Gene expression programming for process parameter optimization during ultrafiltration of surfactant wastewater using hydrophilic polyethersulfone membrane

Abstract

Surfactants are the emerging contaminant and cause a detrimental effect on the ecosystem. In this study, an attempt is made to removal anionic surfactant Sodium dodecyl sulfate (SDS) containing wastewater using hydrophilic polyvinylpyrollidone (PVP) (5–15 wt%) modified polyethersulfone (PES) ultrafiltration membrane. The influence of operating variables on membrane performance was also sequentially analyzed using tests and three numerical modeling methods such as multiple linear regression (MLR), multiple Ln-equation regression (MLnER), and gene expression programming (GEP). Contact angle value of 10 wt% PVP modified PES membrane decreased up to 23.8°, whereas the neat PES membrane is 70.7°. This study indicates that the required hydrophilic property was improved in the modified membrane. The water flux and porosity also enhanced in PVP modified PES membranes. In performance evaluation, the optimum operating variable condition of transmembrane pressure (TMP), feed concentration, and the temperature is found to be 3 bar, 100 ppm, and 25 °C, respectively. Among the models, GEP has a good correlation with experimental anionic surfactant SDS filtration data. GEP performs better than other model with respect to statistical parameter and error terms. This study provides an insight into an adaptation of novel numerical modeling methods for the prediction of membrane performance to the treatment of surfactant wastewater.