Telemac D2.4 1

TELEMAC IST-2000-28156TELEMonitoring and Advanced teleControl of high yield wastewater treatment

Development of two on-line analysersfor the control of the anaerobic digestionprocess

Deliverable 2.4

Deliverable Type: ReportNumber:D 2.4

Contractual Date of Delivery:30-11-03Delivery of the first version: 03-03-04

Actual Date of Delivery:10-05-04

Task WP2.3R&D

Applitek NVIZ De prijkels

Venecoweg 199810 Nazareth

Email: K.de.Neve@AppliTek.com

Other contributors :Usama Zaher, Peter Vanrolleghem

BIOMATH, St Pietersnieuwstraat 25, B-9000 Gent, BelgiumEmail: Peter.vanrolleghem@rug.ac.be, Usama.Zaher@rug.ac.be

Jean-Claude Bouvier, Jean-Philippe SteyerINRA, Avenue des Etangs 11100 Narbonne, France

Email : Bouvier@ensam.inra.fr, Steyer@ensam.inra.fr

Reviewers: Olivier Bernard (INRIA) , Enrique Roca(USC), Gonzalo Ruiz (USC), JoséMartinez (Agralco), Dochain Denis (Cesame), Jean-François Lavigne(DOMECQ)

Abstract: This deliverable presents the work and the results obtained for thedevelopment of two on-line analysers. One analyser based on titrimetry determines VFA,bicarbonate and alkalinities in the liquid phase, the second analyser measures CO2 andCH4 in the biogas based on thermal conductivity.

Keyword List: VFA, bicarbonate, alkalinity, CO2, CH4

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Factual summary for TELEMAC deliverable D2.4

Concrete results and work done in scope of the deliverableAppliTek Biomath INRA

Development of the Liquid Titrimetric AnalyserStudy titration + developing titration methodsLiterature study of titration methods for detection of VFA andtitration algorithms (method of Kapp, Moosbrugger + methoddeveloped at Ghent university: BOMB)

1 1

Adapting and testing the BIOMATH titrimeter for the titrationof vinasses

1

Reporting necessary updates to titrimetric setups for vinassesapplications

0.5

Experiment for INRA and BIOMATH titrimeters comparison 1 0.25Analysis of the comparison results using BOMB software 1Titrimeters comparisons milestone (reporting & validation) 1 0.25Titration tests with standard solutions with Lab-titroprocessor+ calibration of two titrimetric methods (method1 and method2)

1.5 1

Writing report on titrimeters validation 0.15Off-line tests comparing with GC + adapting GC procedure 0.8Design and development of prototype + testing components(micropumps, burette, pH probe and board, … ) + developmentPrinted Circuit Boards for control of the burette and reading pHsignals.

1.5

Developing the model based titration algorithm 0.4 1.8Developing the slope based and mixed algorithm 0.5 0.2Developing simulation module + testing the model basedtitration algorithm with simulations and BOMB software

0.8 0.5

Testing the capabilities of BOMB in determination of differentcombination of buffers + study the requirements of theHardware.

0.3 2

Software development:Upgrade of BOMB software to Buffer Capacity Software(BCS) for on-line applicationBuilding a C++ autoinitialisation module for BOMB 2Building a C++ module to automatically detect and evaluateinterfering buffers (additional buffers other than Bicarbonateand VFA)

2

Building a C++ module for on-line implementation 1Packaging different software modules with BOMB into BCSfor general on-line implementation with any standard titrimetrichard ware

1

Adapting the BCS to work on-line with the TELEMACtitrimetric sensor

0.2 0.5

First stage testing of the BCS for detection of generally anybuffering systems (experiments with standard solutions andanaerobic samples)

0.4 1

Assembly of the Hardware prototype of TitrimetricanalyserConstruction (first version) 1First version of the control software 1.5Implementation of the slope based titration algorithm andmixed algorithm + testing both algorithms + optimalisation

0.75

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Optimalisation of the control software (debugging the program,implementation of caustic dosing for pH-control, rinse cycledata logging, visualization, … )

1.5

First validation of the Titrimetric analyserBased on standards 0.5Installation and maintenance of the anaerobic set-up 1Installation Titrimetric analyser + maintenance + hardwareoptimalisation

1.1

Validation of the titrimetric analyser results using BCS 0.7Development of Gas analyserStudy of commercially available sensors for CO2 and CH4 0.5Assembly of two gas sensors 0.9Testing the sensor with calibration gases + effect oftemperature and moisture

0.8

Development of the gas pretreatment (water and H2S removal) 0.8Testing the gas analyser at the pilot scale reactor of INRA 0.4 0.5Installation of the gas analyser (with gas pretreatment) atENEA + testing

0.2

Consultation and validation 0.5Production of the deliverable documentCoordination and editing of the deliverable document 0.8 0.3

Total for each partner 19.3 20 1

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Deliverable 2.4Contents

1 Introduction

2 Development of on-line titrimetric analyser for VFA, bicarbonate and alkalinities2.1 Introduction

2.1.1 Titration concepts2.1.2 Determination of VFA and bicarbonate

2.2 Study of the titrimetric parameters2.2.1 Titroprocessor set-up (off-line analyses)

2.2.2 Study of stripping CO2 and VFA2.2.3 Influence of NaCl2.2.4 Description of the titrimetric methods

A. INRA method (method 1)B. AppliTek method (method 2)C. Extended method (method3)

2.2.5 Evaluation of the titrimetric methods2.2.6 Off-line tests with anaerobic samples

A. Experimental set-up of the lab-scale reactorB. GC setup for the VFA reference measurementsC. VFA measurementD. Measurement of bicarbonate, total and partial alkalinity.

2.3 Development of on-line titrimetric analyser2.3.1 Development of titration algorithms

A. Model based algorithmB. Slope based algorithm

2.3.2 Description2.4 On-line test results using the on-line titrimetric analyser

2.4.1 Test results for standard solutions2.4.2 Test results for anaerobic digester samples2.4.3 BCS results using the on-line titrimetric analyser

2.4.3.1 Test results for standard solutions2.4.3.2 Test results for anaerobic digester samples

2.4.4 Diagnostics using remote monitoring

3 Development of on-line gas analyser for the determination of CO2 and CH4 inbiogas3.1 Normal gas composition in the anaerobic reactor3.2 Thermal conductivity

3.2.1 Thermal conductivity of the main gases3.2.2 Working principle3.2.3. Linearity of the gas sensor

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3.3 Test of the gas analyser at INRA3.3.1 Set-up of the experiment3.3.2 On-line measurement results

3.4 Gas pretreatment3.4.1 Moisture filtering by Peltier cooler3.4.2 H2S filtering by absorption filter

4. General Conclusion

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1. Introduction titrimetric analyser

The anaerobic digestion process is based on a complex ecosystem of bacterial speciesthat degrade organic matter. Since the late seventies this process has gained considerableimportance at industrial scale. The control of the anaerobic process is based uponmeasurements of Volatile Fatty Acids (VFA), pH, alkalinity and biogas (mixture of CH4

and CO2) production. Since change in VFA concentrations is one of the most sensitiveparameters, VFA measurements are crucial for optimising the anaerobic process.In industry however, few laboratories are equipped for VFA measurements because theexisting techniques like HPLC and GC require specialised equipment, high investmentcosts and highly skilled personnel. Therefore usually pH, alkalinity and gas productionare only measured for the control of the digester. Nevertheless, using only theseparameters the anaerobic process cannot be fully controlled and as a consequence theprocess could be eventually disturbed.Figure 1 shows a simplified overview of the anaerobic process. First the hydrolysis ofcomplex polymers by the enzymes secreted by bacteria to soluble monomers takes place.In a second phase these monomers are consumed to produce volatile fatty acids (aceticacid, propionic acid, butyric acid,…), carbon dioxide and hydrogen. The next step is theproduction of methane out of carbon dioxide, hydrogen and acetate.

Figure 1: Simplified overview of process in anaerobic digester1

From figure 1 it is clear that the anaerobic process is susceptible to little variations andtherefore eventually unstable. If the reactor is overloaded (too much organic load), VFAwill accumulate when the acidogenic organisms metabolize substrate and produce VFAfaster than the acetogenic and methanogenic organisms can consume VFA. This VFA

1 Noykova N., Müller T.G., Gyllenberg M, Timmer J. (2001); Quantitive analyses of anaerobic wastewatertreatment processes: Identifiability and parameter estimation. Biotechnology and Bioengineering, VOL. 78,NO. 1, 89-103.

Complex Organics (Polysaccarides, lipids, Proteins, Nucleic acids)

Simple Organics (Sugars, Fatty acids, Amino Acids, Purines, Pyrimidines, Cellobiose, glycerol)

Volatile Acids (Acetic, propionic, Formic, butyric, Isobutyric)

, Alcohols, CO2, H2

Biogas(CH4,CO2, H2O, N2 H2S)

Hydrolytic Bacteria

Acidogenic Bacteria

Methanogenic Bacteria

Complex Organics (Polysaccarides, lipids, Proteins, Nucleic acids)

Simple Organics (Sugars, Fatty acids, Amino Acids, Purines, Pyrimidines, Cellobiose, glycerol)

Volatile Acids (Acetic, propionic, Formic, butyric, Isobutyric)

, Alcohols, CO2, H2

Biogas(CH4,CO2, H2O, N2 H2S)

Hydrolytic Bacteria

Acidogenic Bacteria

Methanogenic Bacteria

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accumulation may result in the decrease of the pH and the inhibition of the methanogenicbacteria (Moosbrugger et al., 1993) 2. The importance of on-line monitoring of VFA forprocess stability is also described in deliverable 2.1 and 3.1b.

In order to fulfill the need for better control of the anaerobic treatment, AppliTekdeveloped in the framework of the research project TELEMAC an on-line titrimetricanalyser for the determination of VFA, bicarbonate, alkalinity and pH. For more detailedinformation about sample pretreatment (filtration) necessary for the on-lineimplementation of this analyser we refer to deliverable 2.1.

2 Moosbrugger R.E., Wentzel M.C., Loewenthal R.E., Ekama G.E. and Marai Gv.R. (1993) Alkalinitymeasurement: Part 3 – A 5 pH point titration method to determine the carbonate and SCFA weak acid/basesin aqueous solution containing also known concentration of other weak acid/ bases. Water SA 19(1) 29-40.

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2. Development of on-line Analyser for the determination of VFA,bicarbonate and alkalinities

2.1 Introduction

2.1.1 Titration concepts

Titration performed for measuring VFA and bicarbonate can be defined as adding smallamounts of a strong acid to a weak base solution (down titration) and measuring the pHafter each addition. For the analysis, only down titrations are used. This is because the pHof an anaerobic reactor is mostly about 7 and the compounds of interest (bicarbonate andVFA) are buffering below this pH.A titration curve can be obtained by plotting the amount of acid versus the change in pH.An example of a titration curve is presented in figure 2.

Figure 2: Titration curve of a standard solution of 50 meq/l HCO3Na and 250 mg/lCH3COONa

A mathematical representation of the chemical equilibrium is based on three equations: amass balance, a dissociation equation and an electro-neutrality equation.

Equilibrium reaction: HA ? A- + H+

Mass balance: CA = [HA] + [A-]

Dissociation equation: Ka = [H+] [A-]/[HA]

Electro-neutrality equation: [H+] = [OH-] + [A-]

2

3

4

5

6

7

8

0 0.2 0.4 0.6 0.8 1 1.2 1.4

ml HCl (0.5N)

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Definitions:

Alkalinity is the capability of water to neutralize strong acids.Partial alkalinity is defined as the number of millimoles of acid required to titrate thesample between initial pH of the sample and pH 5.75. The total alkalinity is the amountof acid required to titrate from initial pH to pH 4.3.

VFA is a parameter that includes several compounds (see Table 1). All these compoundshave a similar chemical structure and their pKa-values are comparable. Therefore VFAare measured as one parameter and no distinction can be made between the various VFAcompounds by means of titrimetric methods.

Buffer capacity ? of a buffer is the number of moles of strong acid (respectively) base todecrease respectively increase the pH with 1 pH unit of 1 l buffer.

pKa is the minus log of the dissociation constant Ka.pKa = - log (Ka)

pKa-Values: In normal working conditions, the most important compounds in anaerobicdigestion are the VFA and bicarbonate in the buffering range of pH 8.5 to 3. Otherpossible buffering compounds are hydrogen sulfide, phosphorous compounds, ammoniaand phenols. In Table 1 the pKa values for these compounds are listed.

Table 1: pKa-values of the main compounds in anaerobic wastewater

Component pKaVFA

Acetic acid 4.75Propionic acid 4.87n-butyric acid 4.81

Iso-butyric acid 4.84n-Valeric acid 4.82

Iso-Valeric acid 4.77n-Caproic acid 4.83

Iso-Caproic acid 4.84

H2CO3 ? HCO3- + H+ 6.37

HCO3- ? CO3

- + H+ 10.25H3PO4 ? H2PO4

- + H+ 2.12H2PO4

- ? HPO42- + H+ 7.21

HPO42- ? PO4

3- + H+ 12.67NH4

+ ? NH3 + H+ 9.25H2S ? HS- + H+ 7.02

PhenolsArOH ? ArO- + H+ 10

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2.1.2 Determination of VFA/bicarbonate

Because the pH in the digester normally varies between pH 6 and 8 the carbonateconcentration (CO3

2-) in this pH range is negligible in comparison with the bicarbonateconcentration (HCO3

-), it can be assumed that the two main compounds which contributeto the buffer capacity during titration will be VFA and bicarbonate.

VFA- = CH3COO- + C2H5OO- + …Alkalinity = HCO3

- + VFA-

The difficulty for measuring VFA and bicarbonate with titrimetric methods is that thepKa–value of VFA (= 4.78) and the pKa-value of bicarbonate (= 6.3) are too close tomeasure VFA and bicarbonate separately. The bicarbonate buffer capacity overlaps partlythe buffer capacity of the VFA. Figure 3 shows a simulation of the buffer capacities of asample with 0.015M acetate and 0.03M bicarbonate and shows the difficulty if twocompounds are present (overlap).

Figure 3: Simulation of the buffer capacities of 0.015M acetate, 0.03M Bicarbonateand 0.015M acetate + 0.03M bicarbonate

Several titration methods (Kapp, Moosbrugger, … )3are using only a few measuringpoints of the titration curve to solve this problem. The Kapp method and a method thatwas developed during the project are implemented in the on-line analyser because of thegood prediction of the VFA concentration and their relatively easiness forimplementation and interpretation.In real wastewater not only two buffer compounds could be present, but also some otherbuffer compounds could be present like (NH3/NH4

+, HS-/H2S, HPO42- / H2PO4

-).

3 K. Buchauer (1998). A comparison of two simple titration procedures to determine volatile fatty acids ininfluents to waste-water and sludge treatment processes. Water SA, Vol. 24 No. 1 January 1998, 49-56

0.00E+00

4.00E-03

8.00E-03

1.20E-02

1.60E-02

4 4.5 5 5.5 6 6.5 7 7.5 8

pH

VF

Bicarbonate

VFA + Bicarbonate

Telemac D2.4 11

However, these other buffer compounds must be present in high concentrations toinfluence the buffer capacity of the sample. In case the concentrations of thesecompounds are significant, a method based on the modeling of buffer capacitiesdeveloped at the university of Ghent could be promising for getting more informationfrom the same titration data.

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2.2 Study of titrimetric parameters

Before developing the analyser a good understanding of the titration and the behavior ofVFA and bicarbonate during titration was needed. First some tests were performed onstandard solutions of CH3COONa and HCO3Na before starting with titrations on realanaerobic samples. For these tests the titroprocessor 686 was used. These tests were veryimportant to decide the final design of the analyser. Parameters such as the final samplevolume, the shape of the titration vessel, the concentration of the titrant, the titrationalgorithm for the on-line analyser were optimized via these tests.Although the titration methodology has been used for a long time for the determination ofpartial and total alkalinity in order to control the anaerobic digesters, there is still someconfusion of how an anaerobic sample should be titrated. This confusion is mainly causedby the definition of the partial alkalinity, and the fact that by titration of bicarbonatedissolved CO2 is formed, which is volatile and therefore can be stripped. In order tounderstand the stripping of CO2, a study of this phenomenon was made.

2.2.1 Titroprocessor set-up (off-line analysis)

A titroprocessor 686 of Metrohm was used for the off-line experiments. The titrationalgorithm used for the analysis is the MET algorithm (Monotonic equivalence-pointtitration), the titrant used was 0.1N HCl and the volume of the samples was 10 ml.

2.2.2 Study of stripping CO2 and VFA

Figure 4 shows the effect of using air for mixing during titration. The blue curve is atitration curve recorded when the sample is mixed with a magnetic stirrer. The pink curvewas recorded when the sample was mixed with air. From this figure, it is clear that use ofair will have a significant effect on the titration curve.

Figure 4: Titration curves of 0.1M Bicarbonate and 0.05M Acetate with 0.1N HClusing different mixing methods. Each step during the titration a constantvolume of 300µl was added. After each step the titroprocessor waited 10 sto measure the pH before proceeding the titration (65 points to pH 5.5)

2

3

4

5

6

7

8

0 5 10 15 20 25 30 35 40

ml HCl (0.1M)

Filling of the Buret

mixing with air

Without mixing

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From Figure 4 it is clear that there is only a significant difference between the blue andthe pink curve in the higher pH regions (pH > 4.3). In the lower pH region (< 4.3) bothcurves overlap. In the higher pH regions, bicarbonate provides mainly the bufferingeffect. The following reaction explains the effect of air on the titration curve.

HCO3- + H3O+ ? CO2(gas) + H2O

Stripping the CO2 changes the equilibrium and increases the pH by consuming H+ inconverting bicarbonate into dissolved CO2. No stripping effect is noticed in the lower pHranges were mainly VFA buffers; this means that VFA are not stripped significantly.

Figure 5 shows that the stripping is dependant of pH and time. Because of this factstripping of CO2 will also be dependant of the concentrations of other compounds likeVFA. When a MET titration algorithm is used, the analysis time will increase withincreasing concentrations and more CO2 will be stripped and this will influence thelinearity of the method.

Figure 5: Effect of stripping CO2 on the pH of a bicarbonate solution

In the lower pH region CO2 can be stripped more easily. The measurement of the partialalkalinity at pH 5.75 is therefore very sensitive to this stripping. This means that the wayof titration (mixing with air or magnetic stirrer), the speed of titration and even theconcentration of VFA will have an effect on the determination of the partial alkalinity. Toshow the importance of stripping effects, figure 6 shows on-line data from the anaerobicreactor for the partial alkalinity with two methods. The pink curve is the partial alkalinitydetermined with gentle stirring (method1 (see further)) and should normally approachvery closely the theoretical PA. The blue curve is the partial alkalinity when air is used(method2 (see further)) for mixing the sample. There is a huge difference between bothpartial alkalinities and sometimes this difference is more then 15 meq/l, depending on theconcentrations of VFA and bicarbonate. From figure 6 it is clear that if the samples aremixed with air, the PA approaches very closely the concentration of bicarbonate.Although the titration to pH 5.75 is quite fast (addition of 10µl 0.5N HCl each second fora sample of 10 ml). This indicates that stripping of bicarbonate is significant. For the

7.4

7.6

7.8

8

8.2

8.4

8.6

8.8

0 50 100 150 200 250 300

Time (seconds)

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determination of the total alkalinity the effect of stripping is negligible. At a pH of 4.3normally more then 99% of the bicarbonate is titrated.

Figure 6: Effect of stripping of CO2 on the determination of the PA with on-line dataof anaerobic samples

Conclusions

Titration in the higher pH range should be performed with a lot of care. Stripping of CO2

has a non-negligible effect on the partial alkalinity and the shape of the titration curve inthe higher pH ranges (8 - 4.3) At lower pH ranges no effect of stripping of the VFA wasnoticed. It was also noticed that it is possible to remove all the bicarbonate at a pH higherthan 5 without stripping VFA and in a reasonable time (less than 10 minutes). In this wayVFA can be determined more accurately without bicarbonate interference.

15

25

35

45

360 380 400 420 440 460 480

Time (h)

PA: mixing with air

PA: mixing with magnetic stirrer

Bicarbonate (method1)

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2.2.3 Influence of NaCl

The influence of NaCl on the titration results was evaluated. Feitkenhauer et al. (2001) 4,reported that changes in salt concentration in the range of 2.5 - 50 g/l NaCl did notinfluence the determined VFA concentration significantly. The influence of salt on astandard with 500 ppm VFA was checked by increasing the salt concentration from 0 to20 g/l. No significant effect on the result was noticed although a shift in the pKa valuecould be detected.

2.2.4 Description of the titrimetric methods

A. Method1

Method1 developed by INRA is based on the method of Kapp for the detection of VFAand extended for the measurement of bicarbonate. Kapp founded his method on aprocedure suggested by McGhee (1968). According to McGhee the amount of acidrequired to titrate a solution with only VFA is proportional to the content of theconcentration of VFA. The only additional buffer considered in the calculation of Kappis the carbonate subsystem of HCO3

-/CO2 which has a pKa of approximately 6.3. Otherbuffers are assumed to be negligible. By an iteration process the following equation isfound:

Sa = (131340 ? N ? VA5-4) / VS – (3.08 ? ALK4.3) – 10.9

Sa: Concentration of VFA (ppm)N : Normality of the titrant (mol/l)VA5-4: Volume acid (ml) dosed between pH 5 and 4VS: Volume sample (ml)ALK4.3: Total alkalinity (meq/l)

B. Method2

The method of Kapp does not take into account other buffer systems like HPO42-/ H2PO4

-,HS-/H2S and NH3/NH4

+. All these buffer systems together with initial pH and stripping ofCO2 will influence the measurement of the VFA concentration. Therefore the Kappmethod is less suitable for measurement of very low VFA concentrations (0 – 250 ppm).Other methods, like the method according to Moosbrugger3, offer a possibility toconsider the influence of other buffer systems besides VFA and bicarbonate. However,the interfering buffer systems have to be determined using other analysis methods likephotometry.

3 K. Buchauer (1998). A comparison of two simple titration procedures to determine volatile fatty acids ininfluents to waste-water and sludge treatment processes. Water SA, Vol. 24 No. 1 January 1998, 49-564 Feitkenhauer H., von Sachs J., Meyer U. (2001). On-line titration of volatile fatty acids for the processcontrol of anaerobic digestion plants. Water Research 36 (2002), 212-218.

Telemac D2.4 16

In order to provide an accurate and easy measurement of low VFA concentrationswithout determination of other interfering buffer systems, the following titrimetricmethod was developed by AppliTek during the project. This method is also based on themethod of McGhee. It was noticed that bicarbonate and hydrogen sulphide can bestripped from the anaerobic samples (without any visible stripping of the VFA) at a pHhigher than 5 using air. Afterwards the VFA can be directly measured by a down-titrationfrom pH 5 to 4 (Method of McGhee). Because other buffer systems like NH3/NH4

+ andHPO4

2-/ H2PO4- buffer at a pH much higher 5, and the interfering bicarbonate and

hydrogen sulphide are stripped, only the H2PO4-/H3PO4 buffer system could influence the

determination of VFA.

Telemac D2.4 17

C Extended method (method3)

With methods 1 and 2, it is assumed that anaerobic samples would mainly contain twocompounds: VFA and bicarbonate. The reality is that also some other weak bufferingcomponents (H2S, lactate, phenols, NH4, …) can be present. In some cases it is thereforeinteresting to have also an estimation of the concentration of these compounds. Toachieve this a more complex data interpretation method than the previous methods isnecessary. Therefore, the measurement principle is still to successively measure pH infunction of stepwise acid or base addition. In this way the titration curve is built. Fromthis measured titration curve (typically around 30 to 50 data points), the buffer capacity(BC) at each pH point is calculated as the derivative of the amount of base or acid needed(meq l -1 pH -1 ). The buffer capacity curve consists of the sum (hence the linearity of theequation) of the buffer capacities of each individual compound in the solution (VanVooren et al., 2001)5. From the obtained buffer capacity curve, estimates of the differentbuffering compounds are computed using model selection and parameter estimationtechniques. The methods used for model selection and parameter estimation are describedelsewhere (Van De Steene et al., 2002)6.

The automatic model building of BC starts with calibrating an initial model. Theresiduals (differences between model and data) are analysed and used to suggest acandidate model extension, i.e. adding an additional buffer with unknown pKa andconcentration. Then another fitting cycle is performed with the extended model, theresiduals are analysed, a new extension is defined, and so on, until one of the stop criteriais reached. The stop criteria are based on a set of model selection techniques, whichdetermine the optimum BC model and, therefore, pKa’s and concentrations of thesignificant buffers. Also, based on the fitting results the standard deviation is estimatedfor each of the pKa and concentration values. The method is programmed in a C++software (called BOMB) and allows to analyse the titration experiments such as the onesobtained using a standard Metrohm lab titrimetric setup (Appendix: I and II).

Up to this point, the initial model had to be defined by the user. For monitoring anaerobicdigestion the initial model could consist of only bicarbonate and VFA since these are themain buffers in AD systems. An alarm can be generated if the model extension (with newbuffers) is successful and it can be concluded that other buffers are present in the sample.The method has been tested on samples from the anaerobic digester and results wereaccurate compared to the standard methods (for details: Appendix: III).

However, a guaranteed convergence of the model selection is required. This highlydepends on the quality of the initial model (i.e. summarizing prior knowledge of which

5 Van Vooren L., Van De Steene M., Ottoy J.P. and Vanrolleghem P.A. (2001) Automatic buffer capacitymodel building for the purpose of water quality monitoring. Wat. Sci. Technol., 43-7, 105-1146 Van de Steene M., Van Vooren L. Ottoy J.P. and Vanrolleghem P.A (2002) Automatic buffer capacitymodel building for advanced interpretation of titration curves. Environ. Sci. Technol., 36, 715-723.

Telemac D2.4 18

buffers are present in the system) is important. Convergence of the parameter estimation(i.e. estimation of pKa’s and concentrations). Also, it depends on the quality of the initialand boundary values of the parameters, which were defined by the user together with theinitial model.

Therefore to increase the on-line applicability of the method, an extension was made tomeet the following objectives:

- Auto-initialisation: Automatically define the initial model, initial and boundaryvalues of its parameters.

- Other buffers: Detect and measure buffers other than bicarbonate and VFA.- On-line implementation: package different program modules to run on-line and

store the results in TELEMAC format and save the model selection and optimiseresults for further interpretation.

The original program was thus updated and recompiled in C++ and necessary extensionswere programmed in different C++ modules. The result is a package of programs calledBuffer Capacity Software (BCS). BCS runs in three modes:

- On-line with the TELEMAC titrimetric sensor.- On-line with the Metrohm titrimetric setups.- Automatic interpretation of titration results from any off line analyser.

All BCS modules are programmed in C++ and can therefore be compiled to run under astandard system like Windows, Linux or even DOS. A compacted version was compiledto run under DOS and it executes the different modules sequentially to reduce theRandom Access Memory requirement to a minimum. Also, frequent data logging isperformed to release the fixed memory.

Results of BCS interpretation of the on-line titration data are shown in section 2.4.3.

Note:The control software of the titrimetric analyser was written in a PLC language calledB&R automation which has its own, specific Operating System. The good compatibilityof the software with the hardware, results in a robust configuration of hardware/softwareof the titrimetric analyser in comparison with control software that is executed in aDOS/Windows environment. This robustness makes this set-up ideal for operation in anon-line environment.All BCS modules, however, are programmed in C++ and are preferably executed under astandard operating system like Windows, Linux or DOS. It is therefore at this stage notpossible to run the BCS on the hardware of the titrimetric analyser. Nevertheless, withsome adaptations of the control software, it is possible to link the titrimetric analyser viaan ethernet connection (standard in the titrimetric analyser hardware) with a stand-alonePC on which the BCS can run. In this way titration curves can be shared with the BCS forextended measurements.

Telemac D2.4 19

2.2.5 Evaluation of the titrimetric methods

For method1 and method2 calibration curves were developed using standards containingdifferent solutions of CH3COONa and HCO3Na. Table 2 gives the concentration of theprepared standards.

Table 2: Prepared standards for calibration development

Different concentrations of VFA (60-5000 ppm) were measured in standards withvarying concentration of bicarbonate (0-100 meq/l).Method1 is very sensitive to the hardware set-up (titration vessel, stirring), but alsodependant of the used titration algorithm. It was noticed that changes in the set-upresulted in shifts of the calibration curves. For the Kapp method (method1) it is veryimportant to reach the chemical equilibrium. Normally the best way to reach thisequilibrium is to mix the sample very good and to wait when the pH-reading is stabilized.However, it was difficult to know when this equilibrium was reached because of the CO2

stripping. When the stabilisation time was too long (>15 s), the pH of the sample wasincreasing although the equilibrium should already have been achieved. Finally the finalset-up was chosen and all influencing parameters were more or less fixed, and a stablecalibration curve was obtained. In the following figures the correlation between givenand measured concentrations of acetic acid (0 – 5000 mg/l) and bicarbonate (0 – 100meq/l) are plotted for method1 and method2.

Bicarbonate (meq/l) VFA (ppm)100 6060 50040 150010 250025 2500 5000

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Telemac D2.4 20

Figure 7: Correlation between given and measured concentration of VFA obtainedwith method1

Figure 8: Correlation between given and measured concentration of bicarbonatewith method1

Figure 9: Correlation between given and measured concentration of VFA withmethod 2

Figure 10: Correlation between given and measured concentration of bicarbonatewith method2

R2 = 0.9999

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Telemac D2.4 21

2.2.6 Off-line tests with anaerobic samples

The tests on standard solutions of CH3COONa and HCO3Na provided results in goodagreement with the standard concentrations. Afterwards the analysis of real anaerobicsamples was started. An anaerobic reactor set-up was installed and this reactor wasinitially fed with a solution made of starch.

A. Experimental set-up lab-scale reactor

An up-flow anaerobic (USAB) reactor (double walled), was used in the validation of thetitrimetric analyser. The schematic layout is shown in figure 11.

Figure 11: Schematic overview of the anaerobic set-up used in the validation

The UASB reactor has a cylinder shape with height 1.15 m, an inner diameter of 0.08 mand an outer diameter of 0.11 m. The total volume is 6 l. For temperature control, athermostatic bath (T = 37°C) was used to heat the reactor by circulating the warm water.Also a circulation pump and an influent pump are connected to the reactor. A constantcirculation flow of 1 m/h was used and the influent rate was controlled by the influentpump and a timer switching at certain interval ranges. A pH measuring and regulating

InfluentContainer

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Telemac D2.4 22

system was used : pH sensor, a PID controller and a NaOH reservoir with dosing pump.NaOH is dosed directly at the top of the reactor. The gas is captured in two cylindersfilled with water. By the overpressure of the gas, water is replaced by the gas and theproduced gas volume can be measured. The effluent is captured in a 20 l container.

B. GC-setup for the VFA reference measurements

VFA concentration results obtained by titrimetric methods were compared with off-linereference measurements obtained by gas chromatography. The concentrations ofindividual VFA levels in effluent were determined by a SHIMADZU GC equipped with aflame-ionisation detector (FID) and a capillary column (ALLTECH: height = 15m,diameter = 0.53 mm, width = 1.2 µm). The temperature of the injection port and detectorwere 250°C respectively 275°C. Nitrogen gas was used as carrier gas. Before injection ofthe samples into the GC, the samples were pretreated using a centrifuge to remove all thesuspended solids. Afterwards (if necessary) the liquid part was diluted and an internalstandard (a solution of 2-ethyl butyric acid, 1 g/l NaCl and 5% of H3PO4) was added.With this set-up 8 types of VFA can be detected: acetic acid , propionic acid, butyric acidisomer, butyric acid, valeric acid isomer, valeric acid, ceproic acid isomer and ceproicacid. The concentrations in ppm obtained with the GC were recalculated toconcentrations in mol/l and then multiplied by the molecular weight of acetate (60000mg/mol) to obtain concentrations in mg/l (= ppm) acetate equivalent.

C. VFA measurements

For the first samples different results were obtained in comparison with the GC results(see Figure 12).

Figure12:

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concentration of VFA both titrimetric methods predicted higher VFA concentrations. Inorder to evaluate this difference, standards of CH3COONa were analyzed with the GCand the titrimetric analysis method.

Table 3: Comparison of GC with titrimetric results with standard solutions in orderto find the reason for the lack of fit between GC and titrimetric analyser.

The acetate concentrations predicted with GC were too low in comparison with thestandard concentration. Therefore the GC method was evaluated and some changes weremade in the GC procedure. The problems occurred with higher concentration ranges ofVFA and were due to the saturation of the FID detector. Therefore the samples needed tobe diluted for VFA concentrations beyond 250 ppm.

After changes to the GC procedure new samples were analyzed. Figure 13 shows that thetitrimetric VFA results are in good agreement with the GC results.

Figure 13: Analysis of anaerobic samples with two titrimetric methods compared withGC-reference measurements

Sample (ppm)(Acetate)

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Telemac D2.4 24

The following graphs show the correlation between the results obtained via titrimetricmethods and the GC results. Figures 14 and 15 show that for both titrimetric methods thepredicted VFA results correspond with the GC reference results.

Figure 14: Correlation between VFA measurement with GC and titrimetric method1

Figure 15: Correlation between VFA measurement with GC and titrimetric method2

The correlation coefficient R2 computed was 0.9923 respectively 0.9891 for the INRA(method1) respectively AppliTek method (method2). Although the good correlation, bothmethods overestimate the VFA concentration. Both methods give a systematicoverprediction of about 15% in comparison with the GC results. This has already beendescribed by K. Buchauer3. It was attributed to either a consequence of statisticalimperfections of the regression equations or to an influence of other buffer systems notconsidered in the titration methods. However, it seems very strange that one of thesereasons could explain the 15% deviation. Another explanation could be that the reactorwas not working properly and a lot of other volatile fatty acids than acetic acid wereroduced. In Figure 16, the ratio of the different VFA compounds in % is plotted. Indeed ahigh fraction of propionic acid indicates that during the whole experiment the reactor was

3K. Buchauer (1998). A comparison of two simple titration procedures to determine volatile fatty acids ininfluents to waste-water and sludge treatment processes. Water SA, Vol. 24 No. 1 January 1998, 49-56

y = 0.8746x + 23.58

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Telemac D2.4 25

not in such a good condition. This was due to a malfunctioning pH controller causingcomplete acidification (pH < 5.2) during the experiment.

Figure 16: Ratio (%) of specific VFA compounds present in anaerobic samples

However, in the later experiments (paragraph 2.4.1) the condition of the reactor was good(acetic acid > 84% and propionic acid < 7.4%) and a same systematic overprediction of15% was noticed. So the condition of the reactor is not the cause of the overprediction.

D. Measurement of bicarbonate, total and partial alkalinity

For the measurement of bicarbonate, no reference measurements are available. Thereforethe bicarbonate measurement for both titrimetric methods was compared. In Figure 17data of the bicarbonate measurement obtained with the off-line set-up are shown. Bothmethods are comparable for the bicarbonate measurement. During the completeacidification (sample number 5) no bicarbonate was detected (see paragraph 2.4.3). Forsample 1 and 2 no results were obtained for method1.

Figure 17: Bicarbonate measurements with titrimetric methods

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Telemac D2.4 26

Also for the measurement of the total alkalinity both methods are comparable (Figure18). Although method2 seems to give a slight overprediction of the TA in comparisonwith method1.

Figure 18: Measurement of Total alkalinity with titrimetric methods

Figure 19 shows the measurement of the partial alkalinity. As already explained inparagraph 2.2.1, the partial alkalinity measured with both methods is very different. Alsoin these off-line analyses no direct correlation exists between the partial alkalinityobtained via both methods.

Figure 19: Measurement of Partial alkalinity with titrimetric methods

ConclusionBoth titrimetric methods tested are found to be comparable for the VFA and thebicarbonate measurement (as well as for standards as for anaerobic samples). Asystematic deviation for the VFA results in comparison with GC was noticed, butnevertheless a good correlation was obtained. Therefore these titrimetric methods aresuitable for implementation in an on-line titrimetric analyser.

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Telemac D2.4 27

2.3 Development of on-line titrimetric analyser

During the off-line tests the design (titration vessel, concentration of titrant, samplevolume, titration algorithm,…) of the prototype was determined. During the off-lineanalyses no filtration was applied to the anaerobic samples. For on-line purposesfiltration is rather necessary because sludge and other particles in the effluent of ananaerobic reactor could possibly clog the sample tubing. Also the titration algorithm(maybe the most important tool for optimizing the titration (decreasing response time andincreasing the accuracy of the titration)) had to be developed. For the off-line test atitration algorithm with a constant dosing was applied. Such an algorithm (MET) wasfound not suitable for implementation for the on-line analysis.

2.3.1 Development of titration algorithm

In the first experiments using the titroprocessor the Monotonic Equivalence-pointTitration or MET algorithm was used. With this type of titration algorithm, the titrant isadded in constant volume increments throughout the whole titration. For the off-linemeasurement, the volume increment was adapted to the estimated concentrations of thesamples.This titration algorithm was however not suitable for the on-line titrimeter, because of thefollowing reasons. In order to perform a titration in a reasonable time (15 minutes) withsufficient data in each pH interval, the volume increment is a critical parameter. If thevolume increment is too small the titration time will be unnecessarily extended, and whenthe volume increment is too big the number of data points is not sufficient for accurateanalysis. Also, in order to provide accurate analysis using the extended method, it isnecessary to collect a minimum number of data points in each pH range. For the othermethods it is only important to collect a minimum number of data points in the pH rangeof 5.3 to 3.8.

For the on-line set-up other titration algorithms needed to be developed and tested. In thefollowing paragraphs two types of algorithms will be discussed: a model based and aslope based titration algorithm.

A. Model based titration algorithm

To demonstrate the necessity of a titration algorithm, Figure 20 and Figure 21 show thesimulation of an up-titration with a constant volume increment (MET). From thesefigures it is clear that if a homogeneous spreading of the titration points is wanted, a moresophisticated algorithm is required. This algorithm has to be able to estimate the nextdosing step for getting a constant ? ph.

Telemac D2.4 28

Figure 20: Computer simulation of titration curve with 10 ml sample of CH3COOHand titrant 0.2N NaOH

Figure 21: ? pH of simulated titration curve of figure 20

The titration algorithm that was implemented in the simulation program was based on themodel based algorithm decribed by L. Van Vooren7. A model based titration algorithm isan algorithm which uses the theoretical model of titration (mass balance, a dissociationequation and electro-neutrality equation). The use of such an algorithm requires theestimation of the concentration of the various compounds in the sample. In an on-line set-up, the previous measurement data can be used as input parameters.

Figure 22 shows the simulated titration curves using the first version of this model basedalgorithm in a simulation program. It is clear that he ? pH is already more controlled thanwith the MET algorithm, although the desired ? pH of 0.2 is not yet reached over thewhole titration curve. The estimated concentration entered in the algorithm was 0.1M and 7 Van Vooren L. (2000), Buffer capacity based multipropose hard- and software sensor for environmentalapplications, PhD. Thesis. Faculty of agricultural and Applied Biological Science. Ghent University.

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Telemac D2.4 29

the concentration of the simulated sample was 0.07M. This figure shows that if the inputconcentration of the algorithm is above the real concentration an overprediction of thedosing step is made.

Figure 22: Computer simulation with 0.07 M CH3COOH

To improve the results the algorithm was extended and the calculated dosing step(? V(k+1)model) was corrected with a factor ? (k+1). This correction factor is function ofthe previous dosing steps.

? (k+1) = ?(k) - ? ?(k) ? (1) = 1? V(k+1) = ? V(k+1)model ? ? (k+1)

?: correction factor?: setpoint ? pH – actual ? pH? : empirically determined factor

The result of the implemented correction is shown in Figure 23. The concentration of thesimulated sample was 20% higher than the estimated input concentration. Although theincrease of concentration the ? pH step is balancing around the set-point of 0.2

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Telemac D2.4 30

Figure 23: Simulation of ? pH with titration algorithm using a correction factor: theactual concentration was increased with 20 % in comparison with theinput concentration of the algorithm

Using this developed titration algorithm very good titration curves were obtained withalmost every step a constant ? pH. The drawback of this algorithm is that the analysissoftware needs an estimated concentration and also the pKa value of all the compoundspresent in the sample. The model based algorithm was therefore not implemented,although the very good results. Nevertheless, this algorithm is an alternative if thetitration should be very accurate (e.g. in samples with very low concentrations).

B. Slope based titration algorithm

For the slope based titration algorithm no simulations were made and this algorithm wasdirectly implemented and tested.In this approach a moving window containing 3 data points is used to predict the nextdata point. An initial titration sequence to initiate such extrapolation is a necessity. Thena linear regression model is fitted to these three points and an extrapolation is made.Following parameters are needed for the input of the algorithm:

- set-point ? pH: desired pH decrement (for down-titration);- minimum volume: minimum volume that can be dosed;- maximum volume: maximum volume that can be dosed;- Equilibration time: waiting time for pH value acquisition;

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Telemac D2.4 31

- Stop pH: the algorithm stops when the stop pH is reached.

Figure 24: Dosing steps (in ml HCl 0.5N) during titration

Figure 24 shows clearly that this titration algorithm is very different from the METalgorithm. With each dosing step the necessary amount of titrant is calculated and witheach step a different volume of titrant is added to the sample.Figure 25 shows that within certain limits, the ? pH is varying around the setpoint 0.1.Figure 26 shows that the data points are more evenly distributed over the whole pH-range.

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Telemac D2.4 32

Figure 25: ? pH during titration with setpoint 0.1

Figure 26: Titration curve with slope based titration algorithm (60 meq/l bicarbonateand 500 ppm acetate)

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Telemac D2.4 33

Figure 27: Buffer capacity curve obtained from the recorded titration curves

Figure 27 shows the buffer capacity curve which was calculated from the recordedtitration curves. This shows that the titration curves recorded using this titration algorithmwere of good quality and therefore the developed algorithm could also be used for theextended method (see paragraph 2.2.3 part C).

Conclusions

- Using this algorithm the analysis time can be decreased (10 minutes) and theaccuracy of the titration can be increased because the data points are moreevenly distributed over the whole pH range.

- This algorithm has proven its capabilities for the two point methods, but isalso useful for the extended method.

- To improve the titration algorithm, a correction factor like with the modelbased titration algorithm could be implemented to the slope based algorithm.

NoteFor the newly developed method with stripping a mixed algorithm was developed. Usingthis algorithm the whole pH range is divided in several subranges which can be insertedvia the software. In every subrange a choice can be made between the MET and the slopebased algorithm. Also the time between two dosing steps and the waiting time betweensubranges can be defined in the software.

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Telemac D2.4 34

2.3.2 Description

Figure 28 shows the newly developed analyser used for the on-line tests on the anaerobicdigester. It consists of two parts, an upper data processing compartment with an industrialPC (touch screen) and a lower analysis compartment.

Figure 28: Front view of the analyser

Figure 29 shows a detailed view of the analysis compartment and all the componentsused to perform the analysis. Effluent from the reactor was collected in an overflowvessel in order to refresh the sample continuously. A paper filter was used to remove thebig particles (like sludge). Filtration of the samples is not needed for the analysis, but it isnecessary to prevent the sample tubing from clogging. For each analysis a sample volumeof 10 ml is injected via a sample pump into the titration vessel. There are two systems ofmixing the sample available in the analyser : one with air and the other with a magneticstirrer. The compressed air for mixing is provided by a membrane pump mounted in theanalyser. The dosing of the titrant (HCl 0.5 N) is performed by a burette with a minimumdosing step of 1 µl.Sometimes the pH of the reactor can drop below pH 6 or VFA concentrations of theinfluent need to be measured. In these cases it is necessary to adjust the initial pH bydosing NaOH. This is done by means of a micropump installed in the left corner of theanalyser.After each analysis the vessel is drained with a pump, and new sample can be taken foranalysis.

data processingcompartment

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Telemac D2.4 35

Figure 29: Detailed view of the analysis compartment

The analysis procedure implemented in the analyser is shown graphically in Figure 30,each analysis takes about 10 minutes for both methods.

Figure 30: Analysis procedure of the on-line titrimetric analyser

For the control of the analyser, an industrial PC connected with a PLC is implemented inthe analyser. Figure 31 shows the main screen of the control software. Several menus andoptions can be selected via the touch screen.

and 3 ml HCl (0.5N)

Emptying titration vesselRenewing the sample in the tubings

Rinsing titration vessel with sample + Draining

Dosing sample volume into titration vessel

Stabilisation of the sample and pH - probe

Titration

Emptying titration vessel

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After every #titrations the vessel is rinsed with water

Telemac D2.4 36

Figure 31: Control software of the titrimetric analyser (main screen)

The main screen shows the various results of the last measurement : VFA concentration,bicarbonate concentration and alkalinities. The titration process can be followed via thegraph. Via the main screen also other screens (Status, Trending, Timers and Settings) canbe accessed. These screens provide access to the following options:

- Status: enables the operator to control the different components (e.g.pumps) manually;

- Trending: logging data and saving data to floppy disk;- Timers: Adjustments of timers;- Settings: pH adjustments and calibration of the pH-electrode.

Telemac D2.4 37

2.4 On-line test results using the titrimetric analyser

2.4.1 Test results for standard solutions

In order to test the reproducibility of the analysis procedure, a standard solution wasanalysed several times. The reproducibility of the methods was obtained measuring astandard solution of 250 ppm VFA and 25 meq/l bicarbonate. The standard deviation in% of the measured value was calculated. For method1 the reproducibility was 1.9% forVFA and 0.5% for bicarbonate. For the measurement of TA and PA a reproducibility ofrespectively 0.5% and 0.7% was obtained. The measurement of the same parameters withmethod2 gave a reproducibility of 1% for VFA 1.8% for bicarbonate and 1.7% and 4%for TA and PA respectively.

2.4.2 Test results for anaerobic digester samples

In order to test the complete analysis procedure on real anaerobic samples, the titrimetricanalyser was tested on effluent of an anerobic digester (see earlier). The next figuresshow the reactor conditions during the on-line experiment.Diluted wine was used as influent. First the COD of the wine was determined and thendiluted till a COD of 5000 mg/l or 10000 mg/l was obtained. The pH of the reactor duringthe experiment was always higher than 7 and the temperature at 36 to 37°C. The totalnitrogen concentrations varied between 6.8 and 23 mg/l, the total phosphorousconcentration was between 5.04 and 12.06 mg/l, and the suspended solids varied between13.9 and 181.2 mg/l. The VFA were mainly acetic acid (84-100%), propionic acid (0-7.5%) and butyric acid (0-5%).

Figure 32: Loading of the reactor (kg m-3 day-1)

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Telemac D2.4 38

Figure 33: Gas production Qgas per day (l/day)

Using the on-line analyser 4 parameters (VFA, bicarbonate, TA and PA) were measured.

Figure 34: On-line VFA measurement results compared with GC measurement resultson anaerobic digester samples

During on-line analysis the titration methods method1 and method2 were sequentiallyused for the measurement of the 4 parameters. A part of the data was lost due toadaptation of the software (indicated on the graph with number 1). Both methods givesimilar results and are in good agreement with the GC results. Comparison of the analysisresults showed again a systematic deviation of 15% between the GC and titrimetricresults. This overprediction is the same as described in paragraph 2.2.5 with the off-lineanalysis. Although the reactor was during the on-line measurement in good workingconditions. So this deviation cannot by explained by the condition of the reactor and thisdeviation is not due to different types of VFA in the samples.

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Telemac D2.4 39

The measurement of bicarbonate and total alkalinity using both methods provides almostidentical results (see Figure 35 & 36). Although a constant shift is noticed for thebicarbonate measurement between method1 and 2. At very low VFA concentrations(number 2) method2 showed some problems because the titration of the samples was toofast and the buffering in the lower pH regions was almost non-existing. Due to this effectsome data points were not recorded; this was later on solved by adjusting the software.The measurement results of the partial alkalinity for both methods were very different(see Figure 37).

Figure 35: On-line bicarbonate measurements ( pink : method1; blue : method2)

Figure 36: On-line TA measurements (pink : method1; blue : method2)

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Telemac D2.4 40

Figure 37: On-line PA measurements (pink : method1; blue : method2)

The titrimetric analyser tested on a lab-scale reactor during on-line operation was stablethroughout the whole on-line operation period. Although the VFA concentration isoverpredicted with a systematic deviation of 15 % in comparison to gas chromatographyreference measurements, this measured parameter can be used to follow on-line thecondition of the anaerobic process. The reason for this overprediction is unclear, butcould be solved if the analyser is recalibrated with sample and Gas chromatographicreference measurements. Together with the VFA concentration also a good estimation ofthe bicarbonate concentration, the total and partial alkalinity are provided by the newlydeveloped titrimetric analyser. This analyser offers the possibility to control the anaerobictreatment process and as a consequence enables the operator to increase the efficiency ofthe anaerobic treatment.4.3 BCS results using the on-line titrimetric analyser

The BCS measures the buffer compounds present in the sample. The result is theconcentration of each of the buffers, their pKa values and the standard deviation for eachestimated value. Other results like the buffer capacity, data quality, the optimiserperformance and model selection logging are stored for each sample for further analysis.

The accuracy of the estimated concentrations and pKa values depends on the quality ofthe titration experiment. Important factors affecting the quality of the titration experimentare mainly the speed of the titration (determined by titration algorithm). The influence ofthis factor will be presented by the results for the standard solutions, see section 2.4.3.1.

Other factors such as drift and/or shift of the pH probe, temperature and ionic strength aregenerally important for the quality of titration experiments too. However, their effect onthe accuracy of the estimated concentrations is minor. They will cause a shift in the pKavalues but this is easily dealt with by the BCS as long as these conditions are within a

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Telemac D2.4 41

reasonable range. Further assessment of the estimated pKa’s helps in fault detection, e.g.to detect pH probe fouling. Therefore, those factors do not affect the BCS results butBCS rather helps in detecting them.Below results from anaerobic digestion monitoring will be shown in which the mainbuffers (bicarbonate and VFA) are detected accurately.

2.4.3.1 Test results for standard solutions

In this section, three experiments are presented. These experiments were designed to testthe on-line titrimetric analyser. BCS was used to analyse the titration data, determine thepKa values under normal operating conditions and study the effect of the titration timestep on the accuracy.

For one experiment a standard solution of 1000 ppm ( 0.0167 mol/l) VFA and 50 meq/lbicarbonate was analysed. The titration was performed with an average time step of 20 sand 50 titration points, i.e. taking 15 min for each titration experiment. Table 4 shows thepKa values measured for bicarbonate and VFA. Despite the shift in the pKa value thebicarbonate concentration results are accurate as shown in Figure 38. Similarly, the VFAresults were accurate despite the shift in their pKa’s (Table 4), as shown in Figure 39. Forthe concentration results the confidence given as ? ? and estimated by BCS, is plotted.BCS measured the pKa values with a very high accuracy since ? was almost 0.Therefore, it was not plotted.

Compound Theoretical pKa measured pKaBicarbonate (Figure 37) 6.37 6

VFA (Figure 38) 4.75 4.33Bicarbonate (Figure 39) 6.37 6

VFA (Figure 40) 4.75 4.48Table 4: Measured pKa-values with BCS compared with theoretical pKa

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Telemac D2.4 42

Figure 38: Bicarbonate concentration estimates for titration experiments of astandard solution of 1000 ppm (0.0167 mol/l) VFA and 50 meq/l bicarbonate at 20 stitration step.

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Figure 39: VFA concentration estimates for titration experiments of a standardsolution of 1000 ppm (0.0167 mol/l) VFA and 50 meq/l bicarbonate at 20 s titration step.

To investigate the effect of speeding up the titration on the measurement accuracy,the experiment was repeated with the same standards but with a shorter titration timestep of 8 s, i.e. taking 6.6 min for the whole experiment. The influence on theaccuracy was very little as can be seen in figure 40. However, the slower titration isrecommended to guarantee that the chemical equilibrium is achieved at every timestep. Also, such slow titration is required in case detection of interferences (buffersother than bicarbonate and VFA) is needed. In any case, for the monitoring ofanaerobic digestion the results are not required to be more frequent than once every30 min, which allows to use the slow titration approach.

Telemac D2.4 43

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ol/l

Bicarbonate concentration at 20 sec titration stepStandard concentrationBicarbonate concentration at 8 sec titration step

Figure 40: Bicarbonate concentration estimates for titration experiments of a standardsolution of 1000 ppm (0.0167 mol/l) VFA and 50 meq/l bicarbonate at 20 and 8 stitration step.

Similar accuracy levels were found in other experiments with standards. For instance,Figures 41 and 42 show the results from an experiment titrating a standard solution of250 ppm ( 0.004167 mol/l) VFA and 25 meq/l bicarbonate. Titration was performed withan average time step of 20 s and 50 titration points. Despite the fact that a different pKavalue of VFA was detected (4.48 in stead of 4.33) in comparison with the previousdescribed experiment, the accuracy of the concentrations was in the same range. Thisproves that shifts in the pKa values due to experimental conditions or ionic strength doesnot have effect on the BCS method.

It can be noticed that despite the accurate bicarbonate concentration, it was always underestimated. This could be related to the CO2 stripping.

Telemac D2.4 44

0.019

0.020

0.021

0.022

0.023

0.024

0.025

0.026

1 2 3 4 5 6 7 8 9Sample No.

con

cen

trat

ion

mo

l/lBicarbonate concentration +Standard deviation -Standard deviation Standard concentration

Figure 41: Bicarbonate concentration estimates for titration experiments of astandard solution of 250 ppm (0.004167 mol/l) VFA and 25 meq/l bicarbonate at 20 stitration step.

0.000

0.001

0.001

0.002

0.002

0.003

0.003

0.004

0.004

0.005

0.005

1 2 3 4 5 6 7 8 9Sample No.

conc

entr

atio

n m

ol/l

VFA concentration +Standard deviation -Standard deviation Standard concentration

Figure 42: VFA concentration for titration experiment of a standard solution of 250ppm (0.004167 mol/l) VFA and 25 meq/l bicarbonate at 20 s titration step.

Telemac D2.4 45

2.4.3.2 Test results for anaerobic digester samples

Titration data of an anaerobic digester were collected by the titrimetric analyser andanalysed on-line with method 1 and 2. The corresponding VFA results are mentioned inFigure 43. During the high load changes applied to the digester between 350 h and 450 h,three periods of titration data were collected from the titration analyser (382-395 h, 401-418 h and 427-433 h) and interpreted by the third method, BCS. Figure 43 shows theVFA results of the three methods compared to the GC results. The three methods showaccurate results, following the same dynamics and corresponding to the GCmeasurements. However, it can be seen that some outliers appear for all methods. Thereason for these outliers in the BCS data is maybe due to the minor quality of thesetitration curves. This can be caused by anomalies due to the use of fast titration timesteps, the presence of air bubbles, the presence of sludge particles, etc.Therefore it is advised to perform titrations with a titration time step larger than 8 s (20 sseemed sufficient) for the extended method. Similar conclusions can also be drawn fromthe bicarbonate results in Figure 44.

0.000

200.000

400.000

600.000

800.000

1000.000

1200.000

380 390 400 410 420 430

time h

VF

A c

on

cen

trat

ion

pp

m

Method 1 Method 2 Method 3 GC

Figure 43: VFA estimation results from the three titrimetric methods compared to GCmeasurements.

Telemac D2.4 46

0

10

20

30

40

50

60

380 390 400 410 420 430

time h

Bic

arb

on

ate

con

cen

trat

ion

meq

/lMethod 1 Method 2 Method 3

Figure 44: Bicarbonate estimation results from the three titrimetric methods

The results obtained with method 3 (BCS) show that the titration curves correspondingwith the outliers are interpreted as if some other interfering buffers are present in thesample. Since no standard measurement was performed for these interferences during theexperiments and they were only detected at a few points, it can be concluded that theseinterfering buffers are not really present but a result of a misinterpretation. Theirdetection could be related to an irregularity in the buffer capacity curve, caused by forinstance the presence of air bubbles, the presence of sludge particles, or the fast titration.Indeed, these preliminary experiments were mainly aimed at testing the titrimetricanalyser on high frequency measurements (and thus rapid titration) for the detection ofthe main buffers in anaerobic digestion, bicarbonate and VFA and not particularlyfocused on the detection of interfering buffers. This would have required anotherexperimental plan. Important at this stage of the study is that the possibility of BCS todetect interferences is shown to have no effect on the accuracy of the estimates ofbicarbonate and VFA. However, if faults are detected due to an irregularity of the BCcurve, clear outliers will appear and these can be easily filtered out. This characteristiccan be used, for instance, to detect the fouling of the hardware and the titrationexperiments at an early stage.Since no tests with interfering buffers have been performed, the full potential of the BCSin this area have not been examined yet.

Telemac D2.4 47

2.4.4 Diagnostics using remote monitoring

The main focus until now was the operation of the analyser. However, if the results fromthe analyser are transferred to the remote control center, the person responsible for theremote plant does not know how reliable the results are. In order to give the remoteoperator more information about the status of the reactor and the proper functioning ofthe analyser, the complete titration curves (in addition with the final results) can betransferred to the remote control center. From these titration curves the buffer capacitycan be calculated, thus enabling the remote operator to check the operation of theanalyser. The next figures show some buffer capacity curves collected during the test ofthe analyser at the anaerobic reactor (as described in 2.4.2). Figure 45 and 46 showsmooth buffer capacity curves, Figure 47 shows a curve of poor quality.

Figure 45: Buffer capacity curve from an anaerobic sample (predicted concentrationsare 133.18 mg/l of VFA and 46.14 meq/l of bicarbonate)

Figure 46: Buffer capacity curve from an anaerobic sample (predicted concentrationsare 1090.42 mg/l of VFA and 30.27 meq/l of bicarbonate)

0.00E+00

1.00E-02

2.00E-02

3.00E-02

4.00E-02

5.00E-02

6.00E-02

7.00E-02

3 4 5 6 7 8 9 10 11 12

pH

Buf

ferC

apac

ity (

eq l

-1 p

H-1)

0.00E+00

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3.00E-02

4.00E-02

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6.00E-02

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3 4 5 6 7 8 9 10 11 12

pH

Bu

ffer

Cap

acit

y (e

q l-1

pH

-1)

VFA Bicarbonate

VFABicarbonate

Telemac D2.4 48

Fig 47: Buffercapacity curve from an anaerobic sample (predicted concentrations are80.48 mg/l of VFA and 46.89 meq/l of bicarbonate)

The next step is to simulate based on the obtained results a buffer capacity curve whichcan be compared with the calculated buffer capacity curve. This information provides animportant additional source of information concerning the quality of the results andprovides the remote operator with information, for instance, about the calibration of thepH-probe (pH shift, improper calibration).

Figure 48: Buffer capacity from analyser and simulated buffercapacity with the resultsobtained from the analyser.

0.00E+00

1.00E-02

2.00E-02

3.00E-02

4.00E-02

5.00E-02

6.00E-02

7.00E-02

3 4 5 6 7 8 9 10 11 12

pH

Bu

ffer

cap

acit

y (e

q l-1

pH

-1)

0.00E+00

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2.00E-02

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3 4 5 6 7 8 9 10 11 12

pH

Bu

ffer

Cap

acit

y (e

q l-1

pH

-1)

Buffercapacity (Analyser)Simulated Buffercapacity

Telemac D2.4 49

Figure 49 shows a simulation of an improper calibration. Due to this, a shift in the pHreading has occurred.

Figure 49: Problem with a shift of the pH-electrode. The model has different pKa-valuesfor VFA and bicarbonate than the pKa-measured.

Finally, also a measure for the quality of the prediction can be calculated, this so-calledquality index can be used to provide an alarm:

Quality Index = (1-((? (Buffercapacity(x)-Simulated Buffercapacity(x))^2)/( ? (Buffercapacity(x))^2)) ? 100

During the reactor experiment the quality index (calculated for more than 250 samples)was most of the time higher than 98%. Only for one sample this index was lower than95% (Quality Index about 43%) due to poor quality of the titration curve (these data arethose from figure 47).

Using these three tools (visualization of the buffer capacity curves, modelling the buffercapacity based on obtained results, and the Quality Index), the remote operator must beable to intervene if the analyser is malfunctioning or improperly calibrated. The QualityIndex together with the analysis results for VFA and bicarbonate can be used to warn theremote operator. High VFA concentrations and a high Quality Index indicate that thereactor is overloaded. High VFA concentrations and a low Quality Index suggest apossible malfunctioning of the analyser.

0.00E+00

1.00E-02

2.00E-02

3.00E-02

4.00E-02

5.00E-02

6.00E-02

7.00E-02

3 4 5 6 7 8 9 10 11 12

pH

Bu

ffer

cap

acit

y (e

q l-1

pH

-1)

Telemac D2.4 50

3. Development of an on-line gas analyser for the determination of CO2 andCH4 in biogas

One of the parameters used in the industry to control the anaerobic process is the biogasflow. However, the measurement of the main gas compounds CO2 and CH4 in biogas canprovide extra information about the condition of the reactor and can be used forcontrolling the reactor and optimising the ratio CO2/CH4. The latter is important if biogasis used for recovering energy using methane combustion (cogeneration).The on-line gas analyser was developed using a thermal conductivity sensor. This type ofthermal conductivity sensor is insensitive to varying gas flow and is very stable in time.This means that this type of sensor does not need to be calibrated frequently as with othergas sensors (e.g. infrared gas sensors).

3.1 Normal gas composition in biogas

The gas composition of an anaerobic reactor is mainly methane and carbon dioxide andsome traces of other compounds like hydrogen, nitrogen and hydrogen sulfide (see Table5).

Component Normal concentrationCH4 60-80%CO2 40-20%H2S 0.5%H2 0-0.2%N2 0-0.2%

H2O SaturatedNH3 Some ppm

Table 5: Gas composition in anaerobic digesters

In normal working conditions the gas composition is almost binary (only CH4 and CO2),therefore thermal conductivity can be used for measuring CO2 and CH4 in the biogas.

3.2 Thermal conductivity

3.2.1 Thermal conductivity of the main gases

The thermal conductivity sensor uses the thermal conductivity coefficient of the biogas todetermine the concentration of a specific compound present in that gas. The thermalconductivity of the biogas is related to its gas composition and the thermal conductivitycoefficient of each specific compound present in the gas. For example, the thermalconductivity coefficient of a gas mixture of 60 % CH4 and 40 % CO2 is (0.6 x 85.54 +0.4 x 41.74 = Cal.cm.cm-2.s-1.°C-1) . Table 6 shows the conducitivity coefficient for themain gas compounds in biogas.

Telemac D2.4 51

GAS Thermal Conductivity(Cal.cm.cm-2.s-1.°C-1)

CH4 85.54CO2 41.74Air 64.22H2S 36.78H2 458.72N2 64.06CO 61.99NH3 61.58H2O 44.63O2 65.9

Table 6: Thermal conductivity of various gases (T = 37.8°C)

3.2.2 Working principle

In order to measure the thermal conductivity of the gas, two integrated thin film resistorsare structured on a silicon chip (Figure 50).

Figure 50: Schematic view of the sensor head

The sensing resistor Rm is located beneath a membrane that isolates Rm. Rm is heated by acurrent of 4.7 mA and the membrane is heated to a temperature of 60-80°C. Thetemperature of the membrane is dependent of the gas composition. Gases which have ahigher thermal conductivity than a reference gas cause a decrease on the surfacemembrane temperature. Gases with a lower thermal conductivity increase thetemperature. The reference resistor RT is located on the silicon bulk at ambienttemperature. This reference resistor provides the temperature measurement and so atemperature correction is made to the raw measurement signal. The output signal isproportional to Rm/RT and is linear with the target gas concentration. Also, the outputsignal is temperature corrected in a range of 10-45 °C.

Rm

RT

GASHeat Q

Telemac D2.4 52

3.2.3. Linearity of the gas sensor

The linearity of the calibration of the gas sensor was checked with standard gas mixturescontaining different concentrations of CO2 in CH4 (see Figure 51). The calibration of thegas sensor correlates perfectly with the change in CO2 concentration in the standard gasmixtures (correlation coefficient R2 = 0.9989).

y = 0.9981x

R2 = 0.9989

-1

4

9

14

19

24

29

34

39

44

0 5 10 15 20 25 30 35 40

%CO2 (calibration gas)

Figure 51: Linear response of the gas sensor with varying CO2 concentration in CH4

Telemac D2.4 53

3.3 Test of the gas analyser at INRA

3.3.1 Set-up of the experiment

Initially at the implementation of the analyser in the INRA anaerobic set-up, the sensorindicated the maximum concentration of CO2 (i.e. 40 %). This problem was due to thefact that the gas sampling pump needed more gas volume than was provided by thereactor. In this way underpressure was created and air at the outlet of the gas analyserwas sucked into the gas sensor causing inaccurate results. After adapting the gas feed tothe analyser, the analyser worked properly (see following results).

The next figures show the working conditions of the reactor at the INRA institute.

Figure 52: Manipulation of the liquid feed flow (l/h)

Figure 53: Change of the gas flow (l/h) during the experiment

0

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300

120 170 220 270 320

Time (h)

0

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50

60

70

130 180 230 280 330

Time (h)

Telemac D2.4 54

3.3.2 On-line measurement results

The following figures show the on-line results for CO2 and CH4 measurement using anInfra-red gas analyser (IR-ULTRAMAT 22P by Siemens used at INRA) in comparisonwith the thermal conductivity gas analyser (TC) developed by AppliTek.

Figure 54: Measurement of CO2 using IR and TC gas analysers

Figure 55: Measurement of CH4 using IR and TC gas analysers

There is a systematic deviation between the results of the two gas analysers. Theconcentration of CO2 was verified with a third method, whereby some biogas sampleswere analysed with gas chromatography (GC). The results are shown in Figure 56.

Telemac D2.4 55

Figure 56: Measurement of CO2 with 3 analysis methods: IR , off-line GC and TC gasanalyser

The off-line GC results are identical to the results obtained with the IR gas analyser. Asmentioned before the results of the TC gas analyser show a systematic deviation incomparison with the IR gas analyser. This is also shown in Figure 57.

Figure 57: Deviation between IR and TC gas analyser

The deviation is varying between 2.8 and 3.3 % CO2. If we take into account themeasurement increment for both analysers (0.5% for IR and 0.1% for TC), we canconclude that this deviation is constant.

Because of this systematic deviation, the TC result can be used to monitor the gascomposition of the biogas and for more accurate results this analyser can be recalibrated.

2.6

2.7

2.8

2.9

3

3.1

3.2

3.3

3.4

3.5

3.6

190 210 230 250 270 290 310 330

Time (h)

Telemac D2.4 56

3.4 Gas pretreatment

Before sampling the gas to the analyser, the gas should pretreated. First of all, most of thewater vapour should be removed because water vapour has an influence on the thermalconductivity measurement (see Table 6). At INRA a peltier cooler is installed to removeall the water vapour out of the biogas before it is analysed, because water vapour has alsoa huge influence on the IR measurement.The second reason to remove the moisture is because water in combination with H2S orother acids is also very corrosive and could damage the metal parts of the gas sensor.Although the moisture at INRA was removed, some corrosion could be noticed at thecopper wiring of the sensor head. In order to eliminate or minimize this corrosion, a H2Sabsorption filter before the sensing element could be used to protect the copper wiring ofthe sensor head.Also, by incapsulating the copper wiring in a sort of silicon polymer, this corrosion effectwas minimized (no direct contact with biogas).

3.4.1 Moisture filtering by Peltier cooler

The developed cooler consists of a Peltier cooling element, a ventilator and a glasscomponent. The glass component was developed in order to force the biogas to pass thePeltier cooling element. Water vapour in the biogas is cooled down and as a consequencecondenses.

Figure 58: Analyser combined with the moisture removal component.

Moisture condenser

Telemac D2.4 57

3.4.2 H2S filtering by absorption filter

As H2S filter two commercial absorbers were tested: Purafil? and Sulfatreat? .The Purafil? filter was tested at the INRA institute. This filter was not applicablebecause besides the absorption of H2S also CO2 was absorbed by the filter causing faultyanalysis results.The Sulfatreat? filter was tested at AppliTek. This filter consists of iron with a highspecific surface which absorbs very efficiently the H2S present and transforms it intopyrite in the presence of water.The biogas of the anaerobic reactor was forced in a flow cell filled with 200g ofSulfatreat? . The gas flow was averaged at 0.6 l/h. The H2S concentration was measuredwith test kits based on lead acetate. These test kits showed that 0.12 % of H2S waspresent in the incoming biogas; the outcoming biogas (after the Sulfatreat? filter)contained less than 25 ppm H2S. In comparison with the Purafil? filter no significantabsorption of CO2 was observed.

Telemac D2.4 58

4. General conclusion

The newly developed titrimetric analyser was tested on a lab-scale reactor and performedmore than 1800 analyses during the 350 h of on-line operation. During the on-lineexperiments the analyser was calibrated once and also the calibration of the pH electrodeprobe was only once checked. Nevertheless the high number of analyses performed, theanalyser was stable throughout the whole on-line operation period.Although the VFA concentration is overpredicted with a systematic deviation of 15 % incomparison to gas chromatography reference measurements, this measured parameter canbe used to follow on-line the condition of the anaerobic process. Together with the VFAconcentration also a good estimation of the bicarbonate concentration, the total andpartial alkalinity are provided by the newly developed titrimetric analyser.This analyser offers the possibility to control the anaerobic treatment process and as aconsequence enables the operator to increase the efficiency of the anaerobic treatment.Besides both implemented titrimetric methods, the newly developed titrimetric analysercould be up-graded via the integration of the extended method. This offers the possibilityto detect the presence of toxic compounds thus enabling the operator full control of theanaerobic process .

Besides the titrimetric analyser also a biogas analyser based on the thermal conductivityprinciple was developed. This gas analyser has been tested on a pilot scale reactor atINRA and showed that the thermal conductivity principle can be used for biogascomposition measurements. The gas analyser is besides a TC sensor also equipped with aPeltier cooling element for the elimination of moisture and a Sulfatreat? filter for theelimination of H2S. The TC gas analyser is a good and cheap alternative for thecommercially available IR gas analysers.

Telemac D2.4 59

5. APPENDIX

I. Report On Titrators Comparison (First Experimental Stage): Internal Report:see milestone M2.1

II. Report On Titrators Comparison (Second Experimental Stage): InternalReport: see milestone M2.1

III. Comparison of two titrimetric sensors for on-line monitoring of anaerobicdigestion

Telemac D2.4 60

Appendix III: Comparison of two titrimetric sensors foron-line monitoring of anaerobic digestion

U. Zaher*, J.P. Steyer **and Peter A. Vanrolleghem*.

* Department of Applied Mathematics, Biometrics and Process Control (BIOMATH), GhentUniversity, Coupure Links 653,B-9000 Gent, Belgium.(E-mail: usama.zaher@biomath.ugent.be; peter.vanrolleghem@ugent.be)** Laboratoire de Biotechnologie de l'Environnement, Institut National de la RechercheAgronomique, LBE-INRA. Av. des Etangs, F- 11100 Narbonne, France.(E-mail: steyer@ensam.inra.fr)

AbstractVolatile fatty acids and bicarbonate measurement are important toassess a digester’s stability and control it. Titration methods are themost likely cost-effective methods to determine those variables withacceptable accuracy. In this paper, two titrimetric sensors are comparedfor their potential on-line. The necessary background in terms of thesensors' underlying theories and methods is highlighted. An experimentwas designed to evaluate the two sensors during an overload situationof a 1 m3 fixed bed anaerobic reactor. The results of both sensors areanalysed and discussed. The outcome of the comparison is conclusionof each sensor accuracy and recommendation for further research.

KeywordsTitration; Volatile Fatty Acids (VFAs); Bicarbonate alkalinity;Anaerobic digestion; on-line monitoring.

INTRODUCTIONIt is generally accepted that alkalinity is very important to assess the anaerobic digestionstability (Hawkes et al., 1994). In anaerobic reactors the carbonate system is the majorsource of alkalinity (in normal conditions). Ignoring the very small fraction of carbondioxide that associates with water to form carbonic acid, H2CO3 (less than one percent),the carbonate system in water is defined by the concentrations (activities) of five species:CO2, HCO3

-, CO3

2-, H

+, OH

-. In the anaerobic reactor where the pH is (and must be) near

the neutral value of pH 7, the ionic species of the water dissociation and the carbonateconcentration are all much smaller than the bicarbonate concentration. Therefore, for allpractical purposes the alkalinity due to the carbonate system can be taken as identical tothe concentration of bicarbonate, i.e. bicarbonate alkalinity (Van Haandel and Lettinga,1994). Most of the cases, The main source of bicarbonate is the influent wastewater, e.g.domestic wastewater. Anther portion will be formed within the reactor due to CO2 bybiodegradation.

Telemac D2.4 61

However, one should keep in mind that the CO2 and CH4 are produced through theconversion of VFAs in the acetogenic and acetoclastic steps of anaerobic digestion.VFAs are buffering systems at pKa close to 4.75 which is relatively close to bicarbonateat 6.35. VFAs will be considered as well in the total alkalinity measurement. When theacetogenic and acetoclastic step is inhibited VFAs accumulate and the pH drops (e.g.during overload conditions). If the pH is maintained in the optimum range around 7 (i.enormal operation conditions), more VFAs can be converted to CO2 and CH4 andtherefore more bicarbonate alkalinity is produced. Thus the VFAs concentration giveimportant information about the anaerobic process (Pind et al., 2002; Puñal et al., 2002;Mosche and Jordening, 1999; Pind et al.,1999; Rozzi et al., 1997; Ahring et al., 1995).

In most literature the standard method for measuring alkalinity is titration (Vanrolleghemand Lee, 2002; American Society For Testing And Materials, 1999; Smith, 1980).According to Buchauer, (1998) the most common methods for measuring VFAs otherthan titration are expensive, have poor accuracy or are time consuming. Steam distillationrequires certain specialized equipment plus experience, and it is time-consuming(Deutsche Einheitsverfahren, 1971). The colorimetric method is simple procedure(Montgomery et al., 1962) , but is said to be of poor accuracy at low VFA concentrations(Scheer, 1995) and it is rather sensitive towards residual color (Moosbrugger et al.,1993). Chromatographic methods (HPLC, GC) require high investment in technicalequipment and are not commonly available at a WWTP.

On the other hand titrimetric methods for measuring VFAs are mostly very simpleprocedures, which can be conducted with a minimum of time and effort. The titrationprocedures are detailed in (Buchauer, 1997) and listed in (Buchauer, 1998). Amongothers there are methods by (Nordmann, 1977; Moosbrugger et al., 1993&1992; Powelland Archer1989; Colin, 1984; Kapp, 1984; McGhee, 1968).

In this paper two titrimetric sensors are compared for on-line measurement of alkalinityand VFA. For the first sensor necessary updates for the method and components arepresented. Since it was originally measuring buffer systems in the range from pH 2.5 topH 10.5. The update is intended to accurately measure bicarbonate alkalinity and VFA bydown titration in shorter pH range 7 to 2.5. The necessary updates to the Kapp method(Kapp 1992; 1984), which is used in the second sensor, are presented as well. They allowto measure bicarbonate in addition to high concentrations of VFAs. Results from bothsensors are statistically interpreted and compared with off-line GC analysis and standardalkalinity manual analysis.

METHODSBIOMATH Buffer Capacity Hardware and Software SensorThis sensor was developed in Belgium at BIOMATH (Dept. of Applied Mathematics,Biometrics and Process Control- Ghent University) during the PhD research of VanVooren, (2000), therefore it will be called the BIOMATH sensor. It consists of anautomatic titrator (Metohm Titrino 716), sample changer (Metrohm 730) and a PC fordata capture and analysis. For on-line measurement, the sample changer is replaced by a

Telemac D2.4 62

closed titration vessel to prevent CO2 stripping so that bicarbonate alkalinity can bemeasured accurately.

The principle is to successively measure pH in function of stepwise acid or base addition.In this way the titration curve is built. From this measured titration curve (typicallyaround 30 to 50 points), the buffer capacity at each pH point is calculated as thederivative of the amount of base or acid needed (meq l -1 pH -1 ). The buffer capacitycurve consists of the sum ( hence the linearity of the equation) of the buffer capacities ofeach individual component in the solution as given by equation 1 (Van Vooren et al.,2001). From the measured buffer capacity curve, estimates of the different bufferingcomponents are computed using a selected mathematical model followed by parameterestimation. After the parameter estimation, the concentrations and eventuallycorresponding pKa values for the buffering components defined in the model areobtained. The methods used for model selection and parameter estimation are describedelsewhere (Van De Steene et al., 2002) . The operating principle of the hardware andsoftware sensor is illustrated in Figure 1:

Figure (1): Principle of the BIOMATH sensor (Van Vooren, 2000)

The general model for buffer capacity ? is presented by the general equation 1 (VanVooren et al., 2001) For the buffer capacity of a sample containing l monoprotic, mdiprotic and n triprotic weak acids:

Telemac D2.4 63

? ?

??

FHG

IKJ?

? ?? ?

FHG

IKJ

?

? ? ?

? ?? ?

?

??

? ?

? ??

? ? ?

?

? ?

? ?

2 303

11 4

4 9

4

21

1

22 1 2

21 1 2

21

1

42

31 3 2

2

2 1 2 33

12

1 2

. [ ]

([ ] )[ ] [ ]([ ] [ ] )

[ ] [ ] ( ) [ ]

( [ ] )([ ] [ ] [

H

C KH K

C KH K H K KH K H K K

C K

H K H K K K H

H K K K KH K H K K

i aai

l

i

j aa a a

a a a jj

m

j a

a a a a

a a a a

a a a H K K Ka a a

k

k

m

?? ?

F

H

GGGG

I

K

JJJJ? ] )1 2 3

21

(1)

where? =buffer capacity (eq l-1pH-1)[H+] =hydrogen ion concentration (mol l-1)Ci, j, k =concentration of respectively a monoprotic, diprotic and triprotic weak acid(mol l-1)Ka =acidity constant

Update of the BIOMATH sensorIt has already been mentioned that the hardware was updated by replacing the samplechanger by a closed titration vessel to decrease the CO2 stripping and measurebicarbonate alkalinity. More accurately an update of the carbonate system model is alsorequired. In the general model presented by equation 1, the carbonate system is modelledas a diprotic buffer with the following reactions:

pKaH2CO3

K? ?? HCO3- + H+ 6.35

HCO3- K? ?? CO3

2- + H+ 10.33

To consider carbonate as a diprotic buffer, it will require that the titration experimentshould cover both the pKa values. Furthermore, the VFAs buffer, around 4.75, will bemeasured. Therefore with the standard sensor titration method, titration needs to becarried out to cover the range of at least from 3.5 to 10.5. Since the optimum pH in adigestor is around 7, pH adjustment is required to start the titration. With high VFA andbicarbonate concentration in a digester, the standard titration method of the sensor willtake around 90 min.

Therefore, in the updated sensor down titration from the pH of the reactor to 2.5 (around30 min) is performed followed by the up titration (around 60 min). The diprotic model forcarbonate is used to interpret the uptitration. The update is to interpret the down titrationdata by considering only the first reaction, i.e the bicarbonate buffer only. The model in

Telemac D2.4 64

equation 1 is reduced to the term of monoprotic buffer. This is reasonable approximationbecause bicarbonate is the most dominating species of the carbonate system at the pHrange of a digester. Using only the down titration part yields good results in 30 min,Which is appropriate for an on-line measurement.

INRA On-line titrimetric sensorThis sensor has been developed in France at the laboratory of environment biotechnology- INRA (Institut National de la Recherche Agronomique), therefore it will be called theINRA sensor. For description of the sensor principle and the technical details the readeris referred to (Bouvier et al., 2002). The INRA sensor uses the Kapp method (Kapp 1992;1984) but it was modified in order to determine the bicarbonate concentration as well.

The Kapp Method is based on the assumption that the acid required to titrate a sample(VA5-4,meas ml) from pH 5.0 to pH 4.0 can be considered proportional to the VFAsconcentration (Buchauer, 1997; McGhee, 1968). Kapp further considers that thebicarbonate buffer will influence the VA5-4,meas:

VA5-4,meas = VA5-4,VFA + VA5-4,HCO3 (2)

Therefore, according to Kapp two separate experiments must performed. One is to definethe relation of VA5-4 and bicarbonate concentration by titrating a certain range of knownconcentrations of bicarbonate. Another is to define the relation when only a range ofVFA concentrations is titrated. If the two defined relations are mathematically combinedaccording (2), it gives an equation of the general form (3).

VFA a N VA VS b ALK cmeas? ? ? ? ? ??5 4 / (3)

Where VFA is the concenteration of volatile fatty acids in mg/l, N is the normality of thetitrant acid, ALKmeas is the estimated alkalinity of the sample if titrated to pH 4.3 and VSis the volume of sample. The constants a, b, c are first determined to be 127416, 2.99,10.6 respectively from the two individual experiments of Kapp. The concentration ofVFAs is determined by iteration: for each iteration the ALKmeas has to be corrected to thebicarbonate alkalinity by subtracting the estimated VFAs of the previous step. (Buchauer,1997) showed that the iterative calculation will lead only to the adjustment of a, b, cvalues. For example, after three iterations, where further iterations are consideredunnecessary by (Kapp, 1984), a= 131340 , b=3.08 and c=10.9.

The assumption that lead to this adjustment is that the only VFA available is acetate and60% of it is in the form of CH3COOH at pH 4.3. Therefore, the alkalinity due to acetateis evaluated by equation (4).

AlkVFA= 0.6 · VFA / 60 = 0.01 · VFA (4)

The consequences of this assumption on the calculation of the VFA concentrations of anoverloaded anaerobic digester will be discussed below.

Telemac D2.4 65

Update of the Kapp method for the INRA sensor.The first estimates of parameters a, b and c in equation 3 are derived from the twoexperiments of Kapp such that the VFA was added in the form of acetic acid to reachconcentrations ranging from 0 to 70 mmol/l (0 to 420 mg/l). For higher VFAconcenterations (e.g. >7000 mg/l reached in the presented experiment), those parametershave to be redefined experimentally.

Since the measurements are performed on-line, titration will normally start from the pHof the reactor. The INRA sensor titrate from the pH of the reactor (i.e. around pH 7)down to pH 3. Therefore, total alkalinity and partial alkalinity are also measured. Theyare important parameters in many control schemes of the anaerobic digestion.Bicarbonate concentration is estimated since it is seen complimentary to the calculationscheme of the Kapp method

Design of the Experiment

The experiment was designed to provide an over load to 1m3 fixed bed anaerobic reactortreating vinasses at the INRA laboratory of environmental biotechnology. Then VFAsand Alkalinity was on-line measured in the effluent using both sensors. For the detail ofthe reactor design and installation of the reactor the reader is referred to (Steyer et al.,2002). The overload was phased into two steps. First, the flow rate was increased fivetimes while maintaining a dilution of 1:1 to the influent. Second, after reaching a steadystate, the dilution of the influent was stopped to increase the input concentrations as well.After the steady state is reached the reactor was brought back to the normal feedconditions. Consequently, the COD load to the reactor was increased by a factor of 5during the first step and by a factor of 10 during the second step, as shown in the figure 2.Gas pressure, flow and components were recorded on-line and there evolution take thesame pattern of figure 1 (data is not shown) Temperature and pH of the reactor wasregulated to be constant at the optimum 35 ?C and pH 7.2. It was feasible during theoverload to off line analyse 12 samples to determine VFA concentrations using gaschromatography and to manually estimate total and partial alkalinity using the standardtitration methods. In parallel, the INRA sensor was recording on line measurement every30 min. BIOMATH sensor was running careful down- and up-titration experiments tostudy the evolution of the different components during the overload.

Telemac D2.4 66

COD load

0.0

500.0

1000.0

1500.0

2000.0

2500.0

0 1 2 3 4 5 6 7 8 9 10 11 12

Sample No.

load

in g

ram

s / h

Figure (2) :Approximate COD Load projected to Manual Sampling Points

Telemac D2.4 67

RESULTS AND DISCUSSION

Figure (3) shows the buffer capacity measured by the BIOMATH sensor (down titration)during the experiment. As the overload proceeds the highest buffer in the system shiftsfrom the bicarbonate (pKa ? 6.35) to the VFA (pKa ? 4.75). This is very useful to detectthe overload and accumlation of VFAs.

Figure 3: VFA evolution and drop of bicarbonate during the experiment

To interpret the update of the BIOMATH sensor, figure 4 and 5 show the results ofsample 1 down titration and uptitration respectively. The measured buffer capacity (BC1)is plotted together with the simulated results (sim1) by the buffer capacity model. Testingthe linear relationship between BC1 and sim1, one finds to what extent the model fits themeasured buffer capacity. Pearson product moment correlation coefficient (r) foruptitration in the long range 2.5 to 10.5 is less than the case of down titration in the shortrange pH 7 to 2.5. The reason is related to the carbonate system. When starting the downtitration experiment the bicarbonate concentration is simulated using a monoprotic modeland thus related alkalinity is determined accurately. Then as the pH drops and the stirringcontinues the alkalinity is lost as CO2 to the gas phase. Some of the released CO2 will becaptured in the closed titration vessel. When the uptitration starts afterwards, part of thecaptured CO2 will dissolve again. The dissolution rate of CO2 will be as faster as the

Telemac D2.4 68

sample becomes more alkaline. Therefore gradually the carbonate concentration will bemore than it should be from the dissociation of the bicarbonate only. Since simulation ofup titration does not consider the transfer of CO2 but couples both dissociation steps toHCO3

- then to CO3

2- in a diprotic buffer, it under estimates the carbonate while it over

estimates the bicarbonate to get the best fit for the detected buffer capacity curve.Titration results of the rest of the samples lead to the same interpretation (results are notshown)

r = 0.996872

0

2

4

6

8

10

12

14

16

0 1 2 3 4 5 6 7 8

pH

Bu

ffer

Cap

acit

y m

eq/l

BCSim

Figure 4: fitting the buffer capacity determined by down titration (pH 7 to pH 2.5) usingmonoprotic model for bicarbonate

Telemac D2.4 69

r = 0.892785

0

1

2

3

4

5

6

7

8

9

10

0 2 4 6 8 10 12

pH

Buf

fer

capa

city

meq

/l

BC1

Sim1

Figure 5: fitting the buffer capacity determined by uptitration (pH 2.5 to pH 10.5) usingdiprotic model for the carbonate system

On-Line measurements and Graphical interpretation.Figure (6) shows the online VFA as recorded by INRA sensor every 30 min. The figureshows the good reproducibility obtained by the INRA sensor for VFA. It shows nicelythe dynamic effect of the overload on the VFA concentration from 50h to 100h. After100h the process was brought back to normal operation condition and therefore the VFAhas decreased. At 130h, the process reached the initial concentration. Also, off-line GCresults are shown. The evolution of VFA agrees with what detected from the BC resultsevaluated by the BIOMATH sensor as shown in figure 3. In the next section, accuracy ofmeasured VFA concentrations by both sensors will be tested by focusing on the obtainedresults at sampling points.

Telemac D2.4 70

0

2000

4000

6000

8000

10000

0 20 40 60 80 100 120 140

time h

mg/

l

0

2

4

6

8

10On-Line Total VFAs Total VFAs with GC

Figure (6): on-line VFA measurement determined by INRA sensorFigure (7) shows the on-line measurements for partial alkalinity, total alkalinity, andbicarbonate alkalinity recorded by the INRA sensor. During the overload from 50h to100h, it can be seen that the partial alkalinity is decreasing while total alkalinity isincreasing and bicarbonate is usually estimated in between. In comparison with buffercapacity profile detected by the BIOMATH sensor, partial alkalinity agrees with thedecrease pattern of the bicarbonate buffer, sampling points 1 to 10 in figure 3. Also,partial alkalinity increase after the end of the over load conditions at 100h agree with therecovery of the bicarbonate buffer after sampling point 10 in figure 3. Total alkalinitydoes not show the same because it is determined by the amount of acid added to an endpoint of 4.3 and therefore will be influenced by the VFA buffer around 4.75. Partialalkalinity is determined at an end point of 5.75 and therefore it almost avoids the VFAbuffer. This gives an interpretation of the empirical ratio of total alkalinity to partialalkalinity that frequently used to detect/control the overload to a digester. The ratio > 2,e.g at 80h, indicates accumulation of VFAs. Presented in the next section, furtherinterpretation is obtained by testing the correlation of different alkalinity measurement byfocusing on sampling points.

Telemac D2.4 71

0

40

80

120

160

0 20 40 60 80 100 120 140

time h

meq

/l

On-line Partial Alkalinity On-line Total Alkalinity On-Line Bicarbonate Alkalinity

Figure (7): on-line alkalinity measurement determined by INRA sensor

Correlation of on-line and off-line measurement and statistical interpretationIn this section, the linear relationship is tested between the off-line analysis and the on-line measurement at sampling points using Pearson product moment correlationcoefficient (r). Provided that the difference between off-line and on-line measurement iswithin acceptable range, values of (r) indicates the accuracy of VFA measurements sincethe off-line analysis was performed using Gas Chromatography (GC), the most accurateVFA measurement method so far. On the other hand, correlation between differentalkalinity measurements helps to assess the proportion of each measurement that isrelated to the bicarbonate concentration and the influence of VFAs.

First VFA resultFigure (8) shows total VFAs concentration in mol/l obtained by the BIOMATH sensorcompared with GC results at sampling points. For each measurement the BIOMATHsensor estimates the standard deviation ? in the measurement. The value of ? is usuallyestimated from more than 100 trials for model fit and therefore normal distribution isexpected. Confidence interval of 95% is estimated to be the measurement ? 2? . It can beseen from figure 8 that the confidence interval is very concise that indicates the highaccuracy expected from the sensor since 95% of the estimates was very concentratedaround the mean (i.e the measurement). Despite concise is the confidence interval, almostthe GC measurement is falling within this interval and therefore the smaller thedifference expected. Maximum absolute difference between the off-line GC and theBIOMATH sensor was determined at point 3 to be 5.3% of the corresponding GC result.Since the absolute difference at the rest of the points ranges from 0.05% to 2.89% andwith r =0.9995, the BIOMATH sensor has comparable accuracy of VFA measurement toGC. However, more measurement points are indeed needed to assure this result.

Telemac D2.4 72

Figure (8): BIOMATH sensor VFA results compared to GC

Figure (9) shows total VFAs concentration mg/l obtained by the INRA sensor comparedwith GC results at sampling points. During the first step of the overload experiment(figure 2), maximum absolute difference ranges from 2.5% to 9% of the correspondingGC result. When the COD load is increased in the second step the difference reaches 22%and there fore re-calibration of the sensor for this high load is required. The parameters inequation (3) should be re-identified to cover this high range of concentrations. In generalthe measurements from the INRA sensor is highly correlated (r=0.987) to the GC results.

Figure (9) shows slight underestimate during the first step of overload and moreunderestimates during the second step of overload followed by an over estimate.Actually, there are two reasons that cause this combined effect. The first reason isexpressing the results in mg/l, instead of mol/l. second, (Buchauer, 1997) states that Kappmethod over estimates the VFA at higher concentration for unknown reason. Using thesoftware of BIOMATH sensor the reason was found to be due to the increasing total ionconcentration, i.e. ion strength, that occur when titrating high concentrations (data notshown).

Telemac D2.4 73

The Kapp method assumes that VFAs are all acetate so that the Kapp equations arederived in mg/l units for VFA concentration. If there is different VFAs with differentmolecular weight during the overload, expressing the results in mg/l instead of mol/lintroduces an error. For example the effect is more pronounced during the second overload step because higher proportions of propionate and butyrate are formed.

Since there are 8 VFAs and their isomers that each of them has different molecularweight, it is more accurate and reliable to present the total VFA results in mol/l.Differentiation between different VFAs is not yet possible with titration experiments.For example in this experiment, acetate is the main volatile fatty acid. However duringthis overload condition, GC results show the evolution of propionate and butyrate.Therefore if the molecular weight of acetate (60) is used it will lead to under estimate thedetected concentrations of the volatile fatty acids as the propionate and butyrate hashigher molecular weight (74 and 88 respectively).

Figure (9): INRA sensor VFA results compared to GC

Second Alkalinity results:Figures 10 and 11 show different on-line and manual measurement of alkalinity. Sincethe manual measurement is also determined by titration, on-line titration is expected to bemore accurate. However, there are different types of alkalinity recorded. The correlation

Telemac D2.4 74

matrix in table (1) will be analysed to determine how each measurement is influenced bythe two main existing buffers, i.e bicarbonate and VFAs. The BIOMATH sensoraccurately determine the bicarbonate concentration as presented in figure (10) inalkalinity units meq/l. At each measurement point the sensor give the estimated standarddeviation ? so that it is used similarly as VFAs to determine the 95% confidence intervalof the measurement. Testing the correlation matrix in table (1):

- Correlation between manual and INRA sensor total alkalinity measurements has apositive correlation of 0.9607. Therefore they have the same pattern. The pattern asseen in figure 10 is decreasing during the first step of the overload due to the decreaseof the bicarbonate concentration. Then an increase is noticed due to the accumulationof VFAs during the second step of the overload. This pattern is expected since thetotal alkalinity indicates bicarbonate but influenced by VFAs.

- The measurements of bicarbonate from the BIOMATH sensor, partial alkalinity fromthe INRA sensor and manual partial alkalinity have negative correlation with totalalkalinity measurements. Those measurements have less influence of VFAs, i.e. theyare decreasing while total alkalinity is increasing due to the accumulation of VFAs.The correlation of BIOMATH sensor bicarbonate measurements is higher inmagnitude since it is not influenced by VFAs. it is not influenced by VFAs because itis calculated by the fitting the buffer capacity model that include both VFA andbicarbonate terms to the measured buffer capacity points.

- The bicarbonate alkalinity from the INRA sensor has a positive correlation with totalalkalinity and therefore it is influenced by VFAs. Also its positive correlation withbicarbonate alkalinity from the BIOMATH sensor and partial alkalinity is less in thanthe correlation among them. These results of bicarbonate from the INRA sensor couldbe enhanced by re-calibration of equation 3 of the Kapp method for highconcentrations such as those occurred during the second phase of the overloadexperiment.

- The high positive correlation of 0.9394 between the INRA sensor partial alkalinityand the BIOMATH bicarbonate alkalinity indicates that they are the closest estimatesof the bicarbonate concentration.

Telemac D2.4 75

Figure (10) INRA sensor on-line total alkalinity compared to manual total alkalinity

Telemac D2.4 76

Figure (11) on-line and manual bicarbonate and partial alkalinity

Telemac D2.4 77

Table (1): correlation matrix among alkalinity measurements

(1) (2) (3) (4) (5) (6)INRA sensor total alkalinity

(1) 1 0.9607 -0.243

7

0.3614

-0.088

8

-0.033

2Manual total alkalinity

(2) 0.9607

1 -0.333

7

0.2531

-0.175

7

-0.043

5BIOMATH sensor bicarbonatealkalinity

(3)-

0.2437

-0.333

7

1 0.7664

0.9394

0.9104

INRA sensor bicarbonate alkalinity(4) 0.361

40.253

10.766

41 0.894

10.815

3INRA sensor partial alkalinity

(5) -0.088

8

-0.175

7

0.9394

0.8941

1 0.9045

Manual partial alkalinity(6) -

0.0332

-0.043

5

0.9104

0.8153

0.9045

1

CONCLUSIONSINRA sensor, and therefore the updated Kapp method, provides accurate VFAmeasurement up to 3000 mg/l. Further update of the kapp method and identification of itsparameters for higher VFA concentrations is required to maintain the same level ofaccuracy. The sensor provides several alkalinity estimates that are useful for using total topartial alkalinity imperial ratio to control the digester under overload conditions. Itspartial alkalinity measurement is found to provide good estimate of bicarbonateconcentration. The measurement is recorded every 30 min that is appropriate for controlpurposes. Furthermore, the time of the measurement could be reduced to 3 min due to itssimple titration method. Measurement time less than 30 min is not necessary, however,for anaerobic digestion.The BIOMATH sensor gives more accurate results for VFAs upto 0.12 mol/l (i.e 7200mg-acetate/l). Update of its buffer capacity method was necessary to accuratelydetermine the bicarbonate concentration and lower the measurement time to 30min. Itprovides accurate bicarbonate measurement without an influence of VFA. The sensorprovides an estimate of the standard deviation in each measurement and therefore theconfidence interval could be estimated.

Telemac D2.4 78

RECOMMENDATIONS FOR FURTHER RESEARCHWhen titration methods are used to determine the bicarbonate concentration, downtitration is favoured than uptitration to avoid CO2 dissolution from the surroundingatmosphere. Furthermore, titration in a closed vessel is recommended. In general, “whenperforming titration for analysing samples that contains a buffering system whose speciesincludes one form that easily leave or enter the system (i.e the liquid phase) titrationshould be done in the direction where such species leave the system”.

If Kapp method is used to determine VFA concentration higher than the range that wasoriginally designed for the method, titration experiments that cover this higher range areneeded to re-calibrate and identify the method parameters. Adjustment of the calculationunits to be in mol/l is foreseen to provide better results since VFAs other than acetate(different molecular weight) are expected.

When VFA is accurately measured in a digester and either bicarbonate or partialalkalinity is recorded, better control of the digester can be achieved using thoseparameters instead of relaying on the imperial total to partial alkalinity ratio.

ACKNOWLEDGEMENTThe authors would like to thank the financial support of the EU TELEMAC project IST-2000-28156. Acknowledgement is due to Jean-Claude Bouvier, Thierry Conte andMarianne Dupla from INRA for their help in the pilot plant operation and themeasurement campaign during the overload experiment.

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