Control and optimization strategies for thermo-mechanical pulping processes: Nonlinear model predictive control

In this paper, we present nonlinear model predictive control (NMPC) techniques to control and optimize the thermo-mechanical pulping (TMP) process. The TMP process considered in this work has two-stages of refining with a primary and a secondary refiners. TMP processes are, inherently, multi-input multi-output (MIMO) processes with complex dynamics and severe interactions among process variables. We formulate a dynamic optimization problem to simultaneously regulate and optimize the TMP process and compare with regulatory control in the presence of disturbances. Potential economical benefits of the proposed method are demonstrated, through a reduction in total specific energy. The computational burden of the resulting nonlinear programming problem (NLP) is handled with the IPOPT (Interior Point OPTimizer) solver. This is further improved with the advanced step NMPC (asNMPC) controller concept, a sensitivity-based approximation to the solution of the resulting NLP problem in NMPC.