Prediction of Boiler Output Variables Through
the PLS Linear Regression Technique
Abdelmalek Kouadri1, Mimoun Zelmat1, and AlHussein Albarbar2
1Applied Control Laboratory, University of Boumerdes, Algeria
2Department of Engineering and Technology, Manchester Metropolitan University, UK
1Applied Control Laboratory, University of Boumerdes, Algeria
2Department of Engineering and Technology, Manchester Metropolitan University, UK
Abstract: In this work, we propose to use the linear regression partial least square method to predict the output variables of the RA1G boiler. This method consists in finding the regression of an output block regarding an input block. These two blocks represent the outputs and inputs of the process. A criterion of cross validation, based on the calculation of the predicted residual sum of squares, is used to select the components of the model in the partial least square regression. The obtained results illustrate the effectiveness of this method for prediction purposes.
Keywords:Partial least square, principal component analysis, principal component regression, covariance, predicted residual sum of squares.
Received November 26, 2008; accepted May 17, 2009