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What is A QSPR-ANN-driven Predictive Framework For Assessing Organic Micropollutant Rejection in Forward Osmosis Systems about?

This study presents a QSPR-NN model for predicting the rejection of organic micropollutants in forward osmosis systems, utilizing artificial neural networks to analyze various input parameters. The model achieved a high correlation coefficient (R = 0.9895) and low error rates, outperforming previous models in the field. The research highlights the potential of forward osmosis as an alternative water treatment method amidst increasing global water scarcity.

Author

Ho Kah Chun

Language

EN