Machine learning-enhanced life cycle assessment of hydrothermal carbonization for agro-waste valorization: Innovations in sustainable soil remediation

This review synthesizes the environmental impacts of hydrothermal carbonization (HTC) for agro-waste valorization and proposes a machine learning (ML)-enhanced life cycle assessment (LCA) framework to address the complexities of HTC systems. Traditional LCA methods often fail to capture nonlinear interactions among feedstock variability, process parameters (e.g., temperature and residence time), and multicategory environmental impacts (e.g., global warming potential and eutrophication). By integrating ML algorithms (e.g., random forest and neural networks), this approach dynamically models these relationships, optimizing HTC conditions for minimal environmental burdens while maximizing hydrochar's soil-enhancing benefits. Key findings highlight the critical role of renewable energy integration and process water management in achieving a net-negative carbon footprint. The proposed ML-HTC-LCA framework bridges computational innovation with sustainable practices, enabling data-driven decision-making for climate-smart agriculture. Future research should focus on long-term hydrochar stability and scalable ML-LCA tools to ensure robust environmental validation.