Optimized Methane Content Prediction Model for Biogas Production

Abstract

Methane (CH₄) is a key component of biogas, playing a crucial role in renewable energy production. However, accurately predicting CH₄ yield from various feedstocks remains a challenge due to the complex interactions of multiple factors such as feedstock composition, temperature, pH, and retention time. This study developed an optimized CH₄ content prediction model using linear regression, incorporating key process variables to enhance prediction accuracy. The process of producing the biogas from mini-biodigester involves sourcing the feedstocks, mixing them with water to a suitable proportion, pouring the slurry mixture into the mini-biodigester while ensuring that the mini-biodigester is airtight, and allowing anaerobic bacteria to break down the waste. Data from 15 feedstock combinations were obtained using mini-biodigesters in a controlled laboratory environment and analyzed, integrating biogas composition data with environmental parameters. Initially, a basic regression model using only feedstock composition yielded a low predictive accuracy (R² = 0.117). However, after incorporating temperature, pH, and retention time, the optimized model significantly improved performance (R² = 0.968), indicating that these factors strongly influence CH₄ yield. The study further validates the model by comparing predicted and actual CH₄ content, demonstrating a high correlation between key parameters and CH₄ production. The model was deployed in an Excel-based tool for practical use, allowing real-time CH₄ content estimation for biogas plants. The findings underscore the importance of environmental factors in biogas optimization and provide a simple yet effective tool for CH₄ prediction, contributing to improved bioenergy production efficiency.