Boosting the power density and COD removal efficiency of microbial fuel cell using quadratic interpolation optimization algorithm

The performance of the microbial fuel cells (MFCs) preliminarily depends on key operational parameters; the degree of sulfonation (DoS), the amount of catalyst (AoC), and aeration rate (AR). Determining the optimal combination of these parameters is essential to achieve the highest output power density (PD) and COD removal. The proposed methodology consists of four main phases; experimental investigation, ANFIS modelling, parameter identification, and validation. Firstly , experimental work was conducted to evaluate the performance of the MFC by varying the input values of the controlling parameters. SPEEKs with different amounts of sulfonation were fabricated and tested along with different AoC and AR. These membranes have been tested in MFC according to the proposed methodology. Secondly , using the extracted data and with the help of artificial intelligence (AI), the model of MFC is established, considering the DoS, AoC, and AR as the inputs. The output performance of the MFC is measured through PD and COD removal. Thirdly , the quadratic interpolation optimization algorithm (QIOA) is used to identify the best set of inputs that maximizes the outputs. During the optimization procedure, DoS, AoC, and AR are assigned as decision variables, and the objective function is the simultaneous increase in both PD and COD removal. Fourthly , the optimized results are validated experimentally. The experimental validation accuracy at the optimization point for the two outputs reached 98.91% and 99.35%, respectively. This study revealed that the maximum outputs are found at 237 mW/m 2 and 79.88%, respectively, which occur at input values of 64.64%, 0.47 mg/cm², and 92.2 mL/min, for the DoS, AoC, and AR, respectively. In sum, the obtained results demonstrate the superiority of the integration between the AI-based model and the QIOA for improving the performance of the MFC.