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Expert System for Stable Power Generation Prediction in Microbial Fuel Cell

Kathiravan Srinivasan1, Lalit Garg2,*, Bor-Yann Chen3, Abdulellah A. Alaboudi4, N. Z. Jhanjhi5, Chang-Tang Chang6, B. Prabadevi1, N. Deepa1

1 School of Information Technology and Engineering, Vellore Institute of Technology (VIT), Vellore, 632014, India
2 Faculty of Information and Communication Technology, University of Malta, Msida, MSD2080, Malta
3 Department of Chemical and Materials Engineering, National Ilan University, Yilan City, 26047, Taiwan
4 College of Computer Science, Shaqra University, Shaqra, Kingdom of Saudi Arabia
5 School of Computer Science and Engineering, SCE, Taylor’s University, Subang Jaya, 47500, Malaysia
6 Department of Environmental Engineering, National Ilan University, Yilan City, 26047, Taiwan

* Corresponding Author: Lalit Garg. Email:

Intelligent Automation & Soft Computing 2021, 30(1), 17-30. https://doi.org/10.32604/iasc.2021.018380

Abstract

Expert Systems are interactive and reliable computer-based decision-making 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.

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Cite This Article

K. Srinivasan, L. Garg, B. Chen, A. A. Alaboudi, N. Z. Jhanjhi et al., "Expert system for stable power generation prediction in microbial fuel cell," Intelligent Automation & Soft Computing, vol. 30, no.1, pp. 17–30, 2021.

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This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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