Amal Lahkimi

Toward predictable hydrochar properties at scale: a critical review of design of experiments-guided machine learning in hydrothermal carbonization

This review synthesize and critically assess how design of experiments (DoE) and machine learning (ML) can be combined to move HTC from empirical tuning toward data-informed, property-targeted engineering of hydrochar materials.