Application of Artificial Neural Network Modeling in Prediction of The Extraction Yield of Copper-Morin Complex from Aqueous Media Utilizing a Molecularly Imprinted Polymer Coated Stir Bar

Hashemi, Sayyed Hossein; Kaykhaii, Massoud; Shakeri, Mohamad

doi:10.30473/ijac.2020.51710.1167

Document Type : Full research article

Authors

¹ Department of Marine Chemistry, Faculty of Marine Science, Chabahar Maritime University, P.O. Box 98617-85553, Chabahar, Iran

² Department of Chemistry, Faculty of Sciences, University of Sistan and Baluchestan, Zahedan, Iran

³ Department of Chemistry, University of Zabol, Zabol, Iran

https://doi.org/10.30473/ijac.2020.51710.1167

Abstract

In this research, a new modeling method based on three-layer artificial neural network (ANN) technique was applied to predict the extraction yield of copper-morin complex from aqueous samples by means of molecularly imprinted stir bar sorptive extraction. Input variables of the model were pH of the solution, absorption and desorption times, stirring rate, temperature, and amount of morin ligand; while the output was extraction yield of copper ions. It was found that a network with 12 hidden neurons is highly accurate in predicting extraction recovery of copper-morin complex. The mean squared error and correlation coefficient between the experimental data and the ANN predictions were achieved as 0.0009 and 0.9999 for training, 0.0032 and 0.976 for validation and 0.0030 and 0.96666 for testing data sets. Under the optimum conditions, the linear range found to be in the range of 5-1000 μg L^-1 with the detection limit of 0.38 μg L^-1. The relative standard deviation was obtained to be below 5.3%. The method was successfully applied for preconcentration and determination of Cu in a few real samples.

Keywords

20.1001.1.23832207.2020.7.1.10.1

References

[1] N.L. Nemerow, Industrial waste treatment: contemporary practice and vision for the future, Elsevier Inc., Burlington, USA. (2010).

[2] N.L. Nemerow and F.J. Agardy, Strategies of industrial and hazardous waste management, Van Nostrand Reinhold, New York, USA (1998).

[3] N. Pechishcheva and K.Y. Shunyaev, Application of luminescence to the determination of trace copper, J. Anal. Chem. 63 (2008) 412-423.

[4] G.F. Lima, M.O. Ohara, D.N. Clausen, D.R. Nascimento, E.S. Ribeiro, M.G. Segatelli, M.A. Bezerra and C.R. Tarley, Flow injection on-line minicolumn preconcentration and determination of trace copper ions using an alumina/titanium oxide grafted silica matrix and FAAS, Microchim. Acta 178 (2012) 61-70.

[5] M. Csuros and C. Csuros, Environmental sampling and analysis for metals, 1^st edition, CRC Press, NY, USA (2016) p. 20.

[6] W.B. Mills, D.B. Porcella, M. Ungs, S. Gherini and K. Summers, Water-quality assessment: a screening procedure for toxic and conventional pollutants in surface and ground water (1985).

[7] H. Hashemi, M. Khajeh and M. Kaykhaii, Molecularly imprinted stir bar sorptive extraction coupled with atomic absorption spectrometry for trace analysis of copper in drinking water samples, Anal. Methods 5 (2013) 2778-2783.

[8] N. Zhang and B. Hu, Cadmium (II) imprinted 3-mercaptopropyltrimethoxysilane coated stir bar for selective extraction of trace cadmium from environmental water samples followed by inductively coupled plasma mass spectrometry detection, Anal. Chim. Acta 723 (2012) 54-60.

[9] S.H. Hashemi, M. Kaykhaii, A.J. Keikha, Z. Sajjadi and M. Mirmoghaddam, Application of response surface methodology for silver nanoparticle stir bar sorptive extraction of heavy metals from drinking water samples: a Box-Behnken design, Analyst 144 (2019) 3525-3532.

[10] S.H. Hashemi, M. Kaykhaii and F. Tabehzar, Molecularly imprinted stir bar sorptive extraction coupled with high performance liquid chromatography for trace analysis of naphthalene sulfonates in seawater, J. Iran Chem. Soc. 13 (2016) 733-741.

[11] S. Mandal, P. Sivaprasad, S. Venugopal and K. Murthy, Artificial neural network modeling to evaluate and predict the deformation behavior of stainless steel type AISI 304L during hot torsion, Appl. Soft Comput. 9 (2016) 237-244.

[12] M. Khayet and C. Cojocaru, Artificial neural network modeling and optimization of desalination by air gap membrane distillation, Sep. Purif. Technol. 86 (2012) 171-182.

[13] M. Khayet, C. Cojocaru and M. Essalhi, Artificial neural network modeling and response surface methodology of desalination by reverse osmosis, J. Membrane Sci. 368 (2011) 202-214.

[14] D. Baş and İ.H. Boyacı, Modeling and optimization II: Comparison of estimation capabilities of response surface methodology with artificial neural networks in a biochemical reaction, J. Food Eng. 78 (2007) 846-854.

[15] M.A. Akcayol and C. Cinar, Artificial neural network based modeling of heated catalytic converter performance, Appl. Therm. Eng. 25 (2005) 2341-2350.

[16] Y.X. Jin, H.Z. Cheng, J.Y. Yan and L. Zhang, New discrete method for particle swarm optimization and its application in transmission network expansion planning, Electr. Power Syst. Res. 77 (2007) 227-233.

[17] M. Hussain, M.S. Rahman and C. Ng, Prediction of pores formation (porosity) in foods during drying: generic models by the use of hybrid neural network, J. Food Eng. 51 (2002) 239-248.

[18] E. Jorjani, S.C. Chelgani and S. Mesroghli, Application of artificial neural networks to predict chemical desulfurization of Tab as coal, Fuel 87 (2008) 2727-2734.

[19] M.A. Razavi, A. Mortazavi and M. Mousavi, Dynamic modelling of milk ultrafiltration by artificial neural network. J. Membrane Sci. 220 (2003) 47-58.

[20] J.S. Torrecilla, L. Otero and P. Sanz, Optimization of an artificial neural network for thermal/pressure food processing: Evaluation of training algorithms, Comput. Electron. Agric. 56 (2007) 101-110.

[21] A. Ghaffari, H. Abdollahi, M. Khoshayand, I.S. Bozchalooi, A. Dadgar and M. Rafiee-Tehrani, Performance comparison of neural network training algorithms in modeling of bimodal drug delivery, Int. J. Pharm. 327 (2006) 126-138.

[22] M. Khare and S.S. Nagendra, Artificial neural networks in vehicular pollution modelling, Springer-Verlog Berlin Heidelberg (2007).

[23] Y. Erzin, B.H. Rao and D. Singh, Artificial neural network models for predicting soil thermal resistivity, Int. J. Therm. Sci. 47 (2008) 1347-1358.

Iranian Journal of Analytical Chemistry

Application of Artificial Neural Network Modeling in Prediction of The Extraction Yield of Copper-Morin Complex from Aqueous Media Utilizing a Molecularly Imprinted Polymer Coated Stir Bar

References

References

Volume 7, Issue 1 - Serial Number 13
March 2020
Pages 78-87

Application of Artificial Neural Network Modeling in Prediction of The Extraction Yield of Copper-Morin Complex from Aqueous Media Utilizing a Molecularly Imprinted Polymer Coated Stir Bar

References

References

Volume 7, Issue 1 - Serial Number 13March 2020Pages 78-87

Volume 7, Issue 1 - Serial Number 13
March 2020
Pages 78-87