The use of a multilayer perceptron (MLP) for modelling the phenol removal by emulsion liquid membrane

Abstract Extraction of phenol from aqueous solution was studied using emulsion liquid membrane (ELM). In this study, due to the extreme complexity and non linearity of ELM process, a multilayer perceptron (MLP) was developed to predict the extraction efficiency of phenol. The effect of operational parameters such as: the ratios of volume ratio of internal phase to organic phase, volume ratio of emulsion to aqueous external phase, the emulsification speed and time, the surfactant concentration, the extractant and sodium hydroxide concentrations were studied to optimize the conditions for maximum removal of phenol. The results showed that a network with 3 hidden neurons was highly accurate in predicting the extraction efficiency (more than 98%). This accuracy was reflected by high correlation coefficient R = 0.99 and a root mean square error below 0.5. The result indicated that the MLP model explained in this study is an applied tool to predict the extraction efficiency of phenol by ELM.

[1]  L. Forney,et al.  Removal of phenol and substituted phenols by newly developed emulsion liquid membrane process. , 2006, Water research.

[2]  J. R. Carvalho,et al.  Recovery of phenol from aqueous solutions using liquid membranes with Cyanex 923 , 2007 .

[3]  B. Hameed,et al.  Removal of phenol from aqueous solutions by adsorption onto activated carbon prepared from biomass material. , 2008, Journal of hazardous materials.

[4]  O. Hamdaoui,et al.  Extraction of Priority Pollutant 4-Nitrophenol from Water by Emulsion Liquid Membrane: Emulsion Stability, Effect of Operational Conditions and Membrane Reuse , 2014 .

[5]  M. Chakraborty,et al.  Studies on the applicability of artificial neural network (ANN) in emulsion liquid membranes , 2003 .

[6]  W. Kujawski,et al.  Removal of phenol from wastewater by different separation techniques , 2004 .

[7]  Mohamed Meselhy Eltoukhy,et al.  The use of artificial neural network (ANN) for modeling of COD removal from antibiotic aqueous solution by the Fenton process. , 2010, Journal of hazardous materials.

[8]  K. Palanivelu,et al.  Recovery of phenol from aqueous solution by supported liquid membrane using vegetable oils as liquid membrane. , 2006, Journal of hazardous materials.

[9]  A. Ahmad,et al.  Emulsion liquid membrane for heavy metal removal: An overview on emulsion stabilization and destabilization , 2011 .

[10]  N. Messikh,et al.  Application of radial basis function neural network for removal of copper using an emulsion liquid membrane process assisted by ultrasound , 2015 .

[11]  M. Hashim,et al.  Performance evaluation of supported ionic liquid membrane for removal of phenol. , 2011, Journal of hazardous materials.

[12]  P. Gandhidasan,et al.  Predictions of vapor pressures of aqueous desiccants for cooling applications by using artificial neural networks , 2008 .

[13]  N. Messikh,et al.  Neural network analysis of liquid–liquid extraction of phenol from wastewater using TBP solvent , 2007 .

[14]  Qingliang Ma,et al.  Electrochemical catalytic treatment of phenol wastewater. , 2009, Journal of hazardous materials.

[15]  O. Hamdaoui,et al.  Extraction of bisphenol A from aqueous solutions by emulsion liquid membrane , 2010 .

[16]  K. Muthukumar,et al.  Advanced oxidation of phenol: A comparison between Fenton, electro-Fenton, sono-electro-Fenton and photo-electro-Fenton processes , 2012 .

[17]  H. Bart,et al.  Emulsion liquid membrane extraction of Ni(II) and Co(II) from acidic chloride solutions using bis-(2-ethylhexyl) phosphoric acid as extractant , 2010 .