Sludge dewatering process control using principal component analysis (PCA) and partial least square

Abstract

The process control in the sludge dewatering process is to minimalize the water volume in the sludge. However, management of this process control is difficult because of its multi-variables, nonlinearity and long delay. In this paper, a control approach based on the principal component analysis (PCA) is presented. A PCA model, which incorporates time lagged variables is used. The control objective is expressed in the score space of this PCA model. A controller is designed in the model predictive control framework, and it is used to control the equivalent score space representation of the process. The score predictive model for the model predictive control algorithm is built using a partial least squares (PLS). The process control system with PLS was simulated on Matlab and the graphs showed good accuracy and stability.