Hybrid neural modeling of bioprocesses using functional link networks

被引：15

作者：

Harada, LHP ^{[1
]}

Da Costa, AC ^{[1
]}

Maciel, R ^{[1
]}

机构：

[1] Univ Estadual Campinas, FEQ, DPQ, BR-13081970 Campinas, SP, Brazil

来源：

APPLIED BIOCHEMISTRY AND BIOTECHNOLOGY | 2002年 / 98卷 / 1-9期

关键词：

extractive alcoholic fermentation; functional link networks; hybrid model; bioprocess; modeling;

D O I：

10.1385/ABAB:98-100:1-9:1009

中图分类号：

Q5 [生物化学]; Q7 [分子生物学];

学科分类号：

071010 ; 081704 ;

摘要：

The objective of this work was to develop a model for an extractive ethanol fermentation in a simple and rapid way. This model must be sufficiently reliable to be used for posterior optimization and control studies. A hybrid neural model was developed, combining mass and energy balances with neural networks, which describe the process kinetics. To determine the best model, two structures of neural networks were compared: the functional link networks and the feedforward neural networks. The two structures are shown to describe well the process kinetics, and the advantages of using the functional link networks are discussed.

引用

页码：1009 / 1023

页数：15

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