Electricity generation from renewable source- A leachate based microbial fuel cell machine learning approach

Abstract

Abstract This research presents an innovative blend of feature selection and sensitivity analysis techniques, which is an essential yet overlooked aspect in the study of MFCs. The study compared predictive models utilizing various machine learning algorithms to assess the impact of time, dosage, pH and temperature on ammonium nitrogen concentration (NH 4 -N) to predict the power density (PD) output of microbial fuel cells using leachate as a substrate for treatment. Evaluation of six machine learning models demonstrates varying levels of predictive accuracy. CatBoost (R2:0.9969, MSE: 48.8430, RMSE:6.9888) emerges as the most accurate model, followed closely by XGBoost (R2:0.9917, MSE:130.1668, RMSE:11.4091) and Random Forest (R2:0.9830, MSE:267.0929, RMSE:16.3430). Time series plots illustrate the performance of different models in predicting PD over a period, indicating good alignment with observed data. Comparison of Mean Squared Error (MSE) highlights significant variations in prediction accuracy, with CatBoost demonstrating the greatest enhancement and precision. The study directly tackles the deficiencies in existing MFC predictive modeling by incorporating the CatBoost algorithm, which provides enhanced accuracy and a deeper understanding of the nonlinear connections between environmental variables and power density.

Key findings

• ▸CatBoost (R2:0.9969, MSE: 48.8430, RMSE:6.9888) is the most accurate model for predicting power density output.
• ▸XGBoost (R2:0.9917, MSE:130.1668, RMSE:11.4091) and Random Forest (R2:0.9830, MSE:267.0929, RMSE:16.3430) are also accurate models.
• ▸Mean Squared Error (MSE) highlights significant variations in prediction accuracy.

Keywords

Identifiers

DOI

10.21203/rs.3.rs-5119060/v1

Journal

Research Square

Year

2024