Açık Akademik Arşiv Sistemi

Prediction of Ionic Cr (VI) Extraction Efficiency in Flat Sheet Supported Liquid Membrane Using Artificial Neural Networks (ANNs)

Show simple item record

dc.contributor.authors Eyupoglu, V; Eren, B; Dogan, E;
dc.date.accessioned 2020-02-26T08:56:39Z
dc.date.available 2020-02-26T08:56:39Z
dc.date.issued 2010
dc.identifier.citation Eyupoglu, V; Eren, B; Dogan, E; (2010). Prediction of Ionic Cr (VI) Extraction Efficiency in Flat Sheet Supported Liquid Membrane Using Artificial Neural Networks (ANNs). INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH, 4, 470-463
dc.identifier.issn 1735-6865
dc.identifier.uri https://hdl.handle.net/20.500.12619/50093
dc.description.abstract Artificial neural networks (ANNs) are computer techniques that attempt to simulate the functionality and decision-making processes of the human brain. In the past few decades, artificial neural networks (ANNs) have been extensively used in a wide range of engineering applications. There are only a few applications in liquid membrane process. The objective of this research was to develop artificial neural networks (ANNs) model to estimate Cr (VI) extraction efficiency in feed phase. Data set (413 experiment records) were obtained from a laboratory scale experimental study. Various combinations of experimental data, namely % (w/w) extractant Alamine 336 concentration in membrane phase, stirring speed in feed and stripping phase, flat sheet support type, stripping phase NaOH concentration, feed phase pH, diluents type, % (w/w) diluents concentration, polymer support type, extractant type, and time are used as inputs into the ANN so as to evaluate the degree of effect of each of these variables on Cr (VI) extraction efficiency in feed phase. The results of the ANN model is compared with multiple linear regression model (MLR). Mean square error (MSE), average absolute relative error (AARE) and coefficient of determination (R(2)) statistics are used as comparison criteria for the evaluation of the model performances. Based on the comparisons, it was found that the ANN model could be employed successfully in estimating the Cr (VI) extraction efficiency.
dc.language English
dc.publisher UNIV TEHRAN
dc.subject Environmental Sciences & Ecology
dc.title Prediction of Ionic Cr (VI) Extraction Efficiency in Flat Sheet Supported Liquid Membrane Using Artificial Neural Networks (ANNs)
dc.type Article
dc.identifier.volume 4
dc.identifier.startpage 463
dc.identifier.endpage 470
dc.contributor.saüauthor Eyüpoğlu, Volkan
dc.contributor.saüauthor Eren, Beytullah
dc.contributor.saüauthor Doğan, Emrah
dc.relation.journal INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH
dc.identifier.wos WOS:000279971400011
dc.contributor.author Eyüpoğlu, Volkan
dc.contributor.author Eren, Beytullah
dc.contributor.author Doğan, Emrah


Files in this item

Files Size Format View

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record