A convex optimization framework for the identification of homogeneous reaction systems

Al-Matouq, Ali; Vincent, Tyrone L.

doi:10.1016/j.automatica.2020.108823

Cited by 4 publications

(2 citation statements)

References 18 publications

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“…Herein, N T r e denotes concentration change rate in per unit time and physical volume, and is defined as transformed concentration change (due to reaction) that will be consistently expressed as Equation (20).…”

Section: Concentration Modeling and Enumeration Methods For Candidate...mentioning

confidence: 99%

“…However, it creates a nonuniqueness problem that is hard to solve. Al‐Matouq and Vincent 20 present a convex optimization framework to provide unique solutions by selecting an appropriate tuning parameter in an iterative algorithm. Nevertheless, all these methods require a priori information of some candidate reaction stoichiometries and reaction rate laws.…”

Section: Introductionmentioning

confidence: 99%

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Automatic data‐driven stoichiometry identification and kinetic modeling framework for homogeneous organic reactions

et al. 2022

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Data-driven and knowledge-driven methods are two approaches used in studying reaction kinetics. This article proposes a hybrid-modeling framework for homogeneous synthesis reactions, which combines the advantages of high level of automation in the data-driven approach and improved accuracy in the knowledge-driven approach. A constrained enumeration method is proposed to generate possible candidate stoichiometries, and dynamic response surface methodology, target factor analysis, and mass balance are used together for identifying stoichiometries one-byone, without the necessity of an expert-generated candidate list. Then, the previously screened stoichiometries are formed into different groups that represent candidate reaction systems, and the group (or groups) with the greatest likelihood will be identified, based on kinetic fitting and reaction dynamic criteria. This framework has been demonstrated by several examples of different reaction systems. The true reaction stoichiometries are all correctly identified, and the accurate kinetic models are obtained, showing satisfactory performance of the proposed method.

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Section: Concentration Modeling and Enumeration Methods For Candidate...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic data‐driven stoichiometry identification and kinetic modeling framework for homogeneous organic reactions

et al. 2022

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Identification of reaction rate parameters from uncertain spatially distributed concentration data using gradient-based PDE constrained optimization

Ito,

Jeßberger,

Simonis

et al. 2024

Computers & Mathematics with Applications

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Improved understanding of reaction kinetic identification problems using different nonlinear optimization algorithms

Till

Chován

Varga

2020

Journal of the Taiwan Institute of Chemical Engineers

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A convex optimization framework for the identification of homogeneous reaction systems

Cited by 4 publications

References 18 publications

Automatic data‐driven stoichiometry identification and kinetic modeling framework for homogeneous organic reactions

Automatic data‐driven stoichiometry identification and kinetic modeling framework for homogeneous organic reactions

Identification of reaction rate parameters from uncertain spatially distributed concentration data using gradient-based PDE constrained optimization

Improved understanding of reaction kinetic identification problems using different nonlinear optimization algorithms

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