prediction of ionic cr (vi) extraction efficiency in flat sheet supported liquid membrane using artificial neural networks (anns)

نویسندگان

v. eyupoglu

b. eren

e. dogan

چکیده

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 (r2) 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.

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Monthly runoff forecasting by means of artificial neural networks (ANNs)

Over the last decade or so, artificial neural networks (ANNs) have become one of the most promising tools formodelling hydrological processes such as rainfall runoff processes. However, the employment of a single model doesnot seem to be an appropriate approach for modelling such a complex, nonlinear, and discontinuous process thatvaries in space and time. For this reason, this study aims at de...

متن کامل

Prediction of Pressure Drop of Al2O3-Water Nanofluid in Flat Tubes Using CFD and Artificial Neural Networks

In the present study, Computational Fluid Dynamics (CFD) techniques and Artificial Neural Networks (ANN) are used to predict the pressure drop value (Δp ) of Al2O3-water nanofluid in flat tubes. Δp  is predicted taking into account five input variables: tube flattening (H), inlet volumetric flow rate (Qi  ), wall heat flux (qnw  ), nanoparticle volume fraction (Φ) and nanoparticle diameter (dp ...

متن کامل

Rainfall - Runoff Modelling Using Artificial Neural Networks ( ANNs )

Over the last decades or so, artificial neural networks (ANNs) have become one of the most promising tools for modelling hydrological processes such as rainfall-runoff processes. In most studies, ANNs have been demonstrated to show superior result compared to the traditional modelling approaches. They are able to map underlying relationships between input and output data without detailed knowle...

متن کامل

Artificial Neural Networks (ANNs) for EEG Purging using Wavelet Analysis

The Electroencephalogram (EEG) is a biological signal that represents the electrical activity of the brain. Artifacts in EEG signals are caused by various factors, like line interference, EOG (electro-oculogram) and ECG (electrocardiogram). The removal of artifact from scalp EEGs is of considerable importance for analysis of underlying brainwave activity. The presence of artifacts such as muscl...

متن کامل

PREDICTION OF COMPRESSION INDEX OF SOILS USING ARTIFICIAL NEURAL NETWORKS (ANNs)

The behaviour of soil at the location of the project and interactions of the earth materials during and after construction has a major influence on the success, economy and safety of the work. Another complexity associated with some geotechnical engineering materials, such as sand and gravel, is the difficulty in obtaining undisturbed samples and time consuming involving skilled technician. Com...

متن کامل

منابع من

با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید


عنوان ژورنال:
international journal of environmental research

ناشر: university of tehran

ISSN 1735-6865

دوره 4

شماره 3 2010

میزبانی شده توسط پلتفرم ابری doprax.com

copyright © 2015-2023