A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
A New Approach for Improving Microbial Fuel Cell Performance Using Artificial Intelligence
Microbial fuel cells have recently received considerable attention as a potential source of renewable energy. Due to its complex and hybrid nature, it has significant nonlinear features and substantial hysteresis behavior, making it hard to optimize and control its power generation directly. This study modeled power density and COD removal using random forest regression and gradient boost regression trees. System inputs are three key parameters that affect performance and commercialization. There is a range of 0.1–0.5 mg/cm2 of Pt, a degree of sulfonation of sulfonated polyether-etherketone varying from 20% to 80%, and a cathode aeration rate of 10–150 mL/min. Based on the model’s accuracies, gradient boost regression was selected for power density prediction and random forest for COD removal prediction. Particle swarm optimization was used as the optimization algorithm after selecting the best models to maximize COD removal and power density. It was found that DS was the most critical parameter for COD removal, and Pt was the most critical parameter for power density. There is a different optimal input value for each model. In order to maximize power density, DS (%) must be 67.7087, Pt (mg/cm2) must be 0.3943, and Aeration (mL/min) must be 117.7192. To maximize COD removal, the DS (%) must be 75.8816, the Pt (mg/cm2) must be 0.3322, and the Aeration (mL/min) must be 75.1933.
A New Approach for Improving Microbial Fuel Cell Performance Using Artificial Intelligence
Microbial fuel cells have recently received considerable attention as a potential source of renewable energy. Due to its complex and hybrid nature, it has significant nonlinear features and substantial hysteresis behavior, making it hard to optimize and control its power generation directly. This study modeled power density and COD removal using random forest regression and gradient boost regression trees. System inputs are three key parameters that affect performance and commercialization. There is a range of 0.1–0.5 mg/cm2 of Pt, a degree of sulfonation of sulfonated polyether-etherketone varying from 20% to 80%, and a cathode aeration rate of 10–150 mL/min. Based on the model’s accuracies, gradient boost regression was selected for power density prediction and random forest for COD removal prediction. Particle swarm optimization was used as the optimization algorithm after selecting the best models to maximize COD removal and power density. It was found that DS was the most critical parameter for COD removal, and Pt was the most critical parameter for power density. There is a different optimal input value for each model. In order to maximize power density, DS (%) must be 67.7087, Pt (mg/cm2) must be 0.3943, and Aeration (mL/min) must be 117.7192. To maximize COD removal, the DS (%) must be 75.8816, the Pt (mg/cm2) must be 0.3322, and the Aeration (mL/min) must be 75.1933.
A New Approach for Improving Microbial Fuel Cell Performance Using Artificial Intelligence
Yaser Abdollahfard (author) / Mehdi Sedighi (author) / Mostafa Ghasemi (author)
2023
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
Performance of artificial intelligence approach on bridge coating assessment
| Tema Archive | 2002
Comprehensive approach for scour modelling using artificial intelligence
| Taylor & Francis Verlag | 2023
Application of artificial intelligence for improving longwall mine stability
| British Library Conference Proceedings
Performance of microbial fuel cell using chemically synthesized activated carbon coated anode
| American Institute of Physics | 2016