PNNL

Abstract

Currently, as one of the most important link in papermaking industry, the high consistency (HC) refining system is confronted with challenges such as improving pulp quality, saving energy and reducing emissions in production processes. In this correspondence paper, we conduct research on optimal operation of HC refining system and investigate the nonlinear multiobjective model predictive control (MPC) strategies aimed at setpoint tracking objective of pulp quality, economic objective and specific energy (SE) consumption objective, respectively. Firstly, input and output data at different times are employed to construct the subprocess model of the state process model for the HC refining system, and the Wiener-type model can be obtained through combining the mechanism model of Canadian Standard Freeness (CSF) and the state process model that determines their structures based on Akaike Information Criterion (AIC). Secondly, the multiobjective optimization problem optimizing both the setpoint tracking objective of pulp quality and SE consumption objective is proposed simultaneously, which uses NSGA-II approach to obtain the Pareto optimal set. Further, targeting at the setpoint tracking objective of pulp quality, economic objective and SE consumption objective, the sequential quadratic programming (SQP) method is utilized to achieve the optimal predictive controllers. Finally, the simulation results demonstrate that the proposed methods can make the HC refining system provide a better performance of setpoint tracking of pulp quality when these predictive controllers are employed, respectively. In addition, while the optimal predictive controllers orienting with comprehensive economic objective and SE consumption objective, they have made the energy consumption reduced significantly.