Bayesian Symbolic Learning to Build Analytical Correlations from Rigorous Process Simulations: Application to CO(2) Capture Technologies

[Image: see text] 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...

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Detalles Bibliográficos
Autores principales: Negri, Valentina, Vázquez, Daniel, Sales-Pardo, Marta, Guimerà, Roger, Guillén-Gosálbez, Gonzalo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2022
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9670717/
https://www.ncbi.nlm.nih.gov/pubmed/36406548
http://dx.doi.org/10.1021/acsomega.2c04736

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9670717/
https://www.ncbi.nlm.nih.gov/pubmed/36406548
http://dx.doi.org/10.1021/acsomega.2c04736

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