Kinetic Constant Modeling of Zn(II) Ion Removal from Synthetic Wastewater by Gene Expression Programing

Document Type : Research Article

Authors

1 Department of Mining & Metallurgical Engineering, Amirkabir University of Technology, Hafez Ave., Tehran, Iran.

2 Department of chemistry, Amirkabir University of Technology, Tehran, Iran

Abstract

The separation of ions from wastewater and environments such as hydrometallurgy has been a major challenge in the development of ion flotation in recent years. Few studies have been carried out on the kinetics of metal ion removal by ion flotation. In this study, a new model using the gene expression programming (GEP) method is proposed to predict the kinetic constant of zinc ion removal (k-Zn(II)) from synthetic wastewater with sodium dodecyl sulphate as a collector. The efficiency of ion flotation depends on both the amount of ion removal and water removed during the process. In this regard, the water removal kinetics constant (k-W) was also investigated. The effect of important parameters on k-Zn(II) and k-W including the ratio of SDS/Zn(II), the activity coefficient, and the pH were investigated. The values of R2, RMSE, and VAF of the GEP models for the testing data for k-Zn(II) were 0.98, 0.66, and 98.9 and for k-W, they were 0.94, 0.004, and 0.93, respectively. The results indicate the high performance of GEP models for the prediction of k-Zn(II) and k-W. The sensitivity analysis of GEP models showed that k-Zn(II) and k-W are more sensitive to pH and the ratio of SDS/Zn(II), respectively. 

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References

[1] S. Nicol, K. Galvin, M. Engel, Ion flotation-potential applications to mineral processing, Minerals Engineering, 5(10-12) (1992) 1259-1275.
[2] H. Polat, D. Erdogan, Heavy metal removal from waste waters by ion flotation, Journal of Hazardous Materials, 148(1-2) (2007) 267-273.
[3] J. Rubio, M. Souza, R. Smith, Overview of flotation as a wastewater treatment technique, Minerals engineering, 15(3) (2002) 139-155.
[4] F.S. Hoseinian, M. Irannajad, A.J. Nooshabadi, Ion flotation for removal of Ni (II) and Zn (II) ions from wastewaters, International Journal of Mineral Processing, 143 (2015) 131-137.
[5] F.S. Hoseinian, M. Irannajad, M. Safari, Effective factors and kinetics study of zinc ion removal from synthetic wastewater by ion flotation, Separation Science and Technology, 52(5) (2016), 892-902.
[6] A. Bodagh, H. Khoshdast, H. Sharafi, H. Shahbani Zahiri, K. Akbari Noghabi, Removal of cadmium (II) from aqueous solution by ion flotation using rhamnolipid biosurfactant as an ion collector, Industrial & Engineering Chemistry Research, 52(10) (2013) 3910-3917.
[7] M. Reyes, F. Patiño, F.J. Tavera, R. Escudero, I. Rivera, M. Pérez, Kinetics and recovery of xanthate-copper compounds by ion flotation techniques, Journal of the Mexican Chemical Society, 53(1) (2009) 15-22.
[8] Z. Liu, F.M. Doyle, A thermodynamic approach to ion flotation. I. Kinetics of cupric ion flotation with alkylsulfates, Colloids and Surfaces A: Physicochemical and Engineering Aspects, 178(1-3) (2001) 79-92.
[9] M. Reyes, F. Patiño, R. Escudero, M. Pérez, M.U. Flores, I.A. Reyes, Kinetics and hydrodynamics of silver ion flotation, Journal of the Mexican Chemical Society, 56(4) (2012) 408-416.
[10] F.M. Doyle, Z. Liu, The effect of triethylenetetraamine (Trien) on the ion flotation of Cu2+ and Ni2+, Journal of colloid and interface science, 258(2) (2003) 396-403.
[11] Z. Liu, F.M. Doyle, Ion flotation of Co2+, Ni2+, and Cu2+ using dodecyldiethylenetriamine (Ddien), Langmuir, 25(16) (2009) 8927-8934.
[12] E.P. Mavros, K. Matis, Innovations in Flotation Technology,  (1992).
[13] C. McDonald, J. Jaganathan, Ion flotation of nickel using ethylhexadecyldimethylammonium bromide, Microchemical Journal, 27(2) (1982) 240-245.
[14] F.S. Hoseinian, B. Rezai, E. Kowsari, M. Safari, Kinetic study of Ni (II) removal using ion flotation: Effect of chemical interactions, Minerals Engineering, 119 (2018) 212-221.
[15] F.S. Hoseinian, B. Rezai, E. Kowsari, The nickel ion removal prediction model from aqueous solutions using a hybrid neural genetic algorithm, Journal of environmental management, 204 (2017) 311-317.
[16] K. Shakir, A.F. Elkafrawy, H.F. Ghoneimy, S.G.E. Beheir, M. Refaat, Removal of rhodamine B (a basic dye) and thoron (an acidic dye) from dilute aqueous solutions and wastewater simulants by ion flotation, Water research, 44(5) (2010) 1449-1461.
[17] M.A. Soliman, G.M. Rashad, M.R. Mahmoud, Kinetics of ion flotation of Co (II)–EDTA complexes from aqueous solutions, Radiochimica Acta, 103(9) (2015) 643-652.
[18] A. Uribe‐Salas, R. Pérez‐Garibay, F. Nava‐Alonso, M. Castro‐Román, A kinetic model for Pb2+ flotation with sodium dodecylsulfate in a batch column, Separation science and technology, 40(15) (2005) 3225-3237.
[19] F.M. Doyle, Ion flotation—its potential for hydrometallurgical operations, International Journal of Mineral Processing, 72(1-4) (2003) 387-399.
[20] R.S. Faradonbeh, D.J. Armaghani, M. Monjezi, Development of a new model for predicting flyrock distance in quarry blasting: a genetic programming technique, Bulletin of Engineering Geology and the Environment, 75(3) (2016) 993-1006.
[21] C. Ferreira, Gene expression programming in problem solving, in:  Soft computing and industry, Springer, 2002, pp. 635-653.
[22] F.S. Hoseinian, R.S. Faradonbeh, A. Abdollahzadeh, B. Rezai, S. Soltani-Mohammadi, Semi-autogenous mill power model development using gene expression programming, Powder Technology, 308 (2017) 61-69.
[23] C. Ferreira, Gene expression programming: mathematical modeling by an artificial intelligence, Springer, 2006.
[24] D.J. Armaghani, R.S. Faradonbeh, H. Rezaei, A.S.A. Rashid, H.B. Amnieh, Settlement prediction of the rock-socketed piles through a new technique based on gene expression programming, Neural Computing and Applications,  (2016) 1-11.
[25] H.M. Azamathulla, Gene expression programming for prediction of scour depth downstream of sills, Journal of Hydrology, 460 (2012) 156-159.
[26] H.M. Azamathulla, Z. Ahmad, Gene-expression programming for transverse mixing coefficient, Journal of Hydrology, 434 (2012) 142-148.
[27] R.S. Faradonbeh, M. Monjezi, D.J. Armaghani, Genetic programing and non-linear multiple regression techniques to predict backbreak in blasting operation, Engineering with Computers, 32(1) (2016) 123-133.
[28] M.Z. Hashmi, A.Y. Shamseldin, B.W. Melville, Statistical downscaling of watershed precipitation using Gene Expression Programming (GEP), Environmental Modelling & Software, 26(12) (2011) 1639-1646.
[29] M. Khandelwal, R.S. Faradonbeh, M. Monjezi, D.J. Armaghani, M.Z.B.A. Majid, S. Yagiz, Function development for appraising brittleness of intact rocks using genetic programming and non-linear multiple regression models, Engineering with Computers, 33(1) (2017) 13-21.
[30] N.A. Zakaria, H.M. Azamathulla, C.K. Chang, A.A. Ghani, Gene expression programming for total bed material load estimation—a case study, Science of the total environment, 408(21) (2010) 5078-5085.
 
Amirkabir Journal of Civil Engineering
Volume 53, Issue 1
April 2021
Pages 261-272

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