Articles producció científica> Enginyeria Química

Bayesian Symbolic Learning to Build Analytical Correlations from Rigorous Process Simulations: Application to CO2Capture Technologies

  • Identification data

    Identifier: imarina:9286887
    Authors:
    Negri, ValentinaVazquez, DanielSales-Pardo, MartaGuimera, RogerGuillen-Gosalbez, Gonzalo
    Abstract:
    Process modeling has become a fundamental tool to guide experimental work. Unfortunately, process models based on first principles can be expensive to develop and evaluate, and hard to use, particularly when convergence issues arise. This work proves that Bayesian symbolic learning can be applied to derive simple closed-form expressions from rigorous process simulations, streamlining the process modeling task and making process models more accessible to experimental groups. Compared to conventional surrogate models, our approach provides analytical expressions that are easier to communicate and manipulate algebraically to get insights into the process. We apply this method to synthetic data obtained from two basic CO2 capture processes simulated in Aspen HYSYS, identifying accurate simplified interpretable equations for key variables dictating the process economic and environmental performance. We then use these expressions to analyze the process variables' elasticities and benchmark an emerging CO2 capture process against the business as usual technology.
  • Others:

    Author, as appears in the article.: Negri, Valentina; Vazquez, Daniel; Sales-Pardo, Marta; Guimera, Roger; Guillen-Gosalbez, Gonzalo
    Department: Enginyeria Química
    URV's Author/s: Guimera Manrique, Roger / Sales Pardo, Marta
    Keywords: Optimal process design surrogate models storage regression optimization natural-gas flexibility analysis dioxide chemical absorption carbon-capture
    Abstract: Process modeling has become a fundamental tool to guide experimental work. Unfortunately, process models based on first principles can be expensive to develop and evaluate, and hard to use, particularly when convergence issues arise. This work proves that Bayesian symbolic learning can be applied to derive simple closed-form expressions from rigorous process simulations, streamlining the process modeling task and making process models more accessible to experimental groups. Compared to conventional surrogate models, our approach provides analytical expressions that are easier to communicate and manipulate algebraically to get insights into the process. We apply this method to synthetic data obtained from two basic CO2 capture processes simulated in Aspen HYSYS, identifying accurate simplified interpretable equations for key variables dictating the process economic and environmental performance. We then use these expressions to analyze the process variables' elasticities and benchmark an emerging CO2 capture process against the business as usual technology.
    Thematic Areas: Química Interdisciplinar General chemistry General chemical engineering Engenharias ii Ciências agrárias i Chemistry, multidisciplinary Chemistry (miscellaneous) Chemistry (all) Chemical engineering (miscellaneous) Chemical engineering (all)
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    Author's mail: roger.guimera@urv.cat marta.sales@urv.cat
    Author identifier: 0000-0002-3597-4310 0000-0002-8140-6525
    Record's date: 2024-10-19
    Papper version: info:eu-repo/semantics/publishedVersion
    Link to the original source: https://pubs.acs.org/doi/10.1021/acsomega.2c04736
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Papper original source: Acs Omega. 7 (45): 41147-41164
    APA: Negri, Valentina; Vazquez, Daniel; Sales-Pardo, Marta; Guimera, Roger; Guillen-Gosalbez, Gonzalo (2022). Bayesian Symbolic Learning to Build Analytical Correlations from Rigorous Process Simulations: Application to CO2Capture Technologies. Acs Omega, 7(45), 41147-41164. DOI: 10.1021/acsomega.2c04736
    Article's DOI: 10.1021/acsomega.2c04736
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2022
    Publication Type: Journal Publications
  • Keywords:

    Chemical Engineering (Miscellaneous),Chemistry (Miscellaneous),Chemistry, Multidisciplinary
    Optimal process design
    surrogate models
    storage
    regression
    optimization
    natural-gas
    flexibility analysis
    dioxide
    chemical absorption
    carbon-capture
    Química
    Interdisciplinar
    General chemistry
    General chemical engineering
    Engenharias ii
    Ciências agrárias i
    Chemistry, multidisciplinary
    Chemistry (miscellaneous)
    Chemistry (all)
    Chemical engineering (miscellaneous)
    Chemical engineering (all)
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