Modeling and Control of the X-Filter for Enhanced Pulp Mill Performance

Abstract

The dynamic performance of control strategies for an X-filter process is critically analyzed, emphasizing the advantages of model predictive control (MPC) over traditional proportional-integral (PI) control. A detailed simulation model, grounded in the linearized expressions of the X-filter process, was developed using MATLAB-Simulink, with parameters systematically defined and tested under various disturbance scenarios. Key performance indicators, including steady-state behavior, oscillation magnitude, and control effort, were assessed. Results demonstrate that MPC significantly enhances system responsiveness, achieving a steady state more rapidly than PI controllers, with notable reductions in oscillatory behavior across key process variables. Specifically, oscillations in the manipulated variable m_i were effectively mitigated under MPC control, thereby safeguarding hydraulic pump integrity. Statistical analysis of standard deviations for controlled variables revealed that MPC reduces variability in h₁, h₂, and dp by 9%, 24%, and 32% respectively, underscoring its superior ability to maintain stability amidst high-frequency noise and external disturbances. The average position deviation for manipulated variables further illustrates the efficiency of MPC, with reductions of up to 92% in specific instances. Robustness testing confirms MPC's resilience to disturbances in critical input variables, showcasing its adaptability in complex industrial environments. Overall, the findings affirm that MPC not only optimizes set-point tracking but also enhances process control precision, providing a compelling case for its implementation in advanced industrial applications.