Multi-Model Modeling of CFB Boiler Bed Temperature System Based on Principal Component Analysis

Multi-model modeling is an important method for solving the modeling problem of complex non-linear systems. However, there is no such good classification principle of its sub-models’ time span, and the division of sub-windows affects the accuracy of the model, as well as the cost of computing. Through analysis of the characteristics of the bed temperature object, the principal component analysis was introduced into the method of designing the time span division of the sub-windows. The dividing points of the time span of the sub-windows were determined by piecewise analysis, rolling merging, and cyclic validation. The problem with this is that the sub-models in different windows may be different. Aiming at the problems of modeling in the sub-windows, the contribution rates of variables to the principal components were analyzed, the independent variables that play a major role in the dependent variables in the sub-windows were determined with the results of PCA, and the regression models in the the sub-windows were identified by multivariate linear regression. Compared to the sub-window model established by the principal component regression method, the former method had higher accuracy and could better reflect the actual operation of the bed temperature object.