_beta
Calcite
y = -0,0004x4 + 0,0257x3 - 0,5356x2 + 4,1582x + 49,152R² = 0,9975
56,5
57
57,5
58
58,5
59
59,5
60
60,5
0 5 10 15 20 25
28
d s
tren
gth
(M
Pa)
FA_sum (%)
Fly-ash 28d BAS
28d strength
Poly. (28d strength)
99
FA_Sum
Fraction_M1
Hemi-hydrate
Portlandite
Figure 61 - 28d strength variations depending on fly-ash content
The third run is in the model shown in Fel! Hittar inte referenskälla. including significantly fewer
parameters than the other two models.
y = -0,0003x4 + 0,017x3 - 0,3538x2 + 2,5186x + 54,383R² = 1
51
52
53
54
55
56
57
58
59
60
61
0 5 10 15 20 25
28
d s
tren
gth
(M
Pa)
FA_sum (%)
Fly-ash 28d BAS
28d strength
Poly. (28d strength)
100
Figure 62 - 28d strength variations depending on fly-ash content
A4.3.2 Fly-ash on 1d strength The impact of fly-ash on the 1d strength was tested using the scenarios tool in the ANN model, using
the index shown for 1d predictions on BAS-cement.
Figure 63 - Fly-ash impact on 1d strength from HTC model
y = -5E-05x5 + 0,0028x4 - 0,0522x3 + 0,3554x2 - 0,2483x + 54,563R² = 0,9951
56,5
57
57,5
58
58,5
59
59,5
4 6 8 10 12 14 16 18 20 22
Ap
pro
xim
ated
Str
engt
h (
MP
a)
FA_sum (%)
FA_sum - 28d Final model
28d strength
Poly. (28d strength)
y = -0,0003x4 + 0,0164x3 - 0,3239x2 + 2,0216x + 19,426R² = 0,9998
18
19
20
21
22
23
24
4 6 8 10 12 14 16 18 20 22
Ap
pro
x 1
d s
tren
gth
(M
Pa)
FA_sum (%)
1d strength
1d strength
Poly. (1d strength)
101
A5 – Sample selection VBA The following is the Visual Basic for Application code for the sample selection tool. Searchandfill()
selects the samples from “Data” and copies these to a separate sheet “Output”. In this case the samples
are only copied and will still be present in the original data set.
Searchandfillcut() first copies the entire data set to a new sheet, it then sorts out the samples from the
data set and removes the entire line. This leads to 3 sheets with different data sets, “Indata” with the
original data set, “Data” where the samples not selected remains, “Output” where the selected samples
is saved.
Sub Searchandfill()
'Anton Hermansson [email protected]
'Uppdaterad 2019-03-11 ANH
Dim InputRow As Long
Dim SearchRow As Long
Dim CopytoRow As Long
Dim Namn As String
Dim ws As Worksheet
Dim wb As Workbook
Dim i As Long
Dim exist As Boolean
Dim tom As Boolean
On Error GoTo Error_Exec
InputRow = 2
SearchRow = 2
CopytoRow = 2
Set wb = ActiveWorkbook
tom = False
'Output check
For i = 1 To wb.Worksheets.Count
If Worksheets(i).Name = "Output" Then
exists = True
End If
Next i
102
If exists = True Then
Application.DisplayAlerts = False
wb.Worksheets("Output").Delete
Application.DisplayAlerts = True
End If
'Indata check
For i = 1 To wb.Worksheets.Count
If Worksheets(i).Name = "Indata" Then
exists1 = True
End If
Next i
If exists1 = True Then
wb.Sheets("Data").Cells.ClearContents
wb.Worksheets("Indata").Cells.Copy Destination:=Sheets("Data").Cells
Application.DisplayAlerts = False
wb.Worksheets("Indata").Delete
Application.DisplayAlerts = True
End If
Sheets("Data").Select
Set ws = wb.Sheets.Add(Type:=xlWorksheet, After:=Application.ActiveSheet)
ws.Name = "Output"
Sheets("Provnummer").Select
Dim Provnr As Long
Provnr = Cells(InputRow, 1).Value
If IsEmpty(Cells(InputRow, 1)) Then
tom = True
End If
Sheets("Data").Select
Dim Koll As Long
Koll = Cells(2, 1).Value
Sheets("Provnummer").Select
Sheets("Output").Rows(1).EntireRow.Value = Sheets("Data").Rows(1).EntireRow.Value
While tom = False
Provnr = Sheets("Provnummer").Cells(InputRow, 1).Value
If IsEmpty(Sheets("Provnummer").Cells(InputRow, 1)) Then
103
tom = True
End If
Search:
Koll = Sheets("Data").Cells(SearchRow, 1).Value
If (Koll = Provnr) Then
Sheets("Output").Rows(CopytoRow).EntireRow.Value =
Sheets("Data").Rows(SearchRow).EntireRow.Value
CopytoRow = CopytoRow + 1
InputRow = InputRow + 1
SearchRow = 2
Else
SearchRow = SearchRow + 1
GoTo Search
End If
Wend
MsgBox "The request has been completed."
Exit Sub
Error_Exec:
MsgBox "Something went wrong :( Check data and request for formatting errors"
End Sub
Sub Searchandfillcut()
'Anton Hermansson [email protected]
'Uppdaterad 2019-03-11 ANH
Dim InputRow As Long
Dim SearchRow As Long
Dim CopytoRow As Long
Dim Namn As String
Dim ws As Worksheet
Dim ws1 As Worksheet
Dim wb As Workbook
Dim i As Long
Dim exist As Boolean
Dim exist2 As Boolean
104
On Error GoTo Error_Exec
InputRow = 2
SearchRow = 2
CopytoRow = 2
Set wb = ActiveWorkbook
'Output check
For i = 1 To wb.Worksheets.Count
If Worksheets(i).Name = "Output" Then
exists = True
End If
Next i
If exists = True Then
Application.DisplayAlerts = False
wb.Worksheets("Output").Delete
Application.DisplayAlerts = True
End If
'Indata check
For i = 1 To wb.Worksheets.Count
If Worksheets(i).Name = "Indata" Then
exists1 = True
End If
Next i
If exists1 = True Then
wb.Sheets("Data").Cells.ClearContents
wb.Worksheets("Indata").Cells.Copy Destination:=Sheets("Data").Cells
Application.DisplayAlerts = False
wb.Worksheets("Indata").Delete
Application.DisplayAlerts = True
End If
Sheets("Data").Select
Set ws = wb.Sheets.Add(Type:=xlWorksheet, After:=Application.ActiveSheet)
ws.Name = "Output"
wb.Worksheets("Data").Copy After:=Worksheets("Output")
ActiveSheet.Name = "Indata"
105
Sheets("Provnummer").Select
Dim Provnr As Long
Provnr = Cells(2, 1).Value
If IsEmpty(Sheets("Provnummer").Cells(InputRow, 1)) Then
tom = True
End If
Sheets("Data").Select
Dim Koll As Long
Koll = Cells(2, 1).Value
Sheets("Provnummer").Select
Sheets("Output").Rows(1).EntireRow.Value = Sheets("Data").Rows(1).EntireRow.Value
While tom = False
Provnr = Sheets("Provnummer").Cells(InputRow, 1).Value
If IsEmpty(Sheets("Provnummer").Cells(InputRow, 1)) Then
tom = True
End If
Search:
Koll = Sheets("Data").Cells(SearchRow, 1).Value
If (Koll = Provnr) Then
Sheets("Output").Rows(CopytoRow).EntireRow.Value =
Sheets("Data").Rows(SearchRow).EntireRow.Value
Sheets("Data").Rows(SearchRow).Delete
CopytoRow = CopytoRow + 1
InputRow = InputRow + 1
SearchRow = 2
Else
SearchRow = SearchRow + 1
GoTo Search
End If
Wend
MsgBox "The request has been completed."
Exit Sub
Error_Exec:
MsgBox "Something went wrong :( Check data and request for formatting errors"
106
End Sub
A6 – IP21 fetch The IP21-fetch macros are split after the functionality. In the first ones (TransferIP21wrun() and
TransferIP21worun()) both SH and BAS is considered, the first one includes a check on the state of
the mills. If running it copies the data into the user-specified files. The second does not consider the
state of the mills but simply copies the data to the files if the cement type is correct.
The other macros have the same functions but only look at one cement type.
Sub TransferIP21wrun()
'2019-04-15 Anton Hermansson
Dim run1 As Integer
Dim run2 As Integer
Dim run7 As Integer
Dim run8 As Integer
Dim lastrow1 As Long
Dim Emprow1 As Long
Dim lastrow2 As Long
Dim Emprow2 As Long
Dim lastrow3 As Long
Dim Emprow3 As Long
Dim lastrow4 As Long
Dim Emprow4 As Long
Dim sort1 As String
Dim sort2 As String
Dim sort3 As String
Dim sort4 As String
Dim HTCBAS As String
Dim HTCSH As String
Dim HTCBASN As String
Dim HTCSHN As String
Dim IPt As String
Dim fsbas As Object
Dim fssh As Object
Set fsbas = CreateObject("Scripting.FileSystemObject")
Set fssh = CreateObject("Scripting.FileSystemObject")
On Error GoTo Error_exe
IPt = ThisWorkbook.Name
HTCSH = Application.GetOpenFilename(Title:="Specificera den aktuella modellen för
SH", FileFilter:="Excel Files *.xlsm* (*.xlsm*),")
107
HTCBAS = Application.GetOpenFilename(Title:="Specificera den aktuella modellen för
BAS", FileFilter:="Excel Files *.xlsm* (*.xlsm*),")
HTCSHN = fssh.GetFileName(HTCSH)
HTCBASN = fsbas.GetFileName(HTCBAS)
If IsFileOpen(HTCSH) = True Then
Else:
Workbooks.Open (HTCSH)
End If
If IsFileOpen(HTCBAS) = True Then
Else:
Workbooks.Open (HTCBAS)
End If
Workbooks(IPt).Worksheets("Fetch").Activate
run1 = Worksheets("Fetch").Cells(4, 3).Value
run2 = Worksheets("Fetch").Cells(5, 3).Value
run7 = Worksheets("Fetch").Cells(6, 3).Value
run8 = Worksheets("Fetch").Cells(7, 3).Value
_
If run1 = 1 Then
sort1 = Workbooks(IPt).Worksheets("Fetch").Cells(4, 43).Value
If sort1 = "BAS" Then
Workbooks(HTCBASN).Worksheets("Data Example").Activate
If ActiveSheet.ProtectContents Then
ActiveSheet.Unprotect
End If
lastrow1 = Workbooks(HTCBASN).Worksheets("Data
Example").Cells.Find(What:="*", After:=Range("A1"), LookAt:=xlPart,
LookIn:=xlFormulas, SearchOrder:=xlByRows, SearchDirection:=xlPrevious,
MatchCase:=False).Row
Emprov1 = lastrow1 + 1
Workbooks(HTCBASN).Worksheets("Data
Example").Rows(Emprov1).EntireRow.Value =
Workbooks(IPt).Worksheets("Transfer").Rows(1).EntireRow.Value
End If
If sort1 = "SH" Then
Workbooks(HTCSHN).Worksheets("Data Example").Activate
If ActiveSheet.ProtectContents Then
108
ActiveSheet.Unprotect
End If
lastrow1 = Workbooks(HTCSHN).Worksheets("Data Example").Cells.Find(What:="*",
After:=Range("A1"), LookAt:=xlPart, LookIn:=xlFormulas, SearchOrder:=xlByRows,
SearchDirection:=xlPrevious, MatchCase:=False).Row
Emprov1 = lastrow1 + 1
Workbooks(HTCSHN).Worksheets("Data Example").Rows(Emprov1).EntireRow.Value
= Workbooks(IPt).Worksheets("Transfer").Rows(1).EntireRow.Value
End If
End If
If run2 = 1 Then
sort2 = Workbooks(IPt).Worksheets("Fetch").Cells(5, 43).Value
If sort2 = "BAS" Then
Workbooks(HTCBASN).Worksheets("Data Example").Activate
If ActiveSheet.ProtectContents Then
ActiveSheet.Unprotect
End If
lastrow2 = Workbooks(HTCBASN).Worksheets("Data
Example").Cells.Find(What:="*", After:=Range("A1"), LookAt:=xlPart,
LookIn:=xlFormulas, SearchOrder:=xlByRows, SearchDirection:=xlPrevious,
MatchCase:=False).Row
Emprov2 = lastrow2 + 1
Workbooks(HTCBASN).Worksheets("Data
Example").Rows(Emprov2).EntireRow.Value =
Workbooks(IPt).Worksheets("Transfer").Rows(2).EntireRow.Value
End If
If sort2 = "SH" Then
Workbooks(HTCSHN).Worksheets("Data Example").Activate
If ActiveSheet.ProtectContents Then
ActiveSheet.Unprotect
End If
lastrow2 = Workbooks(HTCSHN).Worksheets("Data Example").Cells.Find(What:="*",
After:=Range("A1"), LookAt:=xlPart, LookIn:=xlFormulas, SearchOrder:=xlByRows,
SearchDirection:=xlPrevious, MatchCase:=False).Row
Emprov2 = lastrow2 + 1
Workbooks(HTCSHN).Worksheets("Data Example").Rows(Emprov2).EntireRow.Value
= Workbooks(IPt).Worksheets("Transfer").Rows(2).EntireRow.Value
End If
End If
If run7 = 1 Then
109
sort3 = Workbooks(IPt).Worksheets("Fetch").Cells(6, 43).Value
If sort3 = "BAS" Then
Workbooks(HTCBASN).Worksheets("Data Example").Activate
If ActiveSheet.ProtectContents Then
ActiveSheet.Unprotect
End If
lastrow3 = Workbooks(HTCBASN).Worksheets("Data
Example").Cells.Find(What:="*", After:=Range("A1"), LookAt:=xlPart,
LookIn:=xlFormulas, SearchOrder:=xlByRows, SearchDirection:=xlPrevious,
MatchCase:=False).Row
Emprov3 = lastrow3 + 1
Workbooks(HTCBASN).Worksheets("Data
Example").Rows(Emprov3).EntireRow.Value =
Workbooks(IPt).Worksheets("Transfer").Rows(3).EntireRow.Value
End If
If sort3 = "SH" Then
Workbooks(HTCSHN).Worksheets("Data Example").Activate
If ActiveSheet.ProtectContents Then
ActiveSheet.Unprotect
End If
lastrow3 = Workbooks(HTCSHN).Worksheets("Data Example").Cells.Find(What:="*",
After:=Range("A1"), LookAt:=xlPart, LookIn:=xlFormulas, SearchOrder:=xlByRows,
SearchDirection:=xlPrevious, MatchCase:=False).Row
Emprov3 = lastrow3 + 1
Workbooks(HTCSHN).Worksheets("Data Example").Rows(Emprov3).EntireRow.Value
= Workbooks(IPt).Worksheets("Transfer").Rows(3).EntireRow.Value
End If
End If
If run8 = 1 Then
sort4 = Workbooks(IPt).Worksheets("Fetch").Cells(7, 43).Value
If sort4 = "BAS" Then
Workbooks(HTCBASN).Worksheets("Data Example").Activate
If ActiveSheet.ProtectContents Then
ActiveSheet.Unprotect
End If
lastrow4 = Workbooks(HTCBASN).Worksheets("Data
Example").Cells.Find(What:="*", After:=Range("A1"), LookAt:=xlPart,
LookIn:=xlFormulas, SearchOrder:=xlByRows, SearchDirection:=xlPrevious,
MatchCase:=False).Row
Emprov4 = lastrow4 + 1
110
Workbooks(HTCBASN).Worksheets("Data
Example").Rows(Emprov4).EntireRow.Value =
Workbooks(IPt).Worksheets("Transfer").Rows(4).EntireRow.Value
End If
If sort4 = "SH" Then
Workbooks(HTCSHN).Worksheets("Data Example").Activate
If ActiveSheet.ProtectContents Then
ActiveSheet.Unprotect
End If
lastrow4 = Workbooks(HTCSHN).Worksheets("Data Example").Cells.Find(What:="*",
After:=Range("A1"), LookAt:=xlPart, LookIn:=xlFormulas, SearchOrder:=xlByRows,
SearchDirection:=xlPrevious, MatchCase:=False).Row
Emprov4 = lastrow4 + 1
Workbooks(HTCSHN).Worksheets("Data Example").Rows(Emprov4).EntireRow.Value
= Workbooks(IPt).Worksheets("Transfer").Rows(4).EntireRow.Value
End If
End If
Exit Sub
Error_exe:
MsgBox "Något gick snett, förlåt! :("
End Sub
Function IsFileOpen(filename As String)
Dim filenum As Integer, errnum As Integer
On Error Resume Next ' Turn error checking off.
filenum = FreeFile() ' Get a free file number.
' Attempt to open the file and lock it.
Open filename For Input Lock Read As #filenum
Close filenum ' Close the file.
errnum = Err ' Save the error number that occurred.
On Error GoTo 0 ' Turn error checking back on.
' Check to see which error occurred.
Select Case errnum
' No error occurred.
' File is NOT already open by another user.
Case 0
IsFileOpen = False
' Error number for "Permission Denied."
111
' File is already opened by another user.
Case 70
IsFileOpen = True
' Another error occurred.
Case Else
Error errnum
End Select
End Function
Sub TransferIP21worun()
'2019-04-15 Anton Hermansson
Dim lastrow1 As Long
Dim Emprow1 As Long
Dim lastrow2 As Long
Dim Emprow2 As Long
Dim lastrow3 As Long
Dim Emprow3 As Long
Dim lastrow4 As Long
Dim Emprow4 As Long
Dim sort1 As String
Dim sort2 As String
Dim sort3 As String
Dim sort4 As String
Dim HTCBAS As String
Dim HTCSH As String
Dim HTCBASN As String
Dim HTCSHN As String
Dim IPt As String
Dim fsbas As Object
Dim fssh As Object
Set fsbas = CreateObject("Scripting.FileSystemObject")
Set fssh = CreateObject("Scripting.FileSystemObject")
On Error GoTo Error_exe
IPt = ThisWorkbook.Name
HTCSH = Application.GetOpenFilename(Title:="Specificera den aktuella modellen för
SH", FileFilter:="Excel Files *.xlsm* (*.xlsm*),")
112
HTCBAS = Application.GetOpenFilename(Title:="Specificera den aktuella modellen för
BAS", FileFilter:="Excel Files *.xlsm* (*.xlsm*),")
HTCSHN = fssh.GetFileName(HTCSH)
HTCBASN = fsbas.GetFileName(HTCBAS)
If IsFileOpen(HTCSH) = True Then
Else:
Workbooks.Open (HTCSH)
End If
If IsFileOpen(HTCBAS) = True Then
Else:
Workbooks.Open (HTCBAS)
End If
Workbooks(IPt).Worksheets("Fetch").Activate
sort1 = Worksheets("Fetch").Cells(4, 43).Value
If sort1 = "BAS" Then
Workbooks(HTCBASN).Worksheets("Data Example").Activate
If ActiveSheet.ProtectContents Then
ActiveSheet.Unprotect
End If
lastrow1 = Workbooks(HTCBASN).Worksheets("Data
Example").Cells.Find(What:="*", After:=Range("A1"), LookAt:=xlPart,
LookIn:=xlFormulas, SearchOrder:=xlByRows, SearchDirection:=xlPrevious,
MatchCase:=False).Row
Emprov1 = lastrow1 + 1
Workbooks(HTCBASN).Worksheets("Data
Example").Rows(Emprov1).EntireRow.Value =
Workbooks(IPt).Worksheets("Transfer").Rows(1).EntireRow.Value
End If
If sort1 = "SH" Then
Workbooks(HTCSHN).Worksheets("Data Example").Activate
If ActiveSheet.ProtectContents Then
ActiveSheet.Unprotect
End If
lastrow1 = Workbooks(HTCSHN).Worksheets("Data Example").Cells.Find(What:="*",
After:=Range("A1"), LookAt:=xlPart, LookIn:=xlFormulas, SearchOrder:=xlByRows,
SearchDirection:=xlPrevious, MatchCase:=False).Row
Emprov1 = lastrow1 + 1
113
Workbooks(HTCSHN).Worksheets("Data Example").Rows(Emprov1).EntireRow.Value
= Workbooks(IPt).Worksheets("Transfer").Rows(1).EntireRow.Value
End If
sort2 = Workbooks(IPt).Worksheets("Fetch").Cells(5, 43).Value
If sort2 = "BAS" Then
Workbooks(HTCBASN).Worksheets("Data Example").Activate
If ActiveSheet.ProtectContents Then
ActiveSheet.Unprotect
End If
lastrow2 = Workbooks(HTCBASN).Worksheets("Data
Example").Cells.Find(What:="*", After:=Range("A1"), LookAt:=xlPart,
LookIn:=xlFormulas, SearchOrder:=xlByRows, SearchDirection:=xlPrevious,
MatchCase:=False).Row
Emprov2 = lastrow2 + 1
Workbooks(HTCBASN).Worksheets("Data
Example").Rows(Emprov2).EntireRow.Value =
Workbooks(IPt).Worksheets("Transfer").Rows(2).EntireRow.Value
End If
If sort2 = "SH" Then
Workbooks(HTCSHN).Worksheets("Data Example").Activate
If ActiveSheet.ProtectContents Then
ActiveSheet.Unprotect
End If
lastrow2 = Workbooks(HTCSHN).Worksheets("Data Example").Cells.Find(What:="*",
After:=Range("A1"), LookAt:=xlPart, LookIn:=xlFormulas, SearchOrder:=xlByRows,
SearchDirection:=xlPrevious, MatchCase:=False).Row
Emprov2 = lastrow2 + 1
Workbooks(HTCSHN).Worksheets("Data Example").Rows(Emprov2).EntireRow.Value
= Workbooks(IPt).Worksheets("Transfer").Rows(2).EntireRow.Value
End If
sort3 = Workbooks(IPt).Worksheets("Fetch").Cells(6, 43).Value
If sort3 = "BAS" Then
Workbooks(HTCBASN).Worksheets("Data Example").Activate
If ActiveSheet.ProtectContents Then
ActiveSheet.Unprotect
End If
114
lastrow3 = Workbooks(HTCBASN).Worksheets("Data
Example").Cells.Find(What:="*", After:=Range("A1"), LookAt:=xlPart,
LookIn:=xlFormulas, SearchOrder:=xlByRows, SearchDirection:=xlPrevious,
MatchCase:=False).Row
Emprov3 = lastrow3 + 1
Workbooks(HTCBASN).Worksheets("Data
Example").Rows(Emprov3).EntireRow.Value =
Workbooks(IPt).Worksheets("Transfer").Rows(3).EntireRow.Value
End If
If sort3 = "SH" Then
Workbooks(HTCSHN).Worksheets("Data Example").Activate
If ActiveSheet.ProtectContents Then
ActiveSheet.Unprotect
End If
lastrow3 = Workbooks(HTCSHN).Worksheets("Data Example").Cells.Find(What:="*",
After:=Range("A1"), LookAt:=xlPart, LookIn:=xlFormulas, SearchOrder:=xlByRows,
SearchDirection:=xlPrevious, MatchCase:=False).Row
Emprov3 = lastrow3 + 1
Workbooks(HTCSHN).Worksheets("Data Example").Rows(Emprov3).EntireRow.Value
= Workbooks(IPt).Worksheets("Transfer").Rows(3).EntireRow.Value
End If
sort4 = Workbooks(IPt).Worksheets("Fetch").Cells(7, 43).Value
If sort4 = "BAS" Then
Workbooks(HTCBASN).Worksheets("Data Example").Activate
If ActiveSheet.ProtectContents Then
ActiveSheet.Unprotect
End If
lastrow4 = Workbooks(HTCBASN).Worksheets("Data
Example").Cells.Find(What:="*", After:=Range("A1"), LookAt:=xlPart,
LookIn:=xlFormulas, SearchOrder:=xlByRows, SearchDirection:=xlPrevious,
MatchCase:=False).Row
Emprov4 = lastrow4 + 1
Workbooks(HTCBASN).Worksheets("Data Example").Rows(lastrow).EntireRow.Value
= Workbooks(IPt).Worksheets("Transfer").Rows(4).EntireRow.Value
End If
If sort4 = "SH" Then
Workbooks(HTCSHN).Worksheets("Data Example").Activate
115
If ActiveSheet.ProtectContents Then
ActiveSheet.Unprotect
End If
lastrow4 = Workbooks(HTCSHN).Worksheets("Data Example").Cells.Find(What:="*",
After:=Range("A1"), LookAt:=xlPart, LookIn:=xlFormulas, SearchOrder:=xlByRows,
SearchDirection:=xlPrevious, MatchCase:=False).Row
Emprov4 = lastrow4 + 1
Workbooks(HTCSHN).Worksheets("Data Example").Rows(Emprov4).EntireRow.Value
= Workbooks(IPt).Worksheets("Transfer").Rows(4).EntireRow.Value
End If
Exit Sub
Error_exe:
MsgBox "Något gick snett, förlåt! :("
End Sub
Sub OnlySHwrun()
'2019-04-15 Anton Hermansson
Dim run1 As Integer
Dim run2 As Integer
Dim run7 As Integer
Dim run8 As Integer
Dim lastrow1 As Long
Dim Emprow1 As Long
Dim lastrow2 As Long
Dim Emprow2 As Long
Dim lastrow3 As Long
Dim Emprow3 As Long
Dim lastrow4 As Long
Dim Emprow4 As Long
Dim sort1 As String
Dim sort2 As String
Dim sort3 As String
Dim sort4 As String
Dim HTCSH As String
Dim HTCSHN As String
Dim IPt As String
Dim fssh As Object
Set fssh = CreateObject("Scripting.FileSystemObject")
On Error GoTo Error_exe
116
IPt = ThisWorkbook.Name
HTCSH = Application.GetOpenFilename(Title:="Specificera den aktuella modellen för
SH", FileFilter:="Excel Files *.xlsm* (*.xlsm*),")
HTCSHN = fssh.GetFileName(HTCSH)
If IsFileOpen(HTCSH) = True Then
Else:
Workbooks.Open (HTCSH)
End If
Workbooks(IPt).Worksheets("Fetch").Activate
run1 = Worksheets("Fetch").Cells(4, 3).Value
run2 = Worksheets("Fetch").Cells(5, 3).Value
run7 = Worksheets("Fetch").Cells(6, 3).Value
run8 = Worksheets("Fetch").Cells(7, 3).Value
_
If run1 = 1 Then
sort1 = Workbooks(IPt).Worksheets("Fetch").Cells(4, 43).Value
If sort1 = "SH" Then
Workbooks(HTCSHN).Worksheets("Data Example").Activate
If ActiveSheet.ProtectContents Then
ActiveSheet.Unprotect
End If
lastrow1 = Workbooks(HTCSHN).Worksheets("Data Example").Cells.Find(What:="*",
After:=Range("A1"), LookAt:=xlPart, LookIn:=xlFormulas, SearchOrder:=xlByRows,
SearchDirection:=xlPrevious, MatchCase:=False).Row
Emprov1 = lastrow1 + 1
Workbooks(HTCSHN).Worksheets("Data Example").Rows(Emprov1).EntireRow.Value
= Workbooks(IPt).Worksheets("Transfer").Rows(1).EntireRow.Value
End If
End If
If run2 = 1 Then
sort2 = Workbooks(IPt).Worksheets("Fetch").Cells(5, 43).Value
If sort2 = "SH" Then
Workbooks(HTCSHN).Worksheets("Data Example").Activate
If ActiveSheet.ProtectContents Then
ActiveSheet.Unprotect
End If
117
lastrow2 = Workbooks(HTCSHN).Worksheets("Data Example").Cells.Find(What:="*",
After:=Range("A1"), LookAt:=xlPart, LookIn:=xlFormulas, SearchOrder:=xlByRows,
SearchDirection:=xlPrevious, MatchCase:=False).Row
Emprov2 = lastrow2 + 1
Workbooks(HTCSHN).Worksheets("Data Example").Rows(Emprov2).EntireRow.Value
= Workbooks(IPt).Worksheets("Transfer").Rows(2).EntireRow.Value
End If
End If
If run7 = 1 Then
sort3 = Workbooks(IPt).Worksheets("Fetch").Cells(6, 43).Value
If sort3 = "SH" Then
Workbooks(HTCSHN).Worksheets("Data Example").Activate
If ActiveSheet.ProtectContents Then
ActiveSheet.Unprotect
End If
lastrow3 = Workbooks(HTCSHN).Worksheets("Data Example").Cells.Find(What:="*",
After:=Range("A1"), LookAt:=xlPart, LookIn:=xlFormulas, SearchOrder:=xlByRows,
SearchDirection:=xlPrevious, MatchCase:=False).Row
Emprov3 = lastrow3 + 1
Workbooks(HTCSHN).Worksheets("Data Example").Rows(Emprov3).EntireRow.Value
= Workbooks(IPt).Worksheets("Transfer").Rows(3).EntireRow.Value
End If
End If
If run8 = 1 Then
sort4 = Workbooks(IPt).Worksheets("Fetch").Cells(7, 43).Value
If sort4 = "SH" Then
Workbooks(HTCSHN).Worksheets("Data Example").Activate
If ActiveSheet.ProtectContents Then
ActiveSheet.Unprotect
End If
lastrow4 = Workbooks(HTCSHN).Worksheets("Data Example").Cells.Find(What:="*",
After:=Range("A1"), LookAt:=xlPart, LookIn:=xlFormulas, SearchOrder:=xlByRows,
SearchDirection:=xlPrevious, MatchCase:=False).Row
Emprov4 = lastrow4 + 1
Workbooks(HTCSHN).Worksheets("Data Example").Rows(Emprov4).EntireRow.Value
= Workbooks(IPt).Worksheets("Transfer").Rows(4).EntireRow.Value
End If
End If
Exit Sub
118
Error_exe:
MsgBox "Något gick snett, förlåt! :("
End Sub
Sub OnlySHworun()
'2019-04-15 Anton Hermansson
Dim lastrow1 As Long
Dim Emprow1 As Long
Dim lastrow2 As Long
Dim Emprow2 As Long
Dim lastrow3 As Long
Dim Emprow3 As Long
Dim lastrow4 As Long
Dim Emprow4 As Long
Dim sort1 As String
Dim sort2 As String
Dim sort3 As String
Dim sort4 As String
Dim HTCSH As String
Dim HTCSHN As String
Dim IPt As String
Dim fssh As Object
Set fssh = CreateObject("Scripting.FileSystemObject")
On Error GoTo Error_exe
IPt = ThisWorkbook.Name
HTCSH = Application.GetOpenFilename(Title:="Specificera den aktuella modellen för
SH", FileFilter:="Excel Files *.xlsm* (*.xlsm*),")
HTCSHN = fssh.GetFileName(HTCSH)
If IsFileOpen(HTCSH) = True Then
Else:
Workbooks.Open (HTCSH)
End If
Workbooks(IPt).Worksheets("Fetch").Activate
sort1 = Worksheets("Fetch").Cells(4, 43).Value
If sort1 = "SH" Then
Workbooks(HTCSHN).Worksheets("Data Example").Activate
119
If ActiveSheet.ProtectContents Then
ActiveSheet.Unprotect
End If
lastrow1 = Workbooks(HTCSHN).Worksheets("Data Example").Cells.Find(What:="*",
After:=Range("A1"), LookAt:=xlPart, LookIn:=xlFormulas, SearchOrder:=xlByRows,
SearchDirection:=xlPrevious, MatchCase:=False).Row
Emprov1 = lastrow1 + 1
Workbooks(HTCSHN).Worksheets("Data Example").Rows(Emprov1).EntireRow.Value
= Workbooks(IPt).Worksheets("Transfer").Rows(1).EntireRow.Value
End If
sort2 = Workbooks(IPt).Worksheets("Fetch").Cells(5, 43).Value
If sort2 = "SH" Then
Workbooks(HTCSHN).Worksheets("Data Example").Activate
If ActiveSheet.ProtectContents Then
ActiveSheet.Unprotect
End If
lastrow2 = Workbooks(HTCSHN).Worksheets("Data Example").Cells.Find(What:="*",
After:=Range("A1"), LookAt:=xlPart, LookIn:=xlFormulas, SearchOrder:=xlByRows,
SearchDirection:=xlPrevious, MatchCase:=False).Row
Emprov2 = lastrow2 + 1
Workbooks(HTCSHN).Worksheets("Data Example").Rows(Emprov2).EntireRow.Value
= Workbooks(IPt).Worksheets("Transfer").Rows(2).EntireRow.Value
End If
sort3 = Workbooks(IPt).Worksheets("Fetch").Cells(6, 43).Value
If sort3 = "SH" Then
Workbooks(HTCSHN).Worksheets("Data Example").Activate
If ActiveSheet.ProtectContents Then
ActiveSheet.Unprotect
End If
lastrow3 = Workbooks(HTCSHN).Worksheets("Data Example").Cells.Find(What:="*",
After:=Range("A1"), LookAt:=xlPart, LookIn:=xlFormulas, SearchOrder:=xlByRows,
SearchDirection:=xlPrevious, MatchCase:=False).Row
Emprov3 = lastrow3 + 1
Workbooks(HTCSHN).Worksheets("Data Example").Rows(Emprov3).EntireRow.Value
= Workbooks(IPt).Worksheets("Transfer").Rows(3).EntireRow.Value
End If
120
sort4 = Workbooks(IPt).Worksheets("Fetch").Cells(7, 43).Value
If sort4 = "SH" Then
Workbooks(HTCSHN).Worksheets("Data Example").Activate
If ActiveSheet.ProtectContents Then
ActiveSheet.Unprotect
End If
lastrow4 = Workbooks(HTCSHN).Worksheets("Data Example").Cells.Find(What:="*",
After:=Range("A1"), LookAt:=xlPart, LookIn:=xlFormulas, SearchOrder:=xlByRows,
SearchDirection:=xlPrevious, MatchCase:=False).Row
Emprov4 = lastrow4 + 1
Workbooks(HTCSHN).Worksheets("Data Example").Rows(Emprov4).EntireRow.Value
= Workbooks(IPt).Worksheets("Transfer").Rows(4).EntireRow.Value
End If
Exit Sub
Error_exe:
MsgBox "Något gick snett, förlåt! :("
End Sub
Sub OnlyBASwrun()
'2019-04-15 Anton Hermansson
Dim run1 As Integer
Dim run2 As Integer
Dim run7 As Integer
Dim run8 As Integer
Dim lastrow1 As Long
Dim Emprow1 As Long
Dim lastrow2 As Long
Dim Emprow2 As Long
Dim lastrow3 As Long
Dim Emprow3 As Long
Dim lastrow4 As Long
Dim Emprow4 As Long
Dim sort1 As String
Dim sort2 As String
Dim sort3 As String
Dim sort4 As String
Dim HTCBAS As String
Dim HTCSH As String
Dim HTCBASN As String
121
Dim HTCSHN As String
Dim IPt As String
Dim fsbas As Object
Set fsbas = CreateObject("Scripting.FileSystemObject")
On Error GoTo Error_exe
IPt = ThisWorkbook.Name
HTCBAS = Application.GetOpenFilename(Title:="Specificera den aktuella modellen för
BAS", FileFilter:="Excel Files *.xlsm* (*.xlsm*),")
HTCBASN = fsbas.GetFileName(HTCBAS)
If IsFileOpen(HTCBAS) = True Then
Else:
Workbooks.Open (HTCBAS)
End If
Workbooks(IPt).Worksheets("Fetch").Activate
run1 = Worksheets("Fetch").Cells(4, 3).Value
run2 = Worksheets("Fetch").Cells(5, 3).Value
run7 = Worksheets("Fetch").Cells(6, 3).Value
run8 = Worksheets("Fetch").Cells(7, 3).Value
_
If run1 = 1 Then
sort1 = Workbooks(IPt).Worksheets("Fetch").Cells(4, 43).Value
If sort1 = "BAS" Then
Workbooks(HTCBASN).Worksheets("Data Example").Activate
If ActiveSheet.ProtectContents Then
ActiveSheet.Unprotect
End If
lastrow1 = Workbooks(HTCBASN).Worksheets("Data
Example").Cells.Find(What:="*", After:=Range("A1"), LookAt:=xlPart,
LookIn:=xlFormulas, SearchOrder:=xlByRows, SearchDirection:=xlPrevious,
MatchCase:=False).Row
Emprov1 = lastrow1 + 1
Workbooks(HTCBASN).Worksheets("Data
Example").Rows(Emprov1).EntireRow.Value =
Workbooks(IPt).Worksheets("Transfer").Rows(1).EntireRow.Value
End If
End If
If run2 = 1 Then
122
sort2 = Workbooks(IPt).Worksheets("Fetch").Cells(5, 43).Value
If sort2 = "BAS" Then
Workbooks(HTCBASN).Worksheets("Data Example").Activate
If ActiveSheet.ProtectContents Then
ActiveSheet.Unprotect
End If
lastrow2 = Workbooks(HTCBASN).Worksheets("Data
Example").Cells.Find(What:="*", After:=Range("A1"), LookAt:=xlPart,
LookIn:=xlFormulas, SearchOrder:=xlByRows, SearchDirection:=xlPrevious,
MatchCase:=False).Row
Emprov2 = lastrow2 + 1
Workbooks(HTCBASN).Worksheets("Data
Example").Rows(Emprov2).EntireRow.Value =
Workbooks(IPt).Worksheets("Transfer").Rows(2).EntireRow.Value
End If
End If
If run7 = 1 Then
sort3 = Workbooks(IPt).Worksheets("Fetch").Cells(6, 43).Value
If sort3 = "BAS" Then
Workbooks(HTCBASN).Worksheets("Data Example").Activate
If ActiveSheet.ProtectContents Then
ActiveSheet.Unprotect
End If
lastrow3 = Workbooks(HTCBASN).Worksheets("Data
Example").Cells.Find(What:="*", After:=Range("A1"), LookAt:=xlPart,
LookIn:=xlFormulas, SearchOrder:=xlByRows, SearchDirection:=xlPrevious,
MatchCase:=False).Row
Emprov3 = lastrow3 + 1
Workbooks(HTCBASN).Worksheets("Data
Example").Rows(Emprov3).EntireRow.Value =
Workbooks(IPt).Worksheets("Transfer").Rows(3).EntireRow.Value
End If
End If
If run8 = 1 Then
sort4 = Workbooks(IPt).Worksheets("Fetch").Cells(7, 43).Value
If sort4 = "BAS" Then
Workbooks(HTCBASN).Worksheets("Data Example").Activate
If ActiveSheet.ProtectContents Then
ActiveSheet.Unprotect
123
End If
lastrow4 = Workbooks(HTCBASN).Worksheets("Data
Example").Cells.Find(What:="*", After:=Range("A1"), LookAt:=xlPart,
LookIn:=xlFormulas, SearchOrder:=xlByRows, SearchDirection:=xlPrevious,
MatchCase:=False).Row
Emprov4 = lastrow4 + 1
Workbooks(HTCBASN).Worksheets("Data
Example").Rows(Emprov4).EntireRow.Value =
Workbooks(IPt).Worksheets("Transfer").Rows(4).EntireRow.Value
End If
End If
Exit Sub
Error_exe:
MsgBox "Något gick snett, förlåt! :("
End Sub
Sub OnlyBASworun()
Dim lastrow1 As Long
Dim Emprow1 As Long
Dim lastrow2 As Long
Dim Emprow2 As Long
Dim lastrow3 As Long
Dim Emprow3 As Long
Dim lastrow4 As Long
Dim Emprow4 As Long
Dim sort1 As String
Dim sort2 As String
Dim sort3 As String
Dim sort4 As String
Dim HTCBAS As String
Dim HTCBASN As String
Dim IPt As String
Dim fsbas As Object
Set fsbas = CreateObject("Scripting.FileSystemObject")
On Error GoTo Error_exe
IPt = ThisWorkbook.Name
HTCBAS = Application.GetOpenFilename(Title:="Specificera den aktuella modellen för
BAS", FileFilter:="Excel Files *.xlsm* (*.xlsm*),")
124
HTCBASN = fsbas.GetFileName(HTCBAS)
If IsFileOpen(HTCBAS) = True Then
Else:
Workbooks.Open (HTCBAS)
End If
Workbooks(IPt).Worksheets("Fetch").Activate
sort1 = Worksheets("Fetch").Cells(4, 43).Value
If sort1 = "BAS" Then
Workbooks(HTCBASN).Worksheets("Data Example").Activate
If ActiveSheet.ProtectContents Then
ActiveSheet.Unprotect
End If
lastrow1 = Workbooks(HTCBASN).Worksheets("Data
Example").Cells.Find(What:="*", After:=Range("A1"), LookAt:=xlPart,
LookIn:=xlFormulas, SearchOrder:=xlByRows, SearchDirection:=xlPrevious,
MatchCase:=False).Row
Emprov1 = lastrow1 + 1
Workbooks(HTCBASN).Worksheets("Data
Example").Rows(Emprov1).EntireRow.Value =
Workbooks(IPt).Worksheets("Transfer").Rows(1).EntireRow.Value
End If
sort2 = Workbooks(IPt).Worksheets("Fetch").Cells(5, 43).Value
If sort2 = "BAS" Then
Workbooks(HTCBASN).Worksheets("Data Example").Activate
If ActiveSheet.ProtectContents Then
ActiveSheet.Unprotect
End If
lastrow2 = Workbooks(HTCBASN).Worksheets("Data
Example").Cells.Find(What:="*", After:=Range("A1"), LookAt:=xlPart,
LookIn:=xlFormulas, SearchOrder:=xlByRows, SearchDirection:=xlPrevious,
MatchCase:=False).Row
Emprov2 = lastrow2 + 1
Workbooks(HTCBASN).Worksheets("Data
Example").Rows(Emprov2).EntireRow.Value =
Workbooks(IPt).Worksheets("Transfer").Rows(2).EntireRow.Value
End If
sort3 = Workbooks(IPt).Worksheets("Fetch").Cells(6, 43).Value
125
If sort3 = "BAS" Then
Workbooks(HTCBASN).Worksheets("Data Example").Activate
If ActiveSheet.ProtectContents Then
ActiveSheet.Unprotect
End If
lastrow3 = Workbooks(HTCBASN).Worksheets("Data
Example").Cells.Find(What:="*", After:=Range("A1"), LookAt:=xlPart,
LookIn:=xlFormulas, SearchOrder:=xlByRows, SearchDirection:=xlPrevious,
MatchCase:=False).Row
Emprov3 = lastrow3 + 1
Workbooks(HTCBASN).Worksheets("Data
Example").Rows(Emprov3).EntireRow.Value =
Workbooks(IPt).Worksheets("Transfer").Rows(3).EntireRow.Value
End If
sort4 = Workbooks(IPt).Worksheets("Fetch").Cells(7, 43).Value
If sort4 = "BAS" Then
Workbooks(HTCBASN).Worksheets("Data Example").Activate
If ActiveSheet.ProtectContents Then
ActiveSheet.Unprotect
End If
lastrow4 = Workbooks(HTCBASN).Worksheets("Data
Example").Cells.Find(What:="*", After:=Range("A1"), LookAt:=xlPart,
LookIn:=xlFormulas, SearchOrder:=xlByRows, SearchDirection:=xlPrevious,
MatchCase:=False).Row
Emprov4 = lastrow4 + 1
Workbooks(HTCBASN).Worksheets("Data Example").Rows(lastrow).EntireRow.Value
= Workbooks(IPt).Worksheets("Transfer").Rows(4).EntireRow.Value
End If
Exit Sub
Error_exe:
MsgBox "Något gick snett, förlåt! :("
End Sub