Machine learning-optimized solar thermal pretreatment and low-energy ultrasonic disintegration for enhanced biogas production: Efficiency, carbon footprint, and comparative analysis

Abstract

This experimental and modeling study explores the integration of renewable energy-based pretreatment methods (solar thermal and ultrasonic) with anaerobic digestion (AD) for sustainable sludge management and enhanced biogas production. Building on prior experimental work that utilized microwave pretreatment, the study employs machine learning (ML) to model and optimize AD performance under renewable energy pretreatment. Experimental validation was conducted using lab-scale continuously stirred tank reactors (CSTRs) with a comprehensive dataset of experimental runs. Key findings demonstrate that solar thermal and ultrasonic methods achieve 20.5% ± 1.8% and 18.7% ± 2.1% higher methane production (295 ± 22 and 285 ± 20 mL CH₄/g VS, respectively) and 30.9% ± 2.1% greater chemical oxygen demand (COD) solubilization compared to microwave pretreatment (245 ± 18 mL CH₄/g VS), while reducing energy consumption by 40.1% ± 3.2% and 35.6% ± 2.8%, respectively. ML models (Random Forest and Gradient Boosting) demonstrated high accuracy (R² = 0.952 ± 0.018 and 0.948 ± 0.022, respectively) in predicting biogas yield and identifying optimal pretreatment parameters. Comprehensive life cycle assessment including upstream emissions shows 49% and 37% carbon footprint reduction for solar thermal and ultrasonic systems, respectively, compared to microwave pretreatment. This work provides both experimental validation and theoretical framework for future large-scale implementation and highlights the potential of ML-driven optimization to advance sustainable sludge-to-energy conversion, offering significant implications for reducing operational costs.