THERMAL SCIENCE

International Scientific Journal

Authors of this Paper

External Links

MODELING THE OUTLET TEMPERATURE IN HEAT EXCHANGERS: CASE STUDY

ABSTRACT
This article presents the results of the study of the heat transfer in a heat ex-changer where the working fluid is the crude oil prepared for desalination, and the thermic agent is the re-circulating heavy gasoline fraction. Firstly, the Reynolds numbers have been computed using the temperatures and flow rates of the fluids as input variables. Then, general regression neural network and multi-layer perceptron were used for the outlet temperatures estimation using the inlet temperatures and the Reynolds numbers as input variables. The best models on the training dataset were obtained utilizing a multilayer perceptron with one hidden layer, while the best performance on the validation dataset was obtained using a multilayer perceptron network with two hidden layers.
KEYWORDS
PAPER SUBMITTED: 2019-09-13
PAPER REVISED: 2019-11-05
PAPER ACCEPTED: 2019-11-07
PUBLISHED ONLINE: 2019-12-22
DOI REFERENCE: https://doi.org/10.2298/TSCI190913449B
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2021, VOLUME 25, ISSUE Issue 1, PAGES [591 - 602]
REFERENCES
  1. Angsutorn, N., Siemanond, K., Chuvaree, R., Heat exchanger network synthesis using MINLP stage-wise model with pinch analysis and relaxation, Computer Aided Chemical Engineering, 33(2014), pp. 139-144.
  2. Kryzhanivskyy, V., et al., An inverse V. Kryzhanivskyy et al., An inverse problem for retrieving time dependency of heat flux in metal cutting linear programming, Procedia Manufacturing, 25(2018), pp. 287-293.
  3. Cavallini, A., Heat transfer and heat exchangers, in Organic Rankine Cycle (ORC) Power Systems: Technologies and Applications, E. Macchi and M. Aztolfi, Eds., Elsevier, 2017, pp. 397- 470.
  4. Yeap, B. L., et al., Mitigation of Crude Oil Refinery Heat Exchanger, Chemical Engineering Research & Design, 82 (2004), pp. 53-71.
  5. Yeap, B. L., et al., Retrofitting crude oil refinery heat exchanger networks to minimize fouling while maximizing heat recovery, Heat Transfer Engineering, 26(1) (2005), pp. 23-34.
  6. Pacheco-Vega, et. al., On-line fuzzy-logic-base temperature control of a concentric-tube heat exchanger facility, Heat Transfer Engineering, 30(14)(2009), pp. 1208-1215.
  7. Onishi, V.C., et. al., MINLP Model for the Synthesis of Heat Exchanger Networks with Handling Pressure of Process Streams, Proceedings, The 24th European Symposium on Computer Aided Process Engineering - ESCAPE, Budapest, Hungary, 2014, pp. 1-6.
  8. Xie, G. N., et .al., Heat transfer analysis for shell-and-tube heat exchangers with experimental data by artificial neural networks approach, Applied Thermal Engineering, 27(5-6) (2007), pp. 1096- 1104.
  9. Parry, A. J., Eames, P. C., Agyenim, F. B., Modeling of thermal energy storage shell-and-tube heat exchanger, Heat Transfer Engineering, 35(1)(2014), pp. 1-14.
  10. El-Said, E. M. S., Abou Al-Sood, M. M., Shell and tube heat exchanger with new segmental baffles configurations: A comparative experimental investigation, Applied Thermal Engineering, 150 (2019), pp. 803-810.
  11. Guo, Y., et al., Modeling of plate heat exchanger based on sensitivity analysis and model updating, Chemical Engineering Research & Design, 138(2018), pp. 418-432.
  12. Li, Q., et al., Compact heat exchangers: A review and future applications for a new generation of high temperature solar receivers, Renewable and Sustainable Energy Reviews, 15(2011), pp. 4855-4875.
  13. Shetty, N., et al., Improved threshold fouling models for crude oils, Energy, 111(2016), pp. 453-467.
  14. Trzcinski, P., Markowski, M., Diagnosis of the fouling effects in a shell and tube heat exchanger using artificial neural network, Chemical Engineering Transactions, 70 (2018), pp. 355-360.
  15. Elankavi S., Shankar, U., Study of Flow and Heat Transfer Analysis in Shell and Tube Heat Exchanger using CFD, International Research Journal of Engineering and Technology, 5(10) (2018), pp. 467-473.
  16. Yang, D., et al., Geometric optimization of shell and tube heat exchanger with interstitial twisted tapes outside the tubes applying CFD techniques, Applied Thermal Engineering, 152(2019), pp. 559-572.
  17. Abd, A. A., Qasim Kareem, M., Naji, S. Z., Performance analysis of shell and tube heat exchanger: Parametric study, Case Studies in Thermal Engineering, 12(2018), pp. 563-568.
  18. Islamoglu, Y., A new approach for the prediction of the heat transfer rate of the wire-on-tube type heat exchanger - use of an artificial neural network model, Applied Thermal Engineering, 23(2003), pp. 243-249.
  19. Moya-Rico J. D., et al., Characterization of a triple concentric-tube heat exchanger with corrugated tubes using artificial neural networks (ANN), Applied Thermal Engineering, 147(2019), pp.1036-1046.
  20. Pacheco-Vega, A., et al., Neural network analysis of fin-tube refrigerating heat exchanger with limited experimental data, International Journal of Heat and Mass Transfer, 44(2001), pp. 763-770.
  21. Verma, T. N., et al., ANN: Prediction of an experimental heat transfer analysis of concentric tube heat exchanger with corrugated inner tubes, Applied Thermal Engineering, 120(2017), pp. 219-227.
  22. Dheenamma, M., et .al., In pursuit of the best artificial neural network configuration for the prediction of output parameters of corrugated plate heat exchanger, Fuel, 239(2019), pp. 461-470.
  23. Mohanraj, M., et .al., Applications of artificial neural networks for thermal analysis of heat exchangers - A review, International Journal of Thermal Sciences, 90 (2015), pp. 150-172.
  24. Perry R.J. and D.W. Green, Perry's Chemical Engineers' Handbook, 7th edition (1997), New York, McGraw-Hill, pp. 8- 28.
  25. www.astm.org/standards/D5002.htm
  26. Băutu, E. Bărbulescu, A., Forecasting meteorological time series using soft computing methods: an empirical study, Applied Mathematics and Information Science, 7(4)(2013), pp. 1297 - 1306.
  27. Bărbulescu, A., Barbeș, L., Statistical Analysis and Mathematical Models for the VOCs Concentrations on the omanian Littoral. A case study, Analytical Letters, 49(3)(2016), pp. 387- 399.
  28. Bărbulescu, A., Do the time series statistical properties influence the goodness of fit of GRNN models? Study on financial series, Applied Stochastic Models in Business and Industry, 34(2018), pp. 586 - 596.
  29. Sprecht, F., A General Regression Neural Network, IEEE Transactions on Neural Networks, 2(6)(1991), pp. 568-576.
  30. Rumelhart, D., Hinton, G., Williams, R., Learning Internal Representations by Error Propagation, in: Parallel distributed processing: explorations in the microstructure of cognition, vol 1, MIT Press Cambridge, MA, 1986, pp.318-362.
  31. Shewchuk, J. R., An Introduction to the Conjugate Gradient Method Without the Agonizing Pain, 1994, www.cs.cmu.edu/~quake-papers/painless-conjugate-gradient.pdf.
  32. Gholami, E., Vaferi, B., Ariana, M. A., Prediction of viscosity of several 49 alumina-based nanofluids using various artificial intelligence paradigms comparison with experimental data and empirical correlations, Powder Technology, 323(2018), pp. 495-506..
  33. Hornik, K., et al., Multilayer feedforward networks are universal approximators, Neural Networks, 2(1989), pp. 359-366.
  34. DTREG, www.dtreg.com/solution
  35. Grabowska, K., et al., Construction of an Innovative Adsorbent Bed Configuration in the Adsorption Chiller-Selection Criteria for Effective Sorbent-Glue Pair, Energy, 151(2018), pp. 317-323.
  36. Grabowska, K., et al., The Numerical Comparison of Heat Transfer in a Coated and Fixed Bed of an Adsorption Chiller, Journal of Thermal Science, 27(5)(2018), pp. 421-426.
  37. Krzywanski, J., Grabowska, K., Sosnowski, M., Zylka, A., Sztekler, K., Kalawa, W., Wójcik, T., Nowak, W., An Adaptive Neuro-Fuzzy model of a Re-Heat Two-Stage Adsorption Chiller, Thermal Science, 23 Suppl. 4 (2019), pp. S1053-S1063.

© 2024 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence