A Study of Explorative Moves during Modifier Adaptation with Quadratic Approximation

Gao, Wei; Hernández, Reinaldo; Engell, Sebastian

doi:10.3390/pr4040045

Cited by 11 publications

(2 citation statements)

References 12 publications

(17 reference statements)

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“…The high price of the required metals and ligands restrains its implementation to processes with a highly efficient catalyst recovery concept . The potential benefits of applying RTO based on modifier adaptation with quadratic approximation to the optimal operation of multiphase homogeneously catalyzed processes have been already explored in previous works by the authors, , but in these works, only simulation studies were presented.…”

Section: Introductionmentioning

confidence: 99%

Iterative Real-Time Optimization Scheme for Optimal Operation of Chemical Processes under Uncertainty: Proof of Concept in a Miniplant

Hernández

Dreimann

Vorholt

et al. 2018

Ind. Eng. Chem. Res.

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Real-time optimization (RTO) has gained growing attention during the past few years as a useful approach to boost process performance while safety and environmental constraints are satisfied. Despite the increasing acceptance of RTO in traditional industries such as petrochemical and refineries, its application to novel chemical processes remains limited. This can be partially explained by the fact that only inaccurate models are available and the performance of the traditional RTO scheme suffers in the presence of plant-model mismatch. During the past few years, the so-called modifier-adaptation schemes for real-time optimization have been gaining popularity as an efficient tool to handle plant-model mismatch. So far, there are only few published works regarding experimental implementations. In this contribution, a reliable RTO scheme that is able to deal with model uncertainty and measurement noise is applied to a novel transition metal complex catalyzed process that is performed in a continuously operated miniplant. The experimental results show that the proposed scheme is able to drive the process to an improved operation despite significant plant-model mismatch demonstrating the applicability of the method to real processes.

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Section: Introductionmentioning

confidence: 99%

Iterative Real-Time Optimization Scheme for Optimal Operation of Chemical Processes under Uncertainty: Proof of Concept in a Miniplant

Hernández

Dreimann

Vorholt

et al. 2018

Ind. Eng. Chem. Res.

Self Cite

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“…This special issue on real-time optimization (http://www.mdpi.com/journal/processes/special_ issues/real_time_optimization) includes both methodological and practical contributions [1][2][3][4][5][6][7][8][9][10][11][12][13]. All seven methodological contributions deal with explicit RTO schemes that repeat the optimization when new measurements become available.…”

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confidence: 99%

Special Issue “Real-Time Optimization” of Processes

Bonvin

2017

Processes

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Process optimization is the method of choice for improving the performance of industrial processes, while also enforcing the satisfaction of safety and quality constraints. Long considered an appealing tool but only applicable to academic problems, optimization has now become a viable technology. Still, one of the strengths of optimization-namely, its inherent mathematical rigor-can also be perceived as a weakness, since engineers might sometimes find it difficult to obtain an appropriate mathematical formulation to solve their practical problems. Furthermore, even when process models are available, the presence of plant-model mismatches and process disturbances makes the direct use of model-based optimal inputs hazardous. In the last 30 years, the field of real-time optimization (RTO) has emerged to help overcome the aforementioned modeling difficulties. RTO integrates process measurements into the optimization framework. This way, process optimization does not rely exclusively on a (possibly inaccurate) process model but also on process information stemming from measurements. Various RTO techniques are available in the literature and can be classified in two broad families depending on whether a process model is used (explicit optimization) or not (implicit optimization).This special issue on real-time optimization (http://www.mdpi.com/journal/processes/special_ issues/real_time_optimization) includes both methodological and practical contributions [1][2][3][4][5][6][7][8][9][10][11][12][13]. All seven methodological contributions deal with explicit RTO schemes that repeat the optimization when new measurements become available. The methods covered include modifier adaptation, economic MPC, and the two-step approach of parameter identification and numerical optimization. The six contributions that deal with applications cover various fields including refineries, well networks, combustion, and membrane filtration.This special issue shows that RTO is a very active area of research with excellent opportunities for applications. The guest editor would like to thank all authors for their timely collaboration on this project and excellent scientific contributions. References

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Hierarchical distributed optimization of constraint-coupled convex and mixed-integer programs using approximations of the dual function

Yfantis

Wenzel

Wagner

et al. 2023

EURO Journal on Computational Optimization

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A Study of Explorative Moves during Modifier Adaptation with Quadratic Approximation

Cited by 11 publications

References 12 publications

Iterative Real-Time Optimization Scheme for Optimal Operation of Chemical Processes under Uncertainty: Proof of Concept in a Miniplant

Iterative Real-Time Optimization Scheme for Optimal Operation of Chemical Processes under Uncertainty: Proof of Concept in a Miniplant

Special Issue “Real-Time Optimization” of Processes

Hierarchical distributed optimization of constraint-coupled convex and mixed-integer programs using approximations of the dual function

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