Globally Convergent Composite-Step Trust-Region Framework for Model-Based Real-Time Optimization

Zhang, Duo; Li, Xiang; Wang, Kexin; Shao, Zili

doi:10.1016/b978-0-443-15274-0.50266-3

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…An earlier version of Algorithm 4 was reported in our conference paper [33]. One difficulty in imposing a trust region on a constrained optimization problem is that the feasible region of the original problem may not intersect with the trust region.…”

Section: Algorithm Descriptionmentioning

confidence: 99%

A Globally Convergent Composite-Step Trust-Region Framework for Real-Time Optimization with Plant-Model Mismatch

Zhang,

Li,

Wang

et al. 2023

Preprint

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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.

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Section: Algorithm Descriptionmentioning

confidence: 99%

A Globally Convergent Composite-Step Trust-Region Framework for Real-Time Optimization with Plant-Model Mismatch

Zhang,

Li,

Wang

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

A globally convergent composite‐step trust‐region framework for real‐time optimization

Zhang,

Li,

Wang

et al. 2023

AIChE Journal

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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 guarantees global convergence in the presence of plant‐model mismatch is desired. In this regard, the trust‐region framework is intuitive and simple to implement for unconstrained problems. Constrained RTO problems are converted to unconstrained ones by the penalty function, and global convergence is guaranteed if the penalty coefficient is large enough. However, a sufficiently large penalty coefficient is hard to determine and may lead to numerical difficulties. This paper addresses this issue and proposes a novel composite‐step trust‐region framework for constrained RTO problems that handles inequality constraints directly. The trial step is decomposed into a normal step that improves feasibility and a tangential step that reduces the cost function. A rigorous proof of its global convergence property is given.

show abstract

Globally Convergent Composite-Step Trust-Region Framework for Model-Based Real-Time Optimization

Cited by 2 publications

References 6 publications

A Globally Convergent Composite-Step Trust-Region Framework for Real-Time Optimization with Plant-Model Mismatch

A Globally Convergent Composite-Step Trust-Region Framework for Real-Time Optimization with Plant-Model Mismatch

A globally convergent composite‐step trust‐region framework for real‐time optimization

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