Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/152262
Title: Application of machine learning methods to understand and predict circulating fluidized bed riser flow characteristics
Authors: Chew, Jia Wei
Cocco, Ray A.
Keywords: Engineering::Bioengineering
Issue Date: 2020
Source: Chew, 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-. https://dx.doi.org/10.1016/j.ces.2020.115503
Project: NRF2017-ITS002-013
2019-T1-002-065
Journal: Chemical Engineering Science
Abstract: Machine 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.
URI: https://hdl.handle.net/10356/152262
ISSN: 0009-2509
DOI: 10.1016/j.ces.2020.115503
Rights: © 2020 Elsevier Ltd. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCBE Journal Articles

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