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

RENEWABLE ENERGY

卷:163

Article

Nassef, Ahmed M.^1,2  Olabi, A. G.^3,4  Rodriguez, Cristina⁵  Abdelkareem, Mohammad Ali^3,6,7  Rezk, Hegazy^1,8 

[1] Prince Sattam Bin Abdulaziz Univ, Coll Engn Wadi Addawaser, Al Kharj, Saudi Arabia

[2] Tanta Univ, Fac Engn, Comp & Automat Control Engn Dept, Tanta, Egypt

[3] Univ Sharjah, Dept Sustainable & Renewable Energy Engn, POB 27272, Sharjah, U Arab Emirates

[4] Aston Univ, Sch Engn & Appl Sci, Mech Engn & Design, Birmingham B4 7ET, W Midlands, England

[5] Univ West Scotland, Sch Comp Engn & Phys Sci, Paisley PA1 2BE, Renfrew, Scotland

[6] Menia Univ, Fac Engn, Chem Engn Dept, Al Minya, Egypt

[7] Univ Sharjah, Ctr Adv Mat Res, POB 27272, Sharjah, U Arab Emirates

[8] Menia Univ, Fac Engn, Elect Engn Dept, Al Minya, Egypt

关键词: Renewable energy;    Biomethane;    Biomass;    Algae;    Fuzzy logic;    Optimization;   

DOI  :  10.1016/j.renene.2020.10.069

来源: Elsevier

【 摘 要 】

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.

【 授权许可】

Free