title: A new approach for optimization of small-scale RO membrane using artificial groundwater.
abstract: The present study aims at evaluating a small-scale brackish water reverse osmosis (RO) process using parameter optimization. Experiments were carried out using formulated artificial groundwater, and a predictive model was developed by using response surface methodology (RSM) for the optimization of input process parameters of brackish water RO process to simultaneously maximize water recovery and salt rejection while minimizing energy demand. The result of multiple response optimization along with analysis of variance for RSM predictions showed that the optimal water recovery (19.18%), total dissolved solids rejection (89.21%) and specific energy consumption (17.60 kWh/m³) occurred at 31.94 °C feed water temperature, 0.78 MPa feed pressure, 1500 mg/L feed salt concentration and 6.53 pH. Furthermore, confirmation of RSM predictions was carried out by an artificial neural network (ANN) model trained by RSM experimental data. Predicted values by both RSM and ANN modelling methodologies were compared and found within the acceptable range. Finally, a membrane validation experiment was carried out successfully at proposed optimal conditions, which proves the accuracy of employed RSM and ANN models. Present methodology can be used as a generalized way for the optimization of different RO membranes available in the market in terms of increased water recovery and salt rejection with least energy consumption to make it commercially competent.
authors.affiliation: a Department of Hydrology , Indian Institute of Technology Roorkee , Roorkee , India.
authors.forename: Manoj Chandra
authors.initials: MC
authors.lastname: Garg
authors.forename: Himanshu
authors.initials: H
authors.lastname: Joshi
mesh_terms: D060587:Groundwater; D008567:Membranes, Artificial; D016571:Neural Networks, Computer; D009995:Osmosis; D012492:Salts; D018508:Water Purification / Q000295:instrumentation* / Q000379:methods
chemical_list: D008567:Membranes, Artificial; D012492:Salts
keywords: artificial groundwater; artificial neural network; response surface methodology; small-scale RO process; statistical modelling