mathematical modelling of photocatalytic reaction

8/12/2019 Mathematical modelling of photocatalytic reaction

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Predictive modelling of Photo-Fenton reaction in presence of

Titania for removal of reactive red dye using Artificial Neural

Network

Bachelor of Technology

In

Chemical Engineering

By

NITIN GAUTAM (09CH3018)

Under the Guidance of

Prof. J.K.Basu

&

Prof. S.Sengupta

Department of Chemical Engineering

Indian Institute of Technology


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CERTIFICATE

This is to be certify that the thesis entitled Predictive modelling of Photo-Fenton

reaction in presence of titania for removal of reactive red dye using Artificial

neural network submitted by NITIN GAUTAM to the Department of Chemical

Engineering, Indian Institute of Technology, Kharagpur, for the award of the

degree of Master of Technology in Chemical Engineering is a bonafide work

carried out by him under my personal supervision and guidance during the

academic year 2013-2014. This report is, in our opinion, is worthy of consideration

for the degree of Bachelor of Technology in Chemical Engineering in accordance

with the regulation of the institute.

(Prof. J.K.Basu) (Prof. S.Sengupta)

Dept. of Chemical Engineering Dept. of Chemical Engineering


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ACKNOWLEDGEMENT

I avail this unique opportunity and proud privilege to express my profound

sense of gratitude and indebtedness to my project supervisors Prof. J.K.Basu and

Prof. S.Sengupta for their efficacious advices, perpetual and prolific

encouragement, invaluable guidance, pain taking effort, constant encouragement,

inspiration and creative criticism during each and every step of my project workthroughout the year.

I am sincerely grateful to Prof. N.C.Pradhan, Head, Department of Chemical

Engineering, Indian Institute of Technology, Kharagpur for providing all the

necessary facilities for the successful carryout of my project work.

I wish my thanks to Mr. Bibhas Jana, Mr. D.Sakha, and other staffs for

providing necessary help during course of project work.

I wish to acknowledge the assistance of my colleagues for their help and

support.

Last but not the least, I sincerely acknowledge the warm blessings, unfailing

support of my family members and dear friends who helped me directly or

indirectly for successful carry out of my project work.

Date: 26thNOV, 2013

Place: IIT Kharagpur (NITIN GAUTAM)


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Contents1. INTRODUCTION .................................................................................................................................... 4

1.1 Artificial neural networks ................................................................................................................... 6

1.2 Harmful effects of Reactive Red dye .................................................................................................. 7

2. Problem Statement .................................................................................................................................... 7

2.1 Objectives ........................................................................................................................................... 7

3. Experiement .............................................................................................................................................. 7

3.1 Preparation of Nano-Zerovalent Iron and its characterization............................................................ 7

3.1.1Procedure ..................................................................................................................................... 7

3.2.1Procedure ..................................................................................................................................... 8

3.2.2Factors selection .......................................................................................................................... 8

3.2.3Factors chosen ............................................................................................................................. 8

3.2.4Experimental Condition ............................................................................................................... 9

3.2.5Experiment Procedure ................................................................................................................. 9

4. Experiment Data ..................................................................................................................................... 10

4.1 Removal percentage .......................................................................................................................... 10

4.2 Graph ................................................................................................................................................ 11

4.2 Matlab Code for ANN ...................................................................................................................... 12

5. Results and discussions ........................................................................................................................... 13

5.1Characterization of Nano Zerovalent Iron ....................................................................................... 13

5.2Fitting of photo Fenton reaction with Reactive Red over Titania catalyst using artificial neural

network .................................................................................................................................................... 14

7. Conclusion .............................................................................................................................................. 24

8. Appendix ................................................................................................................................................. 25

9. Reference ................................................................................................................................................ 26


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1. INTRODUCTIONTreatment of hazardous industrial effluents is one of the growing needs of the present time. Advanced

Oxidation Processes (AOPs) have been developed to convert non-biodegradable contaminants into

harmless species .Heterogeneous photo catalysis, a novel process belonging to the class of AOPs, via

combination of photo catalysts, such as TiO2 and ultraviolet (UV) light, is as an attractive alternative

treatment method for the removal of toxic pollutants from wastewater, owing to its ability to degrade the

pollutants into innocuous end-products, such as CO2, H2and mineral acids. The preferential use of TiO2

for the photo catalytic degradation of organic pollutants is based on its availability, low cost and

photochemical stability. The photo catalytic reaction uses photons with energy greater than the band gap

of a semiconductor, usually TiO2, for generation of conduction band electrons and valence band holes to

initiate oxidationreduction reactions. There have been several studies on the assessment of photo

catalytic treatment of hazardous substances from industrial effluents.

The efficiency of a photo catalytic reaction depends on a number of factors, which govern the

performance of photo catalysis. Initial concentration of pollutant, photo catalyst concentration, pH,

volume of solution, radiant flux and agitation, irradiation time, light intensity, irradiation wavelength,

temperature, geometrical parameters of the experimental setup and multiple degradation pathways are the

parameters that can be cited. Due to the complexity and variety of influencing factors, it is difficult to

evaluate the relative significance of several affecting factors, especially in the presence of complex

interactions. In the most recent studies, only traditional one-factor-at-a-time experiments were tested for

evaluating the influence of operating factors on the photo catalytic process efficiency. This technique is

not only time and work demanding, but also completely lacks representation of the effect of interaction

between different factors.

Oxidation with Fentons reagent is based on ferrous ion and hydrogen peroxide, and exploits the

reactivity of the hydroxyl radicals produced in acidic solution by the catalytic decomposition of H2O2.

Fe2++H2O2 Fe3++OH+OH

Hydroxyl radicals may be scavenged by reaction with another Fe2+:

OH+ Fe2+ OH+Fe3+


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Fenton reagent appears to be a very powerful oxidizing agent. Besides, the process is simple and non-

expensive, taking place at low temperatures and at atmospheric pressure. The chemicals are readily

available at moderate cost and there is no need for special equipment.

In photo-Fenton process in addition to the above reactions the formation of hydroxyl radical also occurs

by the following reactions:

Fe3++H2O+h OH+ Fe2++H+

The rate of organic pollutant degradation could be increased by irradiation of Fenton with UV or visible

light (photo-Fenton process). The illumination leads not only to the formation of additional hydroxyl

radicals but also to recycling of ferrous catalyst by reduction of Fe3+

. In this way, the concentration of

Fe2+is increased and the overall reaction is accelerated. Among the AOPs, the oxidation using Fentons

reagent and photo-Fentons reagent has been found to be a promising and attractive treatment method for

the effective degradation of pesticides.

Zero-valent iron has been used successfully in the past to remediate groundwater by construction of a

permeable reactive barrier (PRB) of zero-valent iron to intercept and dechlorinate chlorinated

hydrocarbons such as trichloroethylene (TCE) in groundwater plumes. Currently, zero-valent iron in both

the micro and macro-scale is used in PRBs for the purposes of remediation at contaminated sites. A PRB

most commonly contains granular iron as the reactive medium that degrades chlorinated organics into

potentially nontoxic dehalogenated organic compounds and inorganic chloride that precipitates out of the

water column and becomes part of the sediment. A PRB, in its simplest form, is a trench built across the

flow path of a groundwater plume.

The use of nanoscale zero-valent iron (NZVI) instead of using micro/macro-scale Fe0(zerovalent

iron) materials could potentially eliminate the need for using PRBs and be more effective in both cost

feasibility and contaminant remediation. Laboratory studies indicate that a wider range of chlorinated

hydrocarbons may be dechlorinated using various nanoscale iron particles (principally by abiotic means,

with zero-valent iron serving as the bulk reducing agent), including chlorinated methanes, ethanes,

benzenes, and potentially, polychlorinated biphenyls. Several factors play a role in determining a

nanoscale iron products reactivity, including particle size, the amount of reactive surface area, the

presence or absence of hydrogenation catalysts (e.g., palladium), the method of manufacture, the

morphology of the particle (porosity), the crystalline structure of the particle, impurities and coating, and

whether or not particles have been exposed to acid washing (NZVI synthesis using sodium borohydride

[NaBH]). Two potential advantages of nanoscale zero-valent iron over the construction-grade ZVI used in

conventional PRBs are that nanoparticles may be delivered to deep contamination zones by injection and


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that NZVI may be more effective at degrading some contaminants because of a higher reactivity due to

increased surface area. Despite the potential for the use of manufactured nanoparticles, there are still

particular concerns that must be addressed in regard to the effectiveness and application of this new

technology. These issues include the mobility of nanoparticles under subsurface conditions, the kinetics

and products of contaminant degradation by NZVI, and whether the NZVI maintains its reactivity during

the time period of treatment.

1.1 Artificial neural networks

One type of network sees the nodes as artificial neurons. These are called artificial neural networks

(ANNs). An artificial neuron is a computational model inspired in the natural neurons. Natural neurons

receive signals through synapses located on the dendrites or membrane of the neuron. When the signals

received are strong enough (surpass a certain threshold), the neuron is activated and emits a signal though

the axon. This signal might be sent to another synapse, and might activate other neurons.

The complexity of real neurons is highly abstracted when modelling artificial neurons. These basically

consist of inputs (like synapses), which are multiplied by weights (strength of the respective signals), and

then computed by a mathematical function which determines the activation of the neuron. Another

function (which may be the identity) computes the output of the artificial neuron (sometimes in

dependence of a certain threshold). ANNs combine artificial neurons in order to process information. The

higher a weight of an artificial neuron is, the stronger the input which is multiplied by it will be. Weights

can also be negative, so we can say that the signal is inhibited by the negative weight. Depending on the

weights, the computation of the neuron will be different. By adjusting the weights of an artificial neuron

we can obtain the output we want for specific inputs. But when we have an ANN of hundreds or

thousands of neurons, it would be quite complicated to find by hand all the necessary weights. But we can

find algorithms which can adjust the weights of the ANN in order to obtain the desired output from the

network. This process of adjusting the weights is called learning or training.

The number of types of ANNs and their uses is very high. Since the first neural Model, there have been

developed hundreds of different models considered as ANNs. The differences in them might be the

functions, the accepted values, the topology, the learning algorithms, etc. Also there are many hybrid

models where each neuron has more properties than the ones we are reviewing here. Because of matters

of space, we will present only an ANN which learns using the back propagation algorithm for learning the

appropriate weights, since it is one of the most common models used in ANNs, and many others are

based on it. Since the function of ANNs is to process information, they are used mainly in fields related


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with it. There are a wide variety of ANNs that are used to model real neural networks, and study behavior

and control in animals and machines, but also there are ANNs which are used for engineering purposes,

such as pattern recognition, forecasting, and data compression.

1.2 Harmful effects of Reactive Red dye

Reactive Red Dye is widely utilized in many industries including textile, paper, leather, plastic, food and

painting. However the waste water from these industries contains 10% of dye. The presence of small

amount of dye in water is highly visible and harmful. Dyes are synthetic compounds and most of them are

toxic and carcinogenic. Also they are usually stable and difficult to resolve into innocuous substances.

Therefore the industrial effluents containing dyes are hazardous and can cause fatal harm to animals and

plants.

2. Problem Statement2.1 Objectives

1) To prepare Nano-Zerovalent iron and its characteristization

2) Predictive modelling of dye removal of photo Fenton reaction with Reactive Red over titania

catalyst using Artificial neural network

3) Comparison of Fe activity in removal of dye in nano Zerovalent iron and photo Fenton reaction

3. Experiment

3.1 Preparation of Nano-Zerovalent Iron and its characterization

3.1.1Procedure

The iron nanoparticles were synthesis in a flask reactor in ethanol medium as illustrated in Fig.1. The

following is the reaction,

2FeCl3+ 6NaBH4+ 18H2O 2Fe0+ 6NaCl + 6B(OH)3+ 21H2

For the synthesis of nanoscale Zero Valent Iron (nZVI); 0.5406 g FeCl3.6H2O was dissolved in a 4/1 (v/v)

ethanol/water mixture (24 ml ethanol + 6 ml deionized water) and stirred well. On the other hand, 0.1 M

sodium borohydride solution was prepared i.e., 0.3783 g NaBH4was dissolved in 100 ml of deionized

water; since for better growth of iron nanoparticles excess borohydride is needed. The borohydride

solution is poured in a burette and added drop by drop (1drop per 2 seconds) into iron chloride solution


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with vigorous hand stirring. After the first drop of sodium borohydride solution, black solid particles

immediately appeared and then the remaining sodium borohydride is added completely to accelerate the

reduction reaction. The mixture was left for another 10 minutes of stirring after adding the whole

borohydride solution. The vacuum filtration technique was used to separate the black iron nanoparticles

from the liquid phase. Two sheets of Whatman filter papers were used in filtration. The solid particles

were washed three times with 25 ml portions of absolute ethanol to remove all of the water. This washing

process is probably the key step of synthesis since it prevents the rapid oxidation of zero valent iron

nanoparticles. The synthesized nanoparticles were finally dried in oven at 323 K overnight. For storage, a

thin layer of ethanol was added to preserve the nano iron particles from oxidation.

3.2Predictive modelling of photo Fenton reaction with Reactive Red over Titania catalyst using artificial

neural network

3.2.1ProcedureMatlab Artificial neutral network toolbox was used for curve fitting and input output. Experimental data

was used in inputs and outputs for network. 32 experiments were performed. Out of 32, 70% data was

used for training neural network, 15% for validation and 15% for testing. Neural network was trained 10

times and regression and fit plot were plotted. 1 hidden neuron was used in neural network. Mean

Squared Error and Regression R Values were measured each time of training. Mean Squared Error is the

average squared difference between outputs and targets. Lower values are better. Zero means no error.

Regression R Values measure the correlation between outputs and targets. An R value of 1 means a close

relationship, 0 a random relationship.

3.2.2Factors selection

Efficiency of photo catalysis process depends upon numerous factors like initial concentration of

pollutant, photo catalyst concentration, pH, Volume of solution, radiant flux, agitation, irradiation time,

light intensity, Irradiation wavelength, and temperature. But all these factors cannot be varied to model

photo catalysis process accurately. All the factors affect the photo catalytic process. But due to

experimental limitation in the lab and complexity involved in modeling the entire variables, only 4 factors

were chosen. These factors were chosen because these can be easily varied during process and involved

less modeling complexity.

3.2.3Factors chosen

1. Dye concentration

2. Catalyst Concentration

3. pH

4. Irradiation time


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Each factor is varied in range and is optimized for this range. Experiments are conducted in these ranges.

Table 3.1: The experimental range and levels of the input variables

Factor Units Lower value Upper value

Dye conc. mg/L 100 100

Cat load mg/L 100 2500

pH 4.1 7.1

Time Min 0 70

A response variable has to be chosen for design the ANN. % removal of dye was chosen as response

variable for the photo catalysis process.

3.2.4Experimental Condition

Photo catalytic degradation of Reactive Red dye was studied using TiO2 catalyst in presence of

FeSO4.7H2O and H2O2. Reaction was carried out in a conical glass vessel of 250 ml capacity. Two 8 W

ultraviolet lamp with wavelength 218 nm (Philips) was positioned inside box at a distance of 15 cm from

the conical glass vessel. A typical reaction was carried out using 200 ml solution of Reactive Red dye at

300C with a variation of 1oC. Solution was magnetically stirred at fixed rpm.

3.2.5Experiment Procedure

Solution of Reactive Red dye is prepared. Composition of Titania was varied photo Fenton reaction.

Titania was mixed with reactive red dye. pH of the solution is set by pH meter. The solution was poured

inside 200 ml conical flask. Magnetic stirred was used so that catalyst particle do not deposit at the

bottom of vessel. Photo catalysis reaction is carried out in closed chamber. Glass vessel was filled with

reactive red dye and Titania-Zirconia catalyst is irradiated with UV radiation. Solution was centrifuged

and was analyzed using UV vis spectrophotometer.


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4. Experiment Data4.1 Removal percentage of dye was measured and reported in table 4.1 with respective TiO2 conc, dye

conc., and pH and time.

Table 4.1: Experimental data

TiO2 Conc(mg/l) Dye Conc (mg/l) pH Time (min) % removal

100 100 7.1 0 0

100 100 7.1 10 3.67

100 100 7.1 20 9.06

100 100 7.1 30 10.8

100 100 7.1 40 19.3

100 100 7.1 50 19.7

100 100 7.1 60 19.89100 100 7.1 70 26.1

2500 100 7.1 0 0

2500 100 7.1 10 0.006

2500 100 7.1 20 2.86

2500 100 7.1 30 7.58

2500 100 7.1 40 9.59

2500 100 7.1 50 13.39

2500 100 7.1 60 13.93

2500 100 7.1 70 14.24

500 100 7.1 0 0500 100 7.1 10 1.5

500 100 7.1 20 8.76

500 100 7.1 30 9.28

500 100 7.1 40 9.79

500 100 7.1 50 16.92

500 100 7.1 60 23.38

500 100 7.1 70 24.13

500 100 4.1 0 0

500 100 4.1 10 93.2

500 100 4.1 20 94.77

500 100 4.1 30 94.77

500 100 4.1 40 94.77

500 100 4.1 50 94.77

500 100 4.1 60 94.77

500 100 4.1 70 94.77


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4.2 Graph

For experiment 1,

For experiment 2

0 0.006

2.86

7.58

9.59

13.3913.93 14.24

0

2

4

6

8

10

12

14

16

0 10 20 30 40 50 60 70

PERCENTAGEREMOVALOF

DYE

TIME (MINS)

% R e m o v a l O f D y e V s T i m e ( T i O 2 C o n c = 2 5 0 0

m g / L ) , D y e C o n c ( m g / L ) = 1 0 0 p H = 7 . 1

0

3.67

9.0610.8

19.3 19.7 19.89

26.1

1

0

5

10

15

20

25

30

0 10 20 30 40 50 60 70

PERCENTAGEREMOVALOFDYE

TIME (MINS)

% R e m o v a l O f D y e V s T i m e ( T i O 2 C o n c = 1 0 0

M g / L ) , D y e C o n c ( m g / L ) p H = 7 . 1


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For experiment 3

For experiments 4,

% removal of dye was observed high in acidic medium and when TiO2 conc. was 500 mg/l, a

high conc of TiO2 causes agglomeration and most of TiO2 catalyst surface is not exposed to UVas a result, % removal is observed less. While in acidic medium, high percentage removal is

observed.

4.2 Matlab Code for ANN

See appendix for Matlab code

01.5

8.76 9.289.79

16.92

23.38 24.13

0

5

10

15

20

25

30

0 10 20 30 40 50 60 70

PERCENTAGEREMOVALOFDY

E

TIME (MINS)


M g / L ) , D y e C o n c ( m g / L ) = 1 0 0 p H = 7 . 1

0

93.2 94.77 94.77 94.77 94.77 94.77 94.77

0

10

20

30

40

50

60

70

80

90

100

0 10 20 30 40 50 60 70

PECENTAGER

EMOVALOFDYE

TIME (MINS)


( m g / L ) , D y e c o n c ( m g / L ) = 1 0 0 p H = 4 . 1


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5. Resultsand discussions

5.1Characterization of Nano Zerovalent Iron

In the present work, nanoscaled zero valent irons (nZVI) have been synthesized in ethanol medium by the

method of ferric iron reduction using sodium borohydride as a reducing agent under atmospheric

condition. The iron nanoparticles are mainly in zero valent oxidation state and remain without significant

oxidation for weeks. A systematic characterization of nZVI has been performed using BET studies.

The BET surface area values were determined as 134.76 m2/g for nZVI. Some of the BET surface area

values reported in literature are 14.5 m2/ g, 33.5 m

2/g and 36.5 m

2/g. In comparison, commercially

available Fe powder ( Fe2+(and Fe2+(aq)) + 2BH4

(aq)+ 6H2O(l)> Fe0

(s)+ Fe0

(s)+ 2B (OH)3(aq)+ 7H2 (g)

In the reaction, > Fe2+denotes iron ions attached to a kaolinite surface, > Fe0(s)refers to nZVI dispersed

on kaolinite, and Fe0(s)stands for nZVI retaining its chain-like structure.

Table 5.1: BET ANALYSIS

P/Po Volume (cc/g) 1/(W((Po/P)-1)) STP

4.8409e-02 24.7960 1.641E+00

5.8479e-02 25.7478 1.930E+00

6.8547e-02 26.5949 2.214E+00

7.8631e-02 27.3286 2.499E+00

8.3477e-02 28.0516 2.598E+00

8.8791e-02 28.4590 2.740E+00

9.8581e-02 29.1888 2.998E+00

1.0857e-01 29.9194 3.257E+00

1.3292e-01 31.6027 3.881E+00

1.5792e-01 33.2996 4.506E+00

1.8295e-01 34.9032 5.133E+00

2.0794e-01 36.4888 5.757E+00

2.3289e-01 38.1438 6.368E+00

2.5790e-01 39.7711 6.992E+00

2.8285e-01 41.3903 7.624E+00

3.0805e-01 42.8713 8.308E+00


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5.2Fitting of photo Fenton reaction with Reactive Red over Titania catalyst using artificial

neural network

Using Matlab neural network tool, setting hidden layer to 1, neural network was trained ten times and

respective mean square error correlation was noted.

Run no. Mean square error Regression value

1 1.266 0.98

2 2.769 0.99

3 1.958 0.989

4 45.428 0.9853

5 1.779 0.991

6 1.238 0.999

7 6.96711 0.999

8 9.922 0.999

9 1.110 0.9948

10 1.133 0.9991


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5.3Plots of Each training data

Plot 5.3.1: Plots of 1st

training


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Plot 5.3.2: Plots of 2nd

training


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Plot 5.3.3: Plots of 3rd

training


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Plot 5.3.4: Plots of 4th

training


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Plot 5.3.5: Plots of 5th training


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training


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training


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training


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training


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training

Average value of all the mean square error is 7.3570 and average value of regression correlation is0.99262.

7. ConclusionPredictive modelling of Photo-Fenton reaction in presence of TiO2for removal of Reactive Red dye using

ANN showed better value of Regression, which shows better correlation between input and outputs.


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Prediction was wrong in some training set where mean square error observed was high. Experimental

data plays important role in prediction. Therefore collection of experimental data affects the predictive

model. Since only 1 hidden layer was used, Single sigmoidal function is used in establishing correlation

between inputs and outputs.

Photo-Fenton and Titania UV action gave synergetic effect and better removal of dye was observed.

8. Appendix% Solve an Input-Output Fitting problem with a Neural Network

% Script generated by NFTOOL

% Created Wed Nov 13 03:18:24 IST 2013

%

% this script assumes these variables are defined:

%

% x - input data.

% output - target data.

inputs = x';

targets = output';

% Create a Fitting Network

hiddenLayerSize = 1;

net = fitnet(hiddenLayerSize);

% Choose Input and Output Pre/Post-Processing Functions

% For a list of all processing functions type: help nnprocess

net.inputs{1}.processFcns = {'removeconstantrows','mapminmax'};

net.outputs{2}.processFcns = {'removeconstantrows','mapminmax'};

% Setup Division of Data for Training, Validation, Testing

% For a list of all data division functions type: help nndivide

net.divideFcn = 'dividerand'; % Divide data randomly

net.divideMode = 'sample'; % Divide up every sample

net.divideParam.trainRatio = 70/100;

net.divideParam.valRatio = 15/100;

net.divideParam.testRatio = 15/100;

% For help on training function 'trainlm' type: help trainlm% For a list of all training functions type: help nntrain

net.trainFcn = 'trainlm'; % Levenberg-Marquardt

% Choose a Performance Function

% For a list of all performance functions type: help nnperformance

net.performFcn = 'mse'; % Mean squared error


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% Choose Plot Functions

% For a list of all plot functions type: help nnplot

net.plotFcns = {'plotperform','plottrainstate','ploterrhist', ...

'plotregression', 'plotfit'};

% Train the Network

[net,tr] = train(net,inputs,targets);

% Test the Network

outputs = net(inputs);

errors = gsubtract(targets,outputs);

performance = perform(net,targets,outputs)

% Recalculate Training, Validation and Test Performance

trainTargets = targets .* tr.trainMask{1};

valTargets = targets .* tr.valMask{1};

testTargets = targets .* tr.testMask{1};

trainPerformance = perform(net,trainTargets,outputs)

valPerformance = perform(net,valTargets,outputs)

testPerformance = perform(net,testTargets,outputs)

% View the Network

view(net)

% Plots

% Uncomment these lines to enable various plots.

%figure, plotperform(tr)

%figure, plottrainstate(tr)%figure, plotfit(net,inputs,targets)

%figure, plotregression(targets,outputs)

%figure, ploterrhist(errors)

9. Reference1)Preparation and characterization of zero valent iron nanoparticles, R. Yuvakkumara, V. Elangoa, V.

Rajendrana*, N. Kannanb

2) Preparation of TiO2/activated carbon with fe ions doping photocatalyst and its application to

photocatalytic degradation of reactive brilliant red k2g Li Youji, Li Jing, Ma Mingyuan, Ouyang Yuzhu

& Yan Wenbin

3) Persistence of commercial nanoscaled zero-valent iron (nZVI) and by-products, Adeyemi S. Adeleye

,Arturo A. Keller , Robert J. Miller , Hunter S. Lenihan


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mathematical modelling of photocatalytic reaction

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