Abstract
Concerns in relation to consequences of global warming and climate change have activated worldwide attempts for mitigating the concentration of carbon dioxide (CO2) produced by the industrial sector. Decarbonizing the oil and gas refining (OGR) industries is a challenging problem for policy-makers owing to its potential to prevent economic, environmental, and health risks. In this regard, CO2 capture, utilization, and storage (CCUS) technologies are the most encouraging options to decarbonize. The technologies related to the part of CO2 capture can play a vital role in solving the mentioned problem. Various technologies have been employed for CO2 capture, and choosing the appropriate technology is a complex multi-criteria decision-making (MCDM) issue. This work develops a novel and robust decision support system (DSS). The DSS integrates MCDM techniques of the Delphi and Entropy integration method (DAEIM) and complex proportional assessment of alternatives (COPRAS) method with the interval type-2 trapezoidal fuzzy (IT2TF) environment. The proposed DSS is used to evaluate, prioritize, and choose technologies for CO2 capture. A hybrid criteria system, which involves elements of socio-technical systems perspective has been used for evaluating the candidate technologies. For implementing the DSS of this work, five capture technologies of post-combustion (A_cc1), pre-combustion (A_cc2), oxy-fuel combustion (A_cc3), direct air capture (A_cc4), and indirect air capture (A_cc5) have been chosen for evaluation. The final value of each technology is A_cc1 (0. 2907), A_cc2 (0.2602), A_cc3 (0.1005), A_cc4 (0.2304), and A_cc5 (0.1181) and the preferences of the technologies are A_cc1> A_cc2> A_cc4> A_cc5> A_cc3. The evaluation findings reveal that post-combustion technology with the value of 0.2907 is the most suitable scenario for the capture of CO2 emissions from Iran's OGR systems. The computation results demonstrate that the suggested DSS is feasible and applicable and give reliable and robust findings for acquiring the optimal CO2 capture technology.
Published Version
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