Dynamic Surrogate Modeling for Continuous Processes Control Applications

Pretoro, Alessandro Di; Tomaselli, A.; Manenti, Flavio; Montastruc, Ludovic

doi:10.1016/b978-0-323-95879-0.50016-3

Cited by 1 publication

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“…In order to mitigate these issues, alternative approaches based on constrained sampling or subsets have been proposed and have demonstrated good results, but they have not yet achieved thorough and general validation [11]. Moreover, the vast majority of the proposed studies usually refer to steady-state applications, while the process dynamics domain can only be found in a few works, mostly related to process unit or energy system optimization, that are usually based on explicit analytical or statistical functions with case-specific applications [12][13][14]. If we consider that computationally efficient digital twins for dynamic modeling would substantially increase the effectiveness of tools such as RTDO and optimal control by enabling real-time scheduling and process parameter adjustments, it is evident that the process dynamics field deserves more attention.…”

Section: Introduction 1an Overview Of the Literature On Surrogate Mod...mentioning

confidence: 99%

Exploiting Domain Partition in Response Function-Based Dynamic Surrogate Modeling: A Continuous Crystallizer Study

Di Pretoro,

Montastruc,

Negny

2024

Dynamics

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Given the exponential rise in the amount of data requiring processing in all engineering fields, phenomenological models have become computationally cumbersome. For this reason, more efficient data-driven models have been recently used with the purpose of substantially reducing simulation computational times. However, especially in process engineering, the majority of the proposed surrogate models address steady-state problems, while poor studies refer to dynamic simulation modeling. For this reason, using a response function-based approach, a crystallization unit case study was set up in order to derive a dynamic data-driven model for crystal growth whose characteristic differential parameters are derived via Response Surface Methodology. In particular, multiple independent variables were considered, and a well-established sampling technique was exploited for sample generation. Then, different sample sizes were tested and compared in terms of accuracy indicators. Finally, the domain partition strategy was exploited in order to show its relevant impact on the final model accuracy. In conclusion, the outcome of this study proved that the proposed procedure is a suitable methodology for dynamic system metamodeling, as it shows good compliance and relevant improvement in terms of computational time. In terms of future research perspectives, testing the proposed procedure on different systems and in other research fields would allow for greater improvement and would, eventually, extend its validity.

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