Artificial neural network control of a heat exchanger in a closed flow air circuit

Varshney, Kapil; Panigrahi, Pradipta Kumar

doi:10.1016/j.asoc.2004.10.004

Cited by 54 publications

(28 citation statements)

References 14 publications

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“…Varshney and Panigrahi [9] investigated ANN control of a heat exchanger in a closed flow air circuit.…”

Section: Introductionmentioning

confidence: 99%

ANN modeling of compact heat exchangers

Ermiş

2008

Int. J. Energy Res.

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SUMMARYThis paper focuses on the heat transfer analysis of compact heat exchangers through artificial neural network (ANN). The ANN analysis includes heat transfer coefficient, pressure drop and Nusselt number in the compact heat exchangers by using available experimental results in a case study. In this study, data sets are established in 15 different test channel configurations. A feed-forward back-propagation algorithm is used in the learning process and testing the network. The learning process is applied to correlate the heat transfer analysis for different ratios of rib spacing and height, various Reynolds numbers, different inlet-outlet temperatures, heat transfer areas and hydraulic diameters. Various hidden numbers of the network are trained for the best prediction of the heat transfer analysis. Heat transfer coefficient, pressure drop and Nusselt number values are predicted by the network algorithm. The results are then compared with the experimental results of the case. The trained ANN results perform well in predicting the heat transfer coefficient, pressure drop and Nusselt number with an average absolute mean relative error of less than 6% compared with the experimental results for staggered cylindrical ribbed and staggered triangular ribbed of test channels in the case study. The ANN approach is found to be a suitable method for heat transfer analysis in compact heat exchangers.

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“…Varshney and Panigrahi [9] investigated ANN control of a heat exchanger in a closed flow air circuit.…”

Section: Introductionmentioning

confidence: 99%

ANN modeling of compact heat exchangers

Ermiş

2008

Int. J. Energy Res.

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“…The model consisted of unknown parameters that were rectified during the control process. Kapil Varshney [4] and Gerardo Díaz et al [5] identified the model of a system by artificial neural network. Ansari et al [6] inspected the dynamic behavior of a double tube heat exchanger and derived the PDE by using the conservative energy law.…”

Section: Introductionmentioning

confidence: 99%

“…This paper states that because of the intense dependency of these controllers to the model structures, it is necessary to rectify the model during the control. Kapil Varshney [4] and Gerardo Díaz et al [5] identified a model by artificial neural network (ANN) and then controlled the system by inverse ANN. Alexander Fink et al [10] controlled a heat exchanger by a nonlinear adaptive controller.…”

Section: Introductionmentioning

confidence: 99%

A comparative study between linear and sliding mode adaptive controllers for a hot gas generator

Abedi

Bakhtiari-Nejad

Saffar‐Avval

et al. 2010

Applied Thermal Engineering

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“…In this scenario, most of the contributions that have appeared in literature about advanced control schemes have been tested for nonlinear simulation models (Himmelblau, 2008), while applications with advanced control algorithms over industrial or pilot plants (Frattini et al, 2000) (Varshney and Panigrahi, 2005) (Escano et al, 2009) or even with classical control (Noorai et al, 1999) (Tellez-Anguiano et al, 2009) are hardly found.…”

Section: Introductionmentioning

confidence: 99%