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Photo-Fenton Degradation Process of Styrene in Nitrogen-Sealed Storage Tank

Yiqiang Zhao, Meng Liu, Xiaolong Xu, Chunxu Li Orcid Logo, Jiaji Cheng, Zhimeng Wang, Dong Wang, Wenjuan Qu, Shaoxiang Li

Toxics, Volume: 11, Issue: 1, Start page: 26

Swansea University Author: Chunxu Li Orcid Logo

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DOI (Published version): 10.3390/toxics11010026

Abstract

Using styrene as a proxy for VOCs, a new method was developed to remove styrene gas in nitrogen atmospheres. The effect on the styrene removal efficiency was explored by varying parameters within the continuum dynamic experimental setup, such as ferrous ion concentration, hydrogen peroxide concentra...

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Published in: Toxics
ISSN: 2305-6304
Published: MDPI AG 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa66025
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spelling v2 66025 2024-04-09 Photo-Fenton Degradation Process of Styrene in Nitrogen-Sealed Storage Tank e6ed70d02c25b05ab52340312559d684 0000-0001-7851-0260 Chunxu Li Chunxu Li true false 2024-04-09 ACEM Using styrene as a proxy for VOCs, a new method was developed to remove styrene gas in nitrogen atmospheres. The effect on the styrene removal efficiency was explored by varying parameters within the continuum dynamic experimental setup, such as ferrous ion concentration, hydrogen peroxide concentration, and pH values. The by-products are quantized by a TOC analyzer. The optimal process conditions were hydrogen peroxide at 20 mmol/L, ferrous ions at 0.3 mmol/L and pH 3, resulting in an average styrene removal efficiency of 96.23%. In addition, in this study, we construct a BAS-BP neural network model with experimental data as a sample training set, which boosts the goodness-of-fit of the BP neural network and is able to tentatively predict styrene gas residuals for different front-end conditions. Journal Article Toxics 11 1 26 MDPI AG 2305-6304 styrene; UV/Fenton; nitrogen; VOCs; BAS-BP neural network 27 12 2022 2022-12-27 10.3390/toxics11010026 COLLEGE NANME Aerospace, Civil, Electrical, and Mechanical Engineering COLLEGE CODE ACEM Swansea University Another institution paid the OA fee Shandong Provincial Major Science and Technology Innovation Program (2022CXGC020401). 2024-05-22T15:04:35.3495059 2024-04-09T20:18:46.9358429 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering Yiqiang Zhao 1 Meng Liu 2 Xiaolong Xu 3 Chunxu Li 0000-0001-7851-0260 4 Jiaji Cheng 5 Zhimeng Wang 6 Dong Wang 7 Wenjuan Qu 8 Shaoxiang Li 9 66025__30437__ff0e31cb25a74268924ae78ec46cf3e3.pdf 66025.VoR.pdf 2024-05-22T15:03:10.4500422 Output 7104244 application/pdf Version of Record true © 2022 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. true eng https://creativecommons.org/licenses/by/4.0/
title Photo-Fenton Degradation Process of Styrene in Nitrogen-Sealed Storage Tank
spellingShingle Photo-Fenton Degradation Process of Styrene in Nitrogen-Sealed Storage Tank
Chunxu Li
title_short Photo-Fenton Degradation Process of Styrene in Nitrogen-Sealed Storage Tank
title_full Photo-Fenton Degradation Process of Styrene in Nitrogen-Sealed Storage Tank
title_fullStr Photo-Fenton Degradation Process of Styrene in Nitrogen-Sealed Storage Tank
title_full_unstemmed Photo-Fenton Degradation Process of Styrene in Nitrogen-Sealed Storage Tank
title_sort Photo-Fenton Degradation Process of Styrene in Nitrogen-Sealed Storage Tank
author_id_str_mv e6ed70d02c25b05ab52340312559d684
author_id_fullname_str_mv e6ed70d02c25b05ab52340312559d684_***_Chunxu Li
author Chunxu Li
author2 Yiqiang Zhao
Meng Liu
Xiaolong Xu
Chunxu Li
Jiaji Cheng
Zhimeng Wang
Dong Wang
Wenjuan Qu
Shaoxiang Li
format Journal article
container_title Toxics
container_volume 11
container_issue 1
container_start_page 26
publishDate 2022
institution Swansea University
issn 2305-6304
doi_str_mv 10.3390/toxics11010026
publisher MDPI AG
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering
document_store_str 1
active_str 0
description Using styrene as a proxy for VOCs, a new method was developed to remove styrene gas in nitrogen atmospheres. The effect on the styrene removal efficiency was explored by varying parameters within the continuum dynamic experimental setup, such as ferrous ion concentration, hydrogen peroxide concentration, and pH values. The by-products are quantized by a TOC analyzer. The optimal process conditions were hydrogen peroxide at 20 mmol/L, ferrous ions at 0.3 mmol/L and pH 3, resulting in an average styrene removal efficiency of 96.23%. In addition, in this study, we construct a BAS-BP neural network model with experimental data as a sample training set, which boosts the goodness-of-fit of the BP neural network and is able to tentatively predict styrene gas residuals for different front-end conditions.
published_date 2022-12-27T15:04:34Z
_version_ 1799761873170595840
score 11.01438

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