Unveiling the potential of machine learning in cost-effective degradation of pharmaceutically active compounds: A stirred photo-reactor study

Acosta-Angulo, B., Lara-Ramos, J., Nino-Vargas, A., Diaz-Angulo, J., Benavides-Guerrero, J., Bhattacharya, A., Cloutier, S. et Machuca-Martinez, F.. 2024. « Unveiling the potential of machine learning in cost-effective degradation of pharmaceutically active compounds: A stirred photo-reactor study ». Chemosphere, vol. 358.

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Résumé

In this study, neural networks and support vector regression (SVR) were employed to predict the degradation over three pharmaceutically active compounds (PhACs): Ibuprofen (IBP), diclofenac (DCF), and caffeine (CAF) within a stirred reactor featuring a flotation cell with two non-concentric ultraviolet lamps. A total of 438 datapoints were collected from published works and distributed into 70% training and 30% test datasets while cross-validation was utilized to assess the training reliability. The models incorporated 15 input variables concerning reaction kinetics, molecular properties, hydrodynamic information, presence of radiation, and catalytic properties. It was observed that the Support Vector Regression (SVR) presented a poor performance as the hyperparameter ignored large error over low concentration levels. Meanwhile, the Artificial Neural Networks (ANN) model was able to provide rough estimations on the expected degradation of the pollutants without requiring information regarding reaction rate constants. The multi-objective optimization analysis suggested a leading role due to ozone kinetic for a rapid degradation of the contaminants and most of the results required intensification with hydrogen peroxide and Fenton process. Although both models were affected by accuracy limitations, this work provided a lightweight model to evaluate different Advanced Oxidation Processes (AOPs) by providing general information regarding the process operational conditions as well as know molecular and catalytic properties.

Type de document:	Article publié dans une revue, révisé par les pairs
Professeur:	Professeur Cloutier, Sylvain G.
Affiliation:	Génie électrique
Date de dépôt:	05 juin 2024 18:38
Dernière modification:	07 juin 2024 16:48
URI:	https://espace2.etsmtl.ca/id/eprint/28747

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