Prediction of the isotherms of human IgG adsorption on Ni(II)-IDA-PEVA membrane using artificial neural networks

The use of artificial neural networks (ANNs) to predict the adsorption isotherms of human immunoglobulin G on immobilized Ni(II) affinity hollow fiber membranes was studied. Neural networks were trained using the Levenberg–Marquardt algorithm combined with Bayesian regularization technique and exper...

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Bibliographic Details
Published in:Adsorption : journal of the International Adsorption Society 2014-11, Vol.20 (8), p.959-965
Main Authors: Schmitz, Jones Erni, Lazzarotto Bresolin, Igor Tadeu
Format: Article
Language:English
Subjects:
Adsorption
Affinity
Artificial neural networks
Chemistry
Chemistry and Materials Science
Engineering Thermodynamics
Heat and Mass Transfer
Human
Industrial Chemistry/Chemical Engineering
Interpolation
Isotherms
Membranes
Neural networks
Regularization
Surfaces and Interfaces
Thin Films
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Summary:The use of artificial neural networks (ANNs) to predict the adsorption isotherms of human immunoglobulin G on immobilized Ni(II) affinity hollow fiber membranes was studied. Neural networks were trained using the Levenberg–Marquardt algorithm combined with Bayesian regularization technique and experimental data from different temperatures. The resulting neural network demonstrated to be able to interpolate the behavior of the maximum adsorption capacity and equilibrium concentration in the temperature range (4, 37 °C) with correlation coefficients higher than 0.96. Results demonstrated to be very similar to those achieved with traditionally Langmuir model adjustment. The advantage of interpolation ability of ANNs was also showed.
ISSN:0929-5607
1572-8757
DOI:10.1007/s10450-014-9641-9