Simulation of a conventional water treatment plant for the minimization of new emerging pollutants in drinking water sources: process optimization using response surface methodology

Abstract

This study describes the ability of conventional water treatment plants towards the removal of non-targeted new emerging pollutants (NEPs) by optimizing the variables such as pH and polyaluminium chloride (PAC), activated carbon, and chlorine (Cl2) concentrations. Gas chromatography-mass spectrometry (GC-MS) was used for the separation and quantification of NEPs. Several NEPs, including ibuprofen, a drug expected to exhibit carcinogenicity at lower concentrations, were identified in selected river water samples. The simulation experiments were conducted using jar tests to minimize the turbidity, TOC, and ibuprofen concentrations in water samples. In addition, response surface methodology (RSM) with central composite design (CCD) was chosen for process optimization as well as to study the influences of the four factors viz., pH of the solution, PAC dosage, and activated carbon and Cl2 concentrations, on the treatment process. The quadratic models established for the three responses viz., turbidity, TOC, and ibuprofen removal, evidenced the lower values of 0.51 NTU, 1.21 mg L−1, and 52.53% for the turbidity, TOC, and % removal of ibuprofen, respectively, upon optimization of the selected variables. Moreover, the optimum conditions were evaluated, aiming at 90% ibuprofen removal, which was found to be attained at 26.50 ppm of PAC, 49.20 ppm of activated carbon, and 12.10 ppm of Cl2 concentration at the pH value of 7.99. It was also confirmed that the experimental results are very close to the predicted values. In addition, the removal of other NEPs, turbidity, and TOC was also maximum under the optimized conditions. Finally, our results imply that NEPs are not only removed by coagulation itself, but also by adjusting other parameters such as pH and Cl2 concentration. Herein, the advantages of the RSM approach in achieving good predictions have been explained via conducting minimum number of experiments.