Statistical Modeling for Stability of Emulsion Liquid Membrane for the Removal of Anionic Dyes from Textile Wastewater

• Main Authors: Raja Fahad Qureshi, Khadija Qureshi, Inamullah Bhatti, Zeeshan Khatri
• Format: Article
• Language: English
• Published: Mehran University of Engineering and Technology 2018-07-01
• Series: Mehran University Research Journal of Engineering and Technology
• Subjects: Design of Experiment; Emulsification; Emulsion Liquid Membrane; Stability.
• Online Access: https://doi.org/10.22581/muet1982.1803.16

The importance of statistical modeling is obvious because it imparts an imperative role in predicting the effects of significant factors on any experimental process. In recent days, ELMs (Emulsion Liquid Membranes) are considered as an effective technique for the treatment of industrial wastewater. Despite of many advantages over the other treatment methods, ELM technology encounters one serious drawback of the stability of emulsion. The subject of this research is to identify the factors which are important to study the stability of ELM and also to evaluate the response of these parameters on stability. The membrane used in this study consisted of Span-80 and Hexane as surfactant and diluent respectively. The internal aqueous phase was H2SO4. The experimental setup was designed by using a well-known statistical approach of DoE (Design of Experiment) and the data was analyzed by Taguchi Method using a fractional factorial design. All the parameters including aqueous phase concentration, surfactant concentration, volume ratio of organic to aqueous phase, emulsification speed and emulsification time were selected as key factors to study their effect on the stability of ELM. Using different statistical techniques, it was found that emulsification speed and volume ratio of organic to aqueous phase are two most significant parameters. The significance level of these factors i.e. emulsification speed and volume ratio of organic to aqueous phase was statistically found as 99.7 and 99.9% respectively. A statistical model was also developed and the experimental results were compared with estimated results. The value of correlation coefficient, R2 was calculated as 0.997 indicating that the developed model fits the data very well.