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A Comparative Study of Prediction of Gas Hold up Using ANN

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Computational Intelligence in Communications and Business Analytics (CICBA 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1579))

Abstract

The primary aim of this paper is to present a way by which the gas hold-up can be predicted for gas non-Newtonian liquid flow. The type of conduit used here are the helical coils of different diameter. The helical coils are vertically oriented. The data are collected from our previous experiment and its subsequent publications. A principle component analysis (PCA) based network performance is compared with the Levenberg-Marquardt (LM) algorithm based network. The closeness of the error parameters indicate that both training algorithm can predict both the hydrodynamic parameters with acceptable accuracy. The final analysis and the evaluation of the statistical parameters related to the errors prove the successful nature of the modelling using the PCA based network.

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References

  1. Bar, N., Bandyopadhyay, T.K., Biswas, M.N., Das, S.K.: Prediction of pressure drop using artificial neural network for non-Newtonian liquid flow through piping components. J. Petrol. Sci. Eng. 71(3–4), 187–194 (2010). https://doi.org/10.1016/j.petrol.2010.02.001. http://linkinghub.elsevier.com/retrieve/pii/S0920410510000392

  2. Bar, N., Biswas, A.B., Biswas, M.N., Das, S.K.: Holdup analysis for gas-non-newtonian liquid flow through horizontal helical coils-empirical correlation versus ANN prediction. In: Paruya, S., Kar, S., Roy, S. (eds.) International Conference on Modeling, Optimization and Computing, (ICMOC 2010), vol. 1298, pp. 104–109 (2010). https://doi.org/10.1063/1.3516284. http://aip.scitation.org/doi/abs/10.1063/1.3516284

  3. Bar, N., Biswas, A.B., Biswas, M.N., Das, S.K.: Gas-non-Newtonian liquid flow through horizontally oriented helical coils - prediction of frictional pressure drop using ANN. Artif. Intell. Syst. Mach. Learn. 3(7), 412–418 (2011). http://www.ciitresearch.org/dl/index.php/aiml/article/view/AIML072011002

  4. Bar, N., Biswas, A.B., Das, S.K., Biswas, M.N.: Frictional pressure drop prediction using ANN for gas-non-Newtonian liquid flow through 45\(^{\circ }\) bend. Artif. Intell. Syst. Mach. Learn. 3(9), 608–613 (2011). http://www.ciitresearch.org/dl/index.php/aiml/article/view/AIML082011021

  5. Bar, N., Biswas, M.N., Das, S.K.: Prediction of pressure drop using artificial neural network for gas non-Newtonian liquid flow through piping components. Ind. Eng. Chem. Res. 49(19), 9423–9429 (2010). https://doi.org/10.1021/ie1007739. http://pubs.acs.org/doi/abs/10.1021/ie1007739

  6. Bar, N., Das, S.K.: Comparative study of friction factor by prediction of frictional pressure drop per unit length using empirical correlation and ANN for gas-non-Newtonian liquid flow through 180\(^{\circ }\) circular bend. Int. Rev. Chem. Eng. 3(6), 628–643 (2011)

    Google Scholar 

  7. Bar, N., Das, S.K.: Frictional pressure drop for gas-non-Newtonian liquid flow through 90\(^\circ \) and 135\(^\circ \) circular bend: prediction using empirical correlation and ANN. Int. J. Fluid Mech. Res. 39(5), 416–437 (2012). https://doi.org/10.1615/InterJFluidMechRes.v39.i5.40. http://www.dl.begellhouse.com/journals/71cb29ca5b40f8f8,1d542bee45211141,2e344b6e3fa8f553.html

  8. Bar, N., Das, S.K.: Modeling of gas holdup and pressure drop using ANN for gas-non-Newtonian liquid flow in vertical pipe. Adv. Mater. Res. 917, 244–256 (2014). https://doi.org/10.4028/www.scientific.net/AMR.917.244. http://www.scientific.net/AMR.917.244

  9. Bar, N., Mitra, T., Das, S.K.: Biosorption of Cu(II) ions from industrial effluents by rice husk: experiment, statistical, and ANN modeling. J. Environ. Eng. Landscape Manag. 29(4), 441–448 (2021). https://doi.org/10.3846/jeelm.2021.14386. https://journals.vgtu.lt/index.php/JEELM/article/view/14386

  10. Basheer, I., Hajmeer, M.: Artificial neural networks: fundamentals, computing, design, and application. J. Microbiol. Methods 43(1), 3–31 (2000). https://doi.org/10.1016/S0167-7012(00)00201-3. http://linkinghub.elsevier.com/retrieve/pii/S0167701200002013

  11. Biswas, A.B., Das, S.K.: Holdup characteristics of gas-non-Newtonian liquid flow through helical coils in vertical orientation. Ind. Eng. Chem. Res. 45(21), 7287–7292 (2006). https://doi.org/10.1021/ie060420i. http://pubs.acs.org/doi/abs/10.1021/ie060420i

  12. Biswas, A., Das, S.: Two-phase frictional pressure drop of gas-non-Newtonian liquid flow through helical coils in vertical orientation. Chem. Eng. Process. Process Intensification 47(5), 816–826 (2008). https://doi.org/10.1016/j.cep.2007.01.030. http://linkinghub.elsevier.com/retrieve/pii/S0255270107000438

  13. Biswas, A.B.: Studies on two-phase gas-non-Newtonian liquid flow through helical coils. Ph.D. thesis, University of Calcutta (2007)

    Google Scholar 

  14. Chaudhuri, B.B., Bhattacharya, U.: Efficient training and improved performance of multilayer perceptron in pattern classification. Neurocomputing 34(1–4), 11–27 (2000). https://doi.org/10.1016/S0925-2312(00)00305-2. http://linkinghub.elsevier.com/retrieve/pii/S0925231200003052

  15. Colombo, M., Colombo, L.P., Cammi, A., Ricotti, M.E.: A scheme of correlation for frictional pressure drop in steam-water two-phase flow in helicoidal tubes. Chem. Eng. Sci. 123, 460–473 (2015). https://doi.org/10.1016/j.ces.2014.11.032. https://linkinghub.elsevier.com/retrieve/pii/S0009250914006848

  16. Das, S.K.: Water flow through helical coils in turbulent condition. In: Cheremisinoff, N.P. (ed.) Advances in Engineering Fluid Mechanics: Multiphase Reactor and Polymerization System Hydrodynamics, vol. 71, chap. 13, pp. 379–403. Elsevier (1996). https://doi.org/10.1016/B978-088415497-6/50015-2. http://linkinghub.elsevier.com/retrieve/pii/B9780884154976500152

  17. Gourma, M., Verdin, P.: Two-phase slug flows in helical pipes: slug frequency alterations and helicity fluctuations. Int. J. Multiphase Flow 86, 10–20 (2016). https://doi.org/10.1016/j.ijmultiphaseflow.2016.07.013. https://linkinghub.elsevier.com/retrieve/pii/S0301932216301136

  18. Himmelblau, D.M.: Applications of artificial neural networks in chemical engineering. Korean J. Chem. Eng. 17(4), 373–392 (2000). https://doi.org/10.1007/BF02706848. http://link.springer.com/10.1007/BF02706848

  19. Li, Y.x., Wu, J.h., Wang, H., Kou, L.p., Tian, X.h.: Fluid flow and heat transfer characteristics in helical tubes cooperating with spiral corrugation. Energy Procedia 17, 791–800 (2012). https://doi.org/10.1016/j.egypro.2012.02.172. https://linkinghub.elsevier.com/retrieve/pii/S1876610212005085

  20. Maiti, S.B., Bar, N., Das, S.K.: Terminal settling velocity of solids in the pseudoplastic non-Newtonian liquid system - experiment and ANN modeling. Chem. Eng. J. Adv. 7, 100136 (2021). https://doi.org/10.1016/j.ceja.2021.100136. https://linkinghub.elsevier.com/retrieve/pii/S2666821121000521

  21. Maiti, S.B., Let, S., Bar, N., Das, S.K.: Non-spherical solid-non-Newtonian liquid fluidization and ANN modelling: minimum fluidization velocity. Chem. Eng. Sci. 176, 233–241 (2018). https://doi.org/10.1016/j.ces.2017.10.050. http://linkinghub.elsevier.com/retrieve/pii/S0009250917306668

  22. Mandal, S.N., Das, S.K.: Gas-liquid flow through helical coils in vertical orientation. Ind. Eng. Chem. Res. 42(14), 3487–3494 (2003). https://doi.org/10.1021/ie0200656. http://pubs.acs.org/doi/abs/10.1021/ie0200656

  23. Mandal, S.N., Das, S.K.: Gas-liquid flow through helical coils in horizontal orientation. Can. J. Chem. Eng. 80(5), 979–983 (2008). https://doi.org/10.1002/cjce.5450800522. http://doi.wiley.com/10.1002/cjce.5450800522

  24. Reed, R.: Pruning algorithms—a survey. IEEE Trans. Neural Netw. 4(5), 740–747 (1993). https://doi.org/10.1109/72.248452. http://ieeexplore.ieee.org/document/248452/

  25. Thandlam, A.K., Majumder, S.K.: Dynamic interaction model to analyze hydrodynamics of gas-non-Newtonian-liquid flow in vertical helical coil pipe (VHCP). Int. J. Fluid Mech. Res. 43(4), 281–307 (2016). https://doi.org/10.1615/InterJFluidMechRes.v43.i4.10. http://www.dl.begellhouse.com/journals/71cb29ca5b40f8f8,11d09f2f1b3d1a6b,172338230e99ccb2.html

  26. Vashisth, S., Nigam, K.: Prediction of flow profiles and interfacial phenomena for two-phase flow in coiled tubes. Chem. Eng. Process. Process Intensification 48(1), 452–463 (2009). https://doi.org/10.1016/j.cep.2008.06.006. https://linkinghub.elsevier.com/retrieve/pii/S0255270108001438

  27. Wang, M., et al.: Experimental studies on local and average heat transfer characteristics in helical pipes with single phase flow. Ann. Nuclear Energy 123, 78–85 (2019). https://doi.org/10.1016/j.anucene.2018.09.017. https://linkinghub.elsevier.com/retrieve/pii/S0306454918304869

  28. Xiao, Y., Hu, Z., Chen, S., Gu, H.: Experimental study of two-phase frictional pressure drop of steam-water in helically coiled tubes with small coil diameters at high pressure. Appl. Thermal Eng. 132, 18–29 (2018). https://doi.org/10.1016/j.applthermaleng.2017.12.074. https://linkinghub.elsevier.com/retrieve/pii/S1359431117369041

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Authors and Affiliations

  1. St. James’ School, 165, A. J. C. Bose Road, Kolkata, 700014, West Bengal, India

    Nirjhar Bar

  2. Department of Chemical Engineering, University of Calcutta, 92, A. P. C. Road, Kolkata, 700009, West Bengal, India

    Nirjhar Bar, Asit Baran Biswas & Sudip Kumar Das

Authors
  1. Nirjhar Bar
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  2. Asit Baran Biswas
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  3. Sudip Kumar Das
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Correspondence to Nirjhar Bar .

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Editors and Affiliations

  1. Assam University, Silchar, India

    Somnath Mukhopadhyay

  2. Assam University, Silchar, India

    Sunita Sarkar

  3. Visva-Bharati University, Santiniketan, West Bengal, India

    Paramartha Dutta

  4. University of Kalyani, Nadia, India

    Jyotsna Kumar Mandal

  5. Assam University, Silchar, India

    Sudipta Roy

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Bar, N., Biswas, A.B., Das, S.K. (2022). A Comparative Study of Prediction of Gas Hold up Using ANN. In: Mukhopadhyay, S., Sarkar, S., Dutta, P., Mandal, J.K., Roy, S. (eds) Computational Intelligence in Communications and Business Analytics. CICBA 2022. Communications in Computer and Information Science, vol 1579. Springer, Cham. https://doi.org/10.1007/978-3-031-10766-5_28

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  • DOI: https://doi.org/10.1007/978-3-031-10766-5_28

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  • Online ISBN: 978-3-031-10766-5

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