A synergistic framework integrating statistical design and machine learning for enhancing biogas yield

Li, Yuxuan, Lu, Mahuizi, Campos, Luiza C., Cheng, Shikun, Li, Zifu and Hu, Yukun (2025) A synergistic framework integrating statistical design and machine learning for enhancing biogas yield. Energy Nexus, 20. p. 100583. ISSN 27724271

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Abstract

Anaerobic digestion (AD) of waste-activated sludge is hindered by slow hydrolysis and complex sludge structure, which limit biogas recovery and threaten process stability. Although pretreatment methods such as microwave pretreatment (MP) have shown promise, their energy efficiency and underlying nonthermal effects remain poorly understood. In parallel, existing predictive models often neglect pretreatment conditions, restricting their applicability for process optimization. To bridge these gaps, this study proposes a synergistic framework that integrates statistical optimization with advanced machine learning (ML) to enhance biogas production from MP-assisted AD. Response Surface Methodology (RSM) identified optimal pretreatment conditions—holding time, pH, total solids (TS%), and microwave power—predicting a cumulative biogas yield (CBY) of 1020.5 mL/gVS, closely validated by experiments (1013.8 mL/gVS). These optimized datasets were further used to train ensemble ML models (AdaBoost, LightGBM, XGBoost), achieving high predictive accuracy (XGBoost R2 = 0.992) with strong external validation. SHapley Additive exPlanations (SHAP) revealed TS% as the dominant factor, contrasting with RSM’s linear emphasis on microwave power, thereby highlighting nonlinear interactions and hidden effects. Complementary fluorescence microscopy and ImageJ analysis confirmed microwave-induced structural disruption of extracellular polymeric substances (EPS) and microbial accessibility, particularly at moderate power (600 W), evidencing distinct nonthermal contributions. By combining RSM’s statistical optimization with ML’s predictive and interpretive capacity, this framework not only refines operational conditions for maximum yield but also elucidates the mechanisms of MP, balancing energy input and performance. These insights support the design of more energy-efficient, scalable, and sustainable AD systems.

Item Type:	Article
Sustainable Development Goals:	Goal 6: Clean Water and Sanitation Goal 7: Affordable and Clean Energy
Keywords:	Machine learning algorithms；Response surface methodology；Microwave pretreatment；Nonthermal Effects；Biogas yield
Depositing User:	Dr Kim Li
Date Deposited:	04 Sep 2026 08:25
Last Modified:	04 Sep 2026 08:25
URI:	https://ube.repository.guildhe.ac.uk/id/eprint/292

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