Investigations on optimal discharge pressure in CO2 heat pumps using the GMDH and PSO-BP type neural network—Part A: Theoretical modeling

2019 ◽  
Vol 106 ◽  
pp. 549-557 ◽  
Author(s):  
Xiang Yin ◽  
Feng Cao ◽  
Jing Wang ◽  
Mingjia Li ◽  
Xiaolin Wang
Keyword(s):  
Neural Network ◽  
Heat Pumps ◽  
Theoretical Modeling ◽  
Discharge Pressure

A Generalized Neural Network Model of Refrigerant Mass Flow Through Adiabatic Capillary Tubes and Short Tube Orifices

2007 ◽  
Vol 129 (12) ◽  
pp. 1559-1564 ◽  
Author(s):  
Ling-Xiao Zhao ◽  
Chun-Lu Zhang ◽  
Liang-Liang Shao ◽  
Liang Yang
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Mass Flow ◽  
Transfer Functions ◽  
Heat Pumps ◽  
Capillary Tubes ◽  
Two Phase ◽  
Bp Network ◽  
Inlet Conditions ◽  
Short Tube

Adiabatic capillary tubes and short tube orifices are widely used as expansive devices in refrigeration, residential air conditioners, and heat pumps. In this paper, a generalized neural network has been developed to predict the mass flow rate through adiabatic capillary tubes and short tube orifices. The input/output parameters of the neural network are dimensionless and derived from the homogeneous equilibrium flow model. Three-layer backpropagation (BP) neural network is selected as a universal function approximator. Log sigmoid and pure linear transfer functions are used in the hidden layer and the output layer, respectively. The experimental data of R12, R22, R134a, R404A, R407C, R410A, and R600a from the open literature covering capillary and short tube geometries, subcooled and two-phase inlet conditions, are collected for the BP network training and testing. Compared with experimental data, the overall average and standard deviations of the proposed neural network are 0.75% and 8.27% of the measured mass flow rates, respectively.

scholarly journals Study of a water-source CO2 heat pump for residential use: experimental discharge pressure control and performance analysis

2021 ◽  
Vol 246 ◽  
pp. 06010
Author(s):  
Yantong Li ◽  
Natasa Nord ◽  
Inge Håvard Rekstad ◽  
Stein Kristian Skånøy ◽  
Lars Konrad Sørensen
Keyword(s):  
Heat Pump ◽  
Water Source ◽  
Pressure Control ◽  
Heat Pumps ◽  
Pi Controller ◽  
Experimental Conditions ◽  
Effective Discharge ◽  
Expansion Valve ◽  
Residential Use ◽  
Discharge Pressure

The heat pumps with the refrigerant of carbon dioxide (CO2), i.e., CO2 heat pumps, have the merits of low price and environmentally friendliness in comparison with those with traditional refrigerants, e.g., hydrochlorofluorocarbons and chlorofluorocarbons. Current studies mainly focused on the air-source CO2 heat pumps, while investigations about the CO2 heat pumps gaining heat or cold energy from different mediums, e.g., water, are lacking. In addition, although few studies presented the investigations on the discharge pressure of the CO2 heat pumps (e.g., investigations of optimal discharge pressure), how to realize the effective discharge pressure control in the experimental conditions is still lacking. To remedy these knowledge gaps, this study presented an experimental investigation of a water-source CO2 heat pump for residential use. A PI controller was used to maintain the fixed discharge pressure by adjusting the opening of the electronic expansion valve. The dynamic performance of the CO2 heat pump in the typical discharge pressure of 7,200 to 8,400 kPa were analyzed. The results indicated that the method of using the PI controller to adjust the opening of the electronic expansion valve could effectively maintain the desired discharge pressure of the CO2 heat pump in the experimental conditions.

Experimental Study of Solar-Air Composite Heat Pump System

2013 ◽  
Vol 827 ◽  
pp. 259-263
Author(s):  
Zhen Hua Quan ◽  
Gang Wang ◽  
Yao Hua Zhao ◽  
Yue Chao Deng ◽  
Peng Xu
Keyword(s):  
Heat Source ◽  
Heat Pump ◽  
Heat Pumps ◽  
Pump System ◽  
Heat Pump System ◽  
Operation Conditions ◽  
Low Efficiency ◽  
Source Operation ◽  
Discharge Pressure ◽  
Composite Heat

In the light of low efficiency of photovoltaic power generation and the problems of air source heat pumps application in the cold regions, this paper developed a composite evaporator and designed a new type of solar-air composite heat pump system, which was comprised of independently developed solar photovoltaic-thermal collector based on flat plate micro heat pipe and air source heat pump. The performances of heat pump system were studied experimentally, including water temperature of the heating tank, suction and discharge pressure, and performance of heat pump. When 73L water in the heating tank was heated by 1 HP heat pump from 15°C to 50°C at the ambient temperature of 5°C, the running time of dual heat source operation conditions was 5.14% shorter than that of separate air heat source operation conditions. Meanwhile, COP increased by 5.99%.

scholarly journals A Novel Hybrid Deep Neural Network Model to Predict the Refrigerant Charge Amount of Heat Pumps

2020 ◽  
Vol 12 (7) ◽  
pp. 2914
Author(s):  
Jun Kwon Hwang ◽  
Patrick Nzivugira Duhirwe ◽  
Geun Young Yun ◽  
Sukho Lee ◽  
Hyeongjoon Seo ◽  
...  
Keyword(s):  
Neural Network ◽  
Thermodynamic Model ◽  
Deep Neural Network ◽  
Heat Pumps ◽  
Current Data ◽  
Prediction Performance ◽  
Data Driven ◽  
Electric Heat ◽  
Model Training ◽  
Optimum Charge

Improper refrigerant charge amount (RCA) is a recurring fault in electric heat pump (EHP) systems. Because EHP systems show their best performance at optimum charge, predicting the RCA is important. There has been considerable development of data-driven techniques for predicting RCA; however, the current data-driven approaches for estimating RCA suffer from poor generalization and overfitting. This study presents a hybrid deep neural network (DNN) model that combines both a basic DNN model and a thermodynamic model to counter the abovementioned challenges of existing data-driven approaches. The data for designing models were collected from two EHP systems with different specifications, which were used for the training and testing of models. In addition to the data obtained using the basic DNN model, the hybrid DNN model uses the thermodynamic properties as a thermodynamic model. The testing results show that the hybrid DNN model has a prediction performance of 93%, which is 21% higher than that of the basic DNN model. Furthermore, for model training and model testing, the hybrid DNN model has a 6% prediction performance difference, indicating its reliable generalization capabilities. To summarize, the hybrid DNN model improves data-driven approaches and can be used for designing efficient and energy-saving EHP systems.

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