Abstract
Anaerobic digestion is recognized as being an advantageous waste management technique representing a source of clean and renewable energy. However, biogas production through such practice is complex and it relies on the interaction of several factors including changes in operating and monitoring parameters. Enormous researchers have focused and gave their full attention to mathematical modeling of anaerobic digestion to get good insights about process dynamics, aiming to optimize its efficiency. This paper gives an overview of the different approaches applied to tackle this challenge including mechanistic and data-driven models. This review has led us to conclude that neural networks combined with metaheuristic techniques has the potential to outperform mechanistic and classical machine learning models.
Highlights
Demographic pressure, industrial development, and changes in consumption patterns are leading to an increase in waste volume
anaerobic digestion (AD) is a natural treatment performed by a community of microorganisms in the absence of air, that involves the disintegration of complex organic wastes and generates biogas which is mainly composed of methane (CH4) (60-70%) and carbon dioxide (CO2) (30-40%) [1]
The complex compounds are broken into soluble components, it corresponds to the enzymatic transformation of high molecular weight components into simple molecules, which can be assimilated by the microbial metabolism and used as a source of energy [1, 5]
Summary
Demographic pressure, industrial development, and changes in consumption patterns are leading to an increase in waste volume. One of the powerful techniques of waste recovery is the anaerobic digestion (AD) process known as methanization It is among the economical and effective techniques applied in organic waste treatment and recovery, that is widely used in the agricultural and industrial domains. AD is an advantageous waste management practice since it reduces the polluting organic load in waste and allows the recovery of various types of substrates including sewage sludge, urban, livestock, agricultural, and industrial waste [2]. It represents a source of clean and renewable energy, unlike the conventional energy sources which have negative implications on the environmental balance. The fourth section is dedicated to discussing the advantages and limitations of the presented approaches
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