A Theoretical Study On Reversibly Used Cooling Tower And Coupled Water To Water Heat Pump Optimization In Heating Mode

Cooling tower often acting for heat dissipation harbors unique advantages andapplication prospects when reversibly used to absorb heat as low-temperature heatsource for heat pump. It lies as the basis to optimize the operation and design to fullystudy the complicated heat and mass transfer process between air and water. Sincethere doesn’t exist enough research on this aspect, this paper aims to explore themechanism and characteristics of the heat and mass transfer and coupling between airand water for reversibly used cooling tower with theoretical analysis and experimentaldata. The main content is outlined as follows.Firstly, based on the existing heat and mass exchange model for reversibly usedcounter-flow cooling tower, the calculation deviation due to the assumption thatLewis number is1under low temperature condition is dismissed, an improvedmathematical model is developed, with the temperature difference and moisturecontent difference as the driving force for heat and mass transfer. This model is shownin explicit difference way for easy solution. And the result of this model is comparedwith experimental data, which displays a high accuracy.Secondly, considering the lack of research on reversibly used cooling towerunder cross flow condition, the performance experiment of heat and mass transfer wasconducted for reversibly used cooling tower stuffed with PVC film packing undercross flow condition. Then multiple regression analysis is utilized to deal with theexperimental data, revealing the function equation relating heat absorption efficiency,sensible heat ratio, heat transfer coefficient and Lewis number with inlet dry bulbtemperature, wet bulb temperature, inlet water temperature, water drenching densityand liquid air ratio. Besides, the factors influencing heat and mass transfer areanalyzed. This research offers basis for further theoretical study and practicalapplication.Next, the2-D dimensionless mathematical model is built for reversibly usedcooling tower under cross flow condition, taking account of the water mass and flowchange due to condensation. The heat and mass transfer coefficients are obtainedthrough experimental data regression. The two dimension distribution regularities ofair and water parameters are acquired with discrete numerical solution. Thecalculation results of outlet water temperature validated by experimental result agree well with the experimental data, with the correlation coefficient of0.9904,determination coefficient of0.9809, mean variance error of0.22oC and Lewis numberranging from0.7to1.6. On this basis, a detailed analysis is carried out for theinfluence of ambient parameters and adjustable index on the performance ofreversibly used cooling tower under cross flow condition with the First and SecondLaw of Thermaldynamics.Next, with regard of complex nonlinear relation of various parameters during theheat and mass transfer between moist air and water inside the tower, a GA-BPartificial neural network model is formed or reversibly used cooling tower under crossflow condition. This network structure consists of6inputs,13neutrons at hiddenlayers and8outputs. Outlet dry bulb temperature, wet bulb temperature, outlet watertemperature, heat absorption capacity, sensible heat ratio, heat absorption efficiency,condensate water ratio and Lewis number are predicted and analyzed. This networkmodel is further used for prediction of outlet water temperature and heat absorptioncapacity under different inlet water temperatures. The agreement of simulation resultwith realistic data reveals that this GA-BP artificial neural network model caneffectively forecast heat and mass transfer characteristics reversibly used coolingtower under cross flow condition.In the last part, the model for reversibly used cooling tower under cross flowcondition is coupled with water-water heat pump and circulation pump models. Anoptimization model of heating operation is formed with objective function, constraintconditions and manipulated variables. Quantum genetic algorithm is applied tooptimize the multivariable nonlinear model, with air volume, water flow, inlet andoutlet water temperature of evaporator, evaporating temperature, condensingtemperature, condensate depression and inspiratory superheat as the optimizedvariables. Comparison between simulation results and realistic conditions shows theimprovement of system EERs. Under the prerequisite of fixed heat supply capacity,the experimental system is simulated and analyzed with the ambient temperature of5~13oC and relative humidity of60-90%. It is shown that the liquid air ratio is in therange of0.45~0.47under the peak EER.