Multicriteria Decision-Making System Based on Novel Fermatean Fuzzy Similarity Measure and VIKOR-Based Techniques for Optimizing the Industrial Air Pollution Control Technology

Industrial air pollution is caused by the combustion of fossil fuels for energy, chemical waste, and specific industrial operations such as metal manufacturing, petrochemical refining, and cement manufacturing, and it has a negative impact on human health by causing respiratory and cardiovascular disorders, increasing the risk of cancer, and impairing cognitive and developmental health. Choosing the right technology for controlling industrial air pollution is critical for effective management of industrial air pollution. To address this issue, multiple researchers investigated multicriteria decision-making (MCDM) approaches in a variety of contexts in order to identify the best system for managing industrial air pollution. In this research, we present a new similarity measure (SLM) for Fermatean fuzzy sets (FFSs), as well as its properties and demonstrations of validity. The proposed SLM addresses the limitations of existing SLMs of FFSs, which are unable to classify the unknown patterns with known pattern. Furthermore, we develop a novel MCDM model for FFNs using the proposed SLM and the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) technique. After that, we solve a case study by using the proposed MCDM model of optimal industrial air pollution control technology selection to demonstrate the practical applicability of the proposed MCDM method. In this case study, we select the optimal industrial air pollution control technology from the four industrial air pollution control technologies: “Electrostatic Precipitators”, “Cyclone Separators”, “Fabric Filters”, and “Wet Scrubbers” based on four criteria: “Energy Efficiency”, “Maintenance Simplicity”, “Cost” and “Operating Performance”. The proposed MCDM model identifies “Cyclone Separators” as the best acceptable technology for controlling industrial air pollution. Finally, a comparison with existing MCDM models is performed to demonstrate the effectiveness and benefits of the new technique. In contrast to existing models, the proposed model effectively ranks alternatives, even in cases where other models fail to produce a clear ranking.