Statistical-Based Modeling and Optimization of Chlorophenol Removal from Wastewater Using Reverse Osmosis Process

Abstract

This paper focuses on the modelling and optimisation of the removal of chlorophenol from wastewater using multifactorial analysis. The development of the corresponding mathematical model is based on a specific set of experimental data of chlorophenol removal from wastewater derived from the literature. An analysis of variance (ANOVA) is used to investigate the most important independent variables and their interaction(s), which affect process performance. This in turn is used to develop two empirical model correlations for the rejection of chlorophenol and recovery rate using the multiple linear regression technique. The linear coefficients of the model correlations are estimated using the statistical software SPSS. The predictions of the model developed are compared to observed data and show high confidence level of R². Finally, the optimised control variables, which achieved both optimal rejection and recovery rate were explored further based on the upper and lower limits of the associated independent variables.