A Comparative Study of ANN and GEP Model to Predict the Pressure Drop in the Water Transportation System

Full Text (PDF, 876KB), PP.47-57

Views: 0 Downloads: 0

Author(s)

Rajesh Chakraborty 1,* Uttam Kumar Mandal 1 Rabindra Nath Barman 2

1. National Institute of Technology, Agartala, Department of Production Engineering, Tripura, 799046, India

2. National Institute of Technology, Durgapur, Department of Mechanical Engineering, West Bengal, 713209, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijieeb.2020.05.05

Received: 5 Apr. 2020 / Revised: 18 May 2020 / Accepted: 24 Jun. 2020 / Published: 8 Oct. 2020

Index Terms

A pressure drop, Multi-phase, Volume fraction, ANN, GEP, solid density, solid concentration, Particle diameter.

Abstract

In the present study, the parameter responsible to find out pressure drops in a pipeline network system has been modeled by Gene Expression Programming Based on the experimental data. The different factors like Pipe diameter, Particle diameter, liquid density, Solid density liquid Viscosity, Volume fraction, Velocity, Solid concentration are taken into consideration as the input parameter. GEP model was developed to predict the pressure drop within the pipeline system. GEP model predicts the pressure drop with an accuracy of mean R-Square 0.999153373.As the input parameter is responsible for the selection of soft computing method and both ANN and GEP model is considered in order to validate the output parameters. The result of GEP has been compared with an ANN model, to observe the level of accuracy of the predicted pressure drop with a correlation to predict pressure drop shown by equation 6. The obtained results of both GEP and ANN models are being compared and GEP predicted results are found to be better in predicting the output parameter. The mean absolute error is found to be 15.566 % by the ANN model wherein the GEP model predicts with an accuracy of 8.993 %.The results indicate that the GEP is better tool to predict pressure drop with more accuracy.

Cite This Paper

Rajesh Chakraborty, Uttam Kumar Mandal, Rabindra Nath Barman, "A Comparative Study of ANN and GEP Model to Predict the Pressure Drop in the Water Transportation System", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.12, No.5, pp. 47-57, 2020. DOI:10.5815/ijieeb.2020.05.05

Reference

[1]Ghanta, K.C, and Purohit, N.K, (1999). Pressure drop prediction in hydraulic transport of bi‐dispersed particles of coal and copper ore in the pipeline, The Canadian Journal of Chemical Engineering, 77(1), pp. 127-131.
[2]Gillies, R.G. and Shook, C.A., (2000). Modeling high concentration settling slurry flows. The Canadian Journal of Chemical Engineering, 78 (4), pp. 709-716
[3]S.K. Lahiri, K.C. Ghanta,( 2008) Development of an artificial neural network correlation for prediction of the hold-up of slurry transport in pipelines, Chemical Engineering Science, 63, 6, 1497.
[4]K.C. Wilson, R.S. Sanders, R.G. Gillies, C.A. Shook,(2010) Verification of the near-wall model for slurry flow, Powder Technology, 197, 3, 247.
[5]Manoj Kumar Gopaliya, D. R. Kaushal,( 2015) Analysis of Effect of Grain Size on Various Parameters of Slurry Flow through Pipeline Using CFD, Particulate Science, and Technology, 33, 4, 369.
[6]Wasp EJ, Aude TC. (1970)Deposition velocities, transition velocities, and spatial distribution of solids in slurry pipelines, In Presented at the 1st International British Hydromechanics Research Association Hydraulic Transport of Solids in Pipes Conference, War Wickshire Univ, Coventry, England, Sept 1-4, 1970, (No. H4 Proceeding).
[7]Bandyopadhyay JK, Annamalai S, Gauri KL. (1996) Application of artificial neural networks in modeling limestone–SO2 reaction, AIChE journal. Aug 1; 42 (8):2295-302.
[8]Bowen WR, Jones MG, Yousef HN (1998). Prediction of the rate of cross-flow membrane ultra-filtration of colloids: A neural network approach. Chemical Engineering Science. Nov 1; 53(22):3793-802.
[9]Roy S, Ghosh A, Das AK, Banerjee R.( 2014) A comparative study of GEP and an ANN strategy to model engine performance and emission characteristics of a CRDI assisted single-cylinder diesel engine under CNG dual-fuel operation, Journal of Natural Gas Science and Engineering, Nov 30; 21: 814-28.
[10]Dey, P., Sarkar, A., & Das, A. K. (2015). Prediction of unsteady mixed convection over a circular cylinder in the presence of nanofluid-A comparative study of ANN and GEP. Journal of Naval Architecture and Marine Engineering, 12(1), 57-71.
[11]Dey, P., Sarkar, A., & Das, A. K. (2017). Capability to predict the steady and unsteady reduced aerodynamic forces on a square cylinder by ANN and GEP. Neural Computing and Applications, 28(8), 1933-1945.
[12]S.K. Lahiri, K.C. Ghanta. (2008)Development of an artificial neural network correlation for prediction of the hold-up of slurry transport in pipelines, Chemical Engineering Science 63 1497– 1509.
[13]Ferreira C. (2001) Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst; 13:87–129.
[14]Koza JR. (1995) Survey of genetic algorithms and genetic programming. In: WESCON/ ’95 conference record ’microelectronics communications technology producing quality products mobile and portable power emerging technologies’; p. 589.
[15]Guven A. (2009) Linear genetic programming for time-series modeling of daily flow rate. J Earth Syst Sci; 118:137–46.
[16]Wan Tang, Limei Peng, Ximin Yang, Xia Xie, Yang Cao,( 2010), GEP-based Framework for Immune- Inspired Intrusion Detection, KSII Transactions on Internet and Information Systems Vol. 4, No.6, December 23, doi.10.3837/tiis.2010.12.017.
[17]Xinyu Li, Ping Jiang, Liping Zhang. (2014)Prediction of surface roughness in end milling with gene expression programming. In Proceedings of the 41st International Conference on Computers & Industrial Engineering.
[18]A.R. Fallahpour, A.R. Moghassem. (2013) Yarn Strength Modelling Using Adaptive Neuro-Fuzzy Inference System (ANFIS) and Gene Expression Programming (GEP). Journal of Engineered Fibers and Fabrics Volume 8, Issue 4.
[19]S. Haykins, (1994) Neural Networks: A Comprehensive Foundation, MacMillan, New York.
[20]W.S. McCulloch, W. Pitts,( (1943)) A logical calculus of the ideas immanent in nervous activity, Bull. Math. Biophys. 5, 115–133.
[21]L. Fausett,( 1994.) Fundamentals of Neural Networks, Prentice-Hall, Englewood Cliffs, N.J,