Machine learning method for simulation of adsorption separation: Comparisons of model's performance in predicting equilibrium concentrations Yin, G, Alazzawi, FJI, Mironov, S, Reegu, F, El-Shafay, AS, Rahman, ML, Su, CH, Lu, YZ and Nguyen, Hoang Chinh 2022, Machine learning method for simulation of adsorption separation: Comparisons of model's performance in predicting equilibrium concentrations, Arabian Journal of Chemistry, vol. 15, no. 3, pp. 1-10, doi: 10.1016/j.arabjc.2021.103612. Attached Files Name Description MIMEType Size Downloads Title Machine learning method for simulation of adsorption separation: Comparisons of model's performance in predicting equilibrium concentrations Author(s) Yin, G Alazzawi, FJI Mironov, S Reegu, F El-Shafay, AS Rahman, ML Su, CH Lu, YZ Nguyen, Hoang Chinh Journal name Arabian Journal of Chemistry Volume number 15 Issue number 3 Article ID 103612 Start page 1 End page 10 Total pages 10 Publisher Elsevier Place of publication Amsterdam, The Netherlands Publication date 2022-03 ISSN 1878-5352 1878-5379 Keyword(s) Adsorption Artificial intelligence Chemistry Chemistry, Multidisciplinary MEMBRANES Modeling Physical Sciences Porous materials REGRESSION Science & Technology Separation Language eng DOI 10.1016/j.arabjc.2021.103612 Field of Research 03 Chemical Sciences HERDC Research category C1.1 Refereed article in a scholarly journal Free to Read? Yes Persistent URL http://hdl.handle.net/10536/DRO/DU:30164541 Document type: Journal Article Collections: Faculty of Science, Engineering and Built Environment School of Life and Environmental Sciences Open Access Collection Related Links Link Description Link to full-text (open access)   Connect to Elements publication management system Go to link with your DU access privileges   Connect to published version Go to link with your DU access privileges   Author URL Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au. Versions Version Filter Type Thu, 17 Mar 2022, 20:53:35 EST Fri, 18 Mar 2022, 15:55:43 EST Fri, 18 Mar 2022, 16:12:10 EST Fri, 25 Mar 2022, 12:59:33 EST Fri, 25 Mar 2022, 13:00:44 EST Fri, 25 Mar 2022, 15:51:44 EST Fri, 25 Mar 2022, 15:56:59 EST Fri, 08 Apr 2022, 17:38:05 EST Wed, 20 Apr 2022, 13:36:04 EST Filtered Full Citation counts:   Cited 6 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 10 Abstract Views, 3 File Downloads  -  Detailed Statistics Created: Thu, 17 Mar 2022, 20:53:35 EST