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Optimization of operational conditions in continuous electrodeionization method for maximizing Strontium and Cesium removal from aqueous solutions using artificial neural network

  • Fazel Zahakifar , Alireza Keshtkar EMAIL logo , Ehsan Nazemi and Adib Zaheri
Published/Copyright: January 20, 2017

Abstract

Strontium (Sr) and Cesium (Cs) are two important nuclear fission products which are present in the radioactive wastewater resulting from nuclear power plants. They should be treated by considering environmental and economic aspects. In this study, artificial neural network (ANN) was implemented to evaluate the optimal experimental conditions in continuous electrodeionization method in order to achieve the highest removal percentage of Sr and Ce from aqueous solutions. Three control factors at three levels were tested in experiments for Sr and Cs: Feed concentration (10, 50 and 100 mg/L), flow rate (2.5, 3.75 and 5 mL/min) and voltage (5, 7.5 and 10 V). The obtained data from the experiments were used to train two ANNs. The three control factors were utilized as the inputs of ANNs and two quality responses were used as the outputs, separately (each ANN for one quality response). After training the ANNs, 1024 different control factor levels with various quality responses were predicted and finally the optimum control factor levels were obtained. Results demonstrated that the optimum levels of the control factors for maximum removing of Sr (97.6%) had an applied voltage of 10 V, a flow rate of 2.5 mL/min and a feed concentration of 10 mg/L. As for Cs (67.8%) they were 10 V, 2.55 mL/min and 50 mg/L, respectively.

References

1. Inan, S., Tel, H., Altas, Y.: Adsorption studies of strontium on hydrous zirconium dioxide, J. Radioanal. Nucl. Chem. 267, 615 (2006).10.1007/s10967-006-0094-9Search in Google Scholar

2. Zakrzewska-Trznadel, G., Harasimowicz, M., Chmielewski, A. G.: Concentration of radioactive components in liquid low-level radioactive waste by membrane distillation. J. Membrane Sci. 163, 257 (1999).10.1016/S0376-7388(99)00171-4Search in Google Scholar

3. Cristina Negri, M., Hinchman, R. R. In: B. D. Ensley (Ed.), The Use of Plants for the Treatment of Radionuclides (2000), Ilya Raskin, Wiley, New York, USA, p. 107–150.Search in Google Scholar

4. M. Ugajin, S. Ajuria (Eds.), Inorganic Ion Exchangers and Adsorbents for Chemical Processing in the Nuclear Fuel Cycle, IAEA-TEC DOC-337 (1985), IAEA, Vienna.Search in Google Scholar

5. Balarama, M. V., Krishna, Raoa, S. V., Arunachalam, J., Murali, M. S., Surendra Kumarc, B., Manchandab, V. K.: Removal of 137Cs and 90Sr from actual low level radioactive waste solutions using moss as a phyto-sorbent. J. Separation and Purification Technol. 38, 149 (2004).10.1016/j.seppur.2003.11.002Search in Google Scholar

6. Saleh, H. M.: Water hyacinth for phytoremediation of radioactive waste simulate contaminated with cesium and cobalt radionuclides. Nucl. Eng. Design 242, 425 (2012).10.1016/j.nucengdes.2011.10.023Search in Google Scholar

7. Kang, D. W., Sung, K. B., Lee, S. H., Kim, H. Y.: Wet oxidation of ion exchange resins in Fenton’s reaction system by using the electrode. J. Korea Solid Wastes Eng. Soc. 15, 24 (1998).Search in Google Scholar

8. Liu, F., Zhang, G., Zhang, H., Mo, J.: Performance evaluation of electrodeionization process based on ionic equilibrium with plate and frame modules. Desalination 221, 425 (2008).10.1016/j.desal.2007.01.102Search in Google Scholar

9. Lee, J. H., Choi, J. H.: The production of ultrapure water by membrane capacitive deionization (MCDI) technology. J. Membrane Sci. 409, 251 (2012).10.1016/j.memsci.2012.03.064Search in Google Scholar

10. Lu, J., Wang, Y. X., Lu, Y. Y., Wang, G. L., Kong, L., Zhu, J.: Numerical simulation of the electrodeionization (EDI) process for producing ultrapure water. Electrochim. Acta 55, 7188 (2010).10.1016/j.electacta.2010.07.054Search in Google Scholar

11. Dey, A., Thomas, G.: Electronics Grade Water Preparation (2003), Tall Oaks, Littleton.Search in Google Scholar

12. Arar, Ö., Yüksel, Ü., Kabay, N., Yüksel, M.: Application of electrodeionization (EDI) for removal of boron and silica from reverse osmosis (RO) permeate of geothermal water. Desalination 310, 25 (2013).10.1016/j.desal.2012.10.001Search in Google Scholar

13. Wood, J., Gifford, J., Arba, J., Shaw, M.: Production of ultrapure water by continuous electrodeionization. Desalination 250, 973 (2010).10.1016/j.desal.2009.09.084Search in Google Scholar

14. Boontawana, P., Kanchanathaweeb, S., Boontawanb, A.: Extractive fermentation of l-(+)-lactic acid by Pediococcus pentosaceus using electrodeionization (EDI) technique. Biochem. Eng. J. 54, 192 (2011).10.1016/j.bej.2011.02.021Search in Google Scholar

15. Feng, X., Wu, Z., Chen, X.: Removal of metal ions from electroplating effluent by EDI process and recycle of purified water. Separation Purif Technol. 57, 257 (2007).10.1016/j.seppur.2007.04.014Search in Google Scholar

16. Wen, R., Deng, S., Zhang, Y.: The removal of silicon and boron from ultra-pure water by electrodeionization. Desalination 181, 153 (2005).10.1016/j.desal.2005.02.018Search in Google Scholar

17. Taylor, J. G. Neural Networks and Their Applications (1996), John Wiley & Sons, West Sussex, England.Search in Google Scholar

18. Gallant, A. R., White, H.: On learning the derivatives of an unknown mapping with multilayer feed forward networks. Neural Networks 5, 129 (1992).10.1016/S0893-6080(05)80011-5Search in Google Scholar

19. Nazemi, E., Roshani, G. H., Feghhi, S. A. H., Gholipour Peyvandi, R., Setayeshi, S.: Precise void fraction measurement in two-phase flows independent of the flow regime using gamma-ray attenuation. Nucl. Eng. Technol. 48, 64 (2016).10.1016/j.net.2015.09.005Search in Google Scholar

20. Roshani, G. H., Feghhi, S. A. H., Mahmoudi-Aznaveh, A., Nazemi, E., Adineh-Vand, A.: Precise volume fraction prediction in oil–water–gas multiphase flows by means of gamma-ray attenuation and artificial neural networks using one detector. Measurement 51, 34 (2014).10.1016/j.measurement.2014.01.030Search in Google Scholar

21. Nazemi, E., Feghhi, S. A. H., Roshani, G. H.: Void fraction prediction in two-phase flows independent of the liquid phase density changes. Rad. Measurements 68, 49 (2014).10.1016/j.radmeas.2014.07.005Search in Google Scholar

22. Nazemi, E., Roshani, G. H., Feghhi, S. A. H, Setayeshi, S., Gholipour Peyvandi, R.: A radiation-based hydrocarbon two-phase flow meter for estimating of phase fraction independent of liquid phase density in stratified regime. Flow Measurement and Instrumentations 46, 25 (2015).10.1016/j.flowmeasinst.2015.09.002Search in Google Scholar

23. Nazemi, E., Roshani, G. H., Feghhi, S. A. H., Setayeshi, S., Eftekhari Zadeh, E., Fatehi, A.: Optimization of a method for identifying the flow regime and measuring void fraction in a broad beam gamma-ray attenuation technique. Int. J. Hydrogen Energy 41, 7438 (2016).10.1016/j.ijhydene.2015.12.098Search in Google Scholar

24. Yadollahi, A., Nazemi, E., Zolfaghari, A., Ajorloo, A. M.: Application of artificial neural network for predicting the optimal mixture of radiation shielding concrete. Prog. Nucl. Energy 89, 69 (2016).10.1016/j.pnucene.2016.02.010Search in Google Scholar

25. Yadollahi, A., Nazemi, E., Zolfaghari, A., Ajorloo, A. M.: Optimization of thermal neutron shield concrete mixture using artificial neural network. Nucl. Eng. Design. 305, 146 (2016).10.1016/j.nucengdes.2016.05.012Search in Google Scholar

26. Roshani, G. H., Nazemi, E., Feghhi, S. A. H., Setayeshi, S.: Flow regime identification and void fraction prediction in two-phase flows based on gamma ray attenuation. Measurement 62, 25 (2015).10.1016/j.measurement.2014.11.006Search in Google Scholar

27. Roshani, G. H., Nazemi, E., Roshani, M. M.: Intelligent recognition of gas-oil water three-phase flow regime and determination of volume fraction using radial basis function. Flow Measurement and Instrumentation 54, 39 (2017).10.1016/j.flowmeasinst.2016.10.001Search in Google Scholar

28. Eftekharizadeh, E., Feghhi, S. A. H., Roshani, G. H., Rezaei, A.: Application of artificial neural network in precise prediction of cement elements percentages based on the neutron activation analysis. European Phys. J. Plus 131, 1 (2016).10.1140/epjp/i2016-16167-6Search in Google Scholar

29. Eftekharizadeh, E., Sadighzadeh, A., Salehizadeh, A., Nazemi, E., Roshani, G. H.: Neutron activation analysis for cement elements using an IECF device as a high energy neutron source. Analyt. Methods 8, 2510 (2016).10.1039/C5AY03280FSearch in Google Scholar

30. Roshani, G. H., Nazemi, E., Feghhi, S. A. H.: Investigation of using 60Co source and one detector for determining the flow regime and void fraction in gas-liquid two-phase flows. Flow Measurement and Instrumentation 50, 73 (2016).10.1016/j.flowmeasinst.2016.06.013Search in Google Scholar

31. Hagan, M. T., Menhaj, M.: Training feed forward networks with themarquardt algorithm. IEEE Trans. Neural Networks 5, 989 (1994).10.1109/72.329697Search in Google Scholar PubMed

32. Keramati, N., Moheb, A., Ehsani, M. R.: Effect of operating parameters on NaOH recovery from waste stream of Merox tower using membrane systems: electrodialysis and electrodeionization processes. Desalination 259, 97 (2010).10.1016/j.desal.2010.04.027Search in Google Scholar

Received: 2016-10-5
Accepted: 2016-12-2
Published Online: 2017-1-20
Published in Print: 2017-7-26

©2017 Walter de Gruyter GmbH, Berlin/Boston

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