Improvement of biogas yields in an anaerobic digestion process via optimization technique

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