Novel technology for industrial VOCs exhaust treatment under dual-carbon target based on multivariate statistical analysis
Against the background of the dual-carbon target, the treatment of volatile organic compounds (VOCs) emissions in the industrial sector, especially in the chemical industry, has become an urgent environmental issue. To provide scientific and objective guidance for technology selection, this study co...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Sciendo
2024-01-01
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| Series: | Applied Mathematics and Nonlinear Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/amns-2024-0538 |
| _version_ | 1797279133665853440 |
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| author | Chen Yong Xie Guangyuan |
| author_facet | Chen Yong Xie Guangyuan |
| author_sort | Chen Yong |
| collection | DOAJ |
| description | Against the background of the dual-carbon target, the treatment of volatile organic compounds (VOCs) emissions in the industrial sector, especially in the chemical industry, has become an urgent environmental issue. To provide scientific and objective guidance for technology selection, this study constructs a comprehensive evaluation model based on the combination of entropy weighting method and hierarchical analysis method for the complete progress of VOCs treatment technologies, taking into account the four main factors of environment, economy, management and technology and the thirteen secondary factors under them. The model provides in-depth quantitative analysis and evaluation of ten common VOCs end-of-pipe treatment technologies. The evaluation results show that thermal combustion, catalytic combustion, adsorption-concentration-combustion, and photocatalysis exhibit high comprehensive efficacy in VOCs emission treatment, with relative weight values of 0.1273, 0.1252, 0.1214, and 0.1184, respectively. These findings provide essential references and guidance for chemical companies in selecting suitable VOCs treatment technologies, which help to realize the win-win situation of environmental protection and economic development. This study fills the gap in evaluating VOCs emission management technologies and provides a practical decision-making tool for relevant decision makers. |
| first_indexed | 2024-03-07T16:19:52Z |
| format | Article |
| id | doaj.art-8f894cdfb0fc45adb2df0a87b7f4639e |
| institution | Directory Open Access Journal |
| issn | 2444-8656 |
| language | English |
| last_indexed | 2024-03-07T16:19:52Z |
| publishDate | 2024-01-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Applied Mathematics and Nonlinear Sciences |
| spelling | doaj.art-8f894cdfb0fc45adb2df0a87b7f4639e2024-03-04T07:30:42ZengSciendoApplied Mathematics and Nonlinear Sciences2444-86562024-01-019110.2478/amns-2024-0538Novel technology for industrial VOCs exhaust treatment under dual-carbon target based on multivariate statistical analysisChen Yong0Xie Guangyuan11Hunan Polytechnic of Environment and Biology, Hengyang, Hunan, 421001, China.1Hunan Polytechnic of Environment and Biology, Hengyang, Hunan, 421001, China.Against the background of the dual-carbon target, the treatment of volatile organic compounds (VOCs) emissions in the industrial sector, especially in the chemical industry, has become an urgent environmental issue. To provide scientific and objective guidance for technology selection, this study constructs a comprehensive evaluation model based on the combination of entropy weighting method and hierarchical analysis method for the complete progress of VOCs treatment technologies, taking into account the four main factors of environment, economy, management and technology and the thirteen secondary factors under them. The model provides in-depth quantitative analysis and evaluation of ten common VOCs end-of-pipe treatment technologies. The evaluation results show that thermal combustion, catalytic combustion, adsorption-concentration-combustion, and photocatalysis exhibit high comprehensive efficacy in VOCs emission treatment, with relative weight values of 0.1273, 0.1252, 0.1214, and 0.1184, respectively. These findings provide essential references and guidance for chemical companies in selecting suitable VOCs treatment technologies, which help to realize the win-win situation of environmental protection and economic development. This study fills the gap in evaluating VOCs emission management technologies and provides a practical decision-making tool for relevant decision makers.https://doi.org/10.2478/amns-2024-0538hierarchical analysisentropy weight methodcomprehensive evaluationvocs management00a73 |
| spellingShingle | Chen Yong Xie Guangyuan Novel technology for industrial VOCs exhaust treatment under dual-carbon target based on multivariate statistical analysis Applied Mathematics and Nonlinear Sciences hierarchical analysis entropy weight method comprehensive evaluation vocs management 00a73 |
| title | Novel technology for industrial VOCs exhaust treatment under dual-carbon target based on multivariate statistical analysis |
| title_full | Novel technology for industrial VOCs exhaust treatment under dual-carbon target based on multivariate statistical analysis |
| title_fullStr | Novel technology for industrial VOCs exhaust treatment under dual-carbon target based on multivariate statistical analysis |
| title_full_unstemmed | Novel technology for industrial VOCs exhaust treatment under dual-carbon target based on multivariate statistical analysis |
| title_short | Novel technology for industrial VOCs exhaust treatment under dual-carbon target based on multivariate statistical analysis |
| title_sort | novel technology for industrial vocs exhaust treatment under dual carbon target based on multivariate statistical analysis |
| topic | hierarchical analysis entropy weight method comprehensive evaluation vocs management 00a73 |
| url | https://doi.org/10.2478/amns-2024-0538 |
| work_keys_str_mv | AT chenyong noveltechnologyforindustrialvocsexhausttreatmentunderdualcarbontargetbasedonmultivariatestatisticalanalysis AT xieguangyuan noveltechnologyforindustrialvocsexhausttreatmentunderdualcarbontargetbasedonmultivariatestatisticalanalysis |