Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Prediction of adsorption efficiencies of Ni (II) in aqueous solutions with perlite via artificial neural networks

Prediction of adsorption efficiencies of Ni (II) in aqueous solutions with perlite via artificial... ReferencesAlkan, M. & Doğan, M. (2001). Adsorption of Copper(II) onto Perlite, Journal of Colloid and Interface Science, 243, pp. 280-291.ASCE, 2000, Artifi cial neural networks in hydrology. I: Preliminary concepts, Journal of Hydrologic Engineering, 5(2), pp. 115-123, ASCE Task Committee on Application of Artificial Neural Networks in Hydrology.Bui, H.M., Duong, H.T.G. & Nguyen, C.D. (2016). Applying an artificial neural network to predict coagulation capacity of reactive dying wastewater by chitosan, Polish Journal of Environmental Studies, 25, 2, pp. 545-555.Erdoğan, S., Önal, Y., Akmil-Basar, C. & Bilmez-Erdemoglu, S. (2005). Optimization of Nickel adsorption from aqueous solution by using activated carbon prepared from waste apricot by chemical activation, Applied Surface Science, 252, pp. 1324-1331.García-Vaquero, N., Lee, E., Jiménez Castañeda, R., Cho, J. & López-Ramírez, J.A. (2014). Comparison of drinking water pollutant removal using a nanofiltration pilot plant powered by renewable energy and a conventional treatment facility, Desalination, 347, pp. 94-102.Hagan, M.T., Demuth, H.B. & Beal, M.H. (2003). Neutral network design, PWS, Beston 2003.Hamed, M.M., Khalafallah, M.G. & Hassanien, E.A. (2004). Prediction of wastewater treatment plant performance using artificial neural networks, Environmental Modeling & Software, 19, pp. 919-928.Jiang, S., Huang, L., Nguyen, T.A., Ok, Y.S., Rudolph, V., Yang, H. & Zhang, D. (2016). Copper and zinc adsorption by softwood and hardwood biochars under elevated sulphate-induced salinity and acidic pH conditions, Chemosphere, 142, pp. 64-71.Malkoc, E. & Nuhoğlu, Y. (2006). Removal of Ni(II) ions from aqueous solutions using waste of tea factory: Adsorption on a fixed-bed column, Journal of Hazardous Materials, B135, pp. 328-336.Moradi, M., Fazizadehdavil, M., Pirsaheb, M., Mansouri, Y., Khosravi, T. & Sharafi , K. (2016), Response surface methodology (RSM) and its application for optimization of ammonium ions removal from aqueous solutions by pumice as a natural and low cost adsorbent, Archives of Environmental Protection, 42, 2, pp. 33-43.Nadaroğlu, H., Kalkan, E. & Çelebi, N. (2014). Removal of copper from aqueous solutions by using micritic limestone, Carpathian Journal of Earth and Environmental Sciences, 9, 1, pp. 69-80.Podder , M.S. & Majumder, C.B. (2016). The use of artificial neural network for modelling of phycoremediation of toxic elements As(III) and As(V) from wastewater using Botryococcus braunii, Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 155, pp. 130-145.Prakash, N., Manikandan, S.A., Govindarajan, L. & Vijayagopal, V. (2008). Prediction of Biosorption efficiency for the removal of copper (II) using artifi cial neural networks, Journal of Hazardous Materials, 152, pp. 1268-1275.Ranade, V.V. & Bhandari, V.M. (2014). Industrial Wastewater Treatment, Recycling, and Reuse, Elsevier Ltd, ISBN: 978-0-08-099968-5.Sarkar, A. & Pandey, P. (2015). River Water Quality Modelling Using Artificial Neural Network Technique, Aquatic Procedia, 4, pp. 1070-1077.Yesilnacar, M.I. & Sahinkaya, E. (2012). Artificial neural network prediction of sulfate and SAR in an unconfined aquifer in southeastern Turkey, Environmental Earth Sciences, 67, 4, pp. 1111-1119.Yesilnacar, M.I., Sahinkaya, E., Naz, M. & Ozkaya, B. (2008). Neural network prediction of nitrate in groundwater of Harran Plain, Turkey, Environmental Geology, 56, 1, pp. 19-25. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Archives of Environmental Protection de Gruyter

Prediction of adsorption efficiencies of Ni (II) in aqueous solutions with perlite via artificial neural networks

Archives of Environmental Protection , Volume 43 (4): 7 – Dec 1, 2017

Loading next page...
 
/lp/de-gruyter/prediction-of-adsorption-efficiencies-of-ni-ii-in-aqueous-solutions-uas8Ujsapz
Publisher
de Gruyter
Copyright
© Archives of Environmental Protection
ISSN
2083-4810
eISSN
2083-4810
DOI
10.1515/aep-2017-0034
Publisher site
See Article on Publisher Site

Abstract

ReferencesAlkan, M. & Doğan, M. (2001). Adsorption of Copper(II) onto Perlite, Journal of Colloid and Interface Science, 243, pp. 280-291.ASCE, 2000, Artifi cial neural networks in hydrology. I: Preliminary concepts, Journal of Hydrologic Engineering, 5(2), pp. 115-123, ASCE Task Committee on Application of Artificial Neural Networks in Hydrology.Bui, H.M., Duong, H.T.G. & Nguyen, C.D. (2016). Applying an artificial neural network to predict coagulation capacity of reactive dying wastewater by chitosan, Polish Journal of Environmental Studies, 25, 2, pp. 545-555.Erdoğan, S., Önal, Y., Akmil-Basar, C. & Bilmez-Erdemoglu, S. (2005). Optimization of Nickel adsorption from aqueous solution by using activated carbon prepared from waste apricot by chemical activation, Applied Surface Science, 252, pp. 1324-1331.García-Vaquero, N., Lee, E., Jiménez Castañeda, R., Cho, J. & López-Ramírez, J.A. (2014). Comparison of drinking water pollutant removal using a nanofiltration pilot plant powered by renewable energy and a conventional treatment facility, Desalination, 347, pp. 94-102.Hagan, M.T., Demuth, H.B. & Beal, M.H. (2003). Neutral network design, PWS, Beston 2003.Hamed, M.M., Khalafallah, M.G. & Hassanien, E.A. (2004). Prediction of wastewater treatment plant performance using artificial neural networks, Environmental Modeling & Software, 19, pp. 919-928.Jiang, S., Huang, L., Nguyen, T.A., Ok, Y.S., Rudolph, V., Yang, H. & Zhang, D. (2016). Copper and zinc adsorption by softwood and hardwood biochars under elevated sulphate-induced salinity and acidic pH conditions, Chemosphere, 142, pp. 64-71.Malkoc, E. & Nuhoğlu, Y. (2006). Removal of Ni(II) ions from aqueous solutions using waste of tea factory: Adsorption on a fixed-bed column, Journal of Hazardous Materials, B135, pp. 328-336.Moradi, M., Fazizadehdavil, M., Pirsaheb, M., Mansouri, Y., Khosravi, T. & Sharafi , K. (2016), Response surface methodology (RSM) and its application for optimization of ammonium ions removal from aqueous solutions by pumice as a natural and low cost adsorbent, Archives of Environmental Protection, 42, 2, pp. 33-43.Nadaroğlu, H., Kalkan, E. & Çelebi, N. (2014). Removal of copper from aqueous solutions by using micritic limestone, Carpathian Journal of Earth and Environmental Sciences, 9, 1, pp. 69-80.Podder , M.S. & Majumder, C.B. (2016). The use of artificial neural network for modelling of phycoremediation of toxic elements As(III) and As(V) from wastewater using Botryococcus braunii, Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 155, pp. 130-145.Prakash, N., Manikandan, S.A., Govindarajan, L. & Vijayagopal, V. (2008). Prediction of Biosorption efficiency for the removal of copper (II) using artifi cial neural networks, Journal of Hazardous Materials, 152, pp. 1268-1275.Ranade, V.V. & Bhandari, V.M. (2014). Industrial Wastewater Treatment, Recycling, and Reuse, Elsevier Ltd, ISBN: 978-0-08-099968-5.Sarkar, A. & Pandey, P. (2015). River Water Quality Modelling Using Artificial Neural Network Technique, Aquatic Procedia, 4, pp. 1070-1077.Yesilnacar, M.I. & Sahinkaya, E. (2012). Artificial neural network prediction of sulfate and SAR in an unconfined aquifer in southeastern Turkey, Environmental Earth Sciences, 67, 4, pp. 1111-1119.Yesilnacar, M.I., Sahinkaya, E., Naz, M. & Ozkaya, B. (2008). Neural network prediction of nitrate in groundwater of Harran Plain, Turkey, Environmental Geology, 56, 1, pp. 19-25.

Journal

Archives of Environmental Protectionde Gruyter

Published: Dec 1, 2017

References