Relationship Study of Meltblowning Variables Based on Machine Learning Algorithm

Abstract

For the missing intercalation rate data, we first filled it in, and then split it according to the variable pairs to observe the changes of six indexes before and after intercalation. Then, we made grey correlation analysis between the changes and intercalation rate, and got the influence of intercalation rate on each index. In order to further explore the relationship between process parameters and structural variables, nine models, KNN, linear regression, ridge regression, lasso regression, decision tree, support vector machine, robust model, XGBoost and random forest, are used to train the data, and finally XGBoost regression model has the highest accuracy. Then, using the prediction results of structural variables obtained by XGBoost, we firstly make factor analysis on three indexes of structural variables and product performance, and the index with the largest factor load represents structural variables and product performance, and then make Pearson correlation analysis on these two indexes to get the relationship between structural variables and product performance. Through Pearson analysis of the three internal variables of structural variables and product performance, the internal correlation is obtained. For three indexes in the structural variables, respectively, they are linearly fitted with two variables of process parameters to obtain three fitting equations, and then they are fitted with filtration efficiency to obtain the fitting equation between filtration efficiency and structural variables. Finally, the linear regression equation between filtration efficiency and process parameters is sorted out. However, the effect of changing the linear regression model is not good in model testing, and we think it has a complex nonlinear relationship. Therefore, we use machine learning to carry out regression training on variables. The results show that when the result variables are used to regress the product performance, the effect of using random forest is better, but the filtering efficiency can't be achieved by using many kinds of machine learning. After that, considering the internal influence relationship of product performance, we used structural variables and all other indicators of product performance for regression training, and found that XGBoost algorithm had good effect, so we established a multiple regression model based on machine learning. By controlling the process parameters and observing the predicted structure, it is found that the filtration efficiency is the highest when the receiving distance is 10cm and the hot air speed is 1400r/min. Finally, we set up a multi-objective planning model, and globally optimize the planning model through the sand dune cat population optimization algorithm, and finally get the approximate optimal matching scheme of process parameters.