biogas reactor modeling with adm1 lyseng et al

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HiT Publication no 11/2012

Biogas reactor modelling with ADM1

Benjamin C. Lyseng, Wenche Bergland, Deshai

Botheju, Finn Haugen and Rune Bakke

Faculty of Technology (Porsgrunn)

Telemark University College

Porsgrunn 2012


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HiT Publication no 11/2012

ISBN 978-82-7206-367-1 (online)ISSN 1501-8539 (print)ISSN 1503-3767 (online)Series title:HiT skriftorHiT Publication

Telemark University CollegeP.O. Box 2033901 PorsgrunnTelephone: +47 35 57 50 00Fax: +47 35 57 50 01Website:http://www.hit.no/

The Authors/Telemark University College

No part of this publication may be reproduced except in accordance with the CopyrightAct, or the Act Relating to Rights in Photographic Pictures, or the agreements made withKopinor, The Reproduction Rights Organisation of Norway.
http://www.hit.no/http://www.hit.no/http://www.hit.no/http://www.hit.no/


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PrefaceThis report assumes the reader has at least basic understanding of the anaerobic digestion

process. They should be familiar with the three fundamental steps in the anaerobicoxidation of a substrate: (1) hydrolysis, (2) acidogenesis (fermentation), and (3)methanogenesis, as well as the intermediate products associated with each.

We would like to thank all the master students who conducted lab analyses through thesummer and autumn 2011 that provided the data necessary for this type of research.

Porsgrunn, Norway, 7. February 2013


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Table of Contents

Preface ................................................................................... iii

1. Introduction .................................................................... 1

2. Problem description ........................................................ 2

3. Theory ............................................................................. 3

4. Methods .......................................................................... 44.1 Foss farm pilot plant .................................................................................................. 4

4.2 Data collection ........................................................................................................... 44.3 ADM1 ........................................................................................................................ 4

5. Results ............................................................................ 75.1 Reactor operation....................................................................................................... 7

5.2 Manure composition .................................................................................................. 8

5.3 Biogas flow and composition .................................................................................... 9

5.4 COD ......................................................................................................................... 10

5.5 VFA ......................................................................................................................... 12

5.6 Biomass ................................................................................................................... 13

5.7 Inhibition and pH ..................................................................................................... 14

5.8 Temperature effect ................................................................................................... 16

5.9 Feed flow and washout ............................................................................................ 17

6. Discussion ..................................................................... 18

7. Conclusion .................................................................... 20

8. References .................................................................... 21

Appendix A: Selected ADM1 parameters ............................ 22

Appendix B: Resampling code ............................................. 23


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1. IntroductionAnaerobic digestion (AD) is becoming an increasingly common method of handlingagricultural waste. AD has historically been used to treat high-COD waste because itrequires less energy, additives, and space than conventional aerobic processes. Researchand adoption of AD to other processes has attracted more attention recently due, in large

part, to the biogas (methane) it produces. At the same time, agriculture is under growingpressure to increase production and decrease emissions associated with its operation. ADcan reduce odors, pathogens, and nuisance gas emissions, while preserving nutrients for ahigh quality fertilizer (Cantrell et al., 2008).

The traditional method of handling manure is spreading it on cropland, a very low-costprocedure. So despite the advantages of AD, its high initial capital investment is a majorbarrier in its development. Research at Telemark University College has shown that high

rate, granular sludge reactors are a cost effective solution. However, they requireimprovements to minimize size and energy use, while maximizing the feed rate andmethane production. Computer modeling of the AD process plays a central role inimproving design and operation.

In the past few decades several dynamic models of varying complexity have beendeveloped for anaerobic digestion. The International Water Association (IWA) task forcedeveloped Anaerobic Digestion Model No. 1 (ADM1) to be applicable to a wide range ofAD processes. Batstone et al. (2002) suggest some modified parameters for digestingmanure at 55 C. Page et al. (2008) used bench-scale digesters at 35 C and two full-scale

plug-flow digesters to develop an ADM1 parameter set for dairy manure. This failed topredict biogas production, biomass, inorganic nitrogen, and volatile fatty acids (VFA)

accurately. Zhang et al. (2009) also point out that the method of trial and error is verycase specific and thereby limits its adoption to other reactors. Optimization algorithmswere used by Zhang et al. (2009) to determine parameters for a 1700 L pilot reactoroperated at 37 C.

The goal of the work presented here is to adapt ADM1 to the AD reactor at Foss farm.After introducing the pilot plant, some theory of the model is presented, followed by adescription about how it was developed and adapted to the Foss reactor. Finally, theresults are discussed with some suggestions for further work.


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2. Problem descriptionThe goal of the project is to model the pilot plant at Foss farm in Skien using ADM1.Operation of the reactor began outdoors in 2008, and in 2010 it was moved inside andsubjected to continuous feeding. A current process diagram of the suspended growthanaerobic digester is presented inFigure 2.1.

The ADM1 model, previously implemented in AQUASIM, is to be adapted to theanaerobic digester at Foss farm. Data from online sensor and regular lab analysis shall beused to evaluate and improve the model.

Figure 2.1: Foss farm anaerobic digester process diagram.


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3. TheoryAnaerobic Digestion Model No 1, commonly referred to as ADM1, is a generic model forsimulating anaerobic digestion of various substrates. Developed by the InternationalWater Association (IWA) Anaerobic Digestion Modelling Task Group, ADM1 has 32dynamic state variables, considers both biochemical and physicochemical processes, andcontains several inhibition factors (Batstone et al., 2002). The model can be adapted todifferent applications by adjusting the many parameters, including reaction kinetics andsubstrate composition.

AQUASIM is a modeling and simulation program developed for water treatment in avariety of reactor types. Users define the parameters of the model and the processes(reactions) that occur in the compartment(s) that represents the physical design.

Implementing the ADM1 model in AQUASIM allows for comprehensive analysis andsimulation of waste treatment. Users are able to adapt the model to meet the conditionsof their reactor, simulate its operation, and graphically compare the results to empiricaldata.


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4. Methods

4.1 Foss farm pilot plantA fraction of the manure from the dairy cows on the farm is collected into batches ofroughly 2500 L, which becomes the reactor feed for a few months (depending on feedrate). This manure is diluted about 25% with water as a result of the collection andcleaning process typical on these farms. The feed flow and reactor temperature can becontrolled, while three temperatures (reactor, room, and reservoir), biogas flow, methaneconcentration, and effluent flow are measured with electronic sensors.

4.2 Data collection

Since July 11, 2011 samples have been taken from the influent and effluent three timesper week (typically) for lab analysis. This includes total chemical oxygen demand(tCOD), soluble chemical oxygen demand (sCOD), total solids (TS), volatile solids (VS),total suspended solids (TSS), volatile suspended solids (VSS), inorganic nitrogen (IN),volatile fatty acids (VFA), pH, and alkalinity.

The process is controlled through the LabVIEW program on the local computer whichalso logs sensor data at thirty second intervals. An online connection allows remotemonitoring and control of the reactor. This instrumentation has been in place sinceAugust 13, 2011

4.3 ADM1The model adapted in this project is based on an AQUASIM file that contained theADM1 model with parameters adjusted for an upflow anaerobic sludge blanket (UASB)reactor. The process used for developing the parameters is presented inFigure 4.1. Itrequired many iterations of trial and error, especially to determine the substratecomposition. The ADM1 model divides the influent into several specific constituents,which is rarely know in such detail. Manure composition, as defined by Lbken (2007),Rico (2007), and Zeeman and Gerbens (2002) was used to help describe the manure.

It was decided that the best solution was to have each component defined as a formulavariable equal to a fraction multiplied by the related type of COD (soluble or particulate).For example, for amino acids:

(4-1)where input_sCOD_inf is a list variable of the measured influent sCOD. The influentCOD was observed to vary by more than 50%, so an average value did not give accurateenough simulations. Using the measured influent COD as a dynamic input to the modelallowed for the impacts of other parameters to be observed and corrected more easily.The default definitions of sCOD and pCOD in the AQUASIM model included only thesubstrate, or degradable elements. Gosset and Belser (1982) show that the influent CODcan be largely non-biodegradable in anaerobic conditions. Consequently, the definitionwas modified to include inerts so it would be equivalent to the experimental data thatmeasures the entire COD.


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Steady state analysis was used to determine preliminary values for parameters includingcomponent fractions. The conditions were 25 L/d feed flow, reactor temperature of 24 C

and average COD values. Matching simulated effluent COD values, and, consequently,biogas production to empirical data was dependent mainly on the amount of inerts. Mostof the degradable COD is in the reactor long enough to be fully digested to CH4and CO2.Initial biomass concentrations were also determined under these steady state conditions.

Logged sensor data of effluent flow, biogas flow, methane concentration, and reactortemperature were resampled by decimation using the idresamp function in MATLAB.Setting the new time interval to one hour reduced the number of data points to a fewthousand. The code can be found inAppendix B:Resampling code,which also creates a .txt file. AQUASIM is able to import this data asa list variable, which can be used as an input or simply plotted to compare withsimulation results (e.g. methane concentration). The measured effluent flow is assumedto be equal to the feed flow. Therefore this data was decimated and used as the feed flowinput for the simulation.

Some parameters, like reaction rates, have values as suggested by Page et al. (2008) andZhang et al. (2009). The full list of relevant parameters can be found inAppendix A:Selected ADM1 parameters.

The time period used for adapting the ADM1 model was August 14 to November 21,2011. In this span of one hundred days, new batches of manure were put into use on days30 and 87. Feed flow and reactor temperature were manipulated during the study so thereactors dynamic response could be observed. Simulations in AQUASIM were donewith a step size of 0.005 d.


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No

No

START

Define physical model (sizes, etc.)

Define steady-state conditions(temp., etc.)

Define substrate

Do simulated biogasand COD match

measured values?

Define initial biomassconcentrations

Is biomass steadyfrom start?

Update model

Run dynamic simulation

Do simulated biogasand COD match

measured values?

Do simulated VFAvalues match

measure values?

STOP

Figure 4.1: ADM1 AQUASIM model development process

No

No

Updatesubstratedefinition

Adjustmethanogenreactionrates


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5. Results

5.1 Reactor operationOperational changes in feed flow and temperature during the study are presented inFigure 5.1 andFigure 5.2. The brief drops in feed flow are the result of clogged inlet

pipes. The reactor temperature was increased from 24 C to 30 C on day 60, and to35 C on day 67.

Figure 5.1: Feed flow used in the simulation

Figure 5.2: Reactor temperature used in the simulation


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All of the plots presented below (until section 5.7) are from the same simulation withthese operating conditions.

5.2 Manure compositionThe influent to the reactor is defined in the model as presented inTable 5.1.

Table 5.1: Definition of the manure (feed) used in ADM1.

Symbol Fraction Average Units

Soluble

Amino acids input_S_aa_in 0.31 4.23 gCOD/LFatty acids input_S_fa_in 0.21 2.87 gCOD/L

Sugars input_S_su_in 0.15 2.05 gCOD/L

Degradable sCOD 0.67 9.15 gCOD/L

Inerts input_S_I_in 0.33 4.51 gCOD/L

Total sCOD 1.00 13.66 gCOD/L

Inorganic carbon input_S_IC_in - 0.005 M

Inorganic nitrogen3 input_S_IN_in - 0.065 M

ParticulateCarbohydrates input_X_ch_in 0.01 0.32 gCOD/L

Complex comp. particulates input_X_c_in 0.01 0.32 gCOD/L

lipids input_X_li_in 0.02 0.65 gCOD/L

proteins input_X_pr_in 0.03 0.97 gCOD/L

Degradable pCOD 0.07 2.26 gCOD/L

Inerts input_X_I_in 0.93 30.09 gCOD/L

Total pCOD 1.00 32.35 gCOD/L

Total Degradable COD 0.25 11.42 gCOD/L

Total COD 1.00 46.01 gCOD/L

1. Based on influent from 14.08.2011 through 18.11.2011.

2. Default value from UASB file.

3. Calculated average from lab measurements.


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The substrate was defined in accordance with the screened values of dairy manuredetermined by Rico et al. (2007). Figure 5.3 shows that the unscreened values of Rico et

al. are consistent with others studies that tested only raw manure. The 1.5 mm meshsieve used by Rico et al. is comparable with the 1.4 mm mesh separator used at the farm.The ratios between soluble substrate components (amino acids, fatty acids, and sugars),and particulate substrate components (proteins, lipids, and carbohydrates) are roughly thesame.

Figure 5.3: Model substrate breakdown compared to Lbken et al. (2007), Zeeman

and Gerbens (2002), and Rico (2007).

5.3 Biogas flow and compositionSimulated and measured biogas flow during 100 days of reactor operation is presented inFigure 5.4. Even though the simulations steady-state gas production levels are not exact,the model correctly predicts production changes resulting from step changes in operatingconditions. Feed flow reductions on days 6 and 44 caused decreased biogas productionthat the model accurately predicts. However, when the feed doubles to 50 L/d at day 77,the simulated biogas production is about 25% lower than observed. The sharp spikes in

production at days 60 and 67 are the result of temperature increases. The simulationpredicts the initial rise but does not settle at a higher production rate like themeasurements suggest. Simulated and measured methane content of the biogas can beseen inFigure 5.5. The simulated values are only slightly lower than observed and theeffects of temperature increases on days 60 and 67 are accurately predicted by the model.

0 %

10 %

20 %

30 %

40 %

50 %60 %

70 %

80 %

90 %

100 %

Lbken et al. Zeeman and

Gerbens

Rico et al. -

Raw

Rico et al. -

Screened

Model

Substrate

Carbohydrates

Proteins

Lipids


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Figure 5.4: Biogas flow in the reactor

Figure 5.5: Biogas composition

5.4 CODInFigure 5.6 the simulated COD concentrations are shown for the reactor and effluent.The high pCOD concentration in the reactor is expected because of the long solidsretention time. The high feed flow from day 77 to 98 causes an accumulation of pCOD inthe reactor. In contrast, the sCOD concentration in the reactor remains relatively constantand is the same as the effluent concentration. The sCOD concentration of the influent is

plotted with simulated and measured values of the effluent inFigure 5.7. The same plotfor pCOD concentrations is presented inFigure 5.8. It can be seen that it is mainly sCODthat is converted to biogas in the anaerobic digester. The average sCOD reduction is62%, while pCOD changes by just 3%.1 The simulated effluent pCOD exhibits large step

1


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changes, butFigure 5.6 shows that this is just an outlet phenomenon and the effect on theconditions in the reactor are negligible.

Figure 5.6: Simulated COD values in the reactor and effluent

Figure 5.7: Soluble COD at inlet and outlet


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Figure 5.8: Particulate COD at inlet and outlet

5.5 VFAVolatile fatty acids are an intermediate state of anaerobic digestion. Their relativeconcentrations can indicate if processes are unbalanced. Simulated and measured totalVFA concentrations of the effluent are presented in Figure 5.9. The measured valuesshow much larger variations than the simulation. Figure 5.10 shows that acetic and

propionic acids are the main components in both the simulation and empirical data. Theshape of the simulated acetic acid concentration is very similar to the simulated biogas

flow, which is logical because it is a precursor to methane. The high simulated aceticacid concentration in the effluent explains the lower than expected biogas production inthe last 23 days. Acetate is being produced by acidogenesis faster than aceticlasticmethanogenesis can convert it to methane.

Figure 5.9: Total effluent VFA concentration


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Figure 5.10: Comparison of different VFA concentrations in the effluent

5.6 BiomassSimulated concentrations of acidogens and methanogens in the reactor are presented inFigure 5.11. As expected, the increase in feed flow at day 77 brings more substrate,which increases the number acidogenic organisms. The relatively slow response ofmethanogenic organism growth seen in Figure 5.11 can explain the increase in VFAconcentrations at the same time.


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methanogenesis, H2-buty/val: hydrogen inhibition of butyrate and valerate,H2-propionate: hydrogen inhibition of propionate.

Simulated and observed pH levels in the reactor are presented inFigure 5.13. It isassumed that pH measurements of the effluent are representative of the reactor contents.The simulated pH in the reactor is consistently lower than the measured values.

Figure 5.13: pH in the reactor

A simulation was conducted to observe the possible impact of this difference byforcing the pH in the model to the observed levels. The AQUASIM model wasmodified by changing the definition of hydrogen ion concentration (the basis of pH) froma state variable to a formula variable referencing the measured pH values as shown inequation (5-1).

(5-1)

Figure 5.14 shows increased ammonia inhibition and that its shape is opposite to that ofmeasured pH. This effect is consistent with equation (5-2), the dissociation whenammonia is dissolved in water. As pH increases the reaction shifts to the left, increasingammonia levels and thus inhibition. The influence of this ammonia inhibition on biogas

production can be seen in Figure 5.15. The simulated gas composition also changed,showing 10-15% more methane (figure not shown).

(5-2)


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Figure 5.14: Simulated inhibition factors when pH in reactor set to measured values.pH H2: pH inhibition of hydrogen degrading organisms, NH3-acet. meth.: NH3inhibition of aceticlastic methanogenesis, H2-buty/val: hydrogen inhibition ofbutyrate and valerate, H2 propionate: hydrogen inhibition of propionate.

Figure 5.15: Biogas flow in the reactor using measured pH values in the model

5.8 Temperature effectA simulation was conducted to isolate the effect of temperature increases and the biogas

production is presented inFigure 5.16. The model used for the simulation above wasmodified to have constant, average sCOD and pCOD influent, as well as a flow rate of 25L/d. Temperature started at 24 C and increased to 30 C at day 10, and to 35 C at


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day 60. Biogas production went from 155 L/d to 161 L/d to 166 L/d. These are clearlyless dramatic than the observed increases.

Figure 5.16: Temperature effect on biogas production (24 C, 30 C, 35 C).

5.9 Feed flow and washoutEven though the granular sludge in the reactor is dense enough to remain in the reactorunder high loading conditions, they are still susceptible to washout. A simulation wasdone in an attempt to find the maximum possible loading of the pilot plant. When thefeed flow was increased step-wise to an extremely high level (10 000 L/d) the biogas

production increased congruently without dropping off. This signals that the model isunable to predict washout and therefore cannot be used to determine maximum loadingconditions.

In the model, solids are defined to remain in the reactor longer than the fluid, given byequation (5-3). The constant variable tres_x represents the number of days difference

between solids retention time (SRT) and hydraulic retention time (HRT). To model thegranular sludge in the AD, tres_x was set to 100 days. This is the reason the model isunable to predict washout even when HRT was extremely low.

(5-3)Another simulation was done setting tres_x to 0.1 (the equivalent of 2.4 hours) andincreasing feed flow. In this scenario biogas production eventually crashed, indicatingthat washout conditions had occurred and were possible to predict with a differentdefinition of tres_x.


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6. DiscussionDespite the thorough method used to define the influent, it is still an estimated average.The actual feed composition will vary between batches and constantly throughout theday.

The simulated biogas production fits observed data best between days 15 and 60. This isexpected because operating conditions during that time are similar to the steady-stateconditions when many parameters were determined. Feed flow and temperature increasesafter day 60 cause the simulation to deviate from measured values. This is most likely

because the models reaction rates (uptake, growth, etc.) are defined independent oftemperature. In the description of ADM1, Batstone et al. (2002) state that reaction ratesincrease with increasing temperature as predicted by the Arrhenius equation.Furthermore, van Lier et al. (1997) suggest that mesophilic methanogens grow twice as

fast at 35 C than at 24 C. Figure 5.11 shows the slow growth of methanogens that limitsbiogas production. This growth (and that of other microorganisms) accounts for theconsistent, low effluent sCOD even though the biogas production has not increased

proportional to the feed rate.

The two spikes in biogas production at days 60 and 67 are because of the temperatureincrease. The solubility of methane and carbon dioxide in water decreases withincreasing temperature. As the temperature rises, gases dissolved in the reactor fluid arereleased to the headspace and increase the measure biogas flow. The drop in methaneconcentration and increase in carbon dioxide concentration is because the solubility ofCO2drops more (percentage-wise) than methane, therefore the relative amount of CO 2released is greater.

The large step changes in simulated effluent pCOD are not supported by the lab analysis.They are, however, inversely proportional to the feed flow. When the feed rate dropsfrom 30 to 15 L/d on day 6, HRT doubles while SRT increases by only 7%. In otherwords, solids such as pCOD are leaving the reactor at basically the same rate, while theamount of liquid leaving is half. For this reason the simulation shows that theconcentration of pCOD in the effluent is nearly double. In reality, the retention time of

pCOD is closer to HRT. In an earlier version of the model, simulated VFAconcentrations were an order of magnitude below the measured values. Decreasing theyield of degradation (Y_ac) and increasing the half saturation constant of acetateutilization (Ks_ac) increased simulated concentrations of acetic and propionic acid.These changes reduce the rate and extent to which methanogens grow. As a result it also

lowered simulated methanogen concentration, and is part of the reason the modelunderestimated biogas production after day 60.

The fluctuating measurements of effluent VFA may not be correct. There are knowninstances of overdue liner (filter) changes on the gas chromatograph that may have causedinaccurate measurements.

The ammonia inhibition of aceticlastic methanogenesis in Figure 5.12 changes withtemperature. This is because the temperature dependence of the ammonium ion acidconstant is included in the model. This inhibition contributes after day 60 to simulated

biogas production that is lower than observed.


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The reason for the discrepancy between simulated and observed reactor pH values is notknown, nor are its consequences fully understood. One explanation may be that the lab

analysis is done on samples stored at 4 C, and the pH of most solutions increase astemperature decreases.

The models inability to predict washout is because of the definition of SRT. Asdescribed above, SRT will be at least 100 days with the current model definition.


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7. ConclusionThe ADM1 model developed in this project simulates the anaerobic digestion process atFoss farm quite well. Correct description of the influent is necessary for the model to

predict reactor operation accurately. This is especially important when the model is to beused for process control and optimization. The model is able to accurately predictmethane, effluent COD and VFA concentrations in steady-state and dynamic operatingconditions. Simulations at reactor temperatures greater than 24 C underestimate biogas

production because the models reaction rates are not functions of temperature.Redefining rates to be temperature dependent will allow the model to better predict the

process over a wider range of operating conditions.

Additional research could be done to better understand pH levels in the model and theirrelation to measured values. Another area of work could be to redefine SRT so the model

can simulate washout. For example, SRT could be defined as a constant unless (using theif statement) the vertical velocity was greater than a recommended maximum value,where the velocity can be calculated from feed flow and reactor geometry.

In conclusion, ADM1 is a valuable tool for the design and operation of anaerobicdigesters with dairy manure substrate. ADM1 simulations provide information on manystates in the reactor that can help identify limiting factors in the anaerobic digestion

process. This can be used to improve operating conditions or assist in reactor design. Atthe same time, the sheer number of inputs and parameters that must be specified make ittime-consuming and cumbersome to use.


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8. References

BATSTONE, D. J., KELLER, J., ANGELIDAKI, I., KALYUZHNYI, S. V.,PAVLOSTATHIS, S. G., ROZZI, A., SANDERS, W. T., SIEGRIST, H. & VAVILIN, V.A. 2002. The IWA Anaerobic Digestion Model No 1 (ADM1). Water Sci Technol, 45,65-73.

CANTRELL, K. B., DUCEY, T., RO, K. S. & HUNT, P. G. 2008. Livestock waste-to-bioenergy generation opportunities. Bioresource Technology, 99, 7941-7953.

GOSSETT, J. M. B., R. L. 1982. Anaerobic Digestion of Waste Activated Sludge.

Journal of the Environmental Engineering Division ASCE, 108, 1101-1120.LUBKEN, M., WICHERN, M., SCHLATTMANN, M., GRONAUER, A. & HORN, H.2007. Modelling the energy balance of an anaerobic digester fed with cattle manure andrenewable energy crops. Water Res, 41, 4085-96.

PAGE, D. I., HICKEY, K. L., NARULA, R., MAIN, A. L. & GRIMBERG, S. J. 2008.Modeling anaerobic digestion of dairy manure using Anaerobic Digestion Model No. 1(ADM1). Water Science and Technology, IWA, 58, 689-695.

RICO, J. L., GARCIA, H., RICO, C. & TEJERO, I. 2007. Characterisation of solid andliquid fractions of dairy manure with regard to their component distribution and methane

production. Bioresour Technol, 98, 971-9.

TCHOBANOGLOUS, G., BURTON, F. L. & STENSEL, H. D. 2003. Wastewaterengineering : treatment and reuse, Boston, McGraw-Hill.

VAN LIER, J. B., REBAC, S. & LETTINGA, G. 1997. High-rate anaerobic wastewatertreatment under psychrophilic and thermophilic conditions. Water Science andTechnology, 35, 199-206.

ZEEMAN, G. & GERBENS, S. 2002. CH4 emissions from animal manure. IPCC ExpertMeetings on Good Practice Guidance and Uncertainty Management in NationalGreenhouse Gas Inventories. IPCC.

ZHANG, B., FOWLER, K. R., GRACE, M. D., MONDAL, S. & GRIMBERG, S. J.2009. Optimization of Anaerobic Digestion Model No. 1 (ADM1): Simulation of DairyManure Digestion. 2009 ASABE Annual International Meeting. Reno, Nevada: AmericanSociety of Agricultural and Biological Engineers.


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Appendix A:

Selected ADM1 parameters

Parameter Symbol Value Unit Ref.

Disintegration constant kdis 0.4 1/d 1

Hydrolysis rate of carbs khyd_ch 0.25 1/d 2

Hydrolysis rate of lipids khyd_li 0.1 1/d 2

Hydrolysis rate of proteins khyd_pr 0.2 1/d 2

Max uptake rate of amino acid degrading

organisms

km_aa 50 gCODS/gCODX 3

Max uptake rate of acetic acid degrading

organisms

km_ac 7.927 gCODS/gCODX 4

Max uptake rate of but. and val. degrading

organisms

km_c4 20 gCODS/gCODX 1

Max uptake rate of LCFA degrading organisms km_fa 6 gCODS/gCODX 3

Max uptake rate of hydrogen degrading organisms km_h2 140 gCODS/gCODX 4

Max uptake rate of propionic acid degrading

organisms

km_pro 13 gCODS/gCODX 1

Max uptake rate of monosaccharide degradingorganisms

km_su 30 gCODS/gCODX 3

Yield of biomass on uptake of amino acids Y_aa 0.08 gCOD/gCOD 3

Yield of biomass on uptake of acetate Y_ac 0.03 gCOD/gCOD 5

Yield of biomass on uptake of valerate or butyrate Y_c4 0.06 gCOD/gCOD 1

Yield of biomass on uptake of long chain fatty acids Y_fa 0.06 gCOD/gCOD 3

Yield of biomass on uptake of elemental hydrogen Y_h2 0.02 gCOD/gCOD 5

Yield of biomass on uptake of propionate Y_pro 0.04 gCOD/gCOD 3

Yield of biomass on uptake of monosaccharides Y_su 0.1 gCOD/gCOD 3

References:

1. Default value from original UASB file

2. Batstone et al. (2002)

3. Page et al. (2008)

4. Zhang et al. (2009)

5. Estimated in this study from experimental results


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Appendix B:

Resampling code

%Resampling Biogas Flow Rate- here to obtain fewer data points(i.e. decimation)%A filter is used by default on the original data%The order of the filter is 8. Probably not necessary to changeorder%but can be done using an additional argument in functionidresamp.%Functions iddata and idresamp belongs to System IdentifactionTooolbox.

%Get info about these functions, incl. additional arguments, withHelp in%Matlab.%Alternatively, the resample function in Signal Processing TB canbe used.%Modified by Benjamin Lyseng 22.10.2011

Tsampling=1/24/60/2; %Sampling time of original data. 30 secexperessed in unit of day.iddata_logdata=iddata(biogasflow_bronkhorst_filt,[],Tsampling);%First input is file to be filteredresamp_factor=120; %120 gives hourly data.iddata_logdata_resamp=idresamp(iddata_logdata,resamp_factor);

%Resampling is executed.logdata_resamp=iddata_logdata_resamp.y; %y is an element in thestruct iddata_logdata_resamp.fori=1:length(logdata_resamp)

t_data(i,1)=Tsampling*resamp_factor*(i-1); %Time indexes thedata in days

t_data(i,2)=logdata_resamp(i);endsave('biogasflow_resamp.txt','t_data','-ascii'); %Saving data totext-file.

**The code for resampling other data (e.g. feed flow) is almost identical and notpresented here.


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HiT skrift / HiT Publication

Benjamin C. Lyseng, Wenche Bergland, Deshai Botheju, Finn Haugen & Rune Bakke:Biogas reactormodelling with ADM1. 29 s. (HiT Publication 11/2012)

Jan Heggenes, Frode Bergan, Espen Lydersen & Jostein Sageie: Rehabilitering av elvehabitat i Vallari,Seljord i Telemark: Forslag til tiltak. 72 s. (HiT-skrift 10/2012)

Jan Heggenes, Jostein Sageie, Evie C. Kvisberglien, Bjrn Loland & Signe Stoll: Biologiskeforutsetninger for produksjon av laks- og sjrret i Farris og Siljanelva opp til Lakssj. 62 s. (HiT-skrift

9/2012)

Jan Heggenes:Forslag til tiltak for bedre elvehabitat i Mykleelva, Siljan i Telemark. 108 s. (HiT-skrift8/2012)

Leif Kastdalen & Arne W. Hjeltnes:Vegetasjonskartlegging med satellittdata. 66 s. (HiT-skrift 7/2012)

Inger Hanssen-Bauer:Klima i det 21. rhundre i sydstlige Norge med fokus p kystomrdene. 46 s. (HiT-skrift 6/2012)

Jan Ove Tangen: Golfens helseregnskap. Skisse til en samfunnskonomisk analyse av golfenshelseeffekter. 50 s. (HiT-skrift 5/2012)

Howard Parker & Frank Rosell: Beaver Management in Norway - A Review of Recent Literature andCurrent Problems. 62 s. (HiT Publication 4/2012)

Jan Heggenes: Konsekvenser av utslipp av kjlevann i dam Dale, Mna elv, Tinn i Telemark. 21 s. (HiT-skrift 3/2012)

Ellinor Young: P sporet av adopsjon. 47 s. (HiT-skrift 2/2012)

Nanna Lkka og Geir Vestheim (red.): KulturRikets Tilstand 2011. 73 s. (HiT-skrift 1/2012)

Inger M. Oellingrath, Martin V. Svendsen, Ingunn Fjrtoft og Ingebjrg Hestetun: Kostholds- ogmltidsmnster, fysisk aktivitet og vektutvikling hos barn i grunnskolen i Telemark. 32 s. HiT-skrift 5/2011.

Jan Heggenes, Frode Bergan og Espen Lydersen:Fiskebiologiske underskelser i forbindelse med pleggom fysiske utbedringer i Vallari, Seljord i Telemark. 43 s. (HiT-skrift 4/2011)

Nils E. Srgaard:Pariteter og stabiliseringspolitikk. 87 s. (HiT-skrift 3/2011)

Jens Wollebk, Knut H. Red og Jan Heggenes: Genetisk struktur hos rret i Mjsa. 48 s. (HiT-skrift2/2011)

Per Mangset og Kjrsti Skjeldal (red.): KulturRikets Tilstand 2010. 83 s. (HiT-skrift 1/2011)

Astrid Gundersen og Ellinor Young:Barnevernsarbeideres erfaringer med mdre som har intellektuelle

funksjonshemminger. 43 s. (HiT-skrift 5/2010)Niklas Kreander, Vivien Beattie & Ken McPhail: Charity Ethical Investment in Norway. 46 s. (HiTPublication 4/2010)

Espen Lydersen, Anne Trasti og Jostein Sageie: Tilfrsler av nringsstoffer, metaller og andre miljgiftertil grenlandsfjordene 2008. 74 s. (HiT-skrift 3/2010)

Per Mangset og Espen S. Matheussen (red.): KulturRikets Tilstand 2009. 93 s. (HiT-skrift 2/2010)

Ragnar Prestholdt: Fotomotivunderskelsen i Vrdal og Tinn 2008. 48 s., 1 cd (HiT-skrift 1/2010)Kirsten Palm og Hein Lindquist:Lring i en flersprklig skole. Tosprklig opplring p barnetrinnet eteksempel p en organiseringsmodell. 60 s. (HiT-skrift 3/2009)


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Jan Heggenes, Jostein Sageie og Jostein Kristiansen: Rehabilitering av elvehabitat i Tokkei, Dalen iTelemark: Tilstand og tiltak. 85 s. (HiT-skrift 2/2009)

Sigrun Hvalvik:Skal vi dele en historie? Personlige erfaringer som inntak til forstelse i eldreomsorgen.20 s. (HiT-skrift 1/2009)

Inger M. Oellingrath, Martin V. Svendsen, Michael Reinboth: Kostholds- og mltidsmnster, fysiskaktivitet og vektutvikling hos barn i grunnskolen i Telemark, del 1, 4. klassetrinn. 26 s. (HiT-skrift 4/2008)

Anne Svnaug Haugan, Niels Kayser Nielsen og Peter Stadius (red.): Musikk og nasjonalisme i Norden.162 s. (HiT-skrift 3/2008)

Niklas Kreander, Vivien Beattie & Ken McPhail: Charity ethical investment: Policy practice anddisclosure. 49 s. (HiT Publication 2/2008)

Ragnar Prestholdt:Fotomotivunderskelsen p Geilo, Hovden og i Rauland 2007. 54 s., 1 cd (HiT-skrift1/2008)

Anne Aasmundsen, Per Isaksen og Ragnar Prestholdt: Reiselivsundersking i Setesdal 2006. 47 s.,vedlegg. (HiT-skrift 1/2007)

Jan Heggenes og Jostein Sageie:Rehabilitering av Mna, Tinn i Telemark: Tilstand og tiltak. 73 s. (HiT-skrift 6/2006)

Nils Per Hovland:Bygg nettverkst p! En studie av entreprenrielle prosesser i Buskerud, Telemark ogVestfold. 45 s. (HiT-skrift 5/2006)

Sigrun Hvalvik og Ellinor Young:Et sted hvor hun kan finne seg til rette og bo. Om ugifte mdre ogfdehjem i Telemark i perioden 1916-1965. 36 s. (HiT-skrift 4/2006)

Halvor Kleppen:Etikette i golf. 71 s. (HiT-skrift 3/2006)

Arne Hjeltnes: Kartlegging av habitater til hjort i deler av 4 kommuner i Telemark. Utprving avobjektbasert klassifikasjon p Landsat 5 satellittdata. 35 s., 1 kart. (HiT-skrift 2/2006)

Arne Hjeltnes:Hyopplselige bilder som grunnlag for overvking av endringer i fjellvegetasjon. Skisse tilnytt registreringssystem. 47 s. (HiT-skrift 1/2006)

Ole Martin Hystad: Tempo og paradoks i mentalitetshistoriske endringar. Undset-Elias-Foucault. 40 s.(HiT-skrift 7/2005)

Ole Martin Hystad:Hjertet i hjernen. Det biologiske grunnlaget for kjenslene. 49 s. (HiT-skrift 6/2005)

Else Marie Halvorsen:Forskning gjennom skapende arbeid? 61 s. (HiT-skrift 5/2005)

Synne Kleiven: Overvking av Prestevju rensepark. Sluttrapport 2002-2004. 15 s., vedlegg. (HiT-skrift4/2005)

Anne Aasmundsen, Per Isaksen og Ragnar Prestholdt: Reiselivsundersking i Setesdal 2004. 48 s. (HiT-skrift 3/2005)

Bjrn Egeland, Norvald Fimreite and Olav Rosef: Liver element profiles of red deer with special referenceto copper, and biological implications. 32 s. (HiT Publication 2/2005)

Arne Lande, Kjell Lande og Torstein Lauvdal(2005): Fiskeundersking i 4 kalka vatn p Grhei, Byglandkommune, Aust-Agder. 22 s. (HiT-skrift 1/2005)

Oddvar Hollup: Educational policies, reforms and the role of teachers unions in Mauritius. 37 s. (HiTPublication 8/2004)

Bjrn Kristoffersen: Introduksjon til databaseprogrammering med Java. 33 s. (HiT-skrift 7/2004)


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Inger M. Oellingrath: Kosthold, kroppslig selvbilde og spiseproblemer blant ungdom i Porsgrunn. 45 s.(HiT-skrift 6/2004)

Svein Roald Moen: Knud Lyne Rahbeks Dansk Lsebog og Exempelsamling til de forandrede lrde SkolersBrug. 491 s. (HiT-skrift 5/2004)

Tangen, Jan Ove, red. Kyststientre perspektiver. 27 s. (HiT-skrift 3/2004)

Jan Ove Tangen: Idrettsanlegg og anleggsbrukere-tause forventninger og taus kunnskap. 59 s. (HiT-skrift2/2004)

Greta Hekneby:Fonologisk bevissthet og lesing. 43 s. (HiT-skrift 1/2004)

Ingunn Fjrtoft og Tone Reiten: Barn og unges relasjoner til natur og friluftsliv. 83 s. (HiT-skrift 10/2003)

Else Marie Halvorsen:Teachers understanding of culture and of transference of culture. 40 s. (HiT-skrift

9/2003)

P.G. Rathnasiri and Magnar Otty: Oxygen transfer and transport resistance across Silicone tubularmembranes. 31 s. (HiT Publication 8/2003)

Else Marie Halvorsen: Den estetiske dimensjonen og kunstfeltet - ulike tilnrminger. 17 s. (HiT-skrift7/2003)

Else Marie Halvorsen:Estetisk erfaring. En fenomenlogisk tilnrming i Roman Ingardens perspektiv. 12 s.(HiT-skrift 6/2003)

Steinar Kjosavik: Fra forming til kunst og hndverk, fagutvikling og skolepolitikk 1974-1997. 48 s. (HiT-skrift 5/2003)

Olav Solberg, Herleik Baklid, Peter Fjgesund, red.: Tekst og tradisjon. M. B. Landstad 1802-2002. 106 s.(HiT-skrift 4/2003)

Ella Melbye: Hovedfagsoppgaver i forming Notodden 1976-1999. Faglig innhold sett i lys av det forme.129 s. 1 CD-rom. (HiT-skrift 3/2003)

Olav Rosef m.fl.: Escherichia coli-bakterien som alle har men som noen blir syke av en oversikt. 22 s.(HiT-skrift 2/2003)

Olav Rosef m.fl.: Forekomsten av E.coliO157 (hamburgerbakterien) hos storfe i Telemark og i kjttdeigfra Trndelag (2003) 25 s. (HiT-skrift 1/2003)

Roy Istad: Oppretting av polygon. 24 s. (HiT-skrift 3/2002)

Ella Melbye, red.: Hovedfagsstudium i forming 25 r. 81 s. (HiT-skrift 2/2002)

Olav Rosef m.fl.: Hjorten (Cervus elaphus atlanticus) i Telemark. 29 s. (HiT-skrift 1/2001)

Else Marie Halvorsen:Kulturforstelse hos lrere i Telemark anno 2000. 51 s. (HiT-skrift 4/2000)

Norvald Fimreite, Bjarne Nenseter and Bjrn Steen: Cadmium concentrations in limed and partlyreacidified lakes in Telemark, Norway. 16 s. (HiT-skrift 3/2000)

Tle Bjrnvold:Minimering av omstillingstider ved produksjon av hvellast. 65 s. (HiT-skrift 2/2000)

Sunil R. de Silva, ed.: International Symposium. Reliable Flow of Particulate Solids III Proceedings. 11- 13.August 1999, Porsgrunn, Norway. Vol. 1-2 (HiT-skrift 1/2000)


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Harald Klempe: Overvking av grunnvannsforurensning fra Revdalen kommunale avfallsfylling, B iTelemark. rsrapport 1998. 22 s. (HiT-notat 3/2001)

Sigrun Hvalvik: Tolking av historisk tekstet hermeneutisk perspektiv. Et vitenskapsteoretisk essay. 28 s.(HiT-notat 2/2001)

Sigrun Hvalvik: Georg Henrik von Wright. Explanation of the human action : an analysis of von Wrightsassumptions form the perspective of theory development in nursing history. 27 s. (HiT-notat 1/2001)

Arne Lande og Ralph Stlberg, red.: Bruken av Hardangervidda ressurser, potensiale, konflikter. B iTelemark 8.-9. april 1999. Seminarrapport. 57 s. (HiT-notat 3/2000)

Nils Per Hovland: Studentar i oppdrag: ein rapport som oppsummerer utfrt arbeid og rynsler fr prosjektetNyskaping som samarbeidsprosess mellom SMB og HiT, 1998-2000. 24 s. (HiT-notat 2/2000)

Jan Heggenes: Underskelser av gyteplasser til rret i Tinnelvas utlp fra Tinnsj (Tinnoset), Notodden iTelemark, 1998. 7 s. (HiT-notat 1/2000)

HiT-skrift og HiT-notat kan bestilles fra Hgskolen i Telemark, kopisenteret i B:e-post: [email protected],tlf. +47 35952834

HiT Publications and HiT Working Papers can be ordered from the Copy Centre,Telemark University College, B Campus:

email:[email protected],tel.: +47 35952834

De fleste HiT-skrift og HiT-notat finnes elektronisk i TEORA -Telemark Open Research Archivehttp://teora.hit.no/

You will find most of the HiT Publications and HiT Working Papers in full-text in TEORA -Telemark Open Research Archivehttp://teora.hit.no/

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biogas reactor modeling with adm1 lyseng et al

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