Paper Machine Optimization Using MIMO MPC Along with The Use of Analyzers Predicting Final Reel Properties, TAPPICon24

This study explores the integration of Multi-Input Multi-Output (MIMO) Model Predictive Control (MPC) in the context of paper machine optimization within the pulp and paper industry. The MIMO MPC framework simultaneously manipulates multiple input variables, such as pulp consistency, flow rates, specific energy, chemicals, steam pressure, etc, in an optimal way to meet output objectives. The optimal setting is an economic objective that maintains quality limits while setting inputs for the lowest cost and highest productivity. By leveraging predictive modeling, the controller anticipates future system behavior and computes optimal control actions to steer the paper machine toward desired setpoints, addressing operational constraints and enhancing overall efficiency. In addition to the MIMO MPC framework, this research investigates the role of advanced analyzers in predicting and optimizing final reel properties in paper production. The integration of analyzer feedback into the control strategy provides a means to continuously monitor and adjust the process parameters to achieve the desired final reel properties. This study demonstrates the synergistic effect of combining MIMO MPC with advanced analyzers, offering a comprehensive and adaptive solution for achieving optimal paper quality while ensuring efficient resource utilization in the production process.

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