Chemometric Analysis of NMR Spectra and Machine Learning to Investigate Membrane Fouling.

ABSTRACT: Efficient membrane filtration requires the understanding of the membrane foulants and the functional properties of different membrane types in water purification. In this study, dead-end filtration of aquaculture system effluents was performed and the membrane foulants were investigated via nuclear magnetic resonance (NMR) spectroscopy. Several machine learning models (Random Forest; RF, Extreme Gradient Boosting; XGBoost, Support Vector Machine; SVM, and Neural Network; NN) were constructed, one to predict the maximum transmembrane pressure, for revealing the chemical compounds causing fouling, and the other to classify the membrane materials based on chemometric analysis of NMR spectra, for determining their effect on the properties of the different membrane types tested. Especially, RF

SUBMITTER: Yokoyama D 

PROVIDER: S-EPMC9025983 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature