Advancing Anaerobic Digestion through a Machine Learning Approach Using Gradient Boosting Machines to Optimize Methane Yields from Lignocellulosic Biomass

To optimize methane yields from lignocellulosic biomass, this study harnessed the power of machine learning, specifically gradient boosting machines (GBMs). Through a meticulous analysis of a dataset comprising 18 features related to biomass characteristics and reactor conditions, GBM emerged as the optimal model. Our results revealed a striking accuracy in predicting the specific methane yield (SMY), with an RMSE of 10.53, an MAE of 7.88, and an impressive R2 value of 0.93. These values underscore the model’s ability to capture the complex interrelationships between the variables, offering insights previously elusive from traditional experimental methods. The findings of this study highlight the potential of machine learning in revolutionizing anaerobic digestion modeling, enabling bioenergy facilities to refine operations based on predictive insights. While the results are pioneering, the study also emphasizes the importance of diverse datasets to enhance model generalizability. As the bioenergy sector evolves, the integration of such advanced computational methods promises a pathway to increased efficiency and sustainability.