Decolorization of Acid Red 337 dye with hydroxyl and sulfate radical based advanced oxidation processes using different iron Catalyst: An experimental and statistical Investigation
{"title":"Decolorization of Acid Red 337 dye with hydroxyl and sulfate radical based advanced oxidation processes using different iron Catalyst: An experimental and statistical Investigation","authors":"","doi":"10.1016/j.jphotochem.2024.116105","DOIUrl":null,"url":null,"abstract":"<div><div>The removal efficiencies of Acid Red 337 dye were investigated by combining advanced oxidation processes based on <img>OH and SO<sub>4</sub><img><sup>−</sup> radicals. The photocatalytic oxidation processes were carried out at the natural pH (5.4) and acidic pH (3.0) values of the dye solution. In the experiments, iron (II) sulfate (Fe<sup>2+</sup>) and potassium ferrioxalate (FeOx) were used as catalysts, hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>) and peroxymonosulfate (PMS) were used as oxidants to compare dye removal efficiency and to determine the optimum doses. This comparison provided the opportunity to identify suitable catalysts and oxidants for dye removal. Furthermore, time-based comparisons were conducted from 2 to 10 min using the optimum catalyst doses. It was obtained that PMS was more effective as an oxidant in the presence of both iron catalysts for colour removal. The colour removal efficiency of 98.2 % was achieved in the PMS/Fe<sup>2+</sup>/UV process with doses of 1 mM PMS and 0.1 mM Fe<sup>2+</sup> at pH 5.4 and an irradiation time of 10 min. In experiments using light-sensitive ferrioxalate (FeOx), colour removal was achieved in the PMS/FeOx/UV process with an efficiency of 97.8 %. Additionally, an artificial neural networks (ANNs) model effectively optimized the oxidation parameters, with predicted values closely matching the experimental data. The ANNs model yielded excellent statistical performance, with a root mean square error (RMSE) of 0.0317, 0.0223 and coefficient of determination (R<sup>2</sup>) of 0.9690 and 0.9854 for UV/Fe<sup>2+</sup>/H<sub>2</sub>O<sub>2</sub>-UV/FeOx/H<sub>2</sub>O<sub>2</sub> and UV/Fe<sup>2+</sup>/PMS-UV/FeOx/PMS oxidation systems respectively.</div></div>","PeriodicalId":16782,"journal":{"name":"Journal of Photochemistry and Photobiology A-chemistry","volume":null,"pages":null},"PeriodicalIF":4.1000,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Photochemistry and Photobiology A-chemistry","FirstCategoryId":"92","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S101060302400649X","RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
引用次数: 0
Abstract
The removal efficiencies of Acid Red 337 dye were investigated by combining advanced oxidation processes based on OH and SO4− radicals. The photocatalytic oxidation processes were carried out at the natural pH (5.4) and acidic pH (3.0) values of the dye solution. In the experiments, iron (II) sulfate (Fe2+) and potassium ferrioxalate (FeOx) were used as catalysts, hydrogen peroxide (H2O2) and peroxymonosulfate (PMS) were used as oxidants to compare dye removal efficiency and to determine the optimum doses. This comparison provided the opportunity to identify suitable catalysts and oxidants for dye removal. Furthermore, time-based comparisons were conducted from 2 to 10 min using the optimum catalyst doses. It was obtained that PMS was more effective as an oxidant in the presence of both iron catalysts for colour removal. The colour removal efficiency of 98.2 % was achieved in the PMS/Fe2+/UV process with doses of 1 mM PMS and 0.1 mM Fe2+ at pH 5.4 and an irradiation time of 10 min. In experiments using light-sensitive ferrioxalate (FeOx), colour removal was achieved in the PMS/FeOx/UV process with an efficiency of 97.8 %. Additionally, an artificial neural networks (ANNs) model effectively optimized the oxidation parameters, with predicted values closely matching the experimental data. The ANNs model yielded excellent statistical performance, with a root mean square error (RMSE) of 0.0317, 0.0223 and coefficient of determination (R2) of 0.9690 and 0.9854 for UV/Fe2+/H2O2-UV/FeOx/H2O2 and UV/Fe2+/PMS-UV/FeOx/PMS oxidation systems respectively.
期刊介绍:
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