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A hybrid simulator for improved filtering of noise from oscillating microbial fermentations

Paper ID Volume ID Publish Year Pages File Format Full-Text
4640 235 2008 8 PDF Available
Title
A hybrid simulator for improved filtering of noise from oscillating microbial fermentations
Abstract

Many microbial processes exhibit sustained oscillations under practical conditions. Often these oscillations are corrupted by noise from the environment. It is then important to filter out the noise and recover ‘true’ oscillations for subsequent studies. Previous studies have used algorithmic or neural or hybrid filters in conjunction with mathematical equations for the kinetics. However, this approach is limited because in real situations it is difficult to measure and model some of the variables. Therefore, a hybrid neural simulator (HNS) has been developed here and tested with the continuous fermentation with Saccharomyces cerevisiae. The HNS combines a hybrid neural filter (HNF) for the noise, a hybrid description of the fermentation kinetics and macroscopic balance equations for the bioreactor. The HNS achieved 96% recovery of noise-free oscillations, compared to 91% with an HNF and lower efficiencies with pure neural and algorithmic filters. The commonly employed extended Kalman filter was ineffective as a stand-alone device but contributed to good filtering by the HNF and the HNS, thus indicating that a proper distribution of variables between the mathematical and neural components can significantly improve the performances of both.

Keywords
Saccharomyces cerevisiae; Continuous fermentation; Sustained oscillations; Noise inflow; Hybrid neural simulator
First Page Preview
A hybrid simulator for improved filtering of noise from oscillating microbial fermentations
Publisher
Database: Elsevier - ScienceDirect
Journal: Biochemical Engineering Journal - Volume 39, Issue 2, 15 April 2008, Pages 389–396
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering