祝贺周军辉论文“Machine-learning-aided hydrochar production through hydrothermal carbonization of biomass by engineering operating parameters and/or biomass mixture recipes”被Energy（IF：9.0）接收发表！

Release time:2023-12-07

Description of Publication:Hydrochar serves not only as a fuel source but also as a versatile carbon material that has found extensive application across various domains. The application performance of hydrochar, e.g., energy recovery and carbon stability, is substantially influenced by its mass yield, higher heating value), and compositions, so the prediction and engineering of these properties is promising. In this study, two machine learning algorithms, namely gradient boosting regression (GBR) and random forest (RF), were used to predict the hydrochar properties mentioned above. The interpretation of ML models revealed the importance ranking of features for all targets. Then, engineering hydrochar was carried out through three different optimizations to the as-built multi-target prediction model: i) optimizations of HTC conditions for given biomass samples; ii) optimization of biomass mixture recipes; iii) simultaneous optimization of both biomass mixing recipes and HTC conditions.

Translated or Not:no