Application of machine learning algorithms in predicting the photocatalytic degradation of perfluorooctanoic acid

Navidpour, AH; Hosseinzadeh, A; Huang, Z; Li, D; Zhou, JL

Application of machine learning algorithms in predicting the photocatalytic degradation of perfluorooctanoic acid

Navidpour, AH Hosseinzadeh, A Huang, Z

Li, D Zhou, JL

Permalink

Publisher:: TAYLOR & FRANCIS INC
Publication Type:: Journal Article
Citation:: Catalysis Reviews - Science and Engineering, 2022
Issue Date:: 2022-01-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

The embargo period expires on 6 Jun 2023

Adobe PDF

Download Accepted versionAdobe PDF (1.89 MB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Navidpour, AH
dc.contributor.author	Hosseinzadeh, A
dc.contributor.author	Huang, Z https://orcid.org/0000-0003-1985-0884
dc.contributor.author	Li, D
dc.contributor.author	Zhou, JL
dc.date.accessioned	2023-01-19T03:31:51Z
dc.date.available	2023-01-19T03:31:51Z
dc.date.issued	2022-01-01
dc.identifier.citation	Catalysis Reviews - Science and Engineering, 2022
dc.identifier.issn	0161-4940
dc.identifier.issn	1520-5703
dc.identifier.uri	http://hdl.handle.net/10453/165197
dc.description.abstract	Perfluorooctanoic acid (PFOA) is used in a variety of industries and is highly persistent in the environment, with potential human health risks. Photocatalysis has been extensively used for the decomposition of various organic pollutants, yet its simulation and modeling are challenging. This research aimed to establish different machine learning (ML) algorithms which can simulate and predict the photocatalytic degradation of PFOA. The published results were used to estimate and predict the photocatalytic degradation of PFOA. Statistical criteria including the coefficient of determination (R2), mean absolute error (MAE), and mean squared error (MSE) were considered in assessing the best method of modeling. Among the seven ML algorithms pre-screened, Adaptive Boosting (AdaBoost), Gradient Boosting Machine (GBM), and Random Forest (RF) showed the best performance and were chosen for deep modeling and analysis. Grid search was used to optimize the models developed by AdaBoost, GBM, and RF; and permutation variable importance (PVI) was used to analyze the relative importance of different variables. Based on the modeling results, GBM model (R2 = 0.878, MSE = 106.660, MAE = 6.009) and RF model (R2 = 0.867, MSE = 107.500, MAE = 6.796) showed superior performances compared with AdaBoost model (R2 = 0.574, MSE = 388.369, MAE = 16.480). Furthermore, the PVI results suggested that the GBM model provided the best outcome, with the light irradiation time, type of catalyst, dosage of catalyst, solution pH, irradiation intensity, initial PFOA concentration, oxidizing agents (peroxymonosulfate, ammonium persulfate, and sodium persulfate), irradiation wavelength, and solution temperature as the most important process variables in decreasing order.
dc.language	English
dc.publisher	TAYLOR & FRANCIS INC
dc.relation.ispartof	Catalysis Reviews - Science and Engineering
dc.relation.isbasedon	10.1080/01614940.2022.2082650
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.rights	This is an Accepted Manuscript of an article published by Taylor & Francis in Catalysis Reviews on 2022-06-06, available online: https://www.tandfonline.com/10.1080/01614940.2022.2082650.
dc.subject	0306 Physical Chemistry (incl. Structural), 0904 Chemical Engineering
dc.subject.classification	Physical Chemistry
dc.title	Application of machine learning algorithms in predicting the photocatalytic degradation of perfluorooctanoic acid
dc.type	Journal Article
utslib.for	0306 Physical Chemistry (incl. Structural)
utslib.for	0904 Chemical Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CTWW - Centre for Technology in Water and Wastewater Treatment
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2023-06-06T00:00:00+1000Z
dc.date.updated	2023-01-19T03:31:46Z
pubs.publication-status	Published

Abstract:

Perfluorooctanoic acid (PFOA) is used in a variety of industries and is highly persistent in the environment, with potential human health risks. Photocatalysis has been extensively used for the decomposition of various organic pollutants, yet its simulation and modeling are challenging. This research aimed to establish different machine learning (ML) algorithms which can simulate and predict the photocatalytic degradation of PFOA. The published results were used to estimate and predict the photocatalytic degradation of PFOA. Statistical criteria including the coefficient of determination (R2), mean absolute error (MAE), and mean squared error (MSE) were considered in assessing the best method of modeling. Among the seven ML algorithms pre-screened, Adaptive Boosting (AdaBoost), Gradient Boosting Machine (GBM), and Random Forest (RF) showed the best performance and were chosen for deep modeling and analysis. Grid search was used to optimize the models developed by AdaBoost, GBM, and RF; and permutation variable importance (PVI) was used to analyze the relative importance of different variables. Based on the modeling results, GBM model (R2 = 0.878, MSE = 106.660, MAE = 6.009) and RF model (R2 = 0.867, MSE = 107.500, MAE = 6.796) showed superior performances compared with AdaBoost model (R2 = 0.574, MSE = 388.369, MAE = 16.480). Furthermore, the PVI results suggested that the GBM model provided the best outcome, with the light irradiation time, type of catalyst, dosage of catalyst, solution pH, irradiation intensity, initial PFOA concentration, oxidizing agents (peroxymonosulfate, ammonium persulfate, and sodium persulfate), irradiation wavelength, and solution temperature as the most important process variables in decreasing order.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/165197