Title

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

Journal title

Archives of Environmental Protection

Yearbook

2017

Volume

vol. 43

Issue

No 4

Authors

Turp, Sinan Mehmet

Keywords

wastewater ; treatment efficiency ; adsorption ; perlite ; artificial neural network

Divisions of PAS

Nauki Techniczne

Publisher

Polish Academy of Sciences

Date

2017.12.15

Type

Artykuły / Articles

Identifier

DOI: 10.1515/aep-2017-0034 ; ISSN 2083-4772 ; eISSN 2083-4810

Source

Archives of Environmental Protection; 2017; vol. 43; No 4

References

Alkan (2001), Adsorption of Copper II onto of and, Journal Colloid Interface Science, 243. ; Moradi (2016), Response surface methodology and its application for optimization of ammonium ions removal from aqueous solutions by pumice as a natural and low cost adsorbent of Environmental Protection, Archives, 42, 33. ; García (2014), Comparison of drinking water pollutant removal using a nanofiltration pilot plant powered by renewable energy and a conventional treatment facility, Desalination, 347. ; Nadaroğlu (2014), Removal of copper from aqueous solutions by using micritic limestone, Carpathian Journal of Earth and Environmental Sciences, 9, 69. ; Hagan (2003), Neutral network design, null. ; Hamed (2004), Prediction of wastewater treatment plant performance using artificial neural networks Environmental Modeling Software, null, 19, 919. ; Sarkar (null), River Water Quality Modelling Using Artificial Neural Network Technique Aquatic, null, 2015. ; Jiang (2016), Copper and zinc adsorption by softwood and hardwood biochars under elevated sulphate - induced salinity and acidic pH conditions, Chemosphere, 142. ; Yesilnacar (2012), Artificial neural network prediction of sulfate and SAR in an unconfined aquifer in southeastern Turkey Environmental, Earth Sciences, 67. ; Erdoğan (2005), Optimization of Nickel adsorption from aqueous solution by using activated carbon prepared from waste apricot by chemical activation Surface, Applied Science, 252. ; Prakash (2008), Prediction of Biosorption efficiency for the removal of copper II using artifi cial neural networks of, Journal Hazardous Materials, 152. ; ASCE (2000), cial neural networks in hydrology Preliminary concepts of Task Committee on Application of Artificial Neural Networks in Hydrology, Journal Hydrologic Engineering, 5, 115. ; Podder (2016), The use of artificial neural network for modelling of phycoremediation of toxic elements As III As from wastewater using Botryococcus braunii A and, Spectrochimica Acta Part Molecular Biomolecular Spectroscopy, 155. ; Ranade (2014), Industrial Treatment Recycling and Reuse Elsevier Ltd ISBN, Wastewater, 978. ; Yesilnacar (2008), Neural network prediction of nitrate in groundwater of Harran Turkey, Environmental Geology, 1. ; Bui (2016), Applying an artificial neural network to predict coagulation capacity of reactive dying wastewater by chitosan, Polish Journal of Environmental Studies, 25, 545. ; Malkoc (2006), Removal of II ions from aqueous solutions using waste of tea factory : Adsorption on a fixed - bed column of, Journal Hazardous Materials, 135.

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