Optimization Of Cooling Water System Of Central Air-conditioning

In China’s energy industry structure, the proportion of building energy consumption as high as 30%, and the building energy efficiency is very low. Among them, the central air-conditioning energy consumption accounted for about 60% of the proportion of building energy consumption. The design of the central air conditioning system is designed according to the full load, But the central air conditioning run in the part of the load, this will lead to a reduction in the thermal efficiency of the air conditioning, thus resulting in a waste of energy,so the energy-saving of the central air conditioning system becomes an important issue. The cooling water system is an important part of the central air conditioning, the energy consumption of the cooling water system is related to the temperature of the environment and the load demand of the room, because the air in the air temperature and humidity of the environment changes in real time, and the load of users is also changing in real time, how to match the cooling capacity of the cooling system and the load of the user is one of the most important problems in the energy saving of the central air conditioning system.The operation principle of cooling water system is analyzed in this paper, the heat exchange between the cooling tower and the water chiller is analyzed emphatically, there is an optimal matching point between the chilled water system and the chiller, which makes the minimum energy consumption of the cooling water system. In order to find the working point of the lowest energy consumption of the cooling water system, the mathematical model of the cooling water system was established. The energy consumption model includes chiller,cooling water pump and cooling tower. For the complex problem of heat exchange of cooling water system,the quantum behaved particle swarm optimization algorithm is improved in this paper. first, the interference factor is added to the algorithm according to a certain rule, with the iterative process, the parameters can be adjusted adaptively to effectively, the best position of the particle itself, that is Pbest able to learn from the other particle’s Pbest in a certain probability. the Benchmark function is used to test the improved quantum behaved particle swarm optimization algorithm, the results show that the improved quantum behaved particle swarm optimization algorithm has better convergence.Finally, the improved quantum behaved particle swarm optimization algorithm iscarried out to optimize the air conditioning. For the nomal woking condition, optimize the air conditioning.By simulating the performance curve of the wet bulb temperature and the cooling load of the whole day. The improved quantum behaved particle swarm optimization algorithm was used to optimize the central air conditioning system,reach to the optimal match of between the chiller and cooling system,achieving the destination of system saving energy.