Optimal operating parameter determination and modeling to enhance methane production from macroalgae

This work aims at proposing a robust strategy to determine the optimal operating parameters based on fuzzy modeling for enhancing the productivity of methane using Pelvetia canaliculata. The applied strategy is a combination of fuzzy logic (FL) modeling and particle swarm optimizer (PSO). First, FL is used to build a model that describes methane production using the experimental datasets. Second, a PSO algorithm is used to obtain the best-operating conditions of the production process. The decision vari-ables used in the optimization process are beating time and the feedstock/inoculum ratio (F/I). Each parameter was studied for three different values. The beating time was set at 0, 30, and 60 min while the F/I ratio was set at 0.3, 0.5, and 0.7. To assess the resulting performance, a comparison study was carried out between the optimized results thought proposed strategy and those obtained by using Response Surface Methodology (RSM). The FL model produced a higher accuracy, i.e., lower values of Root Mean Squared Errors (RMSEs), compared with the RSM. Therefore, the obtained results confirmed that the proposed strategy is better than RSM. (c) 2020 Elsevier Ltd. All rights reserved.

Automated summary

This study proposes a robust strategy using fuzzy modeling and particle swarm optimizer to determine the optimal operating parameters for methane production, which is found to be superior to Response Surface Methodology (RSM).