Optimal operating parameter determination and modeling to enhance methane production from macroalgae. (January 2021)

• Record Type: Journal Article
• Authors: Nassef, Ahmed M.; Olabi, A.G.; Rodriguez, Cristina; Abdelkareem, Mohammad Ali; Rezk, Hegazy
• Is Part Of: Renewable energy. Volume 163(2021)
• Page Start: 2190
• Page End: 2197
• Publication Date: 2021-01
• Subjects: Renewable energy -- Biomethane -- Biomass -- Algae -- Fuzzy logic -- Optimization; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2020.10.069
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: 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 variables 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. Highlights: Artificial intelligence used to enhance methane production from macroalgae. Fuzzy logic based modeling and modern optimization were carried out. Particle swarm optimizer (PSO) applied obtain the optimal operating conditions. Beating time and feedstock/inoculum ratio used as the decision variables. Results are better than experiments and Response Surface Methodology (RMS).