Analysis of the Effect of the CaCl2 Mass Fraction on the Efficiency of a Heat Pump Integrated Heat-Source Tower Using an Artificial Neural Network Model

An existing idle cooling tower can be reversibly used as a heat-source tower (HST) to drive a heat pump (HP) in cold seasons, with calcium chloride (CaCl2) aqueous solution commonly selected as the secondary working fluid in an indirect system due to its good thermo-physical properties. This study analyzed the effect of CaCl2 mass fraction on the effectiveness (ε) of a closed HST and the coefficient of performance (COP) of a HP heating system using an artificial neural network (ANN) technique. CaCl2 aqueous solutions with five different mass fractions, viz. 3%, 9%, 15%, 21%, and 27%, were chosen as the secondary working fluids for the HSTHP heating system. In order to collect enough measured data, extensive field tests were conducted on an experimental test rig in Changsha, China which experiences hot summer and cold winter weather. After back-propagation (BP) training, the three-layer (4-9-2) ANN model with a tangent sigmoid transfer function at the hidden layer and a linear transfer function at the output layer was developed for predicting the tower effectiveness and the COP of the HP under different inlet air dry-/wet-bulb temperatures, hot water inlet temperatures and CaCl2 mass fractions. The correlation coefficient (R), mean relative error (MRE) and root mean squared error (RMSE) were adopted to evaluate the prediction accuracy of the ANN model. The results showed that the R, MRE, and RMSE between the training values and the experimental values of ε (COP) were 0.995 (0.996), 2.09% (1.89%), and 0.005 (0.060), respectively, which indicated that the ANN model was reliable and robust in predicting the performance of the HP. The findings of this paper indicated that in order to guarantee normal operation of the system, the freezing point temperature of the CaCl2 aqueous solution should be sufficiently (3–5 K) below its lowest operating temperature or lower than the normal operating temperature by about 10 K. The tower effectiveness increased with increasing CaCl2 mass fraction from 0 to 27%, while the COP of the HP decreased. A tradeoff between the tower effectiveness and the COP of the HP should be considered to further determine the suitable mass fraction of CaCl2 aqueous solution for the HSTHP heating system. The outputs of this study are expected to provide guidelines for selecting brine with an appropriate mass fraction for a closed HSTHP heating system for actual applications, which would be a reasonable solution to improve the system performance. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Xiaoqing Wei [verfasserIn] Nianping Li [verfasserIn] Jinqing Peng [verfasserIn] Jianlin Cheng [verfasserIn] Lin Su [verfasserIn] Jinhua Hu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2016

Schlagwörter:	heat-source-tower heat pump calcium chloride (CaCl2) aqueous solution artificial neural network tower effectiveness coefficient of performance (COP)

Übergeordnetes Werk:	In: Sustainability - MDPI AG, 2009, 8(2016), 5, p 410
Übergeordnetes Werk:	volume:8 ; year:2016 ; number:5, p 410

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/su8050410

Katalog-ID:	DOAJ014760150

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allfields	10.3390/su8050410 doi (DE-627)DOAJ014760150 (DE-599)DOAJd05307c1b121497c9fbc2bfc71db985e DE-627 ger DE-627 rakwb eng TD194-195 TJ807-830 GE1-350 Xiaoqing Wei verfasserin aut Analysis of the Effect of the CaCl2 Mass Fraction on the Efficiency of a Heat Pump Integrated Heat-Source Tower Using an Artificial Neural Network Model 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier An existing idle cooling tower can be reversibly used as a heat-source tower (HST) to drive a heat pump (HP) in cold seasons, with calcium chloride (CaCl2) aqueous solution commonly selected as the secondary working fluid in an indirect system due to its good thermo-physical properties. This study analyzed the effect of CaCl2 mass fraction on the effectiveness (ε) of a closed HST and the coefficient of performance (COP) of a HP heating system using an artificial neural network (ANN) technique. CaCl2 aqueous solutions with five different mass fractions, viz. 3%, 9%, 15%, 21%, and 27%, were chosen as the secondary working fluids for the HSTHP heating system. In order to collect enough measured data, extensive field tests were conducted on an experimental test rig in Changsha, China which experiences hot summer and cold winter weather. After back-propagation (BP) training, the three-layer (4-9-2) ANN model with a tangent sigmoid transfer function at the hidden layer and a linear transfer function at the output layer was developed for predicting the tower effectiveness and the COP of the HP under different inlet air dry-/wet-bulb temperatures, hot water inlet temperatures and CaCl2 mass fractions. The correlation coefficient (R), mean relative error (MRE) and root mean squared error (RMSE) were adopted to evaluate the prediction accuracy of the ANN model. The results showed that the R, MRE, and RMSE between the training values and the experimental values of ε (COP) were 0.995 (0.996), 2.09% (1.89%), and 0.005 (0.060), respectively, which indicated that the ANN model was reliable and robust in predicting the performance of the HP. The findings of this paper indicated that in order to guarantee normal operation of the system, the freezing point temperature of the CaCl2 aqueous solution should be sufficiently (3–5 K) below its lowest operating temperature or lower than the normal operating temperature by about 10 K. The tower effectiveness increased with increasing CaCl2 mass fraction from 0 to 27%, while the COP of the HP decreased. A tradeoff between the tower effectiveness and the COP of the HP should be considered to further determine the suitable mass fraction of CaCl2 aqueous solution for the HSTHP heating system. The outputs of this study are expected to provide guidelines for selecting brine with an appropriate mass fraction for a closed HSTHP heating system for actual applications, which would be a reasonable solution to improve the system performance. heat-source-tower heat pump calcium chloride (CaCl2) aqueous solution artificial neural network tower effectiveness coefficient of performance (COP) Environmental effects of industries and plants Renewable energy sources Environmental sciences Nianping Li verfasserin aut Jinqing Peng verfasserin aut Jianlin Cheng verfasserin aut Lin Su verfasserin aut Jinhua Hu verfasserin aut In Sustainability MDPI AG, 2009 8(2016), 5, p 410 (DE-627)610604120 (DE-600)2518383-7 20711050 nnns volume:8 year:2016 number:5, p 410 https://doi.org/10.3390/su8050410 kostenfrei https://doaj.org/article/d05307c1b121497c9fbc2bfc71db985e kostenfrei http://www.mdpi.com/2071-1050/8/5/410 kostenfrei https://doaj.org/toc/2071-1050 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 8 2016 5, p 410
spelling	10.3390/su8050410 doi (DE-627)DOAJ014760150 (DE-599)DOAJd05307c1b121497c9fbc2bfc71db985e DE-627 ger DE-627 rakwb eng TD194-195 TJ807-830 GE1-350 Xiaoqing Wei verfasserin aut Analysis of the Effect of the CaCl2 Mass Fraction on the Efficiency of a Heat Pump Integrated Heat-Source Tower Using an Artificial Neural Network Model 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier An existing idle cooling tower can be reversibly used as a heat-source tower (HST) to drive a heat pump (HP) in cold seasons, with calcium chloride (CaCl2) aqueous solution commonly selected as the secondary working fluid in an indirect system due to its good thermo-physical properties. This study analyzed the effect of CaCl2 mass fraction on the effectiveness (ε) of a closed HST and the coefficient of performance (COP) of a HP heating system using an artificial neural network (ANN) technique. CaCl2 aqueous solutions with five different mass fractions, viz. 3%, 9%, 15%, 21%, and 27%, were chosen as the secondary working fluids for the HSTHP heating system. In order to collect enough measured data, extensive field tests were conducted on an experimental test rig in Changsha, China which experiences hot summer and cold winter weather. After back-propagation (BP) training, the three-layer (4-9-2) ANN model with a tangent sigmoid transfer function at the hidden layer and a linear transfer function at the output layer was developed for predicting the tower effectiveness and the COP of the HP under different inlet air dry-/wet-bulb temperatures, hot water inlet temperatures and CaCl2 mass fractions. The correlation coefficient (R), mean relative error (MRE) and root mean squared error (RMSE) were adopted to evaluate the prediction accuracy of the ANN model. The results showed that the R, MRE, and RMSE between the training values and the experimental values of ε (COP) were 0.995 (0.996), 2.09% (1.89%), and 0.005 (0.060), respectively, which indicated that the ANN model was reliable and robust in predicting the performance of the HP. The findings of this paper indicated that in order to guarantee normal operation of the system, the freezing point temperature of the CaCl2 aqueous solution should be sufficiently (3–5 K) below its lowest operating temperature or lower than the normal operating temperature by about 10 K. The tower effectiveness increased with increasing CaCl2 mass fraction from 0 to 27%, while the COP of the HP decreased. A tradeoff between the tower effectiveness and the COP of the HP should be considered to further determine the suitable mass fraction of CaCl2 aqueous solution for the HSTHP heating system. The outputs of this study are expected to provide guidelines for selecting brine with an appropriate mass fraction for a closed HSTHP heating system for actual applications, which would be a reasonable solution to improve the system performance. heat-source-tower heat pump calcium chloride (CaCl2) aqueous solution artificial neural network tower effectiveness coefficient of performance (COP) Environmental effects of industries and plants Renewable energy sources Environmental sciences Nianping Li verfasserin aut Jinqing Peng verfasserin aut Jianlin Cheng verfasserin aut Lin Su verfasserin aut Jinhua Hu verfasserin aut In Sustainability MDPI AG, 2009 8(2016), 5, p 410 (DE-627)610604120 (DE-600)2518383-7 20711050 nnns volume:8 year:2016 number:5, p 410 https://doi.org/10.3390/su8050410 kostenfrei https://doaj.org/article/d05307c1b121497c9fbc2bfc71db985e kostenfrei http://www.mdpi.com/2071-1050/8/5/410 kostenfrei https://doaj.org/toc/2071-1050 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 8 2016 5, p 410
allfields_unstemmed	10.3390/su8050410 doi (DE-627)DOAJ014760150 (DE-599)DOAJd05307c1b121497c9fbc2bfc71db985e DE-627 ger DE-627 rakwb eng TD194-195 TJ807-830 GE1-350 Xiaoqing Wei verfasserin aut Analysis of the Effect of the CaCl2 Mass Fraction on the Efficiency of a Heat Pump Integrated Heat-Source Tower Using an Artificial Neural Network Model 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier An existing idle cooling tower can be reversibly used as a heat-source tower (HST) to drive a heat pump (HP) in cold seasons, with calcium chloride (CaCl2) aqueous solution commonly selected as the secondary working fluid in an indirect system due to its good thermo-physical properties. This study analyzed the effect of CaCl2 mass fraction on the effectiveness (ε) of a closed HST and the coefficient of performance (COP) of a HP heating system using an artificial neural network (ANN) technique. CaCl2 aqueous solutions with five different mass fractions, viz. 3%, 9%, 15%, 21%, and 27%, were chosen as the secondary working fluids for the HSTHP heating system. In order to collect enough measured data, extensive field tests were conducted on an experimental test rig in Changsha, China which experiences hot summer and cold winter weather. After back-propagation (BP) training, the three-layer (4-9-2) ANN model with a tangent sigmoid transfer function at the hidden layer and a linear transfer function at the output layer was developed for predicting the tower effectiveness and the COP of the HP under different inlet air dry-/wet-bulb temperatures, hot water inlet temperatures and CaCl2 mass fractions. The correlation coefficient (R), mean relative error (MRE) and root mean squared error (RMSE) were adopted to evaluate the prediction accuracy of the ANN model. The results showed that the R, MRE, and RMSE between the training values and the experimental values of ε (COP) were 0.995 (0.996), 2.09% (1.89%), and 0.005 (0.060), respectively, which indicated that the ANN model was reliable and robust in predicting the performance of the HP. The findings of this paper indicated that in order to guarantee normal operation of the system, the freezing point temperature of the CaCl2 aqueous solution should be sufficiently (3–5 K) below its lowest operating temperature or lower than the normal operating temperature by about 10 K. The tower effectiveness increased with increasing CaCl2 mass fraction from 0 to 27%, while the COP of the HP decreased. A tradeoff between the tower effectiveness and the COP of the HP should be considered to further determine the suitable mass fraction of CaCl2 aqueous solution for the HSTHP heating system. The outputs of this study are expected to provide guidelines for selecting brine with an appropriate mass fraction for a closed HSTHP heating system for actual applications, which would be a reasonable solution to improve the system performance. heat-source-tower heat pump calcium chloride (CaCl2) aqueous solution artificial neural network tower effectiveness coefficient of performance (COP) Environmental effects of industries and plants Renewable energy sources Environmental sciences Nianping Li verfasserin aut Jinqing Peng verfasserin aut Jianlin Cheng verfasserin aut Lin Su verfasserin aut Jinhua Hu verfasserin aut In Sustainability MDPI AG, 2009 8(2016), 5, p 410 (DE-627)610604120 (DE-600)2518383-7 20711050 nnns volume:8 year:2016 number:5, p 410 https://doi.org/10.3390/su8050410 kostenfrei https://doaj.org/article/d05307c1b121497c9fbc2bfc71db985e kostenfrei http://www.mdpi.com/2071-1050/8/5/410 kostenfrei https://doaj.org/toc/2071-1050 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 8 2016 5, p 410
allfieldsGer	10.3390/su8050410 doi (DE-627)DOAJ014760150 (DE-599)DOAJd05307c1b121497c9fbc2bfc71db985e DE-627 ger DE-627 rakwb eng TD194-195 TJ807-830 GE1-350 Xiaoqing Wei verfasserin aut Analysis of the Effect of the CaCl2 Mass Fraction on the Efficiency of a Heat Pump Integrated Heat-Source Tower Using an Artificial Neural Network Model 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier An existing idle cooling tower can be reversibly used as a heat-source tower (HST) to drive a heat pump (HP) in cold seasons, with calcium chloride (CaCl2) aqueous solution commonly selected as the secondary working fluid in an indirect system due to its good thermo-physical properties. This study analyzed the effect of CaCl2 mass fraction on the effectiveness (ε) of a closed HST and the coefficient of performance (COP) of a HP heating system using an artificial neural network (ANN) technique. CaCl2 aqueous solutions with five different mass fractions, viz. 3%, 9%, 15%, 21%, and 27%, were chosen as the secondary working fluids for the HSTHP heating system. In order to collect enough measured data, extensive field tests were conducted on an experimental test rig in Changsha, China which experiences hot summer and cold winter weather. After back-propagation (BP) training, the three-layer (4-9-2) ANN model with a tangent sigmoid transfer function at the hidden layer and a linear transfer function at the output layer was developed for predicting the tower effectiveness and the COP of the HP under different inlet air dry-/wet-bulb temperatures, hot water inlet temperatures and CaCl2 mass fractions. The correlation coefficient (R), mean relative error (MRE) and root mean squared error (RMSE) were adopted to evaluate the prediction accuracy of the ANN model. The results showed that the R, MRE, and RMSE between the training values and the experimental values of ε (COP) were 0.995 (0.996), 2.09% (1.89%), and 0.005 (0.060), respectively, which indicated that the ANN model was reliable and robust in predicting the performance of the HP. The findings of this paper indicated that in order to guarantee normal operation of the system, the freezing point temperature of the CaCl2 aqueous solution should be sufficiently (3–5 K) below its lowest operating temperature or lower than the normal operating temperature by about 10 K. The tower effectiveness increased with increasing CaCl2 mass fraction from 0 to 27%, while the COP of the HP decreased. A tradeoff between the tower effectiveness and the COP of the HP should be considered to further determine the suitable mass fraction of CaCl2 aqueous solution for the HSTHP heating system. The outputs of this study are expected to provide guidelines for selecting brine with an appropriate mass fraction for a closed HSTHP heating system for actual applications, which would be a reasonable solution to improve the system performance. heat-source-tower heat pump calcium chloride (CaCl2) aqueous solution artificial neural network tower effectiveness coefficient of performance (COP) Environmental effects of industries and plants Renewable energy sources Environmental sciences Nianping Li verfasserin aut Jinqing Peng verfasserin aut Jianlin Cheng verfasserin aut Lin Su verfasserin aut Jinhua Hu verfasserin aut In Sustainability MDPI AG, 2009 8(2016), 5, p 410 (DE-627)610604120 (DE-600)2518383-7 20711050 nnns volume:8 year:2016 number:5, p 410 https://doi.org/10.3390/su8050410 kostenfrei https://doaj.org/article/d05307c1b121497c9fbc2bfc71db985e kostenfrei http://www.mdpi.com/2071-1050/8/5/410 kostenfrei https://doaj.org/toc/2071-1050 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 8 2016 5, p 410
allfieldsSound	10.3390/su8050410 doi (DE-627)DOAJ014760150 (DE-599)DOAJd05307c1b121497c9fbc2bfc71db985e DE-627 ger DE-627 rakwb eng TD194-195 TJ807-830 GE1-350 Xiaoqing Wei verfasserin aut Analysis of the Effect of the CaCl2 Mass Fraction on the Efficiency of a Heat Pump Integrated Heat-Source Tower Using an Artificial Neural Network Model 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier An existing idle cooling tower can be reversibly used as a heat-source tower (HST) to drive a heat pump (HP) in cold seasons, with calcium chloride (CaCl2) aqueous solution commonly selected as the secondary working fluid in an indirect system due to its good thermo-physical properties. This study analyzed the effect of CaCl2 mass fraction on the effectiveness (ε) of a closed HST and the coefficient of performance (COP) of a HP heating system using an artificial neural network (ANN) technique. CaCl2 aqueous solutions with five different mass fractions, viz. 3%, 9%, 15%, 21%, and 27%, were chosen as the secondary working fluids for the HSTHP heating system. In order to collect enough measured data, extensive field tests were conducted on an experimental test rig in Changsha, China which experiences hot summer and cold winter weather. After back-propagation (BP) training, the three-layer (4-9-2) ANN model with a tangent sigmoid transfer function at the hidden layer and a linear transfer function at the output layer was developed for predicting the tower effectiveness and the COP of the HP under different inlet air dry-/wet-bulb temperatures, hot water inlet temperatures and CaCl2 mass fractions. The correlation coefficient (R), mean relative error (MRE) and root mean squared error (RMSE) were adopted to evaluate the prediction accuracy of the ANN model. The results showed that the R, MRE, and RMSE between the training values and the experimental values of ε (COP) were 0.995 (0.996), 2.09% (1.89%), and 0.005 (0.060), respectively, which indicated that the ANN model was reliable and robust in predicting the performance of the HP. The findings of this paper indicated that in order to guarantee normal operation of the system, the freezing point temperature of the CaCl2 aqueous solution should be sufficiently (3–5 K) below its lowest operating temperature or lower than the normal operating temperature by about 10 K. The tower effectiveness increased with increasing CaCl2 mass fraction from 0 to 27%, while the COP of the HP decreased. A tradeoff between the tower effectiveness and the COP of the HP should be considered to further determine the suitable mass fraction of CaCl2 aqueous solution for the HSTHP heating system. The outputs of this study are expected to provide guidelines for selecting brine with an appropriate mass fraction for a closed HSTHP heating system for actual applications, which would be a reasonable solution to improve the system performance. heat-source-tower heat pump calcium chloride (CaCl2) aqueous solution artificial neural network tower effectiveness coefficient of performance (COP) Environmental effects of industries and plants Renewable energy sources Environmental sciences Nianping Li verfasserin aut Jinqing Peng verfasserin aut Jianlin Cheng verfasserin aut Lin Su verfasserin aut Jinhua Hu verfasserin aut In Sustainability MDPI AG, 2009 8(2016), 5, p 410 (DE-627)610604120 (DE-600)2518383-7 20711050 nnns volume:8 year:2016 number:5, p 410 https://doi.org/10.3390/su8050410 kostenfrei https://doaj.org/article/d05307c1b121497c9fbc2bfc71db985e kostenfrei http://www.mdpi.com/2071-1050/8/5/410 kostenfrei https://doaj.org/toc/2071-1050 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 8 2016 5, p 410
language	English
source	In Sustainability 8(2016), 5, p 410 volume:8 year:2016 number:5, p 410
sourceStr	In Sustainability 8(2016), 5, p 410 volume:8 year:2016 number:5, p 410
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	heat-source-tower heat pump calcium chloride (CaCl2) aqueous solution artificial neural network tower effectiveness coefficient of performance (COP) Environmental effects of industries and plants Renewable energy sources Environmental sciences
isfreeaccess_bool	true
container_title	Sustainability
authorswithroles_txt_mv	Xiaoqing Wei @@aut@@ Nianping Li @@aut@@ Jinqing Peng @@aut@@ Jianlin Cheng @@aut@@ Lin Su @@aut@@ Jinhua Hu @@aut@@
publishDateDaySort_date	2016-01-01T00:00:00Z
hierarchy_top_id	610604120
id	DOAJ014760150
language_de	englisch
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callnumber-first	T - Technology
author	Xiaoqing Wei
spellingShingle	Xiaoqing Wei misc TD194-195 misc TJ807-830 misc GE1-350 misc heat-source-tower heat pump misc calcium chloride (CaCl2) aqueous solution misc artificial neural network misc tower effectiveness misc coefficient of performance (COP) misc Environmental effects of industries and plants misc Renewable energy sources misc Environmental sciences Analysis of the Effect of the CaCl2 Mass Fraction on the Efficiency of a Heat Pump Integrated Heat-Source Tower Using an Artificial Neural Network Model
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topic_title	TD194-195 TJ807-830 GE1-350 Analysis of the Effect of the CaCl2 Mass Fraction on the Efficiency of a Heat Pump Integrated Heat-Source Tower Using an Artificial Neural Network Model heat-source-tower heat pump calcium chloride (CaCl2) aqueous solution artificial neural network tower effectiveness coefficient of performance (COP)
topic	misc TD194-195 misc TJ807-830 misc GE1-350 misc heat-source-tower heat pump misc calcium chloride (CaCl2) aqueous solution misc artificial neural network misc tower effectiveness misc coefficient of performance (COP) misc Environmental effects of industries and plants misc Renewable energy sources misc Environmental sciences
topic_unstemmed	misc TD194-195 misc TJ807-830 misc GE1-350 misc heat-source-tower heat pump misc calcium chloride (CaCl2) aqueous solution misc artificial neural network misc tower effectiveness misc coefficient of performance (COP) misc Environmental effects of industries and plants misc Renewable energy sources misc Environmental sciences
topic_browse	misc TD194-195 misc TJ807-830 misc GE1-350 misc heat-source-tower heat pump misc calcium chloride (CaCl2) aqueous solution misc artificial neural network misc tower effectiveness misc coefficient of performance (COP) misc Environmental effects of industries and plants misc Renewable energy sources misc Environmental sciences
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title	Analysis of the Effect of the CaCl2 Mass Fraction on the Efficiency of a Heat Pump Integrated Heat-Source Tower Using an Artificial Neural Network Model
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title_full	Analysis of the Effect of the CaCl2 Mass Fraction on the Efficiency of a Heat Pump Integrated Heat-Source Tower Using an Artificial Neural Network Model
author_sort	Xiaoqing Wei
journal	Sustainability
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author_browse	Xiaoqing Wei Nianping Li Jinqing Peng Jianlin Cheng Lin Su Jinhua Hu
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author-letter	Xiaoqing Wei
doi_str_mv	10.3390/su8050410
author2-role	verfasserin
title_sort	analysis of the effect of the cacl2 mass fraction on the efficiency of a heat pump integrated heat-source tower using an artificial neural network model
callnumber	TD194-195
title_auth	Analysis of the Effect of the CaCl2 Mass Fraction on the Efficiency of a Heat Pump Integrated Heat-Source Tower Using an Artificial Neural Network Model
abstract	An existing idle cooling tower can be reversibly used as a heat-source tower (HST) to drive a heat pump (HP) in cold seasons, with calcium chloride (CaCl2) aqueous solution commonly selected as the secondary working fluid in an indirect system due to its good thermo-physical properties. This study analyzed the effect of CaCl2 mass fraction on the effectiveness (ε) of a closed HST and the coefficient of performance (COP) of a HP heating system using an artificial neural network (ANN) technique. CaCl2 aqueous solutions with five different mass fractions, viz. 3%, 9%, 15%, 21%, and 27%, were chosen as the secondary working fluids for the HSTHP heating system. In order to collect enough measured data, extensive field tests were conducted on an experimental test rig in Changsha, China which experiences hot summer and cold winter weather. After back-propagation (BP) training, the three-layer (4-9-2) ANN model with a tangent sigmoid transfer function at the hidden layer and a linear transfer function at the output layer was developed for predicting the tower effectiveness and the COP of the HP under different inlet air dry-/wet-bulb temperatures, hot water inlet temperatures and CaCl2 mass fractions. The correlation coefficient (R), mean relative error (MRE) and root mean squared error (RMSE) were adopted to evaluate the prediction accuracy of the ANN model. The results showed that the R, MRE, and RMSE between the training values and the experimental values of ε (COP) were 0.995 (0.996), 2.09% (1.89%), and 0.005 (0.060), respectively, which indicated that the ANN model was reliable and robust in predicting the performance of the HP. The findings of this paper indicated that in order to guarantee normal operation of the system, the freezing point temperature of the CaCl2 aqueous solution should be sufficiently (3–5 K) below its lowest operating temperature or lower than the normal operating temperature by about 10 K. The tower effectiveness increased with increasing CaCl2 mass fraction from 0 to 27%, while the COP of the HP decreased. A tradeoff between the tower effectiveness and the COP of the HP should be considered to further determine the suitable mass fraction of CaCl2 aqueous solution for the HSTHP heating system. The outputs of this study are expected to provide guidelines for selecting brine with an appropriate mass fraction for a closed HSTHP heating system for actual applications, which would be a reasonable solution to improve the system performance.
abstractGer	An existing idle cooling tower can be reversibly used as a heat-source tower (HST) to drive a heat pump (HP) in cold seasons, with calcium chloride (CaCl2) aqueous solution commonly selected as the secondary working fluid in an indirect system due to its good thermo-physical properties. This study analyzed the effect of CaCl2 mass fraction on the effectiveness (ε) of a closed HST and the coefficient of performance (COP) of a HP heating system using an artificial neural network (ANN) technique. CaCl2 aqueous solutions with five different mass fractions, viz. 3%, 9%, 15%, 21%, and 27%, were chosen as the secondary working fluids for the HSTHP heating system. In order to collect enough measured data, extensive field tests were conducted on an experimental test rig in Changsha, China which experiences hot summer and cold winter weather. After back-propagation (BP) training, the three-layer (4-9-2) ANN model with a tangent sigmoid transfer function at the hidden layer and a linear transfer function at the output layer was developed for predicting the tower effectiveness and the COP of the HP under different inlet air dry-/wet-bulb temperatures, hot water inlet temperatures and CaCl2 mass fractions. The correlation coefficient (R), mean relative error (MRE) and root mean squared error (RMSE) were adopted to evaluate the prediction accuracy of the ANN model. The results showed that the R, MRE, and RMSE between the training values and the experimental values of ε (COP) were 0.995 (0.996), 2.09% (1.89%), and 0.005 (0.060), respectively, which indicated that the ANN model was reliable and robust in predicting the performance of the HP. The findings of this paper indicated that in order to guarantee normal operation of the system, the freezing point temperature of the CaCl2 aqueous solution should be sufficiently (3–5 K) below its lowest operating temperature or lower than the normal operating temperature by about 10 K. The tower effectiveness increased with increasing CaCl2 mass fraction from 0 to 27%, while the COP of the HP decreased. A tradeoff between the tower effectiveness and the COP of the HP should be considered to further determine the suitable mass fraction of CaCl2 aqueous solution for the HSTHP heating system. The outputs of this study are expected to provide guidelines for selecting brine with an appropriate mass fraction for a closed HSTHP heating system for actual applications, which would be a reasonable solution to improve the system performance.
abstract_unstemmed	An existing idle cooling tower can be reversibly used as a heat-source tower (HST) to drive a heat pump (HP) in cold seasons, with calcium chloride (CaCl2) aqueous solution commonly selected as the secondary working fluid in an indirect system due to its good thermo-physical properties. This study analyzed the effect of CaCl2 mass fraction on the effectiveness (ε) of a closed HST and the coefficient of performance (COP) of a HP heating system using an artificial neural network (ANN) technique. CaCl2 aqueous solutions with five different mass fractions, viz. 3%, 9%, 15%, 21%, and 27%, were chosen as the secondary working fluids for the HSTHP heating system. In order to collect enough measured data, extensive field tests were conducted on an experimental test rig in Changsha, China which experiences hot summer and cold winter weather. After back-propagation (BP) training, the three-layer (4-9-2) ANN model with a tangent sigmoid transfer function at the hidden layer and a linear transfer function at the output layer was developed for predicting the tower effectiveness and the COP of the HP under different inlet air dry-/wet-bulb temperatures, hot water inlet temperatures and CaCl2 mass fractions. The correlation coefficient (R), mean relative error (MRE) and root mean squared error (RMSE) were adopted to evaluate the prediction accuracy of the ANN model. The results showed that the R, MRE, and RMSE between the training values and the experimental values of ε (COP) were 0.995 (0.996), 2.09% (1.89%), and 0.005 (0.060), respectively, which indicated that the ANN model was reliable and robust in predicting the performance of the HP. The findings of this paper indicated that in order to guarantee normal operation of the system, the freezing point temperature of the CaCl2 aqueous solution should be sufficiently (3–5 K) below its lowest operating temperature or lower than the normal operating temperature by about 10 K. The tower effectiveness increased with increasing CaCl2 mass fraction from 0 to 27%, while the COP of the HP decreased. A tradeoff between the tower effectiveness and the COP of the HP should be considered to further determine the suitable mass fraction of CaCl2 aqueous solution for the HSTHP heating system. The outputs of this study are expected to provide guidelines for selecting brine with an appropriate mass fraction for a closed HSTHP heating system for actual applications, which would be a reasonable solution to improve the system performance.
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title_short	Analysis of the Effect of the CaCl2 Mass Fraction on the Efficiency of a Heat Pump Integrated Heat-Source Tower Using an Artificial Neural Network Model
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