Optimizing Environmental Impact: MCDM-Based Approaches for Petrochemical Industry Emission Cuts
The petrochemical industry is a major contributor to carbon emissions, necessitating an urgent shift towards effective emission reduction techniques. However, a lack of essential data has hindered the development of strategies to address this issue, calling for a comprehensive approach. This study s...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10445263/ |
| _version_ | 1797243399232815104 |
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| author | Muhammad Ahsan Lixin Tian Ruijin Du Amel Ali Alhussan El-Sayed M. El-Kenawy |
| author_facet | Muhammad Ahsan Lixin Tian Ruijin Du Amel Ali Alhussan El-Sayed M. El-Kenawy |
| author_sort | Muhammad Ahsan |
| collection | DOAJ |
| description | The petrochemical industry is a major contributor to carbon emissions, necessitating an urgent shift towards effective emission reduction techniques. However, a lack of essential data has hindered the development of strategies to address this issue, calling for a comprehensive approach. This study seeks to formulate effective approaches for mitigating carbon emissions in the petrochemical sector by assessing their impact and recognizing potential barriers to reduction. The primary objectives revolve around three key aspects: reducing energy intensity, optimizing CO2 emission reduction, and minimizing associated costs. To attain these objectives, we utilized a dataset represented as a Complex Multi-Fuzzy Hypersoft Set (CMFHSS), specifically designed to address data uncertainties through the incorporation of amplitude and phase terms (P-terms) of complex numbers (C-numbers). The research explores three decision-making techniques, namely Similarity Measures (SM), Entropy (ENT) and TOPSIS within CMFHSS. These techniques are applied to identify the most efficient carbon emission reduction strategy, with the goal of maximizing benefits while minimizing costs. |
| first_indexed | 2024-04-24T18:54:30Z |
| format | Article |
| id | doaj.art-df43bed60ec84fa999fbbff761b51330 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-24T18:54:30Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-df43bed60ec84fa999fbbff761b513302024-03-26T17:46:12ZengIEEEIEEE Access2169-35362024-01-0112353093532410.1109/ACCESS.2024.337016410445263Optimizing Environmental Impact: MCDM-Based Approaches for Petrochemical Industry Emission CutsMuhammad Ahsan0https://orcid.org/0000-0001-8701-0284Lixin Tian1Ruijin Du2Amel Ali Alhussan3https://orcid.org/0000-0001-7530-7961El-Sayed M. El-Kenawy4https://orcid.org/0000-0002-9221-7658School of Mathematical Sciences, Jiangsu University, Zhenjiang, ChinaSchool of Mathematical Sciences, Jiangsu University, Zhenjiang, ChinaSchool of Mathematical Sciences, Jiangsu University, Zhenjiang, ChinaDepartment of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, Saudi ArabiaDepartment of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, EgyptThe petrochemical industry is a major contributor to carbon emissions, necessitating an urgent shift towards effective emission reduction techniques. However, a lack of essential data has hindered the development of strategies to address this issue, calling for a comprehensive approach. This study seeks to formulate effective approaches for mitigating carbon emissions in the petrochemical sector by assessing their impact and recognizing potential barriers to reduction. The primary objectives revolve around three key aspects: reducing energy intensity, optimizing CO2 emission reduction, and minimizing associated costs. To attain these objectives, we utilized a dataset represented as a Complex Multi-Fuzzy Hypersoft Set (CMFHSS), specifically designed to address data uncertainties through the incorporation of amplitude and phase terms (P-terms) of complex numbers (C-numbers). The research explores three decision-making techniques, namely Similarity Measures (SM), Entropy (ENT) and TOPSIS within CMFHSS. These techniques are applied to identify the most efficient carbon emission reduction strategy, with the goal of maximizing benefits while minimizing costs.https://ieeexplore.ieee.org/document/10445263/Decision-makingsimilarity measureentropyTOPSIS |
| spellingShingle | Muhammad Ahsan Lixin Tian Ruijin Du Amel Ali Alhussan El-Sayed M. El-Kenawy Optimizing Environmental Impact: MCDM-Based Approaches for Petrochemical Industry Emission Cuts IEEE Access Decision-making similarity measure entropy TOPSIS |
| title | Optimizing Environmental Impact: MCDM-Based Approaches for Petrochemical Industry Emission Cuts |
| title_full | Optimizing Environmental Impact: MCDM-Based Approaches for Petrochemical Industry Emission Cuts |
| title_fullStr | Optimizing Environmental Impact: MCDM-Based Approaches for Petrochemical Industry Emission Cuts |
| title_full_unstemmed | Optimizing Environmental Impact: MCDM-Based Approaches for Petrochemical Industry Emission Cuts |
| title_short | Optimizing Environmental Impact: MCDM-Based Approaches for Petrochemical Industry Emission Cuts |
| title_sort | optimizing environmental impact mcdm based approaches for petrochemical industry emission cuts |
| topic | Decision-making similarity measure entropy TOPSIS |
| url | https://ieeexplore.ieee.org/document/10445263/ |
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