Multi-task prediction and optimization of hydrochar properties from high-moisture municipal solid waste: Application of machine learning on waste-to-resource

Abstract

Hydrothermal carbonization (HTC) is a promising technology for valuable resources recovery from high moisture wastes without pre-drying, while optimization of operational conditions for desired products preparation through experiments is always energy and time consuming. To accelerate the experiments in an efficient, sustainable, and economic way, machine learning (ML) tools were employed for bridging the inputs and outputs, which can realize the prediction of hydrochar properties, and development of ML based optimization for achieving desired hydrochar. The results showed that deep neural network (DNN) model was the best one for joint prediction of both fuel properties (FP) and carbon capture and storage (CCS) stability of hydrochar with an average R2 and root mean squared error (RMSE) of 0.91 and 3.29. The average testing prediction errors for all the targets were below 20%, furtherly within 10% for HHV, carbon content and H/C predictions. ML-based feature analysis unveiled that both elementary composition and temperature were crucial to FP and CCS. Furthermore, a ML-based software interface was provided for practitioners and researchers to freely access. The insights and Pareto solution provided from ML-based multi-objective optimization benefitted desired hydrochar preparation for the potential application of fuel substitution or carbon sequestration in soil.