Autor según el artículo: Negri V; Vázquez D; Sales-Pardo M; Guimerà R; Guillén-Gosálbez G
Departamento: Enginyeria Química
Autor/es de la URV: Guimera Manrique, Roger / Sales Pardo, Marta
Palabras clave: Optimal process design surrogate models storage regression optimization natural-gas flexibility analysis dioxide chemical absorption carbon-capture
Resumen: 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.
Áreas temáticas: Química Interdisciplinar General chemistry General chemical engineering Engenharias ii Ciências agrárias i Chemistry, multidisciplinary Chemistry (miscellaneous) Chemistry (all) Chemical engineering (miscellaneous) Chemical engineering (all)
Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
Direcció de correo del autor: roger.guimera@urv.cat marta.sales@urv.cat
Identificador del autor: 0000-0002-3597-4310 0000-0002-8140-6525
Fecha de alta del registro: 2024-07-20
Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
Enlace a la fuente original: https://pubs.acs.org/doi/10.1021/acsomega.2c04736
URL Documento de licencia: http://repositori.urv.cat/ca/proteccio-de-dades/
Referencia al articulo segun fuente origial: Acs Omega. 7 (45): 41147-41164
Referencia de l'ítem segons les normes APA: Negri V; Vázquez D; Sales-Pardo M; Guimerà R; Guillén-Gosálbez G (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
DOI del artículo: 10.1021/acsomega.2c04736
Entidad: Universitat Rovira i Virgili
Año de publicación de la revista: 2022
Tipo de publicación: Journal Publications
Repositori URV