Operation Optimization And Experimental Study Of A Closed Cooling Water System

In 2015,China's primary energy consumption accounts for about 23%of the world's total primary energy consumption.At present,China's construction industry consume 30%of total national energy consumption.It is expected that by 2020,it will be slightly higher than the total energy consumption of one-third,reaching 35%.In order to cope with the growing demand for building energy,it is necessary to build an intensive and efficient building energy system,so that the level of sustainable development in building sector has been further improved.In the energy consumption of civil buildings with centralized air conditioning,the energy consumption of air conditioning system is more than 50%,and the energy consumption of cold sources is generally 40%to 70%of air conditioning system energy consumption.Buildings with automated building control systems will save energy by 25%.Therefore,optimizing the operation of closed cooling water system is an effective way to ensure efficient and energy efficient operation of air conditioning system.In this paper,a simplified model of Closed Wet Cooling Tower(CWCT)was established for closed cooling tower,one of the most widely used cooling device,in closed cooling water system.The model includes only two parameters to be determined,which can be used to predict the outlet cooling water temperature,cooling capacity and cooling coefficient of two typical counter-flow CWCTs.These two types of cooling towers include Parallel Counter-Flow Cooling Tower(PCFCT)and Cross Counter-Flow Cooling Tower(CCFCT),which are different from the one-dimensional counter-flow direction of air and cooling water in the tower.In the PCFCT,the counter-flow direction of the air and cooling water can be approximated in the vertical direction,parallel to the top-down spray water;and the counter-flow of the air and cooling water in the CCFCT can be regarded as horizontal,with the top-down spray water cross flow.Based on the experimental data of the existing literature,the Levenberg-Marquardt method is used to determine the two parameters of the model.And the accuracy of the model to predict the temperature of these two types of cooling water outlet is verified.The results show that the model can accurately predict the performance of CWCT.Due to its accuracy and simplicity,the model can be an indispensable part of the performance model of the closed cooling water system,which is convenient for the performance analysis and operation optimization of the closed cooling water system.Based on the model of the determined parameters,the performance of the two types of typical closed cooling towers is analyzed.The simulation results show that although the structure of these two types of towers is different,the influence of individual factors on their performance is similar.In addition,PCFCT is more suitable for large cooling water systems than CCFCT.Based on the nonlinear programming,the constraint optimization model of the energy-saving operation of the closed cooling water system is established.And the equality constraint function and the inequality constraint condition are built on the basis of the existing research results.This paper introduces the penalty function method for converting constrained optimization model into unconstrained model,the Newton Method and Pattern Search Method for solving unconstrained optimization problems.These provide the theoretical basis for analyzing the energy saving operation analysis of the closed cooling water system.The established equation constraints are used to calculate the cooling capacity of the closed cooling water system,the coefficient of performance(COP)of the system and its heat pump unit.A large number of effective test data are obtained on the experimental platform of the closed cooling water system.The least squares(confidence level 95%)of SPSS software was used to fit these test data to obtain the undetermined coefficients in the relevant model.The results of the verification system and its CWCT performance model show that these models have sufficient accuracy for performance prediction.Based on the Monte Carlo method,the influence of different factors on the COP of closed cooling water system and its heat pump unit is investigated.These factors include ambient wet bulb temperature,chilled water inlet temperature and mass flow rate,the input powers of compressor,tower fan and cooling water pump.Firstly,the probability distribution function of these factors is obtained by fitting the experimental data with data processing software.Secondly,the least squares method(95%confidence level)is used to find the multiple linear regression relations between the two target parameters and these factors.Finally,the Monte Carlo method is used to predict the distribution range of the target parameters and its distribution,and quantifies the influence degree of each factor on the target parameters.The results show that the chilled water mass flow rate and the input power of the cooling tower fan have the greatest influence on the two target parameters,and the input power of the cooling water pump has little effect.In the last chapter,we study the energy-efficient operation of closed cooling water system.The exact penalty function method is adopted to transform the constrained optimization problem into unconstrained optimization problem.The mixed L-GDS optimization algorithm(combined L-BFGS algorithm with Greedy Diffusion Search(GDS)algorithm)is select to solve the unconstrained optimization model.Under the condition of a given uncontrollable parameter,viz.ambient air wet bulb temperature,evaporator inlet chilled water parameter,different controllable variables(the input power of each power consuming devices)are analyzed for both scenarios(constant and variable cooling load)of the minimum system power consumption.Under typical experimental conditions,the optimization results of the system operation show that the energy saving potential is about 20.8%.This paper presents a study of the performance of closed cooling water system and its main components and their operation optimization.The findings can provide guidance for low-cost or non-incremental cost optimization of the system's control technology.