Although it is a well-researched topic, the complexity, time for process stabilization, and economic factors related to anaerobic digestion call for simulation of the process offline with the help of computer models. Nature-inspired techniques are a recently developed branch of artificial intelligence wherein knowledge is transferred from natural systems to engineered systems. For soft computing applications, nature-inspired techniques have several advantages, including scope for parallel computing, dynamic behavior, and self-organization. This paper presents a comprehensive review of such techniques and their application in anaerobic digestion modeling. We compiled and synthetized the literature on the applications of nature-inspired techniques applied to anaerobic digestion. These techniques provide a balance between diversity and speed of arrival at the optimal solution, which has stimulated their use in anaerobic digestion modeling.

Anaerobic digestion is a versatile method for wastewater treatment as it not only reduces the waste but also leads to production of renewable energy. Modeling of the anaerobic process requires knowledge of biological and physico-chemical conditions, bacterial growth kinetics, substrate utilization, and product synthesis. However, the complexity of the process calls for highly sophisticated models requiring very high level of expertise and knowledge in the subject. This paper presents an approach for modeling of anaerobic digestion process through which the correlation between various process parameters can be studied, knowledge can be extracted, and system behaviour can be predicted. The datasets have been generated using a synthetic Matlab-Simulink-Excel model and process modelling is done using Kohonen Self organizing maps (KSOM). The resulting KSOM provided a visual interpretation of the inter-relationships between parameters (OLR, Sac, pH, Shco3, Q, Sglu_in, Qgas_out, Sglu_out, and Sch4_gas_out) which would help semi-skilled operators for operation and control of such plants. The model accurately predicts the variations in methane and total gas output with respect to changes in input parameters as the correlation is more than 90% for most of the parameters. This methodology offers a platform for scientists and researchers in comprehending the system behaviour under various operating conditions, even with missing data.

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