Adaptive Neuro-Fuzzy Inference System Modeling and Optimization of Microbial Fuel Cells for Wastewater Treatment
Author:
Abdelkareem Mohammad Ali123ORCID, Alshathri Samah Ibrahim4ORCID, Masdar Mohd Shahbudin3ORCID, Olabi Abdul Ghani15
Affiliation:
1. Sustainable Energy and Power Systems Research Centre, RISE, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates 2. Chemical Engineering Department, Faculty of Engineering, Minia University, Minia 61111, Egypt 3. Fuel Cell Institute, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia 4. Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia 5. Department of Mechanical Engineering and Design, School of Engineering and Applied Science, Aston University, Aston Triangle, Birmingham B4 7ET, UK
Abstract
Due to their toxicity, Cr(VI) levels are subject to strict legislation and regulations in various industries and environmental contexts. Effective treatment technologies are also being developed to decrease the negative impacts on human health and the environment by removing Cr(VI) from water sources and wastewater. As a result, it would be interesting to model and optimize the Cr(VI) removal processes, especially those under neutral pH circumstances. Microbial fuel cells (MFCs) have the capacity to remove Cr(VI), but additional research is needed to enhance their usability, increase their efficacy, and address issues like scalability and maintaining stable operation. In this research work, ANFIS modeling and artificial ecosystem optimization (AEO) were used to maximize Cr(VI) removal efficiency and the power density of MFC. First, based on measured data, an ANFIS model is developed to simulate the MFC performance in terms of the Cu(II)/Cr(VI) ratio, substrate (sodium acetate) concentration (g/L), and external resistance Ω. Then, using artificial ecosystem optimization (AEO), the optimal values of these operating parameters, i.e., Cu(II)/Cr(VI) ratio, substrate concentration, and external resistance, are identified, corresponding to maximum Cr(VI) removal efficiency and power density. In the ANFIS modeling stage of power density, the coefficient-of-determination is enhanced to 0.9981 compared with 0.992 (by ANOVA), and the RMSE is decreased to 0.4863 compared with 16.486 (by ANOVA). This shows that the modeling phase was effective. In sum, the integration between ANFIS and AEO increased the power density and Cr(VI) removal efficiency by 19.14% and 15.14%, respectively, compared to the measured data.
Funder
Princess Nourah bint Abdulrahman University
Subject
Water Science and Technology,Aquatic Science,Geography, Planning and Development,Biochemistry
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