Thermo-mechanical Pulp Refiner Control via Two-Level MPC Strategy
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Solution Overview
Problem
The thermo-mechanical pulping (TMP) process in papermaking is complex and difficult to control due to its multivariable and non-stationary dynamics, requiring tight control of pulp quality variables like fiber length and freeness, which is challenging with existing decentralized control architectures and PID controllers.
Innovation Solution
A two-level control strategy using Model Predictive Control (MPC) is implemented, with a Stabilization Controller for fast dynamics and a Quality Controller for slow dynamics, along with an Optimizer for global optimization, to independently regulate motor loads, pulp quality, and integrate multiple refiner lines, enhancing control precision and energy efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If decentralized control architecture with PID controllers is used, then the control system is easy to understand and implement, but it cannot handle complex multivariable dynamics and control multiple pulp quality variables simultaneously
Solution Approach 1:
The control system is segmented into multiple independent single-variable controllers, each responsible for controlling a specific pulp quality variable (CSF, MFL, shives content) within the quality window. This segmentation allows the complex multivariable control problem to be divided into manageable single-variable control tasks while still achieving comprehensive quality control.
Solution Approach 2:
The controller system is designed with universal functionality to handle multiple pulp quality variables simultaneously. Each controller can independently adjust its setpoint within the quality window, and the system collectively manages CSF, MFL, and shives content control, making it adaptable to different control scenarios and quality requirements.
2Manufacturing precision
If tight control of pulp quality variables is implemented, then pulp quality uniformity improves, but the control complexity increases due to multivariable interactions and non-stationary dynamics
Solution Approach 1:
The controller incorporates dynamic adaptation capabilities where each controller's setpoint can be adjusted in real-time within the quality window based on process conditions. The system handles non-stationary dynamics by allowing flexible setpoint modification rather than relying on fixed setpoints, enabling tight quality control despite changing process characteristics.
Solution Approach 2:
The system changes control parameters dynamically by adjusting individual controller setpoints within the quality window. Instead of changing the overall control strategy or structure, the system achieves adaptability through parameter changes - specifically, modifying the target values for each quality variable based on current process conditions and quality requirements.
3Adaptability or versatility
If manual control is used, then operator flexibility is maintained, but production efficiency and quality consistency are limited
Solution Approach 1:
The control system provides self-service capabilities by automatically managing multiple quality variables within the defined quality window. Each controller independently adjusts its parameters to maintain quality specifications, reducing the need for constant manual intervention while maintaining the flexibility to adapt to changing conditions. Operators benefit from automated quality maintenance while retaining overall system control.
Data Source
AI summary
Thermomechanical pulp is an important process for producing fibrous mass used in papermaking. A two-level control strategy that stabilizes and optimizes the refining process has been developed. The Stabilization layer consists of a multivariable model predicative range controller that regulates the refiner line operations. The Quality Optimization layer provides the pulp quality control as measured by an online pulp quality (freeness, fibre length) sensor. This control startegy leverages the natural decoupling in the process. The modular design technique is able to handle multiple refiner lines that empty into a common latency chest. A global optimizer is also used to integrate and coordinate the two layers for enhanced constraint handling.