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Model-plant mismatch detection for cross-directional processes.

Qiugang Lu1, Michael G Forbes2, Philip D Loewen3

  • 1Department of Chemical Engineering, Texas Tech University, Lubbock, TX, USA, 79409.

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|February 26, 2021
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Summary
This summary is machine-generated.

This study introduces a two-part system to detect model-plant mismatch (MPM) in paper machine cross-directional (CD) processes. The framework uses routine data for system identification and a support vector machine (SVM) to predict MPM effectively.

Keywords:
Cross-directional processesHigh-order ARXProcess monitoringRoutine closed-loop identificationSupport vector machine

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Area of Science:

  • Process Control
  • Machine Learning
  • Paper Manufacturing

Background:

  • Model-predictive control (MPC) is crucial for optimizing paper machine operations.
  • Detecting model-plant mismatch (MPM) is essential for maintaining control performance.
  • Cross-directional (CD) processes present unique challenges for system identification and control.

Purpose of the Study:

  • To develop and validate a two-component framework for detecting model-plant mismatch (MPM) in cross-directional (CD) processes.
  • To enable MPM detection using only routine operating data without external excitations.
  • To differentiate between process model mismatches and noise model changes.

Main Methods:

  • System identification in closed loop using routine operating data.
  • Iterative identification of finite impulse response coefficients for spatial and temporal models.
  • Training a one-class support vector machine (SVM) on normal operation data to detect MPM.

Main Results:

  • The iterative identification method converges, yielding asymptotically consistent parameter estimates.
  • The trained one-class SVM effectively detects MPM in subsequent routine operations.
  • The framework successfully distinguishes process model mismatches from noise model variations.

Conclusions:

  • The proposed two-component framework provides an effective method for MPM detection in CD processes.
  • The approach leverages routine operating data, simplifying implementation.
  • The ability to differentiate between process and noise model changes enhances diagnostic capabilities.