1
|
Acosta-Angulo B, Lara-Ramos J, Niño-Vargas A, Diaz-Angulo J, Benavides-Guerrero J, Bhattacharya A, Cloutier S, Machuca-Martínez F. Unveiling the potential of machine learning in cost-effective degradation of pharmaceutically active compounds: A stirred photo-reactor study. CHEMOSPHERE 2024; 358:142222. [PMID: 38714249 DOI: 10.1016/j.chemosphere.2024.142222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 03/30/2024] [Accepted: 04/30/2024] [Indexed: 05/09/2024]
Abstract
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 ε 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.
Collapse
Affiliation(s)
- B Acosta-Angulo
- Escuela de Ingeniería Química, Universidad Del Valle, Santiago de, Cali, 760026, Valle Del Cauca, Colombia
| | - J Lara-Ramos
- Escuela de Ingeniería Química, Universidad Del Valle, Santiago de, Cali, 760026, Valle Del Cauca, Colombia
| | - A Niño-Vargas
- Escuela de Ingeniería Química, Universidad Del Valle, Santiago de, Cali, 760026, Valle Del Cauca, Colombia
| | - J Diaz-Angulo
- Research and Technological Development in Water Treatment, Processes Modelling and Disposal of Residues - GITAM, Cauca, Colombia
| | - J Benavides-Guerrero
- Department of Electrical Engineering, Ecole de Technologia Superieure, 1100 Notre-Dame West, Montreal, H3C 1K3, Quebec, Canada
| | - A Bhattacharya
- Department of Electrical Engineering, Ecole de Technologia Superieure, 1100 Notre-Dame West, Montreal, H3C 1K3, Quebec, Canada
| | - S Cloutier
- Department of Electrical Engineering, Ecole de Technologia Superieure, 1100 Notre-Dame West, Montreal, H3C 1K3, Quebec, Canada
| | - F Machuca-Martínez
- Escuela de Ingeniería Química, Universidad Del Valle, Santiago de, Cali, 760026, Valle Del Cauca, Colombia.
| |
Collapse
|
2
|
Li Y, Tao C, Fu D, Jafvert CT, Zhu T. Integrating molecular descriptors for enhanced prediction: Shedding light on the potential of pH to model hydrated electron reaction rates for organic compounds. CHEMOSPHERE 2024; 349:140984. [PMID: 38122944 DOI: 10.1016/j.chemosphere.2023.140984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Revised: 12/13/2023] [Accepted: 12/14/2023] [Indexed: 12/23/2023]
Abstract
Hydrated electron reaction rate constant (ke-aq) is an important parameter to determine reductive degradation efficiency and to mitigate the ecological risk of organic compounds (OCs). However, OC species morphology and the concentration of hydrated electrons (e-aq) in water vary with pH, complicating OC fate assessment. This study introduced the environmental variable of pH, to develop models for ke-aq for 701 data points using 3 descriptor types: (i) molecular descriptors (MD), (ii) quantum chemical descriptors (QCD), and (iii) the combination of both (MD + QCD). Models were screened using 2 descriptor screening methods (MLR and RF) and 14 machine learning (ML) algorithms. The introduction of QCDs that characterized the electronic structure of OCs greatly improved the performance of models while ensuring the need for fewer descriptors. The optimal model MLR-XGBoost(MD + QCD), which included pH, achieved the most satisfactory prediction: R2tra = 0.988, Q2boot = 0.861, R2test = 0.875 and Q2test = 0.873. The mechanistic interpretation using the SHAP method further revealed that QCDs, polarizability, volume, and pH had a great influence on the reductive degradation of OCs by e-aq. Overall, the electrochemical parameters (QCDs, pH) related to the solvent and solute are of significance and should be considered in any future ML modeling that assesses the fate of OCs in aquatic environment.
Collapse
Affiliation(s)
- Yi Li
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China
| | - Cuicui Tao
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China
| | - Dafang Fu
- School of Civil Engineering, Southeast University, Nanjing, 210096, China
| | - Chad T Jafvert
- Lyles School of Civil Engineering, and Environmental & Ecological Engineering, Purdue University, West Lafayette, IN, 47907, USA
| | - Tengyi Zhu
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China.
| |
Collapse
|