Treatment of highly iron-contaminated brackish groundwater by MF, UF, and NF membranes for potable water resource production: influence of operating parameters, comparative study, membrane fouling, and machine learning modeling

Abstract

The present investigation focuses on using commercially available MF, UF, and NF membranes to eliminate Fe from real groundwater. The impact of process parameters, including applied pressure, temperature, pH, time, and concentration, on flux and Fe removal% is investigated. Results of the permeation test confirm higher permeability for MF membranes (214.71 L/m2.h.bar) than that for NF (2.708 L/m2.h.bar) and UF (56.52 L/m2.h.bar) membranes. The FESEM-EDS characterization confirms the deposition of dominant foulant Fe particles over the membrane surface. At 2 bar applied pressure (temperature = 30 °C, pH = 6.13, and concentration = 6.62 ppm), MF, UF, and NF can eliminate 93.5, 90.2, and 100% Fe, respectively. However, UF and NF exhibit much lower fluxes (72.43 and 3.85 L/m2.h, respectively) compared to MF (317.13 L/m2.h) at the same process conditions. NF eliminates 100% Fe from start to end with rising pressure, while MF (62.16–100) and UF (87.09–100) show gradually improved removal rates. Above pH 6, all membranes demonstrate higher Fe rejection because the oxidation rate of ferrous iron increases at a high pH. With increasing concentration (6–500 ppm), Fe removal efficiency increases for NF (88.26–94.18%) but decreases for both MF (89.63–23.64%) and UF (27.66–14.4%). To validate experimental findings, five machine learning (ML) regression models are scrutinized using various statistical measures. HIGHLIGHTS Treatment of highly Fe-contaminated real groundwater by MF, UF, and NF flat sheet membranes.; MF shows much higher flux (317.13 L/m2.h) and 100% Fe removal, and is the best membrane.; FESEM-EDS confirms the deposition of dominant foulant Fe over membrane surfaces.; Effects of pressure, pH, temperature, time, and concentration on flux and Fe removal are demonstrated.; Machine learning effectively legitimates the experimental data with fewer errors.;

Keywords

• fouling
• groundwater
• heavy metal removal
• machine learning
• membrane filtration