Title : ( Prediction of MEUF process performance using artificial neural networks and ANFIS approaches )
Authors: Bashir Rahmanian Hoseinabadi , Majid Pakizeh , Seyed Aliakbar Mansoori , morteza esfandyari , dariush jafari , Heidar Maddah , Abdolmajid Maskooki ,
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Abstract
In the present study, a micellar-enhanced ultrafiltration (MEUF) procedure for the separation of lead ions from aqueous solution using response surface methodology (RSM) has been proposed. Due to the extreme complexity and nonlinearity of membrane separation processes, two models, including a feed forward artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS) have been utilized. These simulation methods have been given extreme accurate model that are more efficient than the second quadratic mathematical model for both response variables. The results of ANN and ANFIS models have been shown that the independent predicted rejection and permeate values were compared to measured target values and good correlations were found (R2 > 0.92, R2 > 0.97) for two above mentioned approaches, respectively.
Keywords
, MEUF Simulation RSM ANN, ANFIS
@article{paperid:1028334,
author = {Rahmanian Hoseinabadi, Bashir and Pakizeh, Majid and Mansoori, Seyed Aliakbar and Esfandyari, Morteza and Jafari, Dariush and Heidar Maddah and Abdolmajid Maskooki},
title = {Prediction of MEUF process performance using artificial neural networks and ANFIS approaches},
journal = {Journal of the Taiwan Institute of Chemical Engineers},
year = {2012},
volume = {43},
number = {4},
month = {June},
issn = {1876-1070},
pages = {558--565},
numpages = {7},
keywords = {MEUF
Simulation
RSM
ANN; ANFIS},
}
%0 Journal Article
%T Prediction of MEUF process performance using artificial neural networks and ANFIS approaches
%A Rahmanian Hoseinabadi, Bashir
%A Pakizeh, Majid
%A Mansoori, Seyed Aliakbar
%A Esfandyari, Morteza
%A Jafari, Dariush
%A Heidar Maddah
%A Abdolmajid Maskooki
%J Journal of the Taiwan Institute of Chemical Engineers
%@ 1876-1070
%D 2012
