Adaptive Neuro-Fuzzy Inference System Modeling and Optimization of Microbial Fuel Cells for Wastewater Treatment

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 ohm. 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.