Enhancing ventilation fan performance in underground coal mines: a hybrid approach

Ventilation is critical in underground coal mining (UCM) for safety and productivity. Challenges include mitigating hazardous gases, preventing coal dust accumulation, and achieving a balance between extraction efficiency and environmental concerns. Optimization techniques address these challenges, aiming for enhanced safety, productivity, and sustainability. But, these algorithms need careful tuning and adaptation to dynamic mine conditions to prevent suboptimal solutions and ensure robust optimization performance. Hence, in this research work, a hybrid optimization-based control system is proposed for energy consumption minimization of mine ventilation fan and load management in UCM. The proposed hybrid optimization process is formulated into multi-objective optimization solutions like energy-saving optimization and load management in coal mines. The proposed hybrid optimization technique is the combination of both the Border Collie optimization (BCO) and Aquila optimizer (AO), and hence, it is named as BCOAO technique. At first, based on the ventilation on demand (VOD), the energy consumption is minimized using BCO technique and obtains the optimal main fan speed profile. With this procedure, the energy saving is improved as a consequence, healthy atmosphere with higher performance in the mines. Secondly, based on the time of use (TOU) tariff, the mining schedule starting time is found and analyzed in the load management using AO technique. The performance is carried out in the MATLAB platform and tested with three case studies. The overall energy saving percentage obtained by the proposed hybrid technique for three cases are 25, 38, and 54.5%, respectively.