Batch experiments

2.3.1. Inoculums

Depending on the tests, two different inoculums were used for the batch experiments:

a waste activated sludge acclimated inoculum (WAS-INO) and a pig slurry acclimated temperature was maintained at 36 °C.

PS-INO was sampled from a CSTR fed with a mixture of pig slurry (44% of the total COD) and horse feed (56% of the total COD) mainly consisting of a mixture of wheat gluten feed, oats, straw and sunflower cake. HRT was 27 days and OLR was 3.9 kgCOD.m^-3.d^-1 (feed COD concentration of 106.3gO₂.kg^-1). Based on biodegradability, the OLR was 2.0

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kgCODbiodegradable.m^-3.d^-1 (pH=7.8 ; [N-NH4+

]=3.2gN.L^-1). The temperature of this reactor was 38 °C and its working volume was 87L. Both reactors were kept at steady state conditions throughout the study and were operated for at least for three times their HRT before the first samples were taken.

2.3.2. Batch reactors

For the batch experiments, eight similar reactors with a 1.2 L working volume were used. The contents were continuously mixed by a magnetic stirrer (1200 rpm) and maintained at 38 °C using a specific chamber (Aqualytic, ET637-6, Germany). Each reactor was equipped with a manometer (Vegabar 14, Vega, Germany) and a solenoid valve to allow continuous monitoring of biogas production. Gas production was calculated taking the temperature, the headspace volume of the reactor and pressure into account. Over-pressure was automatically released by opening the solenoid valve when an overpressure of 50 mbar was measured. The released gas was collected in a 1 L Tedlar® Bag (232-01, SKC, USA) which enabled regular determination of CH₄ and CO₂ concentrations in the biogas. CH₄ and CO₂ concentrations were determined using a gas chromatographer equipped with an electron capture detector (Agilent Technologies, 6890N, USA) according to the method described in Lucas et al. (2007).

2.3.3. Experimental procedure

Batch reactors were filled with 1 L of inoculum and incubated to allow the stabilization of biogas and methane production. After one day of incubation (corresponding to time = 0 day in the figures), substrate was added and the methane production rate (MPR) was monitored for 10 days. The mass of substrate added was calculated on the basis of a defined biodegradable substrate to inoculum ratio (gCODbiodegradable.gCODbiomass-1

). The concentration of biomass was calculated as the sum of the biomass in the initial state of experiments for ADM1 (see section 2.4). However, in this study, the total COD of the inoculum cannot be considered as a representative indicator of biomass concentration because it was mainly comprised of inert materials. The concentration of each substrate in biodegradable COD was

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first estimated according to the literature. Then, the real substrate to inoculum ratio was calculated based on the sum of the biodegradable fractions of the substrate (see section 2.5.)

Immediately after setting up the inoculum and the substrate pulse, the headspace of each reactor was purged with a gas mixture of N₂ and CO₂ (70/30). All tests were compared with a control without the substrate pulse.

As the viscosity of the WAS-INO was relatively high which caused problems with stirring, this inoculum was diluted (1/1) with its centrifuge supernatant. Dilution of PS-INO was not necessary. The dilution factor of the WAS-INO was taken into account in the calculation of the amount of substrate added.

Repeatability had been previously tested in parallel in the eight batch reactors with an acetate pulse (results not shown). The relative standard deviation for MPR was below 5%

except in the first and the last hours of the pulse, where a slight time lag was observed.

2.4. Modelling of batch experiments

The ADM1model (Batstone et al., 2002) was used in the simulations to represent the major biochemical and physicochemical processes that occurred during anaerobic digestion.

The ADM1 was implemented in Scilab® and solved with the ordinary differential equation solver “ode” (package ODEPACK, solver Isoda). The stoichiometric parameters from Batstone et al. (2002) were used along with kinetic parameter sets from Girault et al.

(submitted) except for hydrolysis. Parameter values are listed in Table 15.

Parameter Units WAS-INO PS-INO

INH3×km_ac* kgCOD.kgCOD^-1.d^-1 2.73 2.51

Ks_ac kgCOD.m^-3 0.088 0.30

k_{m_pro} kgCOD.kgCOD^-1.d^-1 13 7.8

K_{s_pro} kgCOD.m^-3 0.3 0.077

Table 15: Kinetic parameters for biomass growth from Girault et al. (submitted) used to simulate batch experiments.

Other parameters used were default parameter from Batstone et al. (2002). *the inhibition factor by free ammonia was considered to be constant in all simulations with each inoculum.

Due to the low substrate to inoculum ratios, the substrate added had no significant impact on the concentration of free ammonia.

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Initial biomass concentrations for batch experiments were taken from the results of the steady state ADM1 simulation of each CSTR from which the inoculums were sampled. The inert fraction (X_i) was estimated on each CSTR result to accurately simulate degradation of COD. Biodegradable COD was split into proteins (X_pr), carbohydrates (X_ch) and lipids (X_li) according to the biochemical fractionation of the biodegraded COD into each CSTR (application of Equation 4 on the influent and the effluent of the CSTR). All other input state variables for the influent were considered as equal to zero. Next, the CSTR was simulated for four times the HRT to provide steady state data. Simulated concentrations of each specific biomass were used as initial conditions for the inoculum used in the batch experiments.

Before modelling the batch experiments with added substrate, an accurate simulation of the MPR observed in the control tests (test performed without added substrate) was required.. Indeed, the measured MPR value is the sum of the residual degradation of the inoculum and of the degradation of the added substrate. As in the ADM1, degradation rates are not expressed linearly, accurate simulation of the excess MPR resulting from substrate degradation requires accurate simulation of the MPR related to the residual degradation of the inoculum. The MPR measured in the control batch experiments was thus simulated by optimizing the initial concentration of composite materials (Xc) in the inoculum on the related disintegration constant (kdis). The Xc and kdis values obtained were incorporated in the initial state of the inoculum to model the batch experiments with added substrate. In the following figures, the MPR of the control test has been subtracted from the monitored MPR to remove residual MPR due to residual inoculum degradation.

The initial state for the batch experiments with added substrate was then calculated based on the initial state of the inoculum and the ADM1-consistant COD fractionation of the substrate concerned (see section 2.5.).

2.5. Determination of the set of input state variables for the substrates

To determine an ADM1-consistant COD fractionation for the substrates, hypotheses are necessary. The total COD of each substrate was divided into:

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VFA fractions including acetate (Sac), propionate (Spro), butyrate (Sbu) and valerate (Sva) concentrations.

Biodegradable fractions for which hydrolysis is not rate limiting including concentrations of amino acids (Saa), monosaccharides (Ssu) and long chain fatty acids (Sfa).

Biodegradable fractions for which hydrolysis is rate limiting including proteins (Xpr), carbohydrates (Xch) and lipids (Xli).

A non-biodegradable or inert fraction (X_i).

All other ADM1 COD fractions were set to zero.

Based on these hypotheses, the COD fractionation method is detailed in Figure 45. In this part of the curve, the degradation of COD fractions for which hydrolysis is not rate limiting is finished. As a consequence, only the degradation of the biodegradable fractions for which hydrolysis is rate limiting results in the production of methane. After this step, the concentration of total COD for which hydrolysis is not rate limiting [except VFAs (S_aa+S_su+S_fa)] was calibrated with the same automated tool and the previously calculated fractions to obtain the best simulation of the complete MPR curve. Next, the COD fraction for which hydrolysis is rate limiting (X_pr+ X_ch+ X_li) was split into X_pr, X_ch and X_li based on the biochemical fractionation the total COD of the substrate. Hydrolysis rates for each X-fraction was considered to be equal to the calibrated hydrolysis rate (k_hyd = k_{hyd_pr} = k_{hyd_li} = k_{hyd_ch}) to ensure identifiability. The COD fraction for which hydrolysis is not rate limiting (S_aa+S_su+S_fa) was split into S_aa, S_su and S_fa based on biochemical fractionation of the total COD of the substrate. Finally, the X_i fraction (inert COD) was determined by the total COD balance and the nitrogen content of inerts (Ni) was adjusted to ensure total organic nitrogen balance.

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Time simulation of the end of the

curve

Step 4: Determination of the Xi fraction according to the total COD balance (nitrogen content of Xi is adjusted to respect the total organic nitrogen balance).

Other organic input state variables are considered as empty.

MPR curve obtained for the batch degradation of a standard substrate in « anaerobic respirometry »*

Methane production related to

Figure 45: Framework for the numerical determination of the set of ADM1 input state variables for each substrate studied

*: The MPR curve presented in this figure was obtained by simulating batch degradation tests of a theoretical substrate with the following components: Xpr+Xch+Xli = 50% of the total

COD; Saa+Ssu+Sfa=40% of the total COD; Sac+Spro+Sbu+Sva=10%DCO. To achieve complete COD fractionation according to ADM1, each of these pools of COD were split equally into each constitutive ADM1 state variable. khyd_pr = khyd_li = khyd_ch=0.2days-1

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The automated optimisation of the input state variables was performed using the Simplex method (Nelder and Mead, 1965) implemented in Scilab®. Optimization is performed in order to minimise an objective function J which represents the sum of the squared errors between the experimental and the simulated MPR curve.

Parameter uncertainty and correlations were assessed like in Batstone et al. (2003) using a confidence interval of 95%, and applying an F distribution to the parameter space.

Correlations were assessed for the simultaneous determination of k_hyd and X_pr+ X_ch+ X_li values. Uncertainty was assessed for S_aa+S_su+S_fa fractions. The parameter surface or interval is defined by a critical objective function (Jcrit) which is related to the optimal J (Jmin) as found by the simplex optimisation:







 ×

+ −

= pN ₋p

data

crit F _data

p N

J p

J _min 1 _{0.05, ,}

where p is the number of calibrated parameters, N_data is the number of data points and

p N p _data

F_{0.05, , −} is the F distribution value for a confidence of 95%, p parameters, and Ndata-p degrees of freedom.

2.6. Modelling of anaerobic digestion of the substrates in a CSTR

To investigate if discrepancies observed in the ADM1-consistant substrate fractionation were significant, the simulated effect of hydraulic retention time (HRT) on the performance (in terms of methane production) of a numerical CSTR fed with this substrate was determined. ADM1 was used in a CSTR configuration. Stoichiometric and kinetic parameters were the default parameters described in Batstone et al. (2002) for mesophilic high-rate systems completed with calibrated parameters from Girault et al. (submitted) listed in Table 15 except for hydrolysis, for which the calibrated value was used. Each modelling result was preceded by steady state modelling for four times the HRT concerned. Hence, modelling results are steady state data.

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3. Results

3.1. Physical-chemical characterisation of the substrates

The results of the physical-chemical analysis of the substrates are listed in Table 16.

Three samples of waste activated sludge (WAS) were analysed. The characteristics of pig slurry and waste activated sludge differed significantly. Soluble COD represented 36% of the total COD in pig slurry whereas it represented only 1% of the total COD in WAS. Moreover, WAS were mainly composed of proteins and the concentration of VFAs was not significant, whereas the total COD of pig slurry was mainly composed of carbohydrates and the COD of total VFAs represented 10% of the total COD. This high concentration of VFAs was due to the origin of the pig slurry which was sampled in a livestock fattening unit.

Characteristics Units Pig slurry (PS) Waste activated sludge

WAS1 WAS2 WAS3

Table 16 : Characteristics and biochemical fractionation of the substrates used.

nd = not detected).

3.2. ADM1 initial state for the inoculums

For each inoculum used, specific biomass concentrations based on continuous steady state modelling of the continuous digesters with ADM1, are given in Figure 46. Both the concentration of total biomass and the proportion of specific biomass in the two CSTRs differed due to the different OLRs and to the biochemical composition of the feed. To normalise, results in Figure 46 are expressed as a % of the total biomass in the inoculum. The proportions of methanogenic and acetogenic biomass were similar in WAS and PS-INO.

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Indeed, as these reactions occurred at the end of the degradation chain, the proportions of biomass did not strongly depend on the biochemical characteristics of the feed. Nevertheless PS-INO is better suited for saccharide acidogenesis than WAS-, and WAS-INO is better suited for LCFA and AA acidogenesis. These differences are directly correlated with the differences in the biochemical composition of the CSTR feeds.

0%

10%

20%

30%

40%

50%

60%

Sugar acidogenesis AA acidogenesis LCFA acetogenesis C4 acetogenesis Propionate acetogenesis Acetotrophic methanogenesis Hydrogenotrophic methanogenesis

Specific biomass according to ADM1 Specific biomass concentration (% of total biomass)

WAS-acclimated inoc. PS-acclimated inoc.

Figure 46 : Specific biomass fractions for WAS and PS acclimated inoculums obtained by steady-state simulation of both continuous reactors using the ADM1 (table at top

right).

3.3. Experimental operating conditions for ADM1-consistant substrate fractionation

In this section, the effect of the two major experimental parameters on fractionation results is investigated. The first parameter is the substrate to inoculum ratio for anaerobic respirometric tests (section 3.3.1.). The second is the origin of the inoculum (section 3.3.2.).

3.3.1. Influence of the substrate to inoculum ratio

The effect of the substrate to inoculum ratio used for anaerobic respirometric tests was investigated. To this end, PS-INO was used as inoculum and the pig slurry (PS) described in

Xpr Xch Xli Xi

% of the totalCOD

WAS acclimated inoc. 11% 10% 8% 71%

PS acclimated inoc. 7% 33% 15% 45%

Influent fractionation for the simulation of both CSTRs.

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3.1. was used as substrate. Three different substrate to inoculum ratios were tested simultaneously. After interpretation of experimental results with the method described in section 2.5., the obtained absolute values of the input state variables for ADM1 were compared based on their identifiability. To investigate if the observed discrepancies were significant when ADM1 sensitivity was taken into account, each fractionation was used to simulate a CSTR and to predict the results of the process. The three tested substrate to inoculum ratios were 0.37, 1.30 and 1.90 gDCObiodegradable.gDCO_biomass^-1 (respectively runs A1, A2 and A3 in Figure 47 and Table 17). Expressed as the concentration of added biodegradable COD, each substrate addition represented respectively 2.3, 8.1 and 11.1gCODbiodegradable.Linoculum-1

. These substrate to incoculum ratios were chosen to cover a wider range of values than those used by Yasui et al. (2008).

MPR curves from anaerobic respirometric tests for each substrate to inoculum ratio are presented in Figure 47.

0 10 20 30 40 50 60 70 80 90 100

0 1 2 3 4 5 6 7 8 9

time (days) MPR (NmlCH4.Linoculum-1 .h-1 )

Experimental MPR for run A1 Simulated MPR for run A1 Experimental MPR for run A2 Simulated MPR for run A2 Experimental MPR for run A3 Simulated MPR for run A3

Figure 47: Experimental MPR curves resulting from "anaerobic respirometric" tests for the three substrate to inoculum ratios tested and the related simulation results with

optimal fractionation (substrate: PS and PS-INO inoculum)

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The fractionation procedure described in Figure 45 was applied using these experimental data. The resulting fractionations are presented in Table 17 and the MPR curves from the model with optimal substrate fractionation in Figure 47.

Run Substrate S_ac S_pro S_bu S_va X_i X_pr X_ch X_li S_aa S_su S_fa k_hyd

Table 17 : Sets of ADM1 input state variables estimated from each « anaerobic respirometric » test.

The shapes of the experimental MPR curves matched the theoretical MPR curve presented in Figure 47. These results are consistent with the hypothesis that the biodegradable COD of PS comprises one fraction for which hydrolysis is rate limiting and one fraction for which hydrolysis is not rate limiting. After applying the fractionation method described in section 2.5., the simulated MPR curves accurately matched the experimental curves.

Nevertheless, some discrepancies need to be discussed. In the simulation results, a very strong MPR peak occurred during the first hour of the test. This was the result of the rapid conversion into methane of hydrogen produced during a non rate-limiting acidogenic stage.

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To support this explanation, Figure 48 shows fluctuations in ADM1 state variables related to the substrate in the simulation of run A2. Very rapid degradation of S_aa, S_su and S_fa occurred leading to production of H₂ which was rapidly converted into methane via hydrogenotrophic methanogenesis. This very rapid phenomenon concerned an insignificant volume of methane (4% of the methane production due to degradation of the substrate). The same phenomenon was observed by Zaher et al. (2009) in batch experiments using dairy manure. As it represented such limited methane production, it was not taken into account for data interpretation. Slight discrepancies can be observed between simulated and experimental results during the first MPR peak (which occurred in first day). The non-calibration of acidogenesis could explain these slight inconsistencies. As illustrated in Figure 48 for run A2, in the simulation, acidogenesis was very rapid, and acetate and to a lesser extent, propionate accumulation also occurred during the first MPR peak. On the other hand, for each run, the ends of the MPR curves very accurately matched the experimental curves. This means the hydrolysis of the COD fractions for which hydrolysis is rate limiting, was very accurately simulated. As demonstrated in Figure 48 for run A2, no VFAs accumulated during this part of the MPR curve.

Figure 48 : MPR and variations in X and S concentrations in run A2 with optimal PS fractionation (substrate: PS and PS-INO inoculum).

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Concerning the effect of the substrate to inoculum ratio on the optimal PS fractionation obtained, as indicated in Table 17, COD fractions were similar in each substrate to inoculum ratio tested. A relative standard deviation of 3% was obtained for the determination of the X_pr+ X_ch+ X_li fractions, of 16% for the related k_hyd value, and of 13% for the S_aa+S_su+S_fa fractions. In addition, a relative standard deviation of 9% was obtained for the inert fraction X_i. Some slight discrepancies need to be discussed. The optimal k_hyd and S_aa+S_su+S_fa values increased slightly with a decrease in the substrate to inoculum ratio. This needs to be interpreted given the uncertainty related to the identifiability of each fraction. Using the method described in section 2.5., parameter uncertainty and correlations were assessed with a confidence interval of 95%. Results are presented in Figure49.

0.2

Figure 49: Confidence regions for Xpr+ Xch+ Xli and k_hyd (A) with the related confidence interval for Saa+Ssu+Sfa (B) obtained for the fractionation of pig slurry (PS)

by “anaerobic respirometric” tests with the three substrate to inoculum ratios tested.

Except for the lowest substrate to inoculum ratio (run A1), the identifiability of each fraction was satisfactory indicating low numerical uncertainty for the fitted parameters. In addition, a low correlation between X_pr+ X_ch+ X_li and the related k_hyd was observed. Due to uncertainty, especially for run A1, fractionation results can be considered as quite close. To investigate if these observed discrepancies should be considered significant, results obtained

20

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for the simulation of a mesophilic CSTR fed with PS were compared for each PS fractionation. For each PS fractionation, the effect of the HRT of the CSTR fed with PS on methane production was investigated and results are shown in Figure 50. The results obtained in terms of absolute methane production are quite close. Indeed, the maximum relative standard deviation observed between the three predictive curves was 8.6%. These variations are directly correlated with differences in the inert fraction (Xi) and can be mainly attributed to uncertainty in the fractionation results. However, the shapes of the curves, which were impacted by the fractionation of the biodegradable DCO, are very close. As a result, discrepancies in the fractionation of the biodegradable COD of PS related to the substrate to

for the simulation of a mesophilic CSTR fed with PS were compared for each PS fractionation. For each PS fractionation, the effect of the HRT of the CSTR fed with PS on methane production was investigated and results are shown in Figure 50. The results obtained in terms of absolute methane production are quite close. Indeed, the maximum relative standard deviation observed between the three predictive curves was 8.6%. These variations are directly correlated with differences in the inert fraction (Xi) and can be mainly attributed to uncertainty in the fractionation results. However, the shapes of the curves, which were impacted by the fractionation of the biodegradable DCO, are very close. As a result, discrepancies in the fractionation of the biodegradable COD of PS related to the substrate to

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