Online filtering of CO2 signals from a bioreactor gas outflow using a committee of constructive neural networks.

Abstract:

:This work proposes a committee of Cascade Correlation neural networks as an online smoother for random measurement noise. The goals of this paper are twofold: first it intends to explore the possibilities of using a constructive neural network algorithm to learn how to filter typical noisy data from a bioreactor, CO(2) mol fractions of the effluent gas during the aerobic cultivation of Bacillus megaterium to produce the enzyme penicillin G acylase. Second, to propose a committee of such networks for achieving more realistic results, capturing the inherent trend of the process. In order to do that this paper discusses the advantages of using a constructive neural network algorithm, describes how the committee of NNs operates and evaluates its performance using real CO(2) online data obtained in laboratorial experiments. The paper also presents results obtained with classical filtering algorithms, for comparison.

journal_name

Bioprocess Biosyst Eng

authors

Giordano RC,Bertini JR Jr,Nicoletti MC,Giordano RL

doi

10.1007/s00449-007-0152-x

subject

Has Abstract

pub_date

2008-02-01 00:00:00

pages

101-9

issue

2

eissn

1615-7591

issn

1615-7605

journal_volume

31

pub_type

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