Faculty of Science, Shanghai Maritime University, Shanghai, 201306, China
Olefin is widely used in chemical and pharmaceutical production. In the process of producing C4 olefin, catalyst combination and temperature will affect the formation of C4 olefin. As one of the most important high value-added raw materials in the chemical industry, the synthesis of C4 Olefin by ethanol coupling was of great significance in the field of the chemical industry. Different catalysts and various conditions have different effects on the chemical reaction . This paper mainly explores the relationship between selectivity of C4 olefin and catalyst combination and temperature, and how to optimize catalyst combination and temperature to make the yield of C4 olefin as high as possible. This article is based on relevant data sets. This article explores the relationship between variables such as the amount of Co loading, Co/SiO2 ratio, HAP loading ratio, ethanol concentration, catalyst loading method, temperature, and the selectivity of C4 olefins and the conversion rate of ethanol. It is based on relevant datasets and utilizes the method of controlling variables. Pearson coefficient is used to test the correlation between variables, and a multiple linear regression model is established. Finally, experiments are designed to verify the optimal conditions for achieving the highest yield of C4 olefins. Based on analysis and validity testing, the findings suggest that the proposed model is well-suited for chemical reactions in a wide range of industrial production contexts, facilitating efficient extraction of the desired product during actual manufacturing processes. Future improvements will focus on optimizing the model with a larger dataset, incorporating advanced deep learning techniques, and enhancing both its accuracy and precision.
Regression analysis, Model Control variable method, C4 olefin yield
Aijia Chen. The Regression Analysis Model of C4 Olefin Yield Optimization Study. Academic Journal of Materials & Chemistry (2023) Vol. 4, Issue 5: 36-43. https://doi.org/10.25236/AJMC.2023.040506.
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