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Dynamic optimizers for complex industrial systems via direct data-driven synthesis.

Khalid Alhazmi1, S Mani Sarathy2

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This study introduces a data-driven dynamic real-time optimization (D-RTO) method for the chemical process industry (CPI). The approach uses historical data to enhance operational efficiency, stability, and product quality, challenging traditional methods.

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Area of Science:

  • Chemical Engineering
  • Process Control
  • Data Science

Background:

  • The chemical process industry (CPI) faces challenges in balancing efficiency, sustainability, cost, and complexity.
  • Traditional optimization methods often struggle with dynamic real-time adjustments and uncertainty management.

Purpose of the Study:

  • To develop a data-driven dynamic real-time optimization (D-RTO) approach for the CPI.
  • To improve process efficiency, stability, and product quality by leveraging historical plant data.

Main Methods:

  • Constructing a value function to assess trajectory quality.
  • Employing weighted regression to derive improved optimization policies directly from historical data.
  • Applying the D-RTO method to a plant-wide industrial process control problem.

Main Results:

  • The proposed optimizer demonstrated superior adaptation to disturbances.
  • The method maintained process stability and product quality effectively.
  • Significant efficiency improvements were observed on a realistic industrial benchmark problem.

Conclusions:

  • Data-driven D-RTO offers a practical and effective solution for enhancing operational efficiency in the CPI.
  • The approach leverages readily available historical data, reducing the need for extensive modeling.
  • This study presents a promising avenue for adopting data-driven optimization in real-world industrial applications.