Turning Waste Into Resources With Artificial Intelligence
A recent study published in Biochar demonstrates how artificial intelligence (AI) can optimize the hydrothermal treatment of livestock manure. The approach predicts how nutrients, especially phosphorus, distribute between the solid hydrochar and the nutrient-rich liquid, enabling safer, more valuable end products while reducing environmental pollution. By integrating machine learning with traditional environmental engineering, researchers are moving toward smarter waste management and more sustainable farming practices.
What Is Hydrothermal Treatment And Why It Matters
Hydrothermal treatment converts biowaste into a solid hydrochar and a nutrient-rich liquid without the need for energy-intensive drying. This method is versatile across temperatures and treatment severities and, crucially, can recycle essential nutrients like phosphorus—a limited global resource essential for crop growth. Mismanagement of phosphorus commonly leads to water pollution and ecosystem damage, so improving recovery has wide-reaching environmental and agricultural benefits.
AI Models Behind the Breakthrough
The research team applied several machine learning models—XGBoost, Decision Tree, and Random Forest—to forecast how phosphorus behaves during hydrothermal processing. They focused on how calcium and iron ions influence phosphorus partitioning between hydrochar and the liquid phase. Among the tested models, XGBoost delivered the most accurate predictions and showed strong agreement with experimental results. This demonstrated that AI can reliably guide the treatment conditions to maximize nutrient retention in the solid product while minimizing leaching into water bodies.
Key Findings: Conditions That Drive Phosphorus Recovery
The study identified reaction time and pH as major levers for phosphorus recovery. Longer reaction times and carefully tuned pH levels increased the amount of phosphorus captured in hydrochar. The addition of calcium and iron ions further promoted phosphorus retention in the solid phase, reducing its mobility in the liquid. As treatment severity rose, the forms of phosphorus in hydrochar became more stable and uniform, which is important for storage, handling, and eventual use as a fertilizer.
From Lab To Field: Implications For Policy And Practice
By enabling precise control over key processing conditions, the AI-driven approach offers a practical tool for decision-making in waste treatment plants and farms. It helps producers decide when to apply specific pH levels, how long to process the material, and whether to add calcium or iron to steer phosphorus into hydrochar rather than the liquid. The outcome is a higher-quality hydrochar product suitable for agricultural use and a cleaner liquid that is less likely to pollute waterways. This aligns well with carbon neutrality goals and could support policy development for sustainable livestock waste management on a global scale.
Voices From The Research Team
Lead author Xiaofei Ge from China Agricultural University notes that livestock manure contains sizeable phosphorus reserves that, if mismanaged, threaten water and soil quality. Properly recovered, this phosphorus can be reused as fertilizer to support sustainable agriculture. Co-corresponding author Sabry M. Shaheen from the University of Wuppertal adds that combining machine learning with environmental engineering offers smarter waste-treatment strategies, with broad implications for sustainable farming, environmental protection, and resource recovery.
Looking Ahead
The researchers believe their results can inform policies and technologies for managing livestock waste worldwide, contributing to cleaner ecosystems and more resilient agricultural systems. As AI continues to guide complex treatment processes, hydrothermal methods could become a standard pathway to transform waste into safe, valuable resources while helping reduce the ecological footprint of agriculture.
Journal Reference
Ge, X.; Zhang, T.; Mukherjee, S. et al. Optimizing the conditions of biowastes hydrothermal treatment and predicting phosphorus fate in the hydrochar and liquid phase using machine learning. Biochar 7, 96 (2025). https://doi.org/10.1007/s42773-025-00485-9
