Abstract
An artificial neural networks model to enhance the performance index of mass transfer function in tube flow by means of entry region coil-disc assembly promoter was inserted coaxially is presented in this paper. Popular Backpropagation algorithm was utilized to test, train and normalize the network data to envisage the performance of mass transfer function. The experimental data of the study is separated into two sets one is training sets and second one is validation sets. The 248 sets of the experimental data were used in training and 106 sets for the validation of the artificial neural networks using MATLAB 7.7.0, particularly tool boxes to predict the performance index of the mass transfer in tube for faster convergence and accuracy. The weights were initialized within the range of [–1, 1]. The network limitations in all attempts taken learning rate as 0.10 and momentum term as 0.30. The finest model was selected based on the MSE, STD and R2. In this, network with 5_8_1 configuration is recommended for mass transfer training. This research work reveals that artificial neural networks with adding more number of layers and nodes in hidden layer may not increase the performance of mass transfer function.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Abbreviations
- D:
-
Tube Diameter, m
- Dc :
-
Coil Diameter, m
- Pc :
-
Coil Pitch, m/turn
- Lc :
-
Coil Length, m
- Dd :
-
Diameter of the disc, m
- Hd :
-
Height of the disc, m
- V:
-
Velocity of the fluid, m/s
- \( \bar{g} \) :
-
Mass transfer function
- \( \text{Re}_{m}^{ + } \) :
-
Modified Reynolds number
- Pc/d, Lc/d, dd/d, Hd/d:
-
Dimensionless parameter
References
Jie Zhang., A.J. Morris “A Sequential Learning Approach for Single Hidden Layer Neural Networks” Neural Networks, Vol. 11, Issue 1, 1998, pp 65–80.
Stevan V. Odri., Dusan P. Petrovacki and Gordana A. Krstonosic, “Evolutional development of a multilevel neural network”, Neural Networks, Vol. 6, Issue 4, 1993, pp 583–595.
Simon Haykin, “Neural Networks: A Comprehensive Foundation, 2nd edition” Prentice Hall PTR Upper Saddle River, NJ, USA, 1998, pages 842.
Leonard, J and Kramer, M. A, “Improvement of the back propagation algorithm for training ANN” Computers & Chemical Engineering, Vol. 14, Issue 3, 1990, pp 337–341.
Pollard, J.F., Broussard, M.R., Garrison, D.B and San, K.Y, “Process identification using neural networks” Computers & Chemical Engineering, Vol. 16, Issue 4, 1992, pp 253–270.
Gregory J. Zdaniuk, Louay M. Chamraa and D. Keith Waltersa, “Correlating heat transfer and friction in helically-finned tubes using Artificial Neural Networks” International Journal of Heat and Mass Transfer, Vol. 50, 2007, pp 4713–4723.
Vaka Murali Mohan, “Studies on mass & momentum transfer with coaxially placed entry region coil, disc, coil-disc assembly as turbulence promoter in circular conduit” Ph.D Thesis, Andhra University, Visakhapatnam, INDIA. 2008.
Rajendra Prasad, P, “Studies on ionic MT with coaxially placed spiral coils as turbulence promoter in homogenous flow fluidized beds” Ph.D Thesis, Andhra University, Visakhapatnam, INDIA. 1993.
Vaka Murali Mohan., P. R. Prasad., V. Sujatha & S. Sarveswarao “Flow pattern and model development for coaxially placed entry region coil-disc assembly as turbulence promoter in circular conduits” Expt. Thermal and Fluid Science, Vol. 32, Issue 8, 2008, pp 1748–1753.
Unal Akdag., M. K.Aydin., A. F.Ozguc, “Estimation of heat transfer in oscillating annular flow using artificial neural networks” Advances in Engineering Software, Vol. 40, Iss 9, 2009, pp 864–870.
M.R. Jafari Nasr., A.H Khalaj, S.H. Mozaffari, “Modeling of HT enhancement by wire coil inserts using artificial neural network analysis” Applied Thermal Engineering, Vol. 30, 2010, pp 143–151.
Hoskins, J. C and Him, D. M, “ANN models of knowledge representation in chemical engineering” Computers & Chemical Engineering, Vol.12, Iss9–10, 1988, pp 881–890.
Bhat, N and McAvoy, T. J, “Use of NN for dynamic modeling & control of chemical process systems” Computers & Chemical Engineering, Vol. 14, Iss 4–5, 1990, pp 573-582.
J. Prakash Maran., V. Sivakumar., K. Thirugnanasambandham., R. Sridhar “Artificial neural network and response surface methodology modeling in mass transfer parameters predictions during osmotic dehydration of Carica papaya L” Alexandria Engineering Journal, Volume 52, 2013, pp 507–516.
L.V. Kamble., D.R. Pangavhane., T.P. Singh “Experimental investigation of horizontal tube immersed in gas–solid fluidized bed of large particles using artificial neural network” International Journal of Heat and Mass Transfer, Volume 70, 2014, pp 719–724.
Jing Zhou., Aihuang Guo., Steven Sua “Fault detection and identification spanning multiple processes by integrating PCA with neural network” Applied Soft Computing, Vol. 14, 2014, pp 4–11.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Science+Business Media Singapore
About this paper
Cite this paper
Kanaka Durga, R., Srinivasa Kumar, C., Mohan, V.M., Praveen Kumar, L., Rajendra Prasad, P. (2017). Artificial Neural Networks Model to Improve the Performance Index of the Coil-Disc Assembly in Tube Flow. In: Satapathy, S., Prasad, V., Rani, B., Udgata, S., Raju, K. (eds) Proceedings of the First International Conference on Computational Intelligence and Informatics . Advances in Intelligent Systems and Computing, vol 507. Springer, Singapore. https://doi.org/10.1007/978-981-10-2471-9_4
Download citation
DOI: https://doi.org/10.1007/978-981-10-2471-9_4
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-2470-2
Online ISBN: 978-981-10-2471-9
eBook Packages: EngineeringEngineering (R0)