Machine-Learning-Based Kinetic-Free Hybrid Gray-Box Model to Predict Sulfuric-Acid-Catalyzed Esterification Reaction.
J Phys Chem B
; 128(19): 4770-4782, 2024 May 16.
Article
en En
| MEDLINE
| ID: mdl-38717301
ABSTRACT
The sulfuric-acid-catalyzed esterification reaction of 2-butanol and propionic anhydride is a vital industrial process. In this paper, several experiments are conducted via reaction calorimetry to validate that both sulfuric and propionic acids have discernible catalytic effects on the reaction. This finding complicates the accurate description of the reaction kinetics through traditional methods. So this paper turns to a kinetic-free black-box model, Gaussian process regression (GPR) model via 24 experiments, as a more adaptable approach. Besides, the best GPR model is combined with traditional heat balance model to generate a hybrid gray-box model, which can give complete knowledge of reaction process. The hybrid gray-box model finally achieves maximum root-mean-square error (RMSE) of 0.0069 for conversion, 0.6535 for temperature, and 10.6087 for heat flow rate, underscoring its pretty good predictive ability.
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1
Colección:
01-internacional
Base de datos:
MEDLINE
Idioma:
En
Revista:
J Phys Chem B
Asunto de la revista:
QUIMICA
Año:
2024
Tipo del documento:
Article
País de afiliación:
China
Pais de publicación:
Estados Unidos