Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/106180
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Title: 1-naphthol removal by fenton-like heterogenous photocatalysis : kinetic modelling, optimization, and prediction by response surface methodology
Authors: So, HL 
Chu, W 
Issue Date: Nov-2022
Source: Chemical engineering journal advances, 15 Nov. 2022, v. 12, 100395
Abstract: Degradation of 1-Naphthol by combined photocatalysis and adsorption in column reactor was studied, and the performance could be predicted by employing a proposed multiple regression model. The model includes four variables: concentration of 1-Naphthol, concentration of peroxymonosulfate (PMS), FeOOH dosage and pH, along with their second-order effects and interactions. Statistical analyses were performed to verify the statistical significance of the regressors, and extra experimental sets were conducted to validate the proposed model. The mathematical model proposed exhibited good reproducibility with no significant statistical difference between the experimental and predicted values. Response surface methodology was used to explore the relationship between the four variables and the response. The process optimal ranges can be further refined at pH 2-2.5; [PMS] = 1.2-1.4 mM; and FeOOH 2.75-3 g respectively, according to the proposed overlaid contour plots.
Keywords: Advanced oxidation processes
Prediction model
Multiple regression
Response surface analysis
Process optimization
Peroxymonosulfate
Column reactor
Iron hydroxide
Publisher: Elsevier BV
Journal: Chemical engineering journal advances 
EISSN: 2666-8211
DOI: 10.1016/j.ceja.2022.100395
Rights: © 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
The following publication So, H. L., & Chu, W. (2022). 1-Naphthol removal by Fenton-like heterogenous photocatalysis: Kinetic modelling, optimization, and prediction by response surface methodology. Chemical Engineering Journal Advances, 12, 100395 is available at https://dx.doi.org/10.1016/j.ceja.2022.100395.
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