1. Title, Journal and Authors
Title : Big data analysis of hollow fiber direct contact membrane distillation (HFDCMD) for simulation-based empirical analysis
Journal : Desalination 355 (2015) 56-67
Authors : Seo Jin Kia, Hyeon-Ju Kimb,*, Albert S. Kima,*
a Civil and Environmental Engineering, University of Hawaii at Manoa, 2540 Dole Street Holmes 383, Honolulu, HI 96822, USA
b Seawater Utilization Plant Research Center, Korea Research Institute of Ships and Ocean Engineering, Goseong-gun, Gangwon-do 219-822, Republic of Korea
This study analyses big data of hollow fiber direct contact membrane distillation (HFDCMD) for simulation-based empirical analysis. The self-organizing map(SOM) and multiple linear regression(MLR) methods are used to statistically analyze the big data such as physical and dimensionless data. In the SOM analysis, the mass and heat fluxes are represented as the membrane Peclet and Nusselt numbers, dimensionless numbers, respectively.
And in the MLR analysis, macroscopic parameters such as temperature and radii of lumen and shell sides mostly controlled the MD performance. Also, reduced shell temperature and effective porosity have the highest beta value for mass and hear fluxes, respectively.
Through this study, the authors want to say there are fundamental revisions to include not only fluid- and thermo-dynamic effects but also temperature-dependent fluid properties.
3. Originality & Creativity
This study used the big data analysis such as SOM and MLR to find correlation between physical and dimensionless data and performance of MD.
Sora Shin / Ph.D. program
Environmental Systems Engineering Lab.
School of Earth Sciences & Environmental Engineering
Gwangju Institute of Science and Technology
123 Cheomdangwagi-ro, Buk-gu Gwangju, 61005, Korea
Phone : +82-10-8796-0728
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