A Novel Nonlinear Partial Least Square Integrated With Error-Based Extreme Learning Machine

Abstract

This paper proposes a novel nonlinear partial least square (PLS) approach for dealing with the modeling problem of industrial processes with input variables in collinearity. The new method combines the external linear PLS framework with the internal extreme learning machine (ELM) function. First, PLS is used as the outer framework to extract the input and output latent variables as well as eliminating the collinearity of the original variables, and then ELM is employed to describe the nonlinear relation between pairs of latent variables. Besides, the weight updating strategy based on errors minimization is also involved to improve the prediction accuracy. Then, the pH-neutralization process is taken as a benchmark to verify the validity of the new model. Finally, this method is applied to model the NOx emission of a 1000-MW coal-fired boiler, and root-mean-square error (RMSE) values are 5.9541 for the training dataset and 6.8323 for the testing dataset. Compared with linear PLS and another two nonlinear PLS methods, smaller prediction errors is obtained. The results indicate new nonlinear PLS model can be a better choice for establishing the model of NOx emission for coal-fired boilers.