Overview of Numerical Simulation of Solid-State Anaerobic Digestion Considering Hydrodynamic Behaviors, Phenomena of Transfer, Biochemical Kinetics and Statistical Approaches

Xiaojun Liu (), Arnaud Coutu, Stéphane Mottelet, André Pauss and Thierry Ribeiro ()
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Xiaojun Liu: TIMR (Integrated Transformations of Renewable Matter), Université de Technologie de Compiègne, ESCOM, Centre de Recherches Royallieu, 60203 Compiègne, France
Arnaud Coutu: GéoLab, Institut Polytechnique UniLaSalle, Rue Pierre Waguet, 60026 Beauvais, France
Stéphane Mottelet: TIMR (Integrated Transformations of Renewable Matter), Université de Technologie de Compiègne, ESCOM, Centre de Recherches Royallieu, 60203 Compiègne, France
André Pauss: TIMR (Integrated Transformations of Renewable Matter), Université de Technologie de Compiègne, ESCOM, Centre de Recherches Royallieu, 60203 Compiègne, France
Thierry Ribeiro: Institut Polytechnique UniLaSalle, Université d’Artois, ULR 7519, Rue Pierre Waguet, 60026 Beauvais, France

Energies, 2023, vol. 16, issue 3, 1-31

Abstract: Anaerobic digestion (AD) is a promising way to produce renewable energy. The solid-state anaerobic digestion (SSAD) with a dry matter content more than 15% in the reactors is seeing its increasing potential in biogas plant deployment. The relevant processes involve multiple of evolving chemical and physical phenomena that are not crucial to conventional liquid-state anaerobic digestion processes (LSAD). A good simulation of SSAD is of great importance to better control and operate the reactors. The modeling of SSAD reactors could be realized either by theoretical or statistical approaches. Both have been studied to a certain extent but are still not sound. This paper introduces the existing mathematical tools for SSAD simulation using theoretical, empirical and advanced statistical approaches and gives a critical review on each type of model. The issues of parameter identifiability, preference of modeling approaches, multiscale simulations, sensibility analysis, particularity of SSAD operations and global lack of knowledge in SSAD media evolution were discussed. The authors call for a stronger collaboration of multidisciplinary research in order to further developing the numeric simulation tools for SSAD.

Keywords: biogas; modeling; CFD; diffusion; degradation kinetics; empirical models; machine learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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