modeling and simulation of water softening by nanofiltration using artificial neural network

نویسندگان

mahmoud mousavi

akram avami

چکیده

an artificial neural network has been used to determine the volume flux and rejections of ca2+ , na+ and cl¯, as a function of transmembrane pressure and concentrations of ca2+, polyethyleneimine, and polyacrylic acid in water softening by nanofiltration process in presence of polyelectrolytes. the feed-forward multi-layer perceptron artificial neural network including an eight-neuron hidden layer has the least error in modeling this non-linear process. the overall agreement between the artificial neural network results and experimental data is very good for both the volume flux and rejections, because the maximum values of normalized bias and error are -0.01122 and 1.0737 respectively.

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عنوان ژورنال:
iranian journal of chemistry and chemical engineering (ijcce)

ناشر: iranian institute of research and development in chemical industries (irdci)-acecr

ISSN 1021-9986

دوره 25

شماره 4 2006

میزبانی شده توسط پلتفرم ابری doprax.com

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