There is an urgent need to develop predictive methodologies that will fast-track the industrial implementation of organic solvent nanofiltration (OSN). However, the performance prediction of OSN membranes has been a daunting and challenging task, due to the high number of possible solvents and the complex relationship between solvent-membrane, solute-solvent, and solute-membrane interactions. Therefore, instead of developing fundamental mathematical equations, we have broken away from conventions by compiling a large dataset and building artificial intelligence (AI) based predictive models for both rejection and permeance, based on a collected dataset containing 38,430 datapoints with more than 18 dimensions (parameters). To elucidate the important parameters that affect membrane performance, we have carried out a thorough principal component analysis (PCA), which revealed that the factors affecting both permeance and rejection are surprisingly similar. We have trained three different AI models (artificial neural network, support vector machine, random forest) that predicted the membrane performance with unprecedented accuracy, as high as 98% (permeance) and 91% (rejection). Our findings pave the way towards appropriate data standardization, not only for performance prediction, but also for better membrane design and development.
|Original language||English (US)|
|Journal||Journal of Membrane Science|
|State||Published - Aug 4 2020|
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledgements: The authors thank Murielle Rabiller-Baudry from Université Rennes; Anja Drews from HTW Berlin, Yvonne Thiermeyer from Merck KGaA and TU Dortmund; Stefanie Blumenschein from Merck KGaA and TU Dortmund; Matthias Wessling from RWTH Aachen; Dominic Ormerod from VITO; and Gregory S. Smith from University of Cape Town for the provision of data related to their published articles. The PhD scholarship from King Abdullah University of Science and Technology (KAUST) is gratefully acknowledged (JH). The research reported in this publication was supported by funding from KAUST. JFK thanks the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2019M3E6A1064799, 2019R1G1A109477811, and 2020R1C1C1007876). CSK and JYK thanks the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2019R1F1A106365312).