Response surface methodology and artificial neural network modelling for the performance evaluation of pilot-scale hybrid nanofiltration (NF) & reverse osmosis (RO) membrane system for the treatment of brackish ground water

Artificial neural network (ANN) and response surface methodology (RSM) were employed to develop models for process optimisation of pilot scale nanofiltration (NF) and reverse osmosis (RO) membrane filtration system for the treatment of brackish groundwater. The process variables for this study were feed concentration, temperature, pH and pressure. The performance of NF/RO was assessed in terms of permeate flux, water recovery, salt rejection and specific energy consumption, which were considered as responses. The experimental design was employed to develop both RSM and ANN models. RSM model was validated for the whole range of experimental levels, while the ANN model was considered for the whole range of experimental design. RSM and ANN models were statistically analysed using analysis of variance (ANOVA). Further, the models were graphically compared for its predictive capacity. Numerical optimisation of NF and RO pilot plant to determine the optimum conditions were verified. Finally, using the optimum conditions, three hybrid configurations of NF and RO were studied to determine the best mode for the treatment of brackish groundwater. It was found that parallel NF-RO had a recovery of 57.18% and rejection of 44.89%, for RO-concentrate-NF (RO-C-NF) recovery was 49.55% and rejection of 38.64% & for NF-concentrate-RO (NF-C-RO), the recovery of 39.53% and rejection of 49.66% was obtained. Results obtained also suggested that the mode of configurations and the feed concentration affect the performance of the hybrid system.

Automated summary

In this study, ANN and RSM models were used to optimize the process of nanofiltration and reverse osmosis membrane filtration systems for brackish groundwater treatment. The best hybrid configuration was determined through numerical optimization, with performance influenced by system configurations and feed concentration.