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A Globally Convergent Composite-Step Trust-Region Framework for Real-Time Optimization with Plant-Model Mismatch
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  • Duo Zhang,
  • Xiang Li,
  • Kexin Wang,
  • Zhijiang Shao
Duo Zhang
Zhejiang University

Corresponding Author:[email protected]

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Xiang Li
Queen's University
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Kexin Wang
Zhejiang University
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Zhijiang Shao
Zhejiang University
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Inaccurate models limit the performance of model-based real-time optimization (RTO) and even cause system instability. Therefore, a RTO framework that can guarantee global convergence with the presence of plant-model mismatch is desired. In this regard, the trust-region framework is simple to implement and guarantees globally convergent for unconstrained problems. However, it remains to be seen if the trust-region strategy can handle inequality constraints directly with the common model adaptation method. This paper addresses this issue and proposes a novel composite-step trust-region framework that guarantees global convergence for constrained RTO problems. The trial step is decomposed into a normal step that improves feasibility and a tangential step that reduces the cost function. In each iteration, the model optimization problem with relaxed constraints is solved. The proof of the global convergence property under structural plant-model mismatch is given.
01 Jun 2023Submitted to AIChE Journal
12 Jun 2023Submission Checks Completed
12 Jun 2023Assigned to Editor
12 Jun 2023Review(s) Completed, Editorial Evaluation Pending
16 Jun 2023Reviewer(s) Assigned
22 Aug 2023Editorial Decision: Revise Major
20 Sep 20231st Revision Received
24 Sep 2023Submission Checks Completed
24 Sep 2023Assigned to Editor
24 Sep 2023Review(s) Completed, Editorial Evaluation Pending
24 Sep 2023Reviewer(s) Assigned
01 Nov 2023Editorial Decision: Accept