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Use of Neural Networks for Dynamic Heat Exchanger Modeling

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Chemical and Petroleum Engineering Aims and scope

Use of neural networks for calculating flow temperatures of dynamic dual-flow countercurrent heat exchanger is investigated. The optimum neural network architecture for the problem of dynamic heat exchanger modeling is chosen and the optimum numbers of neurons and layers in the network for minimizing standard error are obtained.

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Authors and Affiliations

  1. Bauman Moscow State Technical University, Moscow, Russia

    N. A. Lavrov & S. I. Khutsieva

  2. Moscow State University of Civil Engineering, Moscow, Russia

    V. A. Shananin

Authors
  1. N. A. Lavrov
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  2. S. I. Khutsieva
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  3. V. A. Shananin
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Correspondence to N. A. Lavrov.

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Translated from Khimicheskoe i Neftegazovoe Mashinostroenie, Vol. 58, No. 11, pp. 14–18, November, 2022.

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Lavrov, N.A., Khutsieva, S.I. & Shananin, V.A. Use of Neural Networks for Dynamic Heat Exchanger Modeling. Chem Petrol Eng 58, 917–924 (2023). https://doi.org/10.1007/s10556-023-01183-8

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  • DOI: https://doi.org/10.1007/s10556-023-01183-8

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