APPLICATION OF EVOLUTIONARY POLYNOMIAL REGRESSION IN ULTRAFILTRATION SYSTEMS CONSIDERING THE EFFECT OF DIFFERENT PARAMETERS ON OILY WASTEWATER TREATMENT

Document Type : Research Paper

Authors

1 Department of Chemical and Petroleum Engineering, Sharif University of Technology, Tehran, Iran.

2 Deputy of Technology and International Affair, Research Institute of Petroleum Industry (RIPI).

3 Polymer Science and Technology Division, Research Institute of Petroleum Industry (RIPI), Tehran, Iran.

4 Department of Chemical Engineering, Sahand University of Technology, Tabriz, Iran.

Abstract

In the present work, the effects of operating conditions including pH, transmembrane pressure, oil concentration, and temperature on fouling resistance and the rejection of turbidity for a polymeric membrane in an ultrafiltration system of wastewater treatment were studied. A new modeling technique called evolutionary polynomial regression (EPR) was investigated. EPR is a method based on regression algorithm, which combines the best properties of the conventional numerical regression technique. This paper employs the capability of EPR as a powerful tool to develop a formula with a variable number of polynomial coefficients. Herein, the evolutionary polynomial regression approach is adopted on two parametric studies, i.e. total fouling resistance and rejection rate. These parameters are all evaluated as a function of some mentioned independent variables. Maximum average error and minimum average error are obtained to be equal to 4.25% and 0.05%, respectively. Therefore, EPR is a practical and useful method to describe a membrane performance.

Keywords

References

[1] Nouzaki K., Nagata J., Arai J., Idemoto Y., Koura N., Yanagishita H., Negishi H., Kitamoto D., Ikegami T. and Haraya K., “Preparation of Polyacrylonitrile Ultra-filtration Membranes for Wastewater Treatment”, Desalination 2002. 144, 53-59.
[2] Salahi A., Abbasi M. and Mohammadi T., “Permeate Flux Decline During UF of Oily Wastewater: Experimental and Modeling”, Desalination 2010, 251, 153-160.
[3] Asatekin A. and Mayes A. M., “Oil Industry Wastewater Treatment with Fouling Resistance Membranes Containing Amphiphilic Comb Copolymers”, Environ-mental Science and Technology. 2009, 43, 4487-4492.
[4] Abbasi M., Salahi A., Mirfendereski M., Mohammadi T. and Pak A., “Dimensional Analysis of Permeation Flux for Microfiltration of Oily Wastewaters Using Mullite Ceramic Membranes”, Desalination 2010, 252, 113-119.
[5] Seo J. and Vogelpohl A., “Membrane Choice for Wastewater Treatment Using External Cross Flow Tubular Membrane Filtration”, Desalination 2009, 249, 197-204.
[6] Pinelo M., Ferrer C., Meyer A. S. and Jonsson G., “Controlling the Rejection of Protein During Membrane Filtration by Adding Selected Polyelectrolytes”, Separation and Purification Technology 2012, 54-60,
[7] Shokrkar H., Salahi A., Kasiri N. and Mohammadi T. , ” Prediction of Permeation Flux Decline during MF of oily Wastewater Using Genetic Programming”, Chemical Engineering Research and Design 2011, CHERD-885, (8).
[8] Hwang T. M., Oh H., Choung Y. K., Oh S., Jeon M., Kim J. H., Nam S. H., Lee S., “Prediction of Membrane Fouling in the Pilot-scale Microfiltration System Using Genetic Programming”, Desalination 2009, 249, 285-294.
[9] Okhovat A. and Mousavi S.M., “Modeling of Arsenic, Chromium and Cadmium Removal by Nanofiltration Process Using Genetic Programming”, Applied Soft Computing 2011, 12, 793-799.
[10] Savic D. A., Giustolisi O. and Laucelli D. B., “Asset Performance Analysis Using Multi-Utility Data and Multi-objective Data Mining Techniques”, 11th International Conference on Urban Drainage, 2008, Edinburgh, Scotland, UK.
[11] Giustolisi O., Laucelli D. and Savic D. A., “Development of Rehabilitation Plans for Water Mains Replacement Considering Risk and Cost-benefit Assessment”, J. of Civil Engineering and Environmental Systems 2006, 23, 175-190.
[12] Rezania M., Javadi A. A. and Giustolisi O., “Evaluation of Liquefaction Potential Based on CPT Results Using Evolutionary Polynomial Regression”, Computers and Geotechnics 2010, 37, 82-92.
[13] Giustolisi O. and Savic D. A.,”A Symbolic Data-driven Technique Based on Evolutionary Polynomial Regression”, Journal of Hydroinformatics 2006, 8(3), in press.
[14] Giustolisi O., Doglioni A., Savic D. A. and DiPierro F., “An Evolutionary Multi-objective Strategy for the Effective Management of Groundwater Resources”, Water Resources Research 2008, 44, W01403.
[15] Giustolisi O. and Berardi L., “Pipe Level Burst Prediction Using EPR and MCS-EPR, Water Management Challenges in Global Changes”, 2007, 1, 39-46.
[16] Ahangar-Asr A., Faramarzi A., Mottaghifard N.and Javadi A.A., “Modeling of Permeability and Compaction Characteristics of Soils Using Evolutionary Polynomial Regression”, Computers & Geosciences 2011, 37, 1860-1869.
[17] Gonder Z.B., Kaya Y., Vergili I. and Barlas H., “Optimization of Filtration Conditions for CIP Wasewater Treatment by Nanofiltration Process Using Taguchi Approach”, Separation And Purification Technology 2010, 70, 265-273,.
[18] Deriszadeh A., Husein M. M. and Harding T. G., “Produced Water Treatment by Micellar-enhanced Ultrafiltration”, Environ-mental Science & Technology 2010, 44, 1767-1772.
[19] Esch J.H. V., “More Than the Sum of its Parts”, Nature 2010, 466, 193-194.
[20] Vincent-Vela M. C., Cuartas-Uribe B., Alvarez-Blanco S.,  Lora-Garcia J., “Analysis of an Ultrafiltration Model: Influence of Operational Conditions”, Desalination 2012, 284, 14-21.
[21] Hlavacek M., “Break-up of Oil-water Emulsions Induced by Permeation through a Microfiltration Membrane”, Journal of Membrane Science. 1995, 102, 1-7.
[22] Rezvanpour A., Roostaazad R., Hesampour M., Nystrom M. and Ghotbi C., “Effective Factors in the Treatment of Kerosene-water Emulsion by Using UF Membranes”, Journal of Hazardous Materials 2009, 161, 1216-1224.
[23] Madaeni S. S., “The Effect of Large Particles on Microfiltration of Small Particles”, Journal of Porous Materials 2001, 18, 143-148.

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