Application of Artificial Neural Network in Countercurrent Spray Saturator

  • Yixing Li
  • Yuzhang Wang
  • Shilie Weng
  • Yonghong Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)


This paper presents the application of artificial neural network (ANN) in saturator. Phase Doppler Anemometry (PDA) is utilized to investigate the distribution of water droplets diameter and velocity in the saturator. The data obtained from experiment is used as input-output of ANN. Before using ANN method, some prerequisites have to be processed, including the selection of the number of input and output variables, hidden layer neurons, the network architecture and the normalization of data etc. The results indicate that the trained ANN can provide accurate prediction values which agree with real experimental data closely.


Artificial Neural Network Hide Layer Artificial Neural Network Model Water Droplet Hide Layer Neuron 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Rao, A.D.: Process for Producing Power, US patent no. 4829763 (1989)Google Scholar
  2. 2.
    Xiao, Y.H., Cai, R.X., Lin, R.M.: Modeling Hat Cycle and Thermodynamic Evaluation. Energy Convers. Mgmt. 38(15), 1605–1612 (1997)CrossRefGoogle Scholar
  3. 3.
    Parente, J.O.S., Traverso, A., Massardo, A.F.: Saturator Analysis for an Evaporative Gas Turbine Cycle. Applied Thermal Engineering 23(10), 1275–1293 (2003)CrossRefGoogle Scholar
  4. 4.
    Makkinejad, N.: Temperature Profile in Countercurrent/Co-current Spray Tower. International Journal of Heat and Mass Transfer 44(2), 429–442 (2000)CrossRefGoogle Scholar
  5. 5.
    Denmark: Dantec Measurement Technology GmbH: Dual PDA Manual, User’s Guide 3 (1996)Google Scholar
  6. 6.
    Ikeda, Y., Mazurkiewicz, D.: Application of Neural Network Technique to Combustion Spray Dynamic Analysis. In: Arikawa, S., Shinohara, A. (eds.) Progress in Discovery Science. LNCS (LNAI), vol. 2281, pp. 408–425. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  7. 7.
    Guardani, R., Nascimento, C.A.O., Onimaru, R.S.: Use of Neural Networks in the Analysis of Particle Size Distribution by Laser Diffraction: Tests with Different Particle Systems. Powder Technology 126(1), 42–50 (2002)CrossRefGoogle Scholar
  8. 8.
    Liu, J.T., Chang, H.B., Hsu, T.Y., et al.: Prediction of the Flow Stress of High-Speed Steel during Hot Deformation Using a BP Artificial Neural Network. Journal of Material Processing Technology 103(2), 200–205 (2000)CrossRefGoogle Scholar
  9. 9.
    Wang, Y.Z., Liu, Y.W., Weng, S.L.: Experimental Investigation of Two-Phase Flow in Countercurrent Spraying Humidifier with Phase Doppler Anemometry. In: 4th European Thermal Sciences Conference, Birmingham, UK, pp. 29–31 (2004)Google Scholar
  10. 10.
    Sozen, A., Arcaklioglu, E., Ozalp, M.: A New Approach to Thermodynamic Analysis of Ejector-Absorption Cycle: Artificial Neural Networks. Applied Thermal Engineering 23(8), 937–952 (2003)CrossRefGoogle Scholar
  11. 11.
    Nascimento, C.A.O., Guardani, R., Giulietti, M.: Use of Neural Networks in the Analysis of Particle Size Distributions by Laser Diffraction. Powder Technology 90(1), 89–94 (1997)CrossRefGoogle Scholar
  12. 12.
    Wang, X.D., Yao, M., Chen, X.F.: Application of BP Neural Network for the Abnormity Monitoring in Slab Continuous Casting. In: Yin, F.-L., Wang, J., Guo, C. (eds.) ISNN 2004. LNCS, vol. 3174, pp. 601–606. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  13. 13.
    Hagan, M.T., Demuth, H.B., Beale, M.H.: Neural Network Design. PWS Publishing Company (1995)Google Scholar
  14. 14.
    Sablani, S.S., Ramaswamy, H., Sreekanth, S., et al.: Neural Network Modeling of Heat Transfer to Liquid Particle Mixtures in Cans Subjected to End-over-End Processing. Food Research International 30(2), 105–116 (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yixing Li
    • 1
  • Yuzhang Wang
    • 1
  • Shilie Weng
    • 1
  • Yonghong Wang
    • 1
  1. 1.Key Laboratory for Power Machinery and Engineering of Ministry of Education, ChinaShanghai Jiao Tong University,

Personalised recommendations