Machine learning-based analysis of microplastic-induced changes in anaerobic digestion parameters influencing methane yield

Microplastics (MPs) present significant challenges for anaerobic digestion (AD) processes used in energy recovery from contaminated organic waste. Given that optimal AD conditions vary widely across studies when MPs are present, a robust predictive model is essential to accurately assess these complex effects. This study applied four machine learning algorithms to predict methane yield using two datasets-one with and one without MPs. Among these, gradient boosting regression demonstrated the highest prediction accuracy, with testing R² values of 0.996 for systems without MP pollution and 0.998 with MP pollution. This model was then further optimized by removing redundant and low-importance features, refining its predictive power. Feature importance analysis revealed that digestion time and substrate organic matter content were key parameters positively correlated with methane production. In the presence of MPs, substrate pH and inoculum total solids emerged as critical factors, with partial dependence plots offering deeper insights into their optimal conditions. This research offers new perspectives on the intricate effects of MPs on methane production, which could inform the optimization of AD processes in environments contaminated by MPs.