Improvements on Model Predictive Control for a Pulp Mill Process

This work constitutes a contribution to the previous one presented by Castro and Doyle (2004a). They decided the incorporation of four Model Predictive Control (MPC) for specific parts of the complex chemical Pulp and Paper plant to improve its global dynamic and economic performance. Meanly the authors supported the decision of including MPC based on the RGA information. In this paper, a deep analysis about each MPC implementation is performed so as to test if the used methodology could guide efficiently for adopting this kind of decisions. Initially, the study begins with a systematic procedure for adjusting the key MPC tuning parameters. The economic and dynamic performance indexes are evaluated to demonstrate for which specific cases a real benefit can be achieved. The results presented here were obtained through dynamic simulations using the computational benchmark model of 8200 states for the same scenarios evaluated by Castro and Doyle (2004b).