冷却塔逆用理论与水—水热泵制热运行优化研究
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摘要
空调用冷却塔普遍用作散热冷却设备，但是，将其逆用吸热作为热泵制热的低温热源，有着独特的优势与应用前景。冷却塔逆用（RUCT）吸热工况下，塔内空气与水进行着复杂的热质交换过程，如何正确认识空气与水之间的热质交换过程是优化运行与优化设计的基础。迄今为止，针对该方面的研究并不充分。鉴于此，本文采用理论分析、数值模拟以及实验研究相结合的方法探讨了冷却塔逆用时空气与水的热质交换过程以及热质耦合机理与特性。进行的主要工作如下：
首先，本文对既有的逆流冷却塔逆用热质交换模型进行了改进，摒弃了低温工况下刘易斯数为1的假设而带来的计算偏差，分别将温差与含湿量差作为空气与水的传热与传质驱动力，建立了较为完善的逆流冷却塔逆用的数学模型，为方便求解，进一步将模型最终以显式差分格式表达，并将模型计算结果与实验数据进行对比，结果表明，模型准确性较高。
其次，本文对横流冷却塔逆用性能进行了理论与实验研究，对采用PVC薄膜式填料的横流冷却塔逆用进行了传热传质性能实验，在此基础之上采用多元回归分析方法处理实验数据，得出了传热传质主要性能参数：吸热效率、显热比、传热系数、传质系数以及刘易斯数与塔进口空气干球温度、湿球温度、进水温度、淋水密度以及液气比的函数关系式，并针对传热与传质系数的影响因素进行了分析，为进一步理论研究与工程运用提供了依据。
在此基础上，本文建立了较为完善的横流冷却塔逆用热质交换二维无量纲数学模型，考虑了冷凝而引起水质量流量变化，利用实验数据回归得到的传热系数与传质系数，通过离散数值求解的方法，得到了水与空气各参数在塔内的二维分布规律，并采用实验数据验证数学模型，出水温度计算值与实验值的相关系数为0.9904，判定系数0.9809，均方差误差为0.22oC，计算结果与实验值吻合较好，且分析得到刘易斯数在0.7~1.6范围内变化。在此基础上，应用热力学第一、第二定律对室外空气参数及可调节参数对横流冷却塔逆用的性能进行了详细分析。
另外，考虑到塔内湿空气与水热质交换过程各个参数之间复杂的非线性关系，本文针对横流冷却塔逆用进一步建立了基于遗传算法优化的BP神经网络模型。本网络模型采用6个输入、13个隐含层节点与8个输出的结构。针对出口空气干球温度、湿球温度、出水温度、吸热量、显热比、吸热效率、冷凝水比例、刘易斯数共8个主要性能参数进行预测分析。并进一步将建立好的网络模型用于不同进水温度对塔出水温度以及吸热量的预测分析。结果表明，模拟结果和实际情况基本一致，即该基于遗传算法优化的BP网络模型能较好地预测横流冷却塔逆用传热传质性能。
最后，本文将横流冷却塔逆用模型与水-水热泵、循环水泵模型耦合，建立系统制热运行的优化模型，确立了目标函数、约束条件与控制变量。以塔风量、水量、蒸发器进出口水温、蒸发温度、冷凝温度、过冷度、吸气过热度为待优化变量，运用量子遗传算法对该多变量非线性模型进行优化计算。通过对优化模型的仿真计算，与实测工况进行了比较，系统最大综合能效比EERs得到提高。本文进一步以实验系统为例，保证系统供热量不变的前提下，针对室外空气温度5~13oC，相对湿度60-90%工况下对系统运行展开模拟研究，结果表明，为保证系统最大综合能效比运行，横流冷却塔逆用的液气比范围为0.44~0.48。
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.
首先，本文对既有的逆流冷却塔逆用热质交换模型进行了改进，摒弃了低温工况下刘易斯数为1的假设而带来的计算偏差，分别将温差与含湿量差作为空气与水的传热与传质驱动力，建立了较为完善的逆流冷却塔逆用的数学模型，为方便求解，进一步将模型最终以显式差分格式表达，并将模型计算结果与实验数据进行对比，结果表明，模型准确性较高。
其次，本文对横流冷却塔逆用性能进行了理论与实验研究，对采用PVC薄膜式填料的横流冷却塔逆用进行了传热传质性能实验，在此基础之上采用多元回归分析方法处理实验数据，得出了传热传质主要性能参数：吸热效率、显热比、传热系数、传质系数以及刘易斯数与塔进口空气干球温度、湿球温度、进水温度、淋水密度以及液气比的函数关系式，并针对传热与传质系数的影响因素进行了分析，为进一步理论研究与工程运用提供了依据。
在此基础上，本文建立了较为完善的横流冷却塔逆用热质交换二维无量纲数学模型，考虑了冷凝而引起水质量流量变化，利用实验数据回归得到的传热系数与传质系数，通过离散数值求解的方法，得到了水与空气各参数在塔内的二维分布规律，并采用实验数据验证数学模型，出水温度计算值与实验值的相关系数为0.9904，判定系数0.9809，均方差误差为0.22oC，计算结果与实验值吻合较好，且分析得到刘易斯数在0.7~1.6范围内变化。在此基础上，应用热力学第一、第二定律对室外空气参数及可调节参数对横流冷却塔逆用的性能进行了详细分析。
另外，考虑到塔内湿空气与水热质交换过程各个参数之间复杂的非线性关系，本文针对横流冷却塔逆用进一步建立了基于遗传算法优化的BP神经网络模型。本网络模型采用6个输入、13个隐含层节点与8个输出的结构。针对出口空气干球温度、湿球温度、出水温度、吸热量、显热比、吸热效率、冷凝水比例、刘易斯数共8个主要性能参数进行预测分析。并进一步将建立好的网络模型用于不同进水温度对塔出水温度以及吸热量的预测分析。结果表明，模拟结果和实际情况基本一致，即该基于遗传算法优化的BP网络模型能较好地预测横流冷却塔逆用传热传质性能。
最后，本文将横流冷却塔逆用模型与水-水热泵、循环水泵模型耦合，建立系统制热运行的优化模型，确立了目标函数、约束条件与控制变量。以塔风量、水量、蒸发器进出口水温、蒸发温度、冷凝温度、过冷度、吸气过热度为待优化变量，运用量子遗传算法对该多变量非线性模型进行优化计算。通过对优化模型的仿真计算，与实测工况进行了比较，系统最大综合能效比EERs得到提高。本文进一步以实验系统为例，保证系统供热量不变的前提下，针对室外空气温度5~13oC，相对湿度60-90%工况下对系统运行展开模拟研究，结果表明，为保证系统最大综合能效比运行，横流冷却塔逆用的液气比范围为0.44~0.48。
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.
引文
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