Expert system for stable power generation prediction in microbial fuel cell

Abstract

Expert Systems are interactive and reliable computer-based decisionmaking systems that use both facts and heuristics for solving complex decision-making problems. Generally, the cyclic voltammetry (CV) experiments are executed a random number of times (cycles) to get a stable production of power. However, presently there are not many algorithms or models for predicting the power generation stable criteria in microbial fuel cells. For stability analysis of Medicinal herbs’ CV profiles, an expert system driven by the augmented K-means clustering algorithm is proposed. Our approach requires a dataset that contains voltage-current relationships from CV experiments on the related subjects (plants/herbs). This new approach uses feature engineering and augmented K-means clustering techniques to determine the cycle number beyond which the CV curve stabilizes. We obtain an excellent estimate of the required CV cycles for getting a stable Voltage versus Current curve in this approach. Moreover, this expert system would reduce the time needed and the money spent on running additional and superfluous CV experiments cycles. Thus, it would streamline the process of Bacterial Fuel Cells production using the CV of medicinal herbs.