A decision support system for sustainability prioritization of air pollution control technologies in energy and carbon management: Oil & gas industry of Iran

Abstract

The environmental, economic, social, and technical challenges associated with implementing the air pollution control technologies (APCTs) in oil, gas, and petrochemical industries (OGPIs) necessitate a sustainability prioritization analysis. Environmental issues, economic parameters, social concerns, and technical performances all must assessed and weighted. Accomplishing such a crucial decision with rigorous potential environmental, economic, social, and technical effects needs a robust decision support system (DSS) for guiding the sustainability prioritization process of the technologies. This study purposes in developing a hybrid DSS by integrating the analytical network process (ANP), decision-making trial and evaluation laboratory (DEMATEL), and multi-objective optimization on the basis of ration analysis (MULTIMOORA) methods based on the fuzzy set theory for sustainability prioritization of implementing APCTs. Three cases of the petrochemical plant, gas refinery, and oil refinery in Iran are selected to assess and prioritize the most commonly used of APCTs including four alternatives of Electrostatic precipitators, Fabric filters, and Wet scrubbers, and Cyclones by the proposed DSS. The sustainability ranking of implementing the four technologies sequentially for three cases is obtained. The suggested DSS is feasible for group decision-making based on experts’ opinions and uncertain data. It can assist the stakeholders/decision-makers to attain the energy and carbon management (ECM) policies in three cases and apply to advance the oil & gas supply chains towards the targets of sustainability, green growth, and low-carbon economy. Finally, sensitivity analysis indicates the robustness of the proposed approach for the prioritization order.