Response surface modeling and optimization of copper removal from aqua solutions using polymer assisted ultrafiltration

Abstract

The present paper discusses response surface methodology (RSM) as an efficient approach for predictive model building and optimization of hybrid complexation–ultrafiltration process. In this work the application of RSM is presented for optimization of dead-end and cross-flow polymer assisted ultrafiltration (PAUF) dealing with the removal of Cu(II) ions from aqua solutions using polyacrilic acid (PAA) as chelating agent. All experiments were performed according to statistical designs in order to develop the predictive regression models used for optimization. The optimization of dead-end PAUF was carried out to ensure a high rejection coefficient. While the goal of cross-flow PAUF optimization was to improve hydrodynamic conditions in membrane apparatus, i.e., to minimize the permeate flux decline and to increase the average permeate flux. In the dead-end PAUF experiments a commercial polymeric membrane made of regenerated cellulose was used. The maximum rejection coefficient of 99.9% was obtained for following optimal conditions: C PAA = 0.3 g/L, r (PAA/Cu) = 2.78 (w/w) and pH 5.56. The cross-flow PAUF experiments were carried out using a commercial metallic tubular membrane equipped with rotating shaft inside (helical module). The optimal hydrodynamics conditions determined in this case by RSM were Δ P = 0.19 bar, Q R = 54 L/h and W = 41.33 Hz (2480 rpm). The analysis of variance (ANOVA) was performed to validate the developed regression models. Also, the response surface plots were drawn for spatial representation of the regression equations.