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Weighted minimum variance based on-line performance assessment of multivariate processes: A data-driven approach.

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  • 1Department of Automation, Xiamen University, Xiamen City 361005, China.

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This study introduces a data-driven method for on-line performance assessment in multivariate processes, eliminating the need for prior process knowledge. The approach enables direct estimation of the weighted minimum variance benchmark from routine operating data.

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Area of Science:

  • Process Control
  • Industrial Automation
  • Data-Driven Modeling

Background:

  • Traditional minimum variance benchmarking lacks on-line applicability in industrial settings due to insufficient process knowledge from routine data.
  • Existing methods often require significant prior process knowledge, hindering real-time industrial implementation.

Purpose of the Study:

  • To develop a data-driven approach for on-line performance assessment of multivariate processes without prior knowledge.
  • To propose a practical scheme for on-line benchmark estimation when prior process knowledge is available.
  • To validate the proposed methods using a case study.

Main Methods:

  • Directly estimating the weighted minimum variance benchmark from closed-loop output data under routine operating conditions.
  • Utilizing the normalizability of the first non-zero impulse response coefficient for benchmark estimation when process knowledge is available.
  • Applying the methods to the "Shell" heavy oil fractionator for verification.

Main Results:

  • Successful on-line estimation of benchmarks/indices for multivariate processes.
  • Demonstrated effectiveness in reducing estimation errors and time costs for on-line updates.
  • Validation of the data-driven approach's applicability in industrial scenarios.

Conclusions:

  • The proposed data-driven approach enables effective on-line performance assessment for multivariate processes, even without prior knowledge.
  • The method offers a practical solution for industrial implementation, improving efficiency and accuracy.
  • The study successfully verified the approach's effectiveness on a real-world industrial case study.