| Cooling water system is a common heat removal system which is widely used in civil buildings,communication buildings and industrial production.Cooling tower cooling system is a kind of energy-saving cooling system that can make full use of outdoor air for natural cooling(free cooling)in transition season and winter,which has been widely used in China.The main energy consuming equipment of cooling tower cooling system includes cooling tower fans,cooling water pumps and chillers.In actual operation,there is often a coupling relationship between the operation parameters of each equipment,which directly affects the optimal operation effect of cooling tower cooling system.In particular,the air-water heat transfer characteristics of the packing in the cooling tower are not clear in the actual operation,and cannot be used in the optimal operation process of the cooling water system,which affects the further improvement of the operation efficiency of the cooling water system.Therefore,it is urgent to carry out the research on the air-water heat transfer characteristics of cooling tower packing and the on-line optimization method for low energy consumption operation of cooling water system.Therefore,based on a systematic review of the research progress of cooling tower heat transfer model and intelligent operation optimization of cooling water system at home and abroad,the on-line optimal operation of cooling water system of cooling tower is studied by combining theoretical analysis,experimental research and simulation research.On the basis of considering the input parameters such as ambient temperature,humidity,cooling load and the air-water heat transfer characteristics of cooling tower packing,the optimization of adjustable parameters such as cooling water flow,air flow and equipment operation quantity is studied.The specific research contents are as follows.First of all,the premise and basis of studying the optimal operation of cooling system of cooling tower is to reveal the law of air-water heat transfer of cooling tower packing and to establish the experimental system.Therefore,based on the theoretical analysis of the air-water heat transfer process of the cooling tower packing,the experimental system of the air-water heat transfer performance of the cooling tower packing is established,and the distributed multiprotocol data acquisition and control system of the experimental system is developed.The results of operation adjustment show that the thermal balance error of the test system is less than 5%.which meets the requirements of the follow-up experimental research in this paper.Secondly,the outlet water temperature and air-water ratio are two crucial control parameters of cooling water system,and the mass transfer coefficient of packing is the crucial basic parameter to study the air-water heat transfer process of packing.In this paper,the airwater heat transfer performance of the cooling tower packing with single parameter and multi parameter changes is studied by using the orthogonal test method with the inlet water temperature,outdoor wet bulb temperature,cooling water flow and cooling air flow as the main factors and the outlet water temperature as the output parameter.The single value function relationship between the outlet water temperature of cooling tower and the air-water ratio is obtained,and the characteristic formula of the air-water heat and mass transfer coefficient of the packing in cooling tower is derived,which is verified by the experiment.Thirdly,the on-line optimization heat transfer model of cooling tower is the basis of online operation optimization of cooling water system.Based on the limitation analysis of enthalpy difference method for on-line optimization of cooling water system,the heat transfer model form of cooling tower is deduced from the perspective of counter flow two-phase convective heat transfer analysis,and the on-line optimization heat transfer model of cooling tower is established based on the characteristic formula of packing air-water mass transfer coefficient.The applicability of the model under off design conditions is verified by different inlet water temperature of cooling tower,outdoor wet bulb temperature and air-water ratio tests.The results show that under different working conditions,the difference between the predicted results and the experimental results is not more than 5%,which meets the requirements of subsequent research and engineering application.Fourthly,the research of suitable on-line optimization method is the key to solve the operation optimization of cooling tower cooling system.On the basis of summarizing the mathematical models of each equipment in the cooling water system,this paper first establishes the mathematical models of the operation optimization problem of the cooling tower cooling system under the natural cooling mode and the chiller cooling mode,and uses the penalty function to realize the de constraint of the constrained optimization mathematical model.Then,the particle swarm optimization(PSO)algorithm is used as the main optimization algorithm,which is improved(including adaptive penalty factor and feasible region,discrete variable optimization method and parallel equipment operation)by simulation.The results show that the improved PSO algorithm is superior to the traditional particle swarm optimization algorithm in the optimization rate,the degree of dispersion and the number of iterations.The improved particle swarm optimization algorithm can better realize the on-line optimization of cooling water system operation energy consumption,and meet the engineering on-line application requirements.Finally,in order to verify the application effect of the above operation optimization method,this paper takes the data center as the application object to simulate the above algorithm.The results show that under the natural cooling mode,when the outdoor wet bulb temperature is low,the optimal value of cooling tower outlet temperature fluctuates less.When the wet bulb temperature is high,the optimal value of outlet water temperature of cooling tower increases linearly with the wet bulb temperature.Under the chiller cooling mode,when the outdoor wet bulb temperature is low,the optimal value of cooling tower outlet temperature does not change with the wet bulb temperature.When the wet bulb temperature is high,the optimal value of outlet water temperature increases with the wet bulb temperature.Compared with the traditional optimization methods such as constant temperature difference set point and variable temperature difference set point,the energy consumption of the system in the natural cooling mode is reduced by 46.7?68.8%,and the energy consumption of the system in the refrigerator cooling mode is reduced by 9.9?21.6%,which means that the improved particle swarm optimization algorithm has significant energy saving effect. |
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