Studies on the applicability of artificial neural network (ANN) in emulsion liquid membranes

被引:32
作者
Chakraborty, M [1 ]
Bhattacharya, C
Dutta, S
机构
[1] SV Reg Coll Engn & Technol, Dept Chem Engn, Surat 395007, India
[2] Jadavpur Univ, Dept Chem Engn, Kolkata 700032, W Bengal, India
关键词
emulsion liquid membrane; artificial neural network; back-propagation algorithm; simulation; solute concentration in feed phase;
D O I
10.1016/S0376-7388(03)00226-6
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
An artificial neural network (ANN) model of emulsion liquid membrane (ELM) process is proposed in the present study which is able to predict solute concentration in feed during extraction operation and ultimate % extraction at different initial solute concentration in feed phase, internal reagent concentration, treat ratio, volume fraction of internal aqueous phase in emulsion and time. Because of the complexity in generalization of the phenomenon of ELM process by any mathematical model, the neural network proves to be a very promising method for the purpose of process simulation. The network uses the back-propagation algorithm (BPA) for evaluating the connection strengths representing the correlations between inputs (initial solute concentration in feed phase, internal reagent concentration, treat ratio, volume fraction of internal aqueous phase in emulsion and time) and outputs (solute concentration in feed during extraction operation and % extraction). The network employed in the present study uses five input nodes corresponding to the operating variables and two output nodes corresponding to the measurement of the performance of the network (solute concentration in feed during extraction and % extraction). Batch experiments are performed for separation of nickel(II) from aqueous sulphate solution of initial concentration in the 200-100 mg/l ranges. The network employed in the present study uses two hidden layers of optimum number of nodes being thirty and twenty. A leaning rate of 0.3 and momentum factor of 0.4 is used. The model predicted results in good agreement with the experimental data and the average deviations for all the cases are found to be well within +/-10%. (C) 2003 Elsevier B.V. All rights reserved.
引用
收藏
页码:155 / 164
页数:10
相关论文
共 25 条
[1]  
Basset J., 1978, VOGELS TXB QUANTITAT
[2]   Studies on transport mechanism of Cr(VI) extraction from an acidic solution using liquid surfactant membranes [J].
Bhowal, A ;
Datta, S .
JOURNAL OF MEMBRANE SCIENCE, 2001, 188 (01) :1-8
[3]  
BRAID RS, 1987, AICHE J, V33, P43
[4]   A DIFFUSION-MODEL FOR REVERSIBLE CONSUMPTION IN EMULSION LIQUID MEMBRANES [J].
BUNGE, AL ;
NOBLE, RD .
JOURNAL OF MEMBRANE SCIENCE, 1984, 21 (01) :55-71
[5]  
CHAN CC, 1987, CHEM ENG SCI, V42, P83
[6]   Use of neural networks for liquid-liquid extraction column modelling: an experimental study [J].
Chouai, A ;
Cabassud, M ;
Le Lann, MV ;
Gourdon, C ;
Casamatta, G .
CHEMICAL ENGINEERING AND PROCESSING-PROCESS INTENSIFICATION, 2000, 39 (02) :171-180
[7]   A PERTURBATION SOLUTION FOR BATCH EXTRACTION WITH DOUBLE EMULSIONS - ROLE OF CONTINUOUS PHASE MASS-TRANSFER RESISTANCE [J].
FALES, JL ;
STROEVE, P .
JOURNAL OF MEMBRANE SCIENCE, 1984, 21 (01) :35-53
[8]  
HO S, 1982, AICHE J, V28, P662
[9]   ARTIFICIAL NEURAL NETWORK MODELS OF KNOWLEDGE REPRESENTATION IN CHEMICAL-ENGINEERING [J].
HOSKINS, JC ;
HIMMELBLAU, DM .
COMPUTERS & CHEMICAL ENGINEERING, 1988, 12 (9-10) :881-890
[10]   Vapour-liquid equilibrium data analysis for mixed solvent-electrolyte systems using neural network models [J].
Iliuta, MC ;
Iliuta, I ;
Larachi, F .
CHEMICAL ENGINEERING SCIENCE, 2000, 55 (15) :2813-2825