Performance improvement of microbial fuel cell through artificial intelligence

The current work introduces an enhancement in the performance of the microbial fuel cell through estimating the optimal set of controlling parameters. The maximization of both power density (PD) and the percentage of chemical oxygen demand (COD) removal were considered as the enhancement in the cell's performance. Three main parameters in terms of performance as well as commercialization are the system's inputs; the Pt which takes the range of 0.1-0.5 mg/cm(2), the degree of sulphonation in sulfonated-poly-ether-ether-ketone that changes in the range of 20-80%, and the rate of aeration of cathode which varies between 10 and 150 mL/min. From the experimental dataset, two robust adaptive neuro-fuzzy inference system models based on the fuzzy logic technique have been constructed. The comparisons between the models' outputs and the experimental data showed well-fitting in both training and testing datasets. The mean squared errors of the PD model, for testing and whole datasets, were found 2.575 and 0.909 while for the COD model it showed 19.242 and 6.791, respectively. Then, based on the two fuzzy models, a Particle Swarm Optimization algorithm has been used to determine the best parameters that maximize both of the PD and the COD removal of the cell. The optimization process was utilized for single and multi-object optimization processes. In the single optimization, the resulting maximums of the PD and the COD removal were found 62.844 (mW/m(2)) and 99.99 (%), respectively. Whereas, in the multi-object optimization, the values of 61.787 (mW/m(2)) and 96.21 (%) were reached as the maximums for the PD and COD, respectively. This implies that, in both cases of optimization processes, the adopted methodology can efficiently enhance the microbial fuel cell performances than the previous work.

Automated summary

This study introduces enhancements to microbial fuel cell performance through optimization of controlling parameters, leading to improved power density and COD removal percentage. Experimental data and model outputs demonstrate the effectiveness of the methodology in enhancing cell performance, achieving maximum values for both power density and COD removal.