INTEREST OF NEURAL NETWORKS FOR THE OPTIMIZATION OF THE CROSS-FLOW FILTRATION PROCESS

被引：17

作者：

DORNIER, M ^{[1
]}

DECLOUX, M ^{[1
]}

TRYSTRAM, G ^{[1
]}

LEBERT, A ^{[1
]}

机构：

[1] INRA,RECH VIANDE STN,F-63122 CEYRAT,FRANCE

来源：

FOOD SCIENCE AND TECHNOLOGY-LEBENSMITTEL-WISSENSCHAFT & TECHNOLOGIE | 1995年 / 28卷 / 03期

关键词：

D O I：

10.1016/S0023-6438(95)94364-1

中图分类号：

TS2 [食品工业];

学科分类号：

0832 ;

摘要：

In order to build up a model representing the effect of transmembrane pressure and crossflow velocity on crossflow filtration results at quasi-steady state, an approach based on neural networks is proposed. For filtrations of various products (raw cane sugar remelt, natural gum solution) on different membranes (micro- and ultrafiltration) with or without co-current permeate flow, the modelling of both permeate flux and retention rate could be obtained after only five experimental trials. Compared to more classical modelling techniques, the neural networks were showed to be sometimes better suited and are useful when the effects of hydrodynamical conditions on filtration results are strongly nonlinear. Thanks to established models, it was possible to determine with a good safety margin, an optimum region in every case studied.

引用

页码：300 / 309

页数：10

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