Predictive modeling and optimization of hydrochar properties from food waste hydrothermal carbonization using machine learning techniques

, and high heating value from 18.4 MJ/kg to 22.7 MJ/kg. These findings demonstrate the potential of ML-driven optimization for real-time control of HTC, reducing reliance on trial-and-error experimentation and advancing sustainable waste-to-energy conversion technologies.