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

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  • Nassef, Ahmed M.
  • Olabi, A.G.
  • Rodriguez, Cristina
  • Abdelkareem, Mohammad Ali
  • Rezk, Hegazy

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.

Suggested Citation

  • Nassef, Ahmed M. & Olabi, A.G. & Rodriguez, Cristina & Abdelkareem, Mohammad Ali & Rezk, Hegazy, 2021. "Optimal operating parameter determination and modeling to enhance methane production from macroalgae," Renewable Energy, Elsevier, vol. 163(C), pages 2190-2197.
    • Handle: RePEc:eee:renene:v:163:y:2021:i:c:p:2190-2197
    DOI: 10.1016/j.renene.2020.10.069
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    1. Rezk, Hegazy & Inayat, Abrar & Abdelkareem, Mohammad A. & Olabi, Abdul G. & Nassef, Ahmed M., 2022. "Optimal operating parameter determination based on fuzzy logic modeling and marine predators algorithm approaches to improve the methane production via biomass gasification," Energy, Elsevier, vol. 239(PB).

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