Multi-criteria decision evaluation of dust suppression technology in complex coal mine environments

Coal mining generates hazardous airborne dust, posing severe health risks and environmental challenges. Traditional dust suppression methods, including water sprays and ventilation, often suffer from high water consumption, inconsistent efficiency, and a lack of systematic optimization. This study addresses these gaps by integrating Computational Fluid Dynamics (CFD) simulations, experimental validation, and Multi-Criteria Decision-Making (MCDM) to optimize dust control strategies. Our findings reveal that ultrasonic nozzle atomization, operating at 0.3–1 MPa, achieves 89% dust reduction efficiency, outperforming high-pressure misting (85%) and foam-based suppression (78%) while maintaining lower water consumption (0.2 L/min). The study identifies optimal nozzle parameters, including a swirl angle of 30°–40°, and a spray cone angle of ~ 45°, to ensure efficient dust capture. A key research gap addressed is the lack of data-driven, structured decision frameworks in dust suppression selection. By employing AHP, TOPSIS, and PROMETHEE, this study objectively ranks dust control methods based on performance, cost, and sustainability. Future work should explore real-time AI-driven optimization and CFD-based adaptive control to enhance dust suppression in mining. This study contributes to scientifically validated, cost-efficient, and environmentally sustainable dust-control solutions for mining operations.