Robust PLS model based product quality control strategy for solvent extraction process

A novel product quality control strategy is presented in this paper. The quality control is achieved by predicting the product quality using a data-driven model and adjusting the manipulated variables when disturbances occur in the measured variables. The data-driven model employs robust partial least squares algorithm to predict offline measured product quality, which can minimize the adverse effect of outliers in the training data set. Base on the robust regression model, the optimal control action are computed by solving a quadratic optimization problem under the constraint that the optimal projected solution must fall within the region of historical scores. The prediction and control performances are examined through a simulated solvent extraction process.