LANCASTER UNIVERSITY 2022 UNDERGRADUATE RESEARCH CONFERENCE
15th MARCH - 17th MARCH 2022
Hanqi Jiang, Xinkai Yuan, Lan Zeng

Hanqi Jiang, Xinkai Yuan, Lan Zeng

Computer Science (BJTU) | Year 2 | Degree: Undergraduate
Study on the process of preparing C4 olefin by catalytic coupling of ethanol

C4 olefins are one of the most important raw materials for chemical products and ethanol can be used as a feedstock for the production and preparation of C4 olefins. In the preparation process, catalyst combinations and reaction temperatures can affect both feedstock conversion and C4 olefin yield, and finding the optimal configuration required for the preparation process can effectively improve yield and efficiency and save costs. Based on the analysis of experimental data, this paper discusses the reasons affecting the magnitude of ethanol conversion, C4 olefin selectivity and C4 olefin yield, and proposes a regression model and grid search based approach to solve for the optimal configuration of catalyst combination and reaction temperature.

Hanqi Jiang, Xinkai Yuan, Lan Zeng
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Hanqi Jiang, Xinkai Yuan, Lan Zeng

Hanqi Jiang, Xinkai Yuan, Lan Zeng

Computer Science (BJTU) | Year 2 | Degree: Undergraduate
Study on the process of preparing C4 olefin by catalytic coupling of ethanol
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C4 olefins are one of the most important raw materials for chemical products and ethanol can be used as a feedstock for the production and preparation of C4 olefins. In the preparation process, catalyst combinations and reaction temperatures can affect both feedstock conversion and C4 olefin yield, and finding the optimal configuration required for the preparation process can effectively improve yield and efficiency and save costs. Based on the analysis of experimental data, this paper discusses the reasons affecting the magnitude of ethanol conversion, C4 olefin selectivity and C4 olefin yield, and proposes a regression model and grid search-based approach to solve for the optimal configuration of catalyst combination and reaction temperature.
2. The following figures show the decision process for the first decision tree of the ethanol conversion regression and the C4 olefin selectivity regression.
1. The LightGBM model was trained using the mean squared error MSE as the objective function, and we used the root mean squared error RMSE and the maximum absolute error MAE to measure the quality of the regression model.
The following figure shows the significance of the characteristics of the two regression models.
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