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Modeling of microbial fuel cell power generation using machine learning-based super learner algorithms
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AI
DOI：10.1016/j.fuel.2023.128646.png)
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Abstract
En 中文
Electricity generation from microbial fuel cells (MFCs) is a potential environment-friendly technology. This study provides Bayesian Algorithm (BA) based Support Vector Regression (SVR) and Boosted Regression Tree (BRT) as prospective super learner modeling tools (BA-SVR, BA-BRT) for predictions of electricity production from MFCs. The membrane thickness, external resistance, and anode area were considered independent variables, while power generation was taken as a response variable. The key novelties of this study include (i) hybridization of BA with SVR and BRT (separately) for forecasting power generation from fuel cells for the first time, (ii) performance comparison of the developed models (BA-SVR and BA-BRT) with the existing Response Surface Methodology (RSM) based on the coefficient of determination (R2), relative error (RE), mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), and computing efficiency, and the (iii) analysis of the models' robustness by utilizing Gaussian white noise. Based on the performance indicators, the proposed super leaner models showed excellent performance compared to the existing M.J. Salar-Garcia et al. RSM model. The BA-SVR model provided the lowest errors (MAE of 2.94, RSME of 7.2926, MAPE of 13.8341) with the highest R2 of 0.9981, compared to the BA-BRT and RSM models. The proposed BA-SVR model showed superior performance to the RSM and BA-BRT models in predicting the MFCs' power generation, with a per-formance improvement of more than 90% regarding MAPE, as an example. The future prediction and high robustness of the proposed super learner model would ensure quick estimation for maximization of electricity generation that may lead to reducing massive lab trials and saving resources.
Keywords:
Electricity generation
Fuel cell
Bayesian algorithm
Response surface methodology
Support vector regression
Boosted regression tree
Journal
IF:
7.5
Papers:
3.8W
Citations:
16.7W