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

doi: 10.3390/en16031108
Overview of Numerical Simulation of Solid-State Anaerobic Digestion Considering Hydrodynamic Behaviors, Phenomena of Transfer, Biochemical Kinetics and Statistical Approaches
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.
- Artois University France
- Normandie Université France
- Normandie Université France
- UNIVERSITE DE TECHNOLOGIE DE COMPIEGNE (UTC) France
- University of Technology of Compiègne France
Technology, 660, T, diffusion, modeling, machine learning, biogas modeling CFD diffusion degradation kinetics empirical models machine learning, degradation kinetics, biogas, [SPI.GPROC]Engineering Sciences [physics]/Chemical and Process Engineering, empirical models, CFD
Technology, 660, T, diffusion, modeling, machine learning, biogas modeling CFD diffusion degradation kinetics empirical models machine learning, degradation kinetics, biogas, [SPI.GPROC]Engineering Sciences [physics]/Chemical and Process Engineering, empirical models, CFD
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