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Algebraic surrogate-based flexibility analysis of process units with complicating process constraints.

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Title: Algebraic surrogate-based flexibility analysis of process units with complicating process constraints.
Authors: Forster, Tim1 (AUTHOR), Vázquez, Daniel1,2 (AUTHOR), Moreno-Palancas, Isabela Fons1 (AUTHOR), Guillén-Gosálbez, Gonzalo1 (AUTHOR) gonzalo.guillen.gosalbez@chem.ethz.ch
Source: Computers & Chemical Engineering. May2024, Vol. 184, pN.PAG-N.PAG. 1p.
Subjects: Chemical engineering, Chemical engineers, Prediction models
Abstract: • A method for solving the flexibility index problem with surrogate models incorporated. • Bypassing complicating constraints to solve the flexibility index problem. • Offline surrogate model training based on symbolic regression. • Application of the method to two bioprocess case studies. Flexibility analyses are widespread in chemical engineering to quantify allowed deviations from nominal conditions. Standard approaches to perform flexibility analysis can be hard to apply if process constraints are difficult to handle, as it happens in bioprocesses with dynamic constraints. Here, focusing on the computation of the traditional flexibility index in problems with complicating constraints, we apply symbolic regression to build algebraic expressions of the said complicating constraints, simplifying the flexibility analysis of complex process models by enabling the application of state-of-the-art deterministic solvers. Our approach is applied to ethanol production in fed-batch operation mode and a chromatographic process. The performance is assessed in terms of model building time, predictive accuracy of the model, and the time required to solve the flexibility formulations. Overall, our approach, which focuses on computing the original flexibility index proposed in the literature, provides an alternative way to analyse the flexibility of processes entailing complicating constraints. [ABSTRACT FROM AUTHOR]
Copyright of Computers & Chemical Engineering is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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An: 176225465
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  Data: • A method for solving the flexibility index problem with surrogate models incorporated. • Bypassing complicating constraints to solve the flexibility index problem. • Offline surrogate model training based on symbolic regression. • Application of the method to two bioprocess case studies. Flexibility analyses are widespread in chemical engineering to quantify allowed deviations from nominal conditions. Standard approaches to perform flexibility analysis can be hard to apply if process constraints are difficult to handle, as it happens in bioprocesses with dynamic constraints. Here, focusing on the computation of the traditional flexibility index in problems with complicating constraints, we apply symbolic regression to build algebraic expressions of the said complicating constraints, simplifying the flexibility analysis of complex process models by enabling the application of state-of-the-art deterministic solvers. Our approach is applied to ethanol production in fed-batch operation mode and a chromatographic process. The performance is assessed in terms of model building time, predictive accuracy of the model, and the time required to solve the flexibility formulations. Overall, our approach, which focuses on computing the original flexibility index proposed in the literature, provides an alternative way to analyse the flexibility of processes entailing complicating constraints. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Computers & Chemical Engineering is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.compchemeng.2024.108630
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        Text: English
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              M: 05
              Text: May2024
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              Y: 2024
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