Development of a Sensitivity Analysis method to highlight key parameters of a dry Anaerobic Digestion reactor model

Alexandre Boutoute (1) , Nathalie Di Miceli Raimondi (1) , Richard Guilet (1) , Michel Cabassud (1) , Corrado Amodeo (2) , Hassen Benbelkacem (2) , Pierre Buffiere (2) , Ruben Teixeira Franco (3) , Stéphane Hattou (3)

1 LGC - Laboratoire de Génie Chimique
2 DEEP - Déchets Eaux Environnement Pollutions
3 Arkolia Energies

Global sensitivity analysis enables to identify model parameters that have the most significant impact on model outputs and therefore require an estimation effort. This work demonstrates the reliability of a global sensitivity analysis methodology based on Definitive Screening Design and multiple linear regression analysis that requires a low number of runs. The method is applied to a simplified anaerobic digestion model. The model is firstly used to simulate a case study of a thermophilic dry anaerobic digestion of a potential agricultural waste. Then, the influence of the kinetic and mass transfer parameters of the model on the biogas flowrate, the percentage of methane in the biogas and the pH are estimated and discussed. The sensitivity analysis reveals that the slow hydrolysis constant and the upper pH inhibition limit of the hydrolytic biomass are decisive in correctly describing the biogas flowrate. The methane percentage in the biogas slightly varies with kinetic parameters, between 53 % and 55 %. Moreover, the mass transfer coefficient has significant impact on the pH through CO2 desorption. The method simplicity and reliability make its application easy to any type of model.

Mots clés: Dry anaerobic digestion; Modelling; Sensitivity analysis; Definitive screening design method

Domaines: Génie des procédés; Génie chimique; Ingénierie de l'environnement

Alexandre Boutoute, Nathalie Di Miceli Raimondi, Richard Guilet, Michel Cabassud, Corrado Amodeo, et al.. Development of a Sensitivity Analysis method to highlight key parameters of a dry Anaerobic Digestion reactor model. Biochemical Engineering Journal, 2021, 173, pp.108085. ⟨10.1016/j.bej.2021.108085⟩. ⟨hal-03293171⟩