Design of Intelligent Network to Predicate Phenol Removal from Waste Water by Emulsion Liquid Membrane

Abstract:

This research introduced Intelligent Network's proposed design for predicting efficiency in the removal of phenol from wastewater by liquid membrane emulsion. In the inner phase of W / O emulsions, phenol extraction from an aqueous solution was investigated using emulsion liquid membrane prepared with kerosene as a membrane phase, Span 80 as a surfactant, and NaOH as a stripping agent. Experiments were conducted to investigate the effect of three emulsion composition variables, namely: surfactant concentration, membrane phase to-internal (V_M / V_I) volume ratio, and removal phase concentration in the internal phase, and two process parameters, feed phase agitation speed at organic acid extraction rates, and emulsion-to-feed volume ratio (V_E / V_F). More than 98% of phenol can be extracted in less than 5 minutes. This article describes compares the performance of different learning algorithms such as GD, RB, GDM, GDX, CG, and LM to predict the efficiency of phenol removal from wastewater through the liquid emulsion membrane. The proposed neural network consisted of (7, 11, 1) neurons in the input , hidden and output layers respectively feed forward ANN with various types of back propagation training algorithms were developed to model the emulsion liquid membrane removal of phenols. The values predicted for the neural network model are found in close agreement with the results of the batch experiment using MATLAB program with a correlation coefficient ( R²) of 0.999 and Mean Squared Error (MSE) of 0.004.

• Periodical: Materials Science Forum (Volume 1021)
• Pages: 115-128
• DOI: https://doi.org/10.4028/www.scientific.net/MSF.1021.115
• Online since: February 2021
• Authors: Suheila Abd Alreda Akkar*, Sawsan Abd Muslim Mohammed
• Keywords: ANN, Emulsion Liquid Membrane (ELM), Phenol Removal, Waste Water

References

[1] N. Messikh, M.H. Samar, and L. Messikh, Neural network analysis of liquid-liquid extraction of phenol from wastewater using TBP solvent, Desalination, 208 (2007) 42–48.

DOI: 10.1016/j.desal.2006.04.073

[2] K. Snigdha, Modeling Phenol Adsorption in Water Environment using Artificial Neural Network, Int. J. Eng. Manag. Sci., 4 (2013) 303–307.

[3] Y.S. Ng, N.S. Jayakumar, and M.A. Hashim, Performance evaluation of organic emulsion liquid membrane on phenol removal, J. Hazard. Mater.,184 (2010) 255–260.

DOI: 10.1016/j.jhazmat.2010.08.030

[4] M. Kadhom, N. Albayati, H. Alalwan, and M. Al-Furaiji, Removal of dyes by agricultural waste, Sustain. Chem. Pharm.,16 (2020) 100259.

DOI: 10.1016/j.scp.2020.100259

[5] H.A. Alalwan, M.A. Kadhom, and A.H. Alminshid, Removal of heavy metals from wastewater using agricultural byproducts, J. Water Supply Res. Technol. - AQUA, 69 (2020) 99–112.

DOI: 10.2166/aqua.2020.133

[6] A. Balasubramanian and S. Venkatesan, Optimization of removal of phenol from aqueous solution by ionic liquid-based emulsion liquid membrane using response surface methodology, Clean - Soil, Air, Water, 42 (2014) 64–70.

DOI: 10.1002/clen.201200168

[7] C. Schöller, J.B. Chaudhuri, and D.L. Pyle, Emulsion liquid membrane extraction of lactic acid from aqueous solutions and fermentation broth, Biotechnol. Bioeng., 42 (1993) 50–58.

DOI: 10.1002/bit.260420108

[8] T.C.T. Chen, C.L. Liu, and H.D. Lin, Advanced artificial neural networks, Algorithms, 11(2018) 10–11.

[9] R.M. Aghav, S. Kumar, and S.N. Mukherjee, Artificial neural network modeling in competitive adsorption of phenol and resorcinol from water environment using some carbonaceous adsorbents, J. Hazard. Mater., 188 (2011) 67–77.

DOI: 10.1016/j.jhazmat.2011.01.067

[10] D. Graupe, Principle of Artificial Neural Networks. World Scientific Publishing, (2007).

[11] K.L. Du and M.N.S. Swamy, Neural networks in a softcomputing framework, Phd thesis at concordia university canada (2006 ) 1-566.

[12] H. a R. Akkar and S.A. Salman, Training Artificial Neural Network Using Back-Propagation & Particle Swarm Optimization for Image Skin Diseases, Eng. Tech. Journal, 29 (2011) 59 - 75.

[13] M.A. Ayoub and B.M. Demiral, Application of Resilient Back-Propagation Neural Networks for Generating a Universal Pressure Drop Model in Pipelines, Khartoum Univ. Eng. J., 1 (2011) 9–21.

[14] V.N.P. Dao and R. Vemuri, A performance comparison of different back propagation neural networks methods in computer network intrusion detection, Differ. Equations Dyn. Syst., 10 (2002) 1–7.

[15] K. Arya and V.P. Vishwakarma, Performance comparison on face recognition system using different variants of back-propagation algorithm with radial basis function neural networks, J. Eng. Res. Appl., 3 (2013) 1588–1595.

[16] I.A. Saleh, Conjugate Gradient Back-propagation with Modified Polack –Rebier updates for training feed forward neural network,, Iraqi J. Stat. Sci., 20 (2011) 164–173.

DOI: 10.33899/iqjoss.2011.27897

[17] M. Ghaedi, A. Daneshfar, A. Ahmadi, and M.S. Momeni, Artificial neural network-genetic algorithm based optimization for the adsorption of phenol red (PR) onto gold and titanium dioxide nanoparticles loaded on activated carbon, J. Ind. Eng. Chem., 21 (2015) 587–598.

DOI: 10.1016/j.jiec.2014.03.024

[18] N. Majeed and M. Adnan, Demulsification of Remaining Waste (Water In Oil Emulsions) After Removal Of Phenol In Emulsion Liquid Membrane Process, J. Eng., 22 (2016) 83 –102.

[19] K. Abbassian and A. Kargari, Modification of membrane formulation for stabilization of emulsion liquid membrane for extraction of phenol from aqueous solutions, J. Environ. Chem. Eng., 4 (2016) 3926–3933.

DOI: 10.1016/j.jece.2016.08.030

[20] G.K. Agrahari, N. Verma, and P.K. Bhattacharya, Removal of benzoic acid from water by reactive extraction using hollow fiber membrane contactor: Experiment and modeling, Clean - Soil, Air, Water, 42 (2014) 901–908.

DOI: 10.1002/clen.201300769

[21] S.A.M. Mohammed, Investigating the performance of ELM systems in separating organic pollutants from industrial wastewater, J. Water Process Eng., 13 (2016) 79–87.

DOI: 10.1016/j.jwpe.2016.08.001

[22] W. Völkel, W. Halwachs, and K. Schügerl, Copper extraction by means of a liquid surfactant membrane process, J. Memb. Sci., 6 (1980) 19–31.

DOI: 10.1016/s0376-7388(00)82146-8

[23] P. Stroeve and P.P.R.A.B.O.D.H. Varanasi, An experimental study on double emulsion drop breakup in uniform shear flow, J. Colloid Interface Sci., 99 (1984) 360–373.

DOI: 10.1016/0021-9797(84)90123-1

[24] J.W. Frankenfeld, R.P. Cahn, and N.N. Li, Extraction of Copper by Liquid Membranes, Sep. Sci. Technol., 16 (1981) 385–402.

[25] Q.M. Li, Q. Liu, Q.F. Zhang, X.J. Wei, and J.Z. Guo, Separation study of cadmium through an emulsion liquid membrane using triisooctylamine as mobile carrier, Talanta, 46 (1998) 927–932.

DOI: 10.1016/s0039-9140(97)00357-3

[26] H. Park and T. Chung, Removal of phenol from aqueous solution by liquid emulsion membrane, Korean J. Chem. Eng, 20 (2003) 731–735.

DOI: 10.1007/bf02706916

[27] S.A.M. Mohammed and M.S. Hameed, Extraction of 4-nitrophenol from aqueous solutions using bulk ionic liquid membranes, Int. J. Curr. Eng. Technol., 6 (2016) 542–550.

[28] Y. Park, A.H.P. Skelland, L.J. Forney, and J.H. Kim, Removal of phenol and substituted phenols by newly developed emulsion liquid membrane process, Water Res., 40 (2006) 1763–1772.

DOI: 10.1016/j.watres.2006.03.005

[29] H.R. Mortaheb, M.H. Amini, F. Sadeghian, B. Mokhtarani, and H. Daneshyar, Study on a new surfactant for removal of phenol from wastewater by emulsion liquid membrane, J. Hazard. Mater., 160 (2008) 582–588.

DOI: 10.1016/j.jhazmat.2008.03.095

[30] N. Jusoh and N. Othman, Stability of Palm Oil-based Emulsion Liquid Membrane for Succinic Acid Extraction from Aqueous Solution, J. Appl. Membr. Sci. Technol., 19 (2017) 1–17.

DOI: 10.11113/amst.v19i1.19