The exergy analysis and finite time thermodynamic methods had been employed to analyze the compound condensation process (CCP). It was based on the air-cooling heat pump unit. The cooling capacity of the chiller unit ...The exergy analysis and finite time thermodynamic methods had been employed to analyze the compound condensation process (CCP). It was based on the air-cooling heat pump unit. The cooling capacity of the chiller unit is about 1 kW, and the work refrigerant is R22/R407C/R410A/CO2. The MATLAB/SIMULINK software was employed to build the simulation model. The thermodynamic simulation model is significant for the optimization of parameters of the unit, such as condensation and evaporation temperature and mass flow of the sanitary hot water and size of hot water storage tank. The COP of the CCP of R410A system is about 3% - 5% higher than the CCP of the R22 system, while CCP of the R407C system is a little lower than the CCP of R22 system. And the CCP of CO2 trans-critical system has advantage in the hot supply mode. The simulation method provided a theoretical reference for developing the production of CCP with substitute refrigerant R407C/R410A/CO2.展开更多
针对冷水机组的采集监控需求,设计了一套基于物联网的冷水机组数据采集监控系统。该系统通过专用物联网传输模块DTU(Data Transfer Unit)完成对冷水机组数据的实时采集和控制。采集到的冷水机组数据通过物联网数据传输单元DTU移动通信网...针对冷水机组的采集监控需求,设计了一套基于物联网的冷水机组数据采集监控系统。该系统通过专用物联网传输模块DTU(Data Transfer Unit)完成对冷水机组数据的实时采集和控制。采集到的冷水机组数据通过物联网数据传输单元DTU移动通信网,远程上传到云端控制中心,控制中心的软件实现了对冷水机组的数据采集监测和远程控制指令的下发。该系统实现了冷水机组的远程监测和控制,降低了系统运营成本,提高了系统工作效率和自动化水平。展开更多
Computer-empowered detection of possible faults for Heating,Ventilation and Air-Conditioning(HVAC)subsystems,e.g.,chillers,is one of the most important applications in Artificial Intelligence(AI)integrated Internet of...Computer-empowered detection of possible faults for Heating,Ventilation and Air-Conditioning(HVAC)subsystems,e.g.,chillers,is one of the most important applications in Artificial Intelligence(AI)integrated Internet of Things(IoT).The cyber-physical system greatly enhances the safety and security of the working facilities,reducing time,saving energy and protecting humans’health.Under the current trends of smart building design and energy management optimization,Automated Fault Detection and Diagnosis(AFDD)of chillers integrated with IoT is highly demanded.Recent studies show that standard machine learning techniques,such as Principal Component Analysis(PCA),Support Vector Machine(SVM)and tree-structure-based algorithms,are useful in capturing various chiller faults with high accuracy rates.With the fast development of deep learning technology,Convolutional Neural Networks(CNNs)have been widely and successfully applied to various fields.However,for chiller AFDD,few existing works are adopting CNN and its extensions in the feature extraction and classification processes.In this study,we propose to perform chiller FDD using a CNN-based approach.The proposed approach has two distinct advantages over existing machine learning-based chiller AFDD methods.First,the CNN-based approach does not require the feature selection/extraction process.Since CNN is reputable with its feature extraction capability,the feature extraction and classification processes are merged,leading to a more neat AFDD framework compared to traditional approaches.Second,the classification accuracy is significantly improved compared to traditional methods using the CNN-based approach.展开更多
Given the distribution feature of resources such as coal and water, the requirements for the development of Chinese power industry, and the fact of monopoly by foreign companies, it is very necessary and significant t...Given the distribution feature of resources such as coal and water, the requirements for the development of Chinese power industry, and the fact of monopoly by foreign companies, it is very necessary and significant to independently research and develop air-cooling technologies. Through experimental research, simulative calculation, process and equipment development, field tests and a demonstration project, the design and operation technologies for air-cooling system are grasped and relevant key equipment is developed. The results of the demonstration project show that the technical indicators for the air-cooling system have met or exceeded the design requirements. Part of the research results have been incorporated into the relevant national design standards. The technologies developed have been applied to more than 23 sets of thermal power units of or above 600 MW in China.展开更多
Buildings worldwide account for nearly 40%of global energy consumption.The biggest energy consumer in buildings is the heating,ventilation and air conditioning(HVAC)systems.In HVAC systems,chillers account for a major...Buildings worldwide account for nearly 40%of global energy consumption.The biggest energy consumer in buildings is the heating,ventilation and air conditioning(HVAC)systems.In HVAC systems,chillers account for a major portion of the energy consumption.Maintaining chillers in good conditions through early fault detection and diagnosis is thus a critical issue.In this paper,the fault detection and diagnosis for an air-cooled chiller with air coming from outside in variable flow rates is studied.The problem is difficult since the air-cooled chiller is operating under major uncertainties including the cooling load,and the air temperature and flow rate.A potential method to overcome the difficulty caused by the uncertainties is to perform fault detection and diagnosis based on a gray-box model with parameters regarded as constants.The method is developed and verified by us in another paper for a water-cooled chiller with the uncertainty of cooling load.The verification used a Kalman filter to predict parameters of a gray-box model and statistical process control(SPC)for measuring and analyzing their variations for fault detection and diagnosis.The gray-box model in the method,however,requires that the air temperature and flow rate be nearly constant.By introducing two new parameters and deleting data points with low air flow rate,the requirement can be satisfied and the method can then be applicable for an air-cooled chiller.The simulation results show that the method with the revised model and some data points dropped improved the fault detection and diagnosis(FDD)performance greatly.It can detect both sudden and gradual air-cooled chiller capacity degradation and sensor faults as well as their recoveries.展开更多
文摘The exergy analysis and finite time thermodynamic methods had been employed to analyze the compound condensation process (CCP). It was based on the air-cooling heat pump unit. The cooling capacity of the chiller unit is about 1 kW, and the work refrigerant is R22/R407C/R410A/CO2. The MATLAB/SIMULINK software was employed to build the simulation model. The thermodynamic simulation model is significant for the optimization of parameters of the unit, such as condensation and evaporation temperature and mass flow of the sanitary hot water and size of hot water storage tank. The COP of the CCP of R410A system is about 3% - 5% higher than the CCP of the R22 system, while CCP of the R407C system is a little lower than the CCP of R22 system. And the CCP of CO2 trans-critical system has advantage in the hot supply mode. The simulation method provided a theoretical reference for developing the production of CCP with substitute refrigerant R407C/R410A/CO2.
文摘针对冷水机组的采集监控需求,设计了一套基于物联网的冷水机组数据采集监控系统。该系统通过专用物联网传输模块DTU(Data Transfer Unit)完成对冷水机组数据的实时采集和控制。采集到的冷水机组数据通过物联网数据传输单元DTU移动通信网,远程上传到云端控制中心,控制中心的软件实现了对冷水机组的数据采集监测和远程控制指令的下发。该系统实现了冷水机组的远程监测和控制,降低了系统运营成本,提高了系统工作效率和自动化水平。
基金supported by two Ministry of Education(MoE)Singapore Tier 1 research grants under grant numbers R-296-000-208-133 and R-296-000-241-114.
文摘Computer-empowered detection of possible faults for Heating,Ventilation and Air-Conditioning(HVAC)subsystems,e.g.,chillers,is one of the most important applications in Artificial Intelligence(AI)integrated Internet of Things(IoT).The cyber-physical system greatly enhances the safety and security of the working facilities,reducing time,saving energy and protecting humans’health.Under the current trends of smart building design and energy management optimization,Automated Fault Detection and Diagnosis(AFDD)of chillers integrated with IoT is highly demanded.Recent studies show that standard machine learning techniques,such as Principal Component Analysis(PCA),Support Vector Machine(SVM)and tree-structure-based algorithms,are useful in capturing various chiller faults with high accuracy rates.With the fast development of deep learning technology,Convolutional Neural Networks(CNNs)have been widely and successfully applied to various fields.However,for chiller AFDD,few existing works are adopting CNN and its extensions in the feature extraction and classification processes.In this study,we propose to perform chiller FDD using a CNN-based approach.The proposed approach has two distinct advantages over existing machine learning-based chiller AFDD methods.First,the CNN-based approach does not require the feature selection/extraction process.Since CNN is reputable with its feature extraction capability,the feature extraction and classification processes are merged,leading to a more neat AFDD framework compared to traditional approaches.Second,the classification accuracy is significantly improved compared to traditional methods using the CNN-based approach.
文摘Given the distribution feature of resources such as coal and water, the requirements for the development of Chinese power industry, and the fact of monopoly by foreign companies, it is very necessary and significant to independently research and develop air-cooling technologies. Through experimental research, simulative calculation, process and equipment development, field tests and a demonstration project, the design and operation technologies for air-cooling system are grasped and relevant key equipment is developed. The results of the demonstration project show that the technical indicators for the air-cooling system have met or exceeded the design requirements. Part of the research results have been incorporated into the relevant national design standards. The technologies developed have been applied to more than 23 sets of thermal power units of or above 600 MW in China.
文摘Buildings worldwide account for nearly 40%of global energy consumption.The biggest energy consumer in buildings is the heating,ventilation and air conditioning(HVAC)systems.In HVAC systems,chillers account for a major portion of the energy consumption.Maintaining chillers in good conditions through early fault detection and diagnosis is thus a critical issue.In this paper,the fault detection and diagnosis for an air-cooled chiller with air coming from outside in variable flow rates is studied.The problem is difficult since the air-cooled chiller is operating under major uncertainties including the cooling load,and the air temperature and flow rate.A potential method to overcome the difficulty caused by the uncertainties is to perform fault detection and diagnosis based on a gray-box model with parameters regarded as constants.The method is developed and verified by us in another paper for a water-cooled chiller with the uncertainty of cooling load.The verification used a Kalman filter to predict parameters of a gray-box model and statistical process control(SPC)for measuring and analyzing their variations for fault detection and diagnosis.The gray-box model in the method,however,requires that the air temperature and flow rate be nearly constant.By introducing two new parameters and deleting data points with low air flow rate,the requirement can be satisfied and the method can then be applicable for an air-cooled chiller.The simulation results show that the method with the revised model and some data points dropped improved the fault detection and diagnosis(FDD)performance greatly.It can detect both sudden and gradual air-cooled chiller capacity degradation and sensor faults as well as their recoveries.