Physical-chemical analysis and biochemical methanogenic potential

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2. Methods

2.2. Physical-chemical analysis and biochemical methanogenic potential

Classical parameters including total solids (TS), volatile solids (VS), total Kjeldahl nitrogen (TKN), total ammonia nitrogen (TAN) and total chemical oxygen demand (COD) were measured using standard methods (APHA, AWWA, WEF, 1998).

Total lipid content was determined by soxhlet extraction on substrate dry matter. Each substrate was first dried at 105 °C and ground to powder (<1 mm). Soxhlet extraction was carried out for 5 h using hexan/isopropanol (60/40) solvent. After evaporation of the solvent, the percentage of hexane extractable materials (HEM) was determined by gravimetry.

For the biochemical fractionation of the total COD of each substrate, the following hypotheses were considered:

( )


g g TKN TAN gO g




proteins(% )=100× 6.25 protein/ organicN × − ×1.42 2 / protein /


gO g HEM




lipids(% )=100× 2.86 2 / lipid × /

) (%

) (%

100 )

(%COD proteins COD lipids COD


carbohydra = − −

Equation 5: Equation for calculation of the biochemical fractionation of total COD.

TKN in ; TAN in ; COD in ; HEM in

In addition, volatile fatty acids (VFAs) were analysed on a high performance liquid chromatographer (HPLC, Varian, U3000) equipped with an evaporative light scattering detector. For VFAs, raw samples were first centrifuged and only the supernatant was used for analysis. The biochemical methanogenic potential (BMP) of each substrate was determined using a previously described method (Vedrenne et al., 2008). Samples were incubated at 38

°C for 40 days.

2.3. Batch experiments for “anaerobic respirometry”

2.3.1. Inoculum

The inoculum used for batch experiments 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 a

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organic loading rate (OLR) was 3.9 kgCOD.m-3.d-1 (feed COD concentration of Based on biodegradability, the OLR was 2.0 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 87 L. The reactor was kept at steady state conditions throughout the study and run for at least three times its HRT before the first samples were taken.

2.3.2. Batch reactor device

For the batch experiments performed for characterisation, 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 amount of substrate added was calculated to maintain a substrate to inoculum ratio between 0.4 and 1.0gCODbiodegradable.gCODbiomass-1. This value is in accordance with recommendations in Girault et al. (submitted). The concentration of biomass was calculated as the sum of the biomass in the initial state of experiments for ADM1 (see section 2.4).

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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.

Repeatability was tested beforehand in the eight batch reactors using 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 resulting in a higher deviation for a period of a few hours.

2.4. Modelling of batch experiments

The ADM1 modelling platform (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. 2011 (submitted), except for hydrolysis. Concerning acetotrophic methanogenesis, the INH3×km_ac(kgCOD.kgCOD-1.d-1)/Ks_ac(kgCOD.m-3) parameter was set at 2.51/0.30. For propionate acetogenesis, the km_pro(kgCOD.kgCOD-1.d-1)/Ks_pro(kgCOD.m-3) parameter was set at 7.8/0.077. Other kinetic parameters were default parameters from Batstone et al. (2002).

The initial biomass concentrations for the batch experiments were taken from the results of the steady state ADM1 simulation of each CSTR for which the inoculums were sampled. The inert fraction (Xi) was fitted to each CSTR result to accurately simulate degradation of COD. Biodegradable COD was split into proteins (Xpr), carbohydrates (Xch) and lipids (Xli) according to the biochemical fractionation of the biodegraded COD in each CSTR (application of Equation 5 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 length of 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

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required. The measured MPR value is the sum of the residual degradation of the inoculum and of the degradation of the added substrate. Like in the ADM1, degradation rates are not expressed linearly, instead 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 fitting the initial concentration of composite materials (Xc) contained in the inoculum to the appropriate 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 Figure 57 and Figure 58, 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

Hypotheses are required for the determination of ADM1-consistant COD fractionation for the substrates. The total COD of each substrate was divided into:

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 (Xi).

All other ADM1 COD fractions were set to zero.

Based on these hypotheses, the COD fractionation method is detailed in Figure 56. Firstly, VFA fractions were directly linked with VFA analysis. Secondly, the concentration of the biodegradable fractions for which hydrolysis is rate limiting (Xpr+ Xch+ Xli) and the associated mean hydrolysis rates (khyd) were fitted with an automated tool to accurately simulate the MPR curve (from 0.5 days after the end of the first MPR peak, see Figure 56). In

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this part of the curve, the degradation of COD fractions for which hydrolysis is not rate limiting is finished. Consequently, only the degradation of the biodegradable fractions for which hydrolysis is rate limiting, produces methane. After this step, the concentration of total COD for which hydrolysis is not rate limiting [except VFAs (Saa+Ssu+Sfa)] 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 (Xpr+ Xch+ Xli) was split into Xpr, Xch and Xli based on the biochemical fractionation of the total COD of the substrate. All hydrolysis rates (i.e. for each X fraction) were considered to be equal to the calibrated hydrolysis rate (khyd = khyd_pr = khyd_li = khyd_ch) to ensure identifiability. The COD fraction for which hydrolysis is not rate limiting (Saa+Ssu+Sfa) was split into Saa, Ssu and Sfa based on biochemical fractionation of the total COD of the substrate. Finally, the Xi fraction (inert COD) was determined by the total COD balance and the nitrogen content of inerts (Ni) was adjusted to ensure organic nitrogen balance.

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Figure 56: 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 component ADM1 state variable. khyd_pr = khyd_li = khyd_ch=0.2days-1

The automated optimisation of the input state variables was performed using the Simplex method (Nelder and Mead, 1965) implemented in Scilab®. Optimisation 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.

Time simulation of the end of the


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

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