Global optimization of symbolic surrogate process models based on Bayesian learning

Tim Forster, Daniel Vázquez, Gonzalo Guillé-Gosálbez

Producció científica: Capítol de llibreCapítolAvaluat per experts


In this work, we address the global optimization of process surrogates using Bayesian symbolic regression and deterministic global optimization algorithms. In contrast to other surrogates of process models that are hard to (globally) optimize, e.g., artificial neural networks or Gaussian processes, symbolic regression leads to a closed-form mathematical expression describing the observed data that can subsequently be globally optimized using off-the-shelf deterministic solvers. After providing an introductory example, we show the capabilities of our approach in the optimization of a methanol production plant. We further discuss the model accuracy, CPU times for model building and optimization, and outline the advantages and limitations of the proposed strategy.

Idioma originalAnglès
Títol de la publicacióComputer Aided Chemical Engineering
EditorElsevier B.V.
Nombre de pàgines6
Estat de la publicacióPublicada - de gen. 2023
Publicat externament

Sèrie de publicacions

NomComputer Aided Chemical Engineering
ISSN (imprès)1570-7946


Navegar pels temes de recerca de 'Global optimization of symbolic surrogate process models based on Bayesian learning'. Junts formen un fingerprint únic.

Com citar-ho