Operation Optimization of a Refrigeration Ventilation System for the Deep Metal Mine

With the increment of the depth of metal mines, the energy conservation of refrigeration ventilation for deep metal mines has attracted more attention. This study uses the heat current method and artificial neural network to model the refrigeration ventilation system of deep metal mines, and establishes the systematic flow constraints using the driving-drag balance relationship. A global operational optimization model is built using a Lagrange multiplier method to minimize the pumping power consumption. An iterative algorithm combined with the stope model is proposed by coupling the water outlet temperature of the evaporator and condenser. Subsequently, the optimal frequency of the variable frequency pumps is obtained. The operation of the optimal data on the experimental platform showed that the pumping power consumption of the cooling tower loop decreased the most by 72.84%, and the total energy consumption of system decreased by 25.79%. Additionally, the influences of the inlet air temperature of the air cooler, fan frequency, and inlet air temperature of the cooling tower on the system power consumption are analyzed and provides guidance on energy saving and consumption reduction for refrigeration ventilation systems in deep metal mines.