C4 olefin production conditions optimizing based on a hybrid model - Yancong Zhou; Chenheng Xu; Yongqiang Chen; Shanshan Li (Mathematical Biosciences and Engineering, 2023)
The yield of C4 olefin is often low due to the complexity of the associated products. Finding the optimal ethanol reaction conditions requires repeated manual experiments, which results in a large consumption of resources. Therefore, it is challenging to design ethanol reaction conditions to make the highest possible yield of C4 olefin. This paper introduces artificial intelligence technology to the optimization problem of C4 olefin production conditions. A sample incremental eXtreme Gradient Boosting tree based on Gaussian noise (GXGB) is proposed to establish the objective function of the variables to be optimized. The Sparrow Search Algorithm (SSA), which has an improved advantage in the optimization efficiency, is used to combine with GXGB. Therefore, a kind of hybrid model GXGB-SSA that can solve the optimization of complex problems is proposed. The purpose of this model is to find the combination of ethanol reaction conditions that makes the maximum yield of C4 olefin. In addition, due to the insufficient interpretation ability of GXGB on the data, the SHAP (SHapley Additive exPlanations) value method is creatively introduced to investigate the effect of each ethanol reaction condition on the yield of C4 olefin. The constraints of each decision variable for optimization are adjusted according to the analysis results. The experimental results have showed that the proposed GXGB-SSA model obtained the combination of ethanol reaction conditions that maximized the yield of C4 olefin. (i.e., when the Co loading is 1.1248 wt%, the Co/SiO2 and HAP mass ratio is 1.8402, the ethanol concentration is 0.8992 ml/min, the total catalyst mass is 400 mg, and the reaction temperature is 420.37 ℃, the highest C4 olefin yield is obtained as 5611.46%). It is nearly 25.46 % higher compared to the current highest yield of 4472.81 % obtained from manual experiments.