{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T13:16:15Z","timestamp":1742994975747,"version":"3.40.3"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031107658"},{"type":"electronic","value":"9783031107665"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-10766-5_28","type":"book-chapter","created":{"date-parts":[[2022,7,21]],"date-time":"2022-07-21T14:14:51Z","timestamp":1658412891000},"page":"351-362","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Comparative Study of\u00a0Prediction of\u00a0Gas Hold up\u00a0Using ANN"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4786-5223","authenticated-orcid":false,"given":"Nirjhar","family":"Bar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Asit Baran","family":"Biswas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9177-8381","authenticated-orcid":false,"given":"Sudip Kumar","family":"Das","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,7,22]]},"reference":[{"key":"28_CR1","doi-asserted-by":"publisher","unstructured":"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\u20134), 187\u2013194 (2010). https:\/\/doi.org\/10.1016\/j.petrol.2010.02.001. http:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0920410510000392","DOI":"10.1016\/j.petrol.2010.02.001"},{"key":"28_CR2","doi-asserted-by":"publisher","unstructured":"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\u2013109 (2010). https:\/\/doi.org\/10.1063\/1.3516284. http:\/\/aip.scitation.org\/doi\/abs\/10.1063\/1.3516284","DOI":"10.1063\/1.3516284"},{"key":"28_CR3","unstructured":"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\u2013418 (2011). http:\/\/www.ciitresearch.org\/dl\/index.php\/aiml\/article\/view\/AIML072011002"},{"key":"28_CR4","unstructured":"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\u2013613 (2011). http:\/\/www.ciitresearch.org\/dl\/index.php\/aiml\/article\/view\/AIML082011021"},{"key":"28_CR5","doi-asserted-by":"publisher","unstructured":"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\u20139429 (2010). https:\/\/doi.org\/10.1021\/ie1007739. http:\/\/pubs.acs.org\/doi\/abs\/10.1021\/ie1007739","DOI":"10.1021\/ie1007739"},{"issue":"6","key":"28_CR6","first-page":"628","volume":"3","author":"N Bar","year":"2011","unstructured":"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\u2013643 (2011)","journal-title":"Int. Rev. Chem. Eng."},{"key":"28_CR7","doi-asserted-by":"publisher","unstructured":"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\u2013437 (2012). https:\/\/doi.org\/10.1615\/InterJFluidMechRes.v39.i5.40. http:\/\/www.dl.begellhouse.com\/journals\/71cb29ca5b40f8f8,1d542bee45211141,2e344b6e3fa8f553.html","DOI":"10.1615\/InterJFluidMechRes.v39.i5.40"},{"key":"28_CR8","doi-asserted-by":"publisher","unstructured":"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\u2013256 (2014). https:\/\/doi.org\/10.4028\/www.scientific.net\/AMR.917.244. http:\/\/www.scientific.net\/AMR.917.244","DOI":"10.4028\/www.scientific.net\/AMR.917.244"},{"key":"28_CR9","doi-asserted-by":"publisher","unstructured":"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\u2013448 (2021). https:\/\/doi.org\/10.3846\/jeelm.2021.14386. https:\/\/journals.vgtu.lt\/index.php\/JEELM\/article\/view\/14386","DOI":"10.3846\/jeelm.2021.14386"},{"key":"28_CR10","doi-asserted-by":"publisher","unstructured":"Basheer, I., Hajmeer, M.: Artificial neural networks: fundamentals, computing, design, and application. J. Microbiol. Methods 43(1), 3\u201331 (2000). https:\/\/doi.org\/10.1016\/S0167-7012(00)00201-3. http:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0167701200002013","DOI":"10.1016\/S0167-7012(00)00201-3"},{"key":"28_CR11","doi-asserted-by":"publisher","unstructured":"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\u20137292 (2006). https:\/\/doi.org\/10.1021\/ie060420i. http:\/\/pubs.acs.org\/doi\/abs\/10.1021\/ie060420i","DOI":"10.1021\/ie060420i"},{"key":"28_CR12","doi-asserted-by":"publisher","unstructured":"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\u2013826 (2008). https:\/\/doi.org\/10.1016\/j.cep.2007.01.030. http:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0255270107000438","DOI":"10.1016\/j.cep.2007.01.030"},{"key":"28_CR13","unstructured":"Biswas, A.B.: Studies on two-phase gas-non-Newtonian liquid flow through helical coils. Ph.D. thesis, University of Calcutta (2007)"},{"key":"28_CR14","doi-asserted-by":"publisher","unstructured":"Chaudhuri, B.B., Bhattacharya, U.: Efficient training and improved performance of multilayer perceptron in pattern classification. Neurocomputing 34(1\u20134), 11\u201327 (2000). https:\/\/doi.org\/10.1016\/S0925-2312(00)00305-2. http:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0925231200003052","DOI":"10.1016\/S0925-2312(00)00305-2"},{"key":"28_CR15","doi-asserted-by":"publisher","unstructured":"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\u2013473 (2015). https:\/\/doi.org\/10.1016\/j.ces.2014.11.032. https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0009250914006848","DOI":"10.1016\/j.ces.2014.11.032"},{"key":"28_CR16","doi-asserted-by":"publisher","unstructured":"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\u2013403. Elsevier (1996). https:\/\/doi.org\/10.1016\/B978-088415497-6\/50015-2. http:\/\/linkinghub.elsevier.com\/retrieve\/pii\/B9780884154976500152","DOI":"10.1016\/B978-088415497-6\/50015-2"},{"key":"28_CR17","doi-asserted-by":"publisher","unstructured":"Gourma, M., Verdin, P.: Two-phase slug flows in helical pipes: slug frequency alterations and helicity fluctuations. Int. J. Multiphase Flow 86, 10\u201320 (2016). https:\/\/doi.org\/10.1016\/j.ijmultiphaseflow.2016.07.013. https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0301932216301136","DOI":"10.1016\/j.ijmultiphaseflow.2016.07.013"},{"key":"28_CR18","doi-asserted-by":"publisher","unstructured":"Himmelblau, D.M.: Applications of artificial neural networks in chemical engineering. Korean J. Chem. Eng. 17(4), 373\u2013392 (2000). https:\/\/doi.org\/10.1007\/BF02706848. http:\/\/link.springer.com\/10.1007\/BF02706848","DOI":"10.1007\/BF02706848"},{"key":"28_CR19","doi-asserted-by":"publisher","unstructured":"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\u2013800 (2012). https:\/\/doi.org\/10.1016\/j.egypro.2012.02.172. https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1876610212005085","DOI":"10.1016\/j.egypro.2012.02.172"},{"key":"28_CR20","doi-asserted-by":"publisher","unstructured":"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","DOI":"10.1016\/j.ceja.2021.100136"},{"key":"28_CR21","doi-asserted-by":"publisher","unstructured":"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\u2013241 (2018). https:\/\/doi.org\/10.1016\/j.ces.2017.10.050. http:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0009250917306668","DOI":"10.1016\/j.ces.2017.10.050"},{"key":"28_CR22","doi-asserted-by":"publisher","unstructured":"Mandal, S.N., Das, S.K.: Gas-liquid flow through helical coils in vertical orientation. Ind. Eng. Chem. Res. 42(14), 3487\u20133494 (2003). https:\/\/doi.org\/10.1021\/ie0200656. http:\/\/pubs.acs.org\/doi\/abs\/10.1021\/ie0200656","DOI":"10.1021\/ie0200656"},{"key":"28_CR23","doi-asserted-by":"publisher","unstructured":"Mandal, S.N., Das, S.K.: Gas-liquid flow through helical coils in horizontal orientation. Can. J. Chem. Eng. 80(5), 979\u2013983 (2008). https:\/\/doi.org\/10.1002\/cjce.5450800522. http:\/\/doi.wiley.com\/10.1002\/cjce.5450800522","DOI":"10.1002\/cjce.5450800522"},{"key":"28_CR24","doi-asserted-by":"publisher","unstructured":"Reed, R.: Pruning algorithms\u2014a survey. IEEE Trans. Neural Netw. 4(5), 740\u2013747 (1993). https:\/\/doi.org\/10.1109\/72.248452. http:\/\/ieeexplore.ieee.org\/document\/248452\/","DOI":"10.1109\/72.248452"},{"key":"28_CR25","doi-asserted-by":"publisher","unstructured":"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\u2013307 (2016). https:\/\/doi.org\/10.1615\/InterJFluidMechRes.v43.i4.10. http:\/\/www.dl.begellhouse.com\/journals\/71cb29ca5b40f8f8,11d09f2f1b3d1a6b,172338230e99ccb2.html","DOI":"10.1615\/InterJFluidMechRes.v43.i4.10"},{"key":"28_CR26","doi-asserted-by":"publisher","unstructured":"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\u2013463 (2009). https:\/\/doi.org\/10.1016\/j.cep.2008.06.006. https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0255270108001438","DOI":"10.1016\/j.cep.2008.06.006"},{"key":"28_CR27","doi-asserted-by":"publisher","unstructured":"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\u201385 (2019). https:\/\/doi.org\/10.1016\/j.anucene.2018.09.017. https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0306454918304869","DOI":"10.1016\/j.anucene.2018.09.017"},{"key":"28_CR28","doi-asserted-by":"publisher","unstructured":"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\u201329 (2018). https:\/\/doi.org\/10.1016\/j.applthermaleng.2017.12.074. https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1359431117369041","DOI":"10.1016\/j.applthermaleng.2017.12.074"}],"container-title":["Communications in Computer and Information Science","Computational Intelligence in Communications and Business Analytics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-10766-5_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,16]],"date-time":"2022-11-16T17:16:06Z","timestamp":1668618966000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-10766-5_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031107658","9783031107665"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-10766-5_28","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"22 July 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CICBA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Intelligence in Communications and Business Analytics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Silchar","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 January 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 January 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cicba2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.cicba.in","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"107","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"21","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"13","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"20% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}