To address the challenges of preparing C4 olefins, this study processes the data by using regression analysis, calculating its Spearman correlation coefficient, analyzing the test results of a given catalyst combination at 350 degrees at different times in an experiment, as well as analyzing various catalyst combinations and temperature to determine the effects of ethanol conversion rate and C4 olefin selectivity. Moreover, based on the machine learning XGBoost algorithm, this study establishes a regression model between the catalyst combination and temperature and the yield of C4 olefins. At the same time, this study further explores the variable values when the C4 olefins yield is as high as possible. For instance, the catalyst carrier, Co loading, total Co/SiO2, Co/SiO2 and HAP loading ratio, ethanol concentration, temperature, and six other elements are considered as the independent variables. Then, C4 olefin yield is considered as the dependent variable. The XGBoost algorithm is then used for integrated training. Then, the six most important ranking features are selected. Finally, the trained model is reverse-optimized and, to further optimize the catalyst combination and temperature values when the C4 olefin yield is as high as possible, the grid search method is applied.
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