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Mining the Synergistic Effect in Hydrothermal Co-liquefaction of Real Feedstocks Through Machine Learning Approaches by Jie Yu; Xiaomei Zhong; Zhilin Huang; Xiaoyu Lin; Haiyong Weng; Dapeng Ye; Quan (Sophia) He; Jie Yang is a Engineering article available to read on EtoBox.
What is Mining the Synergistic Effect in Hydrothermal Co-liquefaction of Real Feedstocks Through Machine Learning Approaches about?
Hydrothermal co-liquefaction (co-HTL) of different feedstocks has received much research attention, not only because its significant importance in real industrial applications, but also due to the potential synergy in biocrude yield by tuning mixed feedstock’s biochemical composition and reaction conditions. Although some attempts have been made to search for the synergy from co-liquefying various feedstocks, these processes were remarkably time and labor consuming, and often with low rate of success. Therefore, this study for the first time employed machine learning algorithms to mine the synergistic effect in co-HTL. Started with single task prediction, three machine learning algorithms, including Adaboost, Gradient Boosting Regression and Random Forest, were trained and tested for predicting co-HTL biocrude yield and relative co-liquefaction effect (CE). It was found that their prediction performances were favorable over traditional mathematical equations, in which Gradient Boosting Regression exhibited the best performance for co-HTL biocrude yield prediction (training and testing R2 of 0.976 and 0.812 respectively), and Adaboost better estimated relative CE. Feature importance
Who reads Mining the Synergistic Effect in Hydrothermal Co-liquefaction of Real Feedstocks Through Machine Learning Approaches?
It is typically read by researchers, students, and practitioners in Engineering.
- Author
- Jie Yu; Xiaomei Zhong; Zhilin Huang; Xiaoyu Lin; Haiyong Weng; Dapeng Ye; Quan (Sophia) He; Jie Yang
- Publisher
- Elsevier BV
- Published
- 2023
- Language
- EN
- Field
- Engineering (Physical Sciences)