Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/161990
Title: Application of machine learning-based models to understand and predict critical flux of oil-in-water emulsion in crossflow microfiltration
Authors: Tanudjaja, Henry Jonathan
Chew, Jia Wei
Keywords: Engineering::Chemical engineering
Issue Date: 2022
Source: Tanudjaja, H. J. & Chew, J. W. (2022). Application of machine learning-based models to understand and predict critical flux of oil-in-water emulsion in crossflow microfiltration. Industrial and Engineering Chemistry Research, 61(24), 8470-8477. https://dx.doi.org/10.1021/acs.iecr.1c04662
Project: A20B3a0070
A2083c0049
2019-T1-002-065 
RG100/19
MOE-MOET2EP10120-0001 
Journal: Industrial and Engineering Chemistry Research
Abstract: Random Forest (RF) and Neural Network (NN), respectively, were employed to understand and predict the critical flux (Jcrit) of oil-in-water emulsions in crossflow microfiltration. A total of 223 data sets from various studies were compiled, with nine operational parameters and one target variable of critical flux. RF indicated crossflow velocity (CFV) as the most dominant parameter in determining critical flux, outweighing surfactant and oil variations. Exceptions were found in specific cases when casein concentration was the most dominant, since the smaller sizes of casein significantly decreased Jcrit. The NN model predicted the best when all nine input parameters were integrated and the worst when CFV was the sole parameter used for model development, even though CFV was identified as the most dominant. The results here demonstrate the usefulness of machine learning tools to enhance the understanding on and prediction of critical flux without any governing equations.
URI: https://hdl.handle.net/10356/161990
ISSN: 0888-5885
DOI: 10.1021/acs.iecr.1c04662
Rights: © 2022 American Chemical Society. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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