Comprehensive determination of input state variables dataset required for anaerobic digestion modelling (ADM1) based on characterisation of organic substrates

Fisgativa, Henry; Zennaro, Bastien; Charnier, Cyrille; Richard, Charlotte; Accarion, Guillaume; Béline, Fabrice

doi:10.1016/j.dib.2020.105212

Cited by 9 publications

(6 citation statements)

References 4 publications

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“…As for the sludge, based on the results from the sensitivity analysis and from measurements, and given the predominance of the particulate matter, the determination of soluble components other than VFAs can be avoided. In this case, the biodegradability of the particulate and soluble COD fractions can be assumed to be equal to the overall degradability and the remaining biodegradable soluble COD can be divided into three equal parts (S su , S aa , S li ) [13,19,24] or using the same proportion found in the particulate matter, as suggested in a previous work [7].…”

Section: Discussionmentioning

confidence: 99%

“…AcoD modelling plays a crucial role in the selection of the effective mixing of two or more waste streams in a digester [5]. Indeed, a modelling approach allows the assessment of optimal operational conditions (co-substrate selection, feeding composition, applied loads) of the AcoD process while minimizing the time and costs associated with laboratory experiments [6,7]. Since the development of the current state-of-the-art model for describing anaerobic digestion processes (Anaerobic Digestion Model No.…”

Section: Introductionmentioning

confidence: 99%

“…Several methods have been proposed to determine ADM1 input state variables with minimal analytical efforts. The most common way is to estimate substrate fractionation from direct analysis of the main biochemical fractions: carbohydrates, proteins, and lipids [7,9,[24][25][26]. An alternative approach relies on the determination of the elemental mass fractions of carbon, hydrogen, oxygen, nitrogen, and phosphorus starting from practical chemical measurements [23,27,28] or directly from the elemental analysis [20]; fractions for proteins, carbohydrates, lipids, and VFAs are then calculated using stoichiometric coefficients and an ideal molecular formula for each component.…”

Section: Introductionmentioning

confidence: 99%

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Optimizing ADM1 Calibration and Input Characterization for Effective Co-Digestion Modelling

Catenacci

Grana

Malpei

et al. 2021

Water

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Anaerobic co-digestion in wastewater treatment plants is looking increasingly like a straightforward solution to many issues arising from the operation of mono-digestion. Process modelling is relevant to predict plant behavior and its sensitivity to operational parameters, and to assess the feasibility of simultaneously feeding a digester with different organic wastes. Still, much work has to be completed to turn anaerobic digestion modelling into a reliable and practical tool. Indeed, the complex biochemical processes described in the ADM1 model require the identification of several parameters and many analytical determinations for substrate characterization. A combined protocol including batch Biochemical Methane Potential tests and analytical determinations is proposed and applied for substrate influent characterization to simulate a pilot-scale anaerobic digester where co-digestion of waste sludge and expired yogurt was operated. An iterative procedure was also developed to improve the fit of batch tests for kinetic parameter identification. The results are encouraging: the iterative procedure significantly reduced the Theil’s Inequality Coefficient (TIC), used to evaluate the goodness of fit of the model for alkalinity, total volatile fatty acids, pH, COD, volatile solids, and ammoniacal nitrogen. Improvements in the TIC values, compared to the first iteration, ranged between 30 and 58%.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimizing ADM1 Calibration and Input Characterization for Effective Co-Digestion Modelling

Catenacci

Grana

Malpei

et al. 2021

Water

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“…Xs which comprises carbohydrates (Xch), proteins (Xpr) and lipids (Xli) fraction and Xi were computed from the experimental data obtained through Weender analysis and Van soest estension described in [27]. Ss such as monosacharride (Ssu), amino acids (Saa), long chain fatty acids (Sfa), total acetate (Sac) was determined by multiplying the total COD of the considered substrates with percentages calculated from the work of [29]. Table 1 gives an overview of the calculated basic data used for substrate characterization.…”

Section: Substrates Characterizationmentioning

confidence: 99%

Modeling Anaerobic Co-digestion of Food Wastes and Cattle Manure in an Industrial Plant: a System Dynamic Approach

Azeez

Gwandangaji

2022

Preprint

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Even though the Anaerobic Digestion Model number 1 (ADM1) is acceptable and widely used model for anaerobic digestion, it is associated with large number of experimental data and large number of model parameter identification thereby making it difficult to be utilized for optimization and control design. In this study, the applicability of Anaerobic Digestion Model number 2 (AM2) model to co-digestion of food wastes and cattle manure in an industrial biogas plant’s digester is explored. The model was modified and reconstructed in system dynamic modeling software (STELLA) for better interpretation and understanding. The predictive ability of the model using the model parameters estimated in Premium Solver was checked by comparing model results (biogas flow and compositions) with ADMI simulated results and the experimental data. In order to gain insight on variability of biogas flow and compositions as a result of change in some important process variables, sensitivity analysis was conducted. The simulation results of the modified AM2 model were found to correspond with the measured value with percentages errors of 1.42% and 0.6% for methane concentration and biogas flow respectively. The process variables associated with methanogenesis stage were obtained to be the most sensitive variables. The use of system dynamic modeling linked to AM2 model which is tractable, reduced and robust is therefore recommended for simulation of full scale biogas plant’s digester.

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“…Missing input state variables required for ADM1 have been found from a dataset containing the characterization and fractionation of organic substrates coming from literature [29]. Some kinetic rate equations have been modified to take into account inhibition linked to substrate concentration.…”

Section: Modelling and Simulationmentioning

confidence: 99%

Co-digestion of Extended Aeration Sewage Sludge With Whey, Grease and Septage: Experimental and Modelling Determination

Merlin¹,

Outin²,

Boileau³

2021

Preprint

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Potential of co-digestion mixing thickened secondary sludge (TS) from extended aeration wastewater treatment plant and locally available substrates (whey, grease and septage) has been studied using three steps. The first step was a batch test to determine biological methane potential (BMP) of different mixtures of the three co-substrates with TS. The second step has been carried out with lab-scale reactors (20 L) simulating anaerobic continuous stirred tank reactors fed by three mixtures of co-substrates determined according to previous step results. Modelling using ADM1 as a mechanistic model was applied in the third step to help understanding the co-digestion process. According to BMP step, septage used as co-substrate has a negative effect on performance and addition of 10 to 30% grease or 10% whey would lead to a higher production of biogas and with an increase of the methane content. The results from the reactor showed less evi-dence of the positive effects observed with the BMP assay. Protein and lipid fractions of particu-late biodegradable COD are important variables for digester stability and methane production as predicted by modelling. Results of simulations with ADM1 model adapted to co-digestion confirmed that this model is a powerful tool to optimize the process of biogas production.

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Comprehensive determination of input state variables dataset required for anaerobic digestion modelling (ADM1) based on characterisation of organic substrates

Cited by 9 publications

References 4 publications

Optimizing ADM1 Calibration and Input Characterization for Effective Co-Digestion Modelling

Optimizing ADM1 Calibration and Input Characterization for Effective Co-Digestion Modelling

Modeling Anaerobic Co-digestion of Food Wastes and Cattle Manure in an Industrial Plant: a System Dynamic Approach

Co-digestion of Extended Aeration Sewage Sludge With Whey, Grease and Septage: Experimental and Modelling Determination

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