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dc.contributor.authorChew, Jia Weien_US
dc.contributor.authorCocco, Ray A.en_US
dc.identifier.citationChew, J. W. & Cocco, R. A. (2020). Application of machine learning methods to understand and predict circulating fluidized bed riser flow characteristics. Chemical Engineering Science, 217, 115503-.
dc.description.abstractMachine learning methods were applied to circulating fluidized bed (CFB) riser data. The goals were to (i) provide insights on various fluidization phenomena through determining the relative dominance of the process variables, and (ii) develop a model to provide predictive capability in the absence of first-principles understanding that remains elusive. The Random Forest results indicate radial position had the most dominant influence on local mass flux and species segregation, overall mass flux was the most dominant for local particle concentration, while no variable was particularly dominant or negligible for the local clustering characteristics. Furthermore, the Neural Network can be trained to provide good predictive capability, without any mechanistic understanding needed, if a sufficiently large dataset is used for training and if the input variables fully account for all the effects at play. This study underscores the value of machine learning methods in fluidization to advance understanding and provide adequate predictions.en_US
dc.description.sponsorshipMinistry of Education (MOE)en_US
dc.description.sponsorshipNational Research Foundation (NRF)en_US
dc.relation.ispartofChemical Engineering Scienceen_US
dc.rights© 2020 Elsevier Ltd. All rights reserved.en_US
dc.titleApplication of machine learning methods to understand and predict circulating fluidized bed riser flow characteristicsen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Chemical and Biomedical Engineeringen_US
dc.contributor.researchSingapore Membrane Technology Centreen_US
dc.contributor.researchNanyang Environment and Water Research Instituteen_US
dc.subject.keywordsMachine Learningen_US
dc.subject.keywordsMass Fluxen_US
dc.description.acknowledgementThe authors thank the financial support from the Singapore National Research Foundation 2nd Intra-CREATE Seed Collaboration Grant (NRF2017-ITS002-013) and the Singapore Ministry of Education Tier 1 Grant (2019-T1-002-065).en_US
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