祝贺张伟进论文“A novel intelligent system based on machine learning for hydrochar multi-target prediction from the hydrothermal carbonization of biomass”被Biochar（IF：12.7）接收发表！

Release time:2023-12-23

Description of Publication:Hydrothermal carbonization (HTC) is a thermochemical conversion technology to produce hydrochar from wet biomass without drying, but it is time-consuming and expensive to experimentally determine the optimal HTC operational conditions of specific biomass to produce desired hydrochar. Therefore, a machine learning (ML) approach was used to predict and optimize hydrochar properties. Specifically, the biochemical components (proteins, lipids, and carbohydrates) of biomass were predicted and analyzed first via elementary compositions. Then, the accurate single-biomass (no mixtures) basis ML multi-target models (average R2 = 0.93 and RMSE = 2.36) were built to predict and optimize the hydrochar properties (yield, elemental composition, elemental atomic ratio, and higher heating values). This study advances the field by integrating predictive modeling and mechanistic insights, offering a holistic approach to the precise control and optimization of hydrochar production through HTC.

Translated or Not:no