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.

Modeling and optimization of small-scale NF/RO seawater desalination using the artificial neural network (ANN)

“…According to Garg et al [26] et salami et al [50] and further confirmation of the permeate recovery prediction of the performance membrane desalination process, the ANN model proposed in this study yielded satisfactory results that can be used as a model for controlling the desalination membrane filtration performance. Since the neural network model shows good accuracy in predicting these parameters, it could be considerate that the ANN the best-simulated model and is suitable for prediction of seawater desalination performance hybrid process NF/RO in the mean of improving the quality of the water produced and reduce water production costs.…”

“…Abbas et al [25] identified the variable of permeate flow rate to be controlled in RO seawater and brackish plant with ANN. Garg et al [26] used ANN simulation to study the performance of small-scale RO membrane which control the permeate recovery, the TDS and specific energy consumption. Aish et al [27] considered the permeate flow rate and TDS adequate variables to control and modeling of five large and small scale brackish water plants in Gaza strip.…”

Effects of temperature, pH, feed, and fertilizer draw solution concentrations on the performance of forward osmosis process for textile wastewater treatment

“…Apart from the major ions, azo‐based dyes (blue and red dyes) were used in the textile industry for the dying process. Artificial textile wastewater was produced with the help of the inverse matrix method (Adams & Bubucis, 1998; Garg & Joshi, 2014, 2017). Additionally, 30 ppm of methylene blue dye (molecular weight = 319.86 Da, dye content = 70%) acquired from the Central Drug House Pvt., Ltd. (India) was dissolved with the desired quantity of salts obtained through the inverse matrix method in distilled water.…”

Optimization of the Forward‐Osmosis Performance with a Low‐Concentration Draw Solution Using Response Surface Modeling

“…where y is the response variable, β 0 is the intercept, β i are the first‐order model coefficients for x i , β ij are the multi‐parameter interaction coefficients for x i x j , β ii are the quadratic coefficients of x i , k is the total number of variables, and ε is the error of the model 25.…”