Performance assessment and optimization of forward osmosis-low pressure ultrafiltration hybrid system using machine learning for rhodamine B removal

초록

Here, an in-series forward osmosis-low pressure ultrafiltration membrane system was devised for water treat-ment. Response surface methodology (RSM) and artificial neural network (ANN) machine learning techniques were applied to evaluate this system's performance and optimization with respect to water flux and rhodamine B (RhB) removal. The effects of initial feed solution (FS) concentration, draw solution (DS) concentration, initial feed pH, and natural organic matter (NOM) concentrate were examined using a central composite design. Models developed using RSM and ANN could successfully fit and predict the data with > 99.5 % accuracy. DS and FS concentrations were the most influential factors on water flux and RhB removal, respectively. Statistical parameters demonstrated that ANN approach (water flux = 98.46 % and RhB removal = 99.85 %) was more reliable than RSM; it also better predicted system performance (water flux = 95.06 %; RhB removal = 97.01 %). The predicted optimum conditions for water flux and RhB removal were FS concentration = 47.01 mg L-1, DS concentration = 4.91 M, initial pH = 10.99, and NOM concentration = 7.97 mg L-1. Under these conditions, RSM and ANN models predicted water fluxes of 16.37 L m(-2) h(-1) and 16.38 L m(-2) h(-1), respectively, and RhB removal of 99.99 % and 99.95 %, respectively.

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