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Nonlinear mill control.

G Martin1, S McGarel

  • 1Business Development, Pavilion Technologies, Inc, Austin, TX 78758, USA. gmartin@pav.com

ISA Transactions
|October 2, 2001
PubMed
Summary
This summary is machine-generated.

This study introduces an on-line control application for cement mills, utilizing nonlinear model predictive control (NMPC) with a neural network to manage process variations. This approach addresses historical control failures caused by model mismatch in milling operations.

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Area of Science:

  • Chemical Engineering
  • Process Control
  • Artificial Intelligence

Background:

  • Milling processes, particularly in cement production, exhibit significant deadtime and nonlinear behavior.
  • Previous attempts at continuous mill control over 25 years have largely failed due to model mismatch from changing process gains.

Purpose of the Study:

  • To describe an on-line control application for a closed-circuit cement mill.
  • To implement nonlinear model predictive control (NMPC) technology to overcome limitations of traditional control methods.

Main Methods:

  • Utilized nonlinear model predictive control (NMPC) technology for mill control.
  • Developed an on-line neural network model to calculate nonlinear process gains.
  • Applied the NMPC with on-line gain calculation to a closed-circuit cement mill.

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Main Results:

  • Successfully implemented an on-line control system for a cement mill.
  • The neural network accurately estimated nonlinear gains, mitigating model mismatch issues.
  • Demonstrated the feasibility of NMPC in managing complex milling dynamics.

Conclusions:

  • Nonlinear model predictive control, enhanced by on-line neural network gain estimation, offers a viable solution for cement mill control.
  • This advanced control strategy overcomes the persistent challenges of deadtime and nonlinearity in milling operations.
  • The developed approach provides a pathway for improved efficiency and stability in industrial grinding processes.