Machine Learning-Optimized Hydrothermal Carbonization Enhances Agricultural Waste Valorization

A recent study led by researchers from the Xinjiang Institute of Ecology and Geography (XIEG) of the Chinese Academy of Sciences demonstrates how machine learning and statistical modelling can optimize hydrothermal carbonization (HTC) processes for hybrid agricultural waste valorization. Published in Industrial Crops & Products, the research highlights innovative approaches to overcome feedstock variability challenges in producing high-quality hydrochar (HC) for soil improvement.

The research team developed a hybrid modelling approach combining machine learning algorithms with statistical techniques to optimize the HTC process parameters. This innovative methodology addressed the inherent variability in hybrid agricultural feedstocks by analyzing critical reaction pathways including Maillard reactions, depolymerization, and decarboxylation. Their integrated approach not only improved hydrochar quality but also revealed how different waste streams could be synergistically combined to enhance the process efficiency.

"Our findings demonstrate how machine learning can uncover complex patterns in HTC processes that traditional trial-and-error methods often miss," said Dr. Collins Elendu, first author of the study. The research particularly emphasizes how lignin-rich and cellulose-dominant feedstocks require hybrid optimization and modelling strategies to maximize nutrient retention and soil amendment potential.

The study also explores how optimized HTC conditions can promote beneficial soil microorganisms like Rhizobia and Mycorrhizal fungi, while life cycle assessments confirm the process's environmental advantages and sustainability over conventional waste management methods. These insights provide valuable guidance for developing climate-smart agricultural practices and sustainable waste valorization strategies.

Read the full article: https://doi.org/10.1016/j.indcrop.2025.121147

Optimized hybrid agro-waste valorization and sustainability. (Image by XIEG)

Contact

LONG Huaping

Xinjiang Institute of Ecology and Geography

E-mail: longhp@ms.xjb.ac.cn

Web: http://english.egi.cas.cn