Polypropylene melt index hybrid modeling method based on integrated neural network

The invention discloses a polypropylene melt index hybrid modeling method based on an integrated neural network. The method comprises steps: firstly, through acquiring key process variable data and melt index offline analysis data, preprocessing such as outlier detection and standardization is carried out on original data, and a training sample data set is built; then, a mechanism analysis method and a steepest descent method are adopted to build a polypropylene melt index simplified mechanism model, and a Bagging ensemble learning algorithm and an information entropy method are adopted to build a mechanism model prediction error compensation model based on an integrated BP neural network; and finally, the simplified mechanism model and the error compensation model are combined, the error compensation model is built for realizing online melt index estimation. Compared with current other modeling methods, the method of the invention has the advantages that the model generalization performance is improved; the polypropylene production process can be guided; and the polypropylene quality control can be effectively realized.