{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:53:42Z","timestamp":1750208022860,"version":"3.40.3"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031199578"},{"type":"electronic","value":"9783031199585"}],"license":[{"start":{"date-parts":[[2022,10,21]],"date-time":"2022-10-21T00:00:00Z","timestamp":1666310400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,10,21]],"date-time":"2022-10-21T00:00:00Z","timestamp":1666310400000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-19958-5_3","type":"book-chapter","created":{"date-parts":[[2022,10,20]],"date-time":"2022-10-20T11:06:59Z","timestamp":1666264019000},"page":"24-33","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Comparative Performance Assessment of Multi Linear Regression and Artificial Neural Network for Prediction of Permeate Flux of Disc Shaped Membrane"],"prefix":"10.1007","author":[{"given":"Anirban","family":"Banik","sequence":"first","affiliation":[]},{"given":"Tarun Kanti","family":"Bandyopadhyay","sequence":"additional","affiliation":[]},{"given":"Sushant Kumar","family":"Biswal","sequence":"additional","affiliation":[]},{"given":"Vladimir","family":"Panchenko","sequence":"additional","affiliation":[]},{"given":"Sunil","family":"Garhwal","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,21]]},"reference":[{"key":"3_CR1","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1038\/nature09440","volume":"467","author":"CJ V\u00f6r\u00f6smarty","year":"2010","unstructured":"V\u00f6r\u00f6smarty, C.J., et al.: Global threats to human water security and river biodiversity. Nature 467, 555\u2013561 (2010). https:\/\/doi.org\/10.1038\/nature09440","journal-title":"Nature"},{"key":"3_CR2","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1016\/j.ecolind.2013.06.012","volume":"34","author":"Z Zeng","year":"2013","unstructured":"Zeng, Z., Liu, J., Savenije, H.H.G.: A simple approach to assess water scarcity integrating water quantity and quality. Ecol. Indic. 34, 441\u2013449 (2013). https:\/\/doi.org\/10.1016\/j.ecolind.2013.06.012","journal-title":"Ecol. Indic."},{"key":"3_CR3","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1016\/j.polymer.2019.05.054","volume":"175","author":"M Mehrdad","year":"2019","unstructured":"Mehrdad, M., Moosavi, A.: An efficient graphyne membrane for water desalination. Polymer (Guildf) 175, 310\u2013319 (2019). https:\/\/doi.org\/10.1016\/j.polymer.2019.05.054","journal-title":"Polymer (Guildf)"},{"key":"3_CR4","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.cej.2019.01.111","volume":"363","author":"D Ji","year":"2019","unstructured":"Ji, D., Xiao, C., An, S., et al.: Preparation of high-flux PSF\/GO loose nanofiltration hollow fiber membranes with dense-loose structure for treating textile wastewater. Chem. Eng. J. 363, 33\u201342 (2019). https:\/\/doi.org\/10.1016\/j.cej.2019.01.111","journal-title":"Chem. Eng. J."},{"key":"3_CR5","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1016\/j.ultras.2017.06.013","volume":"83","author":"L Borea","year":"2018","unstructured":"Borea, L., et al.: Wastewater treatment by membrane ultrafiltration enhanced with ultrasound: effect of membrane flux and ultrasonic frequency. Ultrasonics 83, 42\u201347 (2018). https:\/\/doi.org\/10.1016\/j.ultras.2017.06.013","journal-title":"Ultrasonics"},{"key":"3_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.jwpe.2019.101046","volume":"33","author":"M Yaqub","year":"2020","unstructured":"Yaqub, M., Lee, S.H.: Micellar enhanced ultrafiltration (MEUF) of mercury-contaminated wastewater: experimental and artificial neural network modeling. J. Water Process. Eng. 33, 101046 (2020). https:\/\/doi.org\/10.1016\/j.jwpe.2019.101046","journal-title":"J. Water Process. Eng."},{"issue":"9","key":"3_CR7","doi-asserted-by":"publisher","first-page":"2977","DOI":"10.1007\/s11696-020-01136-y","volume":"74","author":"A Banik","year":"2020","unstructured":"Banik, A., Biswal, S.K., Bandyopadhyay, T.K.: Predicting the optimum operating parameters and hydrodynamic behavior of rectangular sheet membrane using response surface methodology coupled with computational fluid dynamics. Chem. Pap. 74(9), 2977\u20132990 (2020). https:\/\/doi.org\/10.1007\/s11696-020-01136-y","journal-title":"Chem. Pap."},{"issue":"1","key":"3_CR8","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1007\/s11696-021-01838-x","volume":"76","author":"A Banik","year":"2021","unstructured":"Banik, A., Majumder, M., Biswal, S.K., Bandyopadhyay, T.K.: Polynomial neural network-based group method of data handling algorithm coupled with modified particle swarm optimization to predict permeate flux (%) of rectangular sheet-shaped membrane. Chem. Pap. 76(1), 79\u201397 (2021). https:\/\/doi.org\/10.1007\/s11696-021-01838-x","journal-title":"Chem. Pap."},{"key":"3_CR9","doi-asserted-by":"publisher","first-page":"1004","DOI":"10.1016\/j.saa.2014.08.011","volume":"137","author":"M Ghaedi","year":"2015","unstructured":"Ghaedi, M., Ansari, A., Bahari, F., Ghaedi, A.M., Vafaei, A.: A hybrid artificial neural network and particle swarm optimization for prediction of removal of hazardous dye Brilliant Green from aqueous solution using Zinc sulfide nanoparticle loaded on activated carbon. Spectrochimica Acta Part A: Mol. Biomol. Spectrosc. 137, 1004\u20131015 (2015). https:\/\/doi.org\/10.1016\/j.saa.2014.08.011","journal-title":"Spectrochimica Acta Part A: Mol. Biomol. Spectrosc."},{"key":"3_CR10","doi-asserted-by":"publisher","first-page":"566","DOI":"10.1016\/j.biortech.2015.08.017","volume":"196","author":"H Akba\u015f","year":"2015","unstructured":"Akba\u015f, H., Bilgen, B., Turhan, A.M.: An integrated prediction and optimization model of biogas production system at a wastewater treatment facility. Bioresour. Technol. 196, 566\u2013576 (2015). https:\/\/doi.org\/10.1016\/j.biortech.2015.08.017","journal-title":"Bioresour. Technol."},{"key":"3_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.envres.2020.109158","volume":"183","author":"LY Jun","year":"2020","unstructured":"Jun, L.Y., et al.: Modeling and optimization by particle swarm embedded neural network for adsorption of methylene blue by jicama peroxidase immobilized on buckypaper\/polyvinyl alcohol membrane. Environ. Res. 183, 109158 (2020). https:\/\/doi.org\/10.1016\/j.envres.2020.109158","journal-title":"Environ. Res."},{"key":"3_CR12","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1139\/cjce-2018-0007","volume":"46","author":"A Banik","year":"2019","unstructured":"Banik, A., Dutta, S., Bandyopadhyay, T.K., Biswal, S.K.: Prediction of maximum permeate flux (%) of disc membrane using response surface methodology (RSM). Can. J. Civ. Eng. 46, 299\u2013307 (2019). https:\/\/doi.org\/10.1139\/cjce-2018-0007","journal-title":"Can. J. Civ. Eng."},{"key":"3_CR13","doi-asserted-by":"publisher","first-page":"1263","DOI":"10.1016\/j.envsoft.2004.09.001","volume":"20","author":"SA Abdul-Wahab","year":"2005","unstructured":"Abdul-Wahab, S.A., Bakheit, C.S., Al-Alawi, S.M.: Principal component and multiple regression analysis in modelling of ground-level ozone and factors affecting its concentrations. Environ. Model. Softw. 20, 1263\u20131271 (2005). https:\/\/doi.org\/10.1016\/j.envsoft.2004.09.001","journal-title":"Environ. Model. Softw."},{"key":"3_CR14","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1016\/j.cej.2011.10.046","volume":"178","author":"U \u00d6zdemir","year":"2011","unstructured":"\u00d6zdemir, U., \u00d6zbay, B., Veli, S., Zor, S.: Modeling adsorption of sodium dodecyl benzene sulfonate (SDBS) onto polyaniline (PANI) by using multi linear regression and artificial neural networks. Chem. Eng. J. 178, 183\u2013190 (2011). https:\/\/doi.org\/10.1016\/j.cej.2011.10.046","journal-title":"Chem. Eng. J."},{"key":"3_CR15","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1007\/BF02478259","volume":"5","author":"WS McCulloch","year":"1943","unstructured":"McCulloch, W.S., Pitts, W.A.: A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5, 115\u2013133 (1943). https:\/\/doi.org\/10.1007\/BF02478259","journal-title":"Bull. Math. Biophys."},{"key":"3_CR16","doi-asserted-by":"publisher","first-page":"1017","DOI":"10.1016\/j.rser.2015.12.275","volume":"59","author":"S Ghosh","year":"2016","unstructured":"Ghosh, S., Chakraborty, T., Saha, S., Majumder, M., Pal, M.: Development of the location suitability index for wave energy production by ANN and MCDM techniques. Renew. Sustain. Energy Rev. 59, 1017\u20131028 (2016). https:\/\/doi.org\/10.1016\/j.rser.2015.12.275","journal-title":"Renew. Sustain. Energy Rev."},{"issue":"1","key":"3_CR17","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1615\/InterJFluidMechRes.2019021943","volume":"47","author":"S Debnath","year":"2020","unstructured":"Debnath, S., Banik, A., Bandyopadhyay, T.K., Majumder, M., Saha, A.K.: Estimation of pressure drop for non-newtonian liquid flow through bends using adaptive non-parametric model. Int. J. Fluid Mech. Res. 47(1), 59\u201369 (2020). https:\/\/doi.org\/10.1615\/InterJFluidMechRes.2019021943","journal-title":"Int. J. Fluid Mech. Res."}],"container-title":["Lecture Notes in Networks and Systems","Intelligent Computing & Optimization"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-19958-5_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,20]],"date-time":"2022-10-20T11:48:42Z","timestamp":1666266522000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-19958-5_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,21]]},"ISBN":["9783031199578","9783031199585"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-19958-5_3","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"type":"print","value":"2367-3370"},{"type":"electronic","value":"2367-3389"}],"subject":[],"published":{"date-parts":[[2022,10,21]]},"assertion":[{"value":"21 October 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICO","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing & Optimization","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hua Hin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Thailand","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":"27 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ico2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.icico.info\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}