A machine learning approach to feature selection and uncertainty analysis for biogas production in wastewater treatment plants

Abstract

The growing demand for efficient waste management solutions and renewable energy sources has driven research into predicting biogas production at wastewater treatment plants. This study outlines a methodology starting with data collection from a full-scale plant, followed by detailed analysis to resolve potential issues. A notable advancement is the use of a robust machine learning model, fine-tuned with advanced optimization techniques. To enhance its utility, prediction intervals were incorporated to quantify uncertainty, providing decision-makers with reliable insights. Results revealed that the developed model performed well, explaining 82% of the variability in test data and delivering predictions closely aligned with actual biogas production. This reliability empowers more confident decision-making in wastewater treatment operations. The study also identified key factors influencing biogas output, categorizing them into sludge characteristics, operational practices, and sludge quantity. By focusing on most important adjustable parameters, operators can optimize processes and significantly improve biogas yields. This predictive capability, combined with an understanding of influencing factors and quantified reliability, offers notable advantages. It enables operators to enhance biogas production while providing decision-makers with reliable predictions to guide policy and resource management. These developments contribute to more sustainable and efficient waste management practices.