Exergo-ecological evaluation of heat exchanger

Thermodynamic optimization of thermal devices requires information about the influence of operational and structural parameters on its behaviour. The interconnections among parameters can be estimated by tools such as CFD, experimental statistic of the deviceetc. Despite precise and compreh...

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Main Authors: Stanek Wojciech, Czarnowska Lucyna, Szczygiel Ireneusz, Rojczyk Marek
Format: Article
Language:English
Published: VINCA Institute of Nuclear Sciences 2014-01-01
Series:Thermal Science
Subjects:
Online Access:http://www.doiserbia.nb.rs/img/doi/0354-9836/2014/0354-98361403853S.pdf
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spelling doaj-3a2b0a1f162142d5af0a99ee9c00f7692021-01-02T01:09:58ZengVINCA Institute of Nuclear SciencesThermal Science0354-98362014-01-0118385386210.2298/TSCI1403853S0354-98361403853SExergo-ecological evaluation of heat exchangerStanek Wojciech0Czarnowska Lucyna1Szczygiel Ireneusz2Rojczyk Marek3Institute of Thermal Technology, Silesian University of Technology, Gliwice, PolandInstitute of Thermal Technology, Silesian University of Technology, Gliwice, PolandInstitute of Thermal Technology, Silesian University of Technology, Gliwice, PolandInstitute of Thermal Technology, Silesian University of Technology, Gliwice, PolandThermodynamic optimization of thermal devices requires information about the influence of operational and structural parameters on its behaviour. The interconnections among parameters can be estimated by tools such as CFD, experimental statistic of the deviceetc. Despite precise and comprehensive results obtained by CFD, the time of computations is relatively long. This disadvantage often cannot be accepted in case of optimization as well as online control of thermal devices. As opposed to CFD the neural network or regression is characterized by short computational time, but does not take into account any physical phenomena occurring in the considered process. The CFD model of heat exchanger was built using commercial package Fluent/Ansys. The empirical model of heat exchanger has been assessed by regression and neural networks based on the set of pseudo-measurements generated by the exact CFD model. In the paper, the usage of the developed empirical model of heat exchanger for the minimisation of TEC is presented. The optimisationconcerns operational parameters of heat exchanger. The TEC expresses the cumulative exergy consumption of non-renewable resources. The minimization of the TEC is based on the objective function formulated by Szargut. However, the authors extended the classical TEC by the introduction of the exergy bonus theory proposed by Valero. The TEC objective function fulfils the rules of life cycle analysis because it contains the investment expenditures (measured by the cumulative exergy consumption of non-renewable natural resources), the operation of devices and the final effects of decommissioning the installation.http://www.doiserbia.nb.rs/img/doi/0354-9836/2014/0354-98361403853S.pdfthermo-ecological costheat exchangerCFD modellingoptimizationneural networks
collection DOAJ
language English
format Article
sources DOAJ
author Stanek Wojciech
Czarnowska Lucyna
Szczygiel Ireneusz
Rojczyk Marek
spellingShingle Stanek Wojciech
Czarnowska Lucyna
Szczygiel Ireneusz
Rojczyk Marek
Exergo-ecological evaluation of heat exchanger
Thermal Science
thermo-ecological cost
heat exchanger
CFD modelling
optimization
neural networks
author_facet Stanek Wojciech
Czarnowska Lucyna
Szczygiel Ireneusz
Rojczyk Marek
author_sort Stanek Wojciech
title Exergo-ecological evaluation of heat exchanger
title_short Exergo-ecological evaluation of heat exchanger
title_full Exergo-ecological evaluation of heat exchanger
title_fullStr Exergo-ecological evaluation of heat exchanger
title_full_unstemmed Exergo-ecological evaluation of heat exchanger
title_sort exergo-ecological evaluation of heat exchanger
publisher VINCA Institute of Nuclear Sciences
series Thermal Science
issn 0354-9836
publishDate 2014-01-01
description Thermodynamic optimization of thermal devices requires information about the influence of operational and structural parameters on its behaviour. The interconnections among parameters can be estimated by tools such as CFD, experimental statistic of the deviceetc. Despite precise and comprehensive results obtained by CFD, the time of computations is relatively long. This disadvantage often cannot be accepted in case of optimization as well as online control of thermal devices. As opposed to CFD the neural network or regression is characterized by short computational time, but does not take into account any physical phenomena occurring in the considered process. The CFD model of heat exchanger was built using commercial package Fluent/Ansys. The empirical model of heat exchanger has been assessed by regression and neural networks based on the set of pseudo-measurements generated by the exact CFD model. In the paper, the usage of the developed empirical model of heat exchanger for the minimisation of TEC is presented. The optimisationconcerns operational parameters of heat exchanger. The TEC expresses the cumulative exergy consumption of non-renewable resources. The minimization of the TEC is based on the objective function formulated by Szargut. However, the authors extended the classical TEC by the introduction of the exergy bonus theory proposed by Valero. The TEC objective function fulfils the rules of life cycle analysis because it contains the investment expenditures (measured by the cumulative exergy consumption of non-renewable natural resources), the operation of devices and the final effects of decommissioning the installation.
topic thermo-ecological cost
heat exchanger
CFD modelling
optimization
neural networks
url http://www.doiserbia.nb.rs/img/doi/0354-9836/2014/0354-98361403853S.pdf
work_keys_str_mv AT stanekwojciech exergoecologicalevaluationofheatexchanger
AT czarnowskalucyna exergoecologicalevaluationofheatexchanger
AT szczygielireneusz exergoecologicalevaluationofheatexchanger
AT rojczykmarek exergoecologicalevaluationofheatexchanger
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