Abstract
Biomethane optimization and upscaling are major challenges in biorefinery due to the recalcitrant properties of lignocellulose biomass and non-linearities of anaerobic digestion. This study integrates the experimental investigation of the influence of Natural Deep Eutectic Solvents (NADES) pretreatment on the biomethane yield of groundnut shells and advanced data-driven techniques encompassing k-means clustering, SHapley Additive exPlanations (SHAP)-based feature interpretations to overcome the black-box nature of conventional machine learning (ML) models and ensemble learning-based predictive modeling. Ethyl glycerol combined with choline chloride at a 1:1 ratio was applied to groundnut shells for 60 min using temperatures of 80 degrees C and 100 degrees C, and solid-to-liquid ratios of 1:2 and 1:4 before anaerobic digestion. It was observed that all the pretreatment conditions considered improved the biomethane yield, and the optimum biomethane yield of 365.70 mL CH4/ gVSadded was recorded. Optimum pretreatment conditions were 1:1 of ethyl glycerol and choline chloride for 60 min at a 1:2 solid-to-liquid ratio at 100 degrees C. SHAP-based feature importance assessment revealed retention times as the dominant driver, with an average SHAP value of about 70. K-means clustering revealed 3 distinct operational clusters reflecting the low, moderate, and high-yield digestion scenarios. XGBoost exhibits a superior predictive performance among others with RMSE, MAE, MAD, MAPE, VAF, and R2 values of 0.9641, 0.6614, 0.3576, 9.6905, 99.9, and 0.999, respectively, at training. This research demonstrates that NADES pretreatment, along with SHAP-based interpretable predictive modeling and clustering, can substantially improve biomethane recovery and provide actionable insights for the expansion of lignocellulosic bioenergy systems.