International Scientific Journal


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.
PAPER REVISED: 2014-02-27
PAPER ACCEPTED: 2014-03-04
DOI REFERENCE: 10.2298/TSCI1403853S
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