Modeling and optimization of chemical reaction based on XGBoost-PSO

This paper uses machine learning algorithm and intelligent optimization algorithm for chemical data modeling, build based on XGBoost and PSO of chemical reaction prediction and optimization model, for chemical reaction data, especially in organic reaction is low experimental efficiency, low prediction accuracy, we innovative use XGBoost machine learning model, for small batch chemical reaction data regression modeling, mining data high dimension of small sample characteristics. On the basis of the constructed regression model, the particle swarm optimization algorithm is used to optimize the reaction conditions to find the balance point in the chemical reaction, which overcomes the problems of low product rate and difficult raw material ratio in the chemical reaction.Based on this, we designed a set of universality algorithm for chemical reaction optimization, conducted data modeling through XGBoost, and quickly found the optimal reaction conditions by PSO, and applied them to ethanol-coupled C-4 olefin reaction preparation. Through experimental analysis, the MAE, MSE and R2 scores of our XGBoost model in regression analysis are 29.82, 4.01 and 0.93, all better than other machine learning models, which has certain statistical significance. Secondly, in the comparative literature and experiments, the optimal solution obtained by PSO search conforms to the principle and reality of chemical preparation, which has certain industrial value. The modeling algorithm can be further extended to the fields of biopharmaceutical and machine molecular preparation, to provide the basis for decision-making for researchers and find new experimental ideas and methods. The algorithm has strong general adaptation and popularization significance.