Optimal operating parameter determination based on fuzzy logic modeling and marine predators algorithm approaches to improve the methane production via biomass gasification

The current study is related to optimizing methane production from steam gasification of palm kernel shell (PKS) utilizing coal bottom ash as a catalyst. Based on the experimental dataset, the fuzzy logic technique, represented by the Adaptive Network-based Fuzzy Inference System (ANFIS) structure, is used to create a robust model to simulate methane production via biomass gasification. Then, a Marine Predator Algorithm (MPA) is used to determine the optimal operating parameter of the gasification process. The temperature, particle size, CaO/PKS ratio, and coal bottom ash are used as the decision variables, whereas methane production is used as the objective function. The main findings confirmed that the yield of methane gasification reached 52.82 vol% when the associated temperature, particle size, CaO/PKS Ratio, and coal bottom ash are at 678 °C, 0.42 mm, 3.03, and 0.037 wt%, respectively. Accordingly, the proposed strategy provides a better result than the Analysis of Variance (ANOVA) methodology reported in the literature. Furthermore, the proposed strategy's output exceeds both the results obtained experimentally and ANOVA methods by 20.26% and 29.69%, respectively. •An accurate ANFIS model is developed to simulate the bio-methane production.•Three parameters: temperature, particle size, CaO/PKS ratio, and coal bottom ash are considered.•New integration between the ANFIS model and the marine predator optimization algorithm is proposed.•The optimal system parameters for increasing the bio-methane production are determined.•Statistical analysis is presented to prove the superiority of the proposed strategy.