article · Environment Development and Sustainability
Anaerobic digestion converts waste into biogas, yet the process often faces issues with substrate recalcitrance, low efficiency, high operational costs, energy demands, and long retention times. This review evaluates more than 90 published studies to assess how pre-treatment technologies and mathematical modelling can optimise methane and biogas yields. The findings indicate that mathematical approaches, specifically artificial neural networks, predict and enhance biogas yields with high accuracy and efficiency. Meanwhile, pre-treatment methods offer unique operational mechanisms whose effectiveness depends heavily on feedstock substrate type, composition, geographic location, and specific conversion processes. By comparing these disparate approaches, the synthesis establishes a clearer framework to guide the selection of appropriate optimisation strategies to improve anaerobic digestion performance sustainably.
Biogas production provides renewable energy and manages solid waste, but process inefficiencies frequently limit practical output. By identifying how computational models and physical pre-treatments can be combined, this work helps waste-to-energy operators choose the most suitable techniques to boost methane yields, shorten processing times, and lower energy and operating costs.
The review serves plant engineers, waste-to-energy facility managers, and technology developers looking to enhance anaerobic digestion infrastructure. Artificial neural networks can be deployed as software tools for operational monitoring and process control, while matching pre-treatment protocols to specific substrates can increase methane generation. Because this is a literature review synthesising existing studies, implementing these optimisation strategies represents early-stage to applied engineering requiring site-specific adaptation.
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Abstract Anaerobic digestion for biogas production was first used in 1895 for electricity generation and treating municipal solid waste in 1939. Since then, overcoming substrate recalcitrance and methane production has been one way to assess the quality of biogas production in a sustainable manner. These are achieved through pre-treatment methods and mathematical modeling predictions. However, previous studies have shown that optimisation techniques (pre-treatment and mathematical modeling) improve biogas yield efficiently and effectively. The good news about these techniques is that they address the challenges of low efficiency, cost, energy, and long retention time usually encountered during anaerobic digestion. Therefore, this paper aims to comprehensively review different promising pre-treatment technologies and mathematical models and discuss their latest advanced research and development, thereby highlighting their contribution towards improving the biogas yield. The comparison, application, and significance of findings from both techniques, which are still unclear and lacking in the literature, are also presented. With over 90 articles reviewed from academic databases (Springer, ScienceDirect, SCOPUS, Web of Science, and Google Scholar), it is evident that artificial neural network (ANN) predicts and improves biogas yield efficiently and accurately. On the other hand, all the pre-treatment techniques are unique in their mode of application in enhancing biogas yield. Hence, this depends on the type of substrate used, composition, location, and conversion process. Interestingly, the study reveals research findings from authors concerning the enhancement of biogas yield to arrive at a conclusion of the best optimization technique, thereby making the right selection technique. Graphical Abstract
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DOI: 10.1007/s10668-024-04540-6
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