Szczegóły

Tytuł artykułu

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

Tytuł czasopisma

Archives of Environmental Protection

Rocznik

2017

Wolumin

vol. 43

Numer

No 4

Autorzy

Słowa kluczowe

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

Wydział PAN

Nauki Techniczne

Wydawca

Polish Academy of Sciences

Data

2017.12.15

Typ

Artykuły / Articles

Identyfikator

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

Referencje

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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