title: 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.
authors.affiliation: Departamento de Engenharia Química, Universidade Federal de S. Carlos, São Carlos, SP, Brazil. roberto@ufscar.br
authors.forename: Roberto C
authors.initials: RC
authors.lastname: Giordano
authors.forename: João R
authors.initials: JR
authors.lastname: Bertini
authors.forename: Maria C
authors.initials: MC
authors.lastname: Nicoletti
authors.forename: Raquel L C
authors.initials: RL
authors.lastname: Giordano
mesh_terms: D000465:Algorithms; D001410:Bacillus megaterium / Q000378:metabolism*; D019149:Bioreactors / Q000382:microbiology*; D002245:Carbon Dioxide / Q000302:isolation & purification* / Q000378:metabolism*; D017022:Flow Injection Analysis / Q000379:methods*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D012815:Signal Processing, Computer-Assisted