Abstract
In this study, a soft-sensor modeling algorithm with adaptive partial least squares nonnegative garrote is developed by incorporating nonstationary disturbance. The approach is capable of monitoring the stationary and nonstationary behaviors of the process dynamics. The procedure of adaptive variable selection ensures that a compact and robust input-output relation is obtained online. Hence, in addition to simply tracking prediction, the model can be used for the detection of structural model change and the emergence of disturbance. The advantages of the proposed method are demonstrated with a simulation example and two industrial applications to predict the temperature of a blast furnace hearth wall and to estimate impurity composition of a distillation column. © 2013 Elsevier Ltd.