Affiliations: [a]
School of Information Engineering, Tianjin University of Commerce, Tianjin, China
| [b]
School of Economics, Tianjin University of Commerce, Tianjin, China
| [c]
School of Science, Tianjin University of Commerce, Tianjin, China
Correspondence:
[*]
Corresponding author. Yancong Zhou, School of Information
Engineering, Tianjin University of Commerce, Tianjin 300134,China. E-mail: [email protected].
Note: [1] Yancong Zhou and Chenheng Xu contributed equally to this work.
Abstract: Due to the complexity of the products from the ethanol coupling reaction, the C4 olefin yield tends to be low. Finding the optimal ethanol reaction conditions requires repeated manual experiments. In this paper, a novel learning framework based on least squares support vector machine and tree-structured parzen estimator is proposed to solve the optimization problem of C4 olefin production conditions. And shapley value is introduced to improve the interpretation ability of modeling method. The experimental results show that the proposed learning framework can obtain the combination of ethanol reaction conditions that maximized the C4 olefin yield It is nearly 17.30% higher compared to the current highest yield of 4472.81% obtained from manual experiments.
Keywords: C4 olefin production, complex problem optimization, model interpretability, LSSVM, SHAP, TPE
