A decision support system for integration of decarbonization strategies in the oil refining industry

Mitigating the impacts of pollution caused by the oil and natural gas energy systems by applying an optimal combination of decarbonization strategies has advantages for environmental, economic, social, and health goals. The implementation of these strategies has become one of the most important problems of the project-oriented oil refining industry. This industry is thus confronted with a growing need to decarbonize its operations. Therefore, this work aims at developing an intelligent decision support system (DSS) for choosing and implementing a sustainable and optimal combination of decarbonization strategies in the studied industry.The proposed DSS involves the weights by envelope and slope (WENSLO) and Bernardo methods under the m-polar fuzzy N-soft (mFNS) information. For applying this DSS, 16 criteria-based sustainability and feasibility policies and six decarbonization strategies including Improving energy efficiency (S1), Waste-heat and over-bottom pressure recovery (S2), Improving design performance (S3), Increasing use of renewable energy sources (S4), Adoption of low-carbon hydrogen (S5), and Adoption of CCUS technologies (S6) have been considered.The oil refineries located in Iran are studied as a case to implement the performance of the DSS of the present work. The results indicate that the DSS can be applied in real-world conditions and it has acceptable performance under uncertainty states. Also, it can affect the long-term success and profitability of the industry and positively guide project managers towards making robust decisions for the projects in the long-term.