Comparing the performance of multilayer perceptrons networks and neuro-fuzzy systems for on-line inference of Bacillus megaterium cellular concentrations.
Penicillin G acylase (PGA) is one of the most important enzymes for the pharmaceutical industry. Bacillus megaterium has the advantage of producing extra-cellular PGA. This work compares two neural networks (NNs) architectures for on-line inference of B. megaterium cell mass in an aerated stirred tank bioreactor, during the production of PGA. Nowadays, intelligent computing tools such as artificial NNs and fuzzy logic are commonly applied for state inference and modeling of bioreactors. Combining these two approaches in hybrid, neuro-fuzzy systems, may be advantageous. Our results indicate that a neuro-fuzzy inference system showed a better performance to infer cell concentrations, when compared to multilayer perceptrons networks.
Nucci
Edson R
ER
Departamento de Engenharia Química, Universidade Federal de São Carlos, PO Box 676, São Carlos, São Paulo 13565-905, Brazil.
Silva
Rosineide G
RG
Souza
Vanessa R
VR
Giordano
Raquel L C
RL
Giordano
Roberto C
RC
Cruz
Antonio J G
AJ
D001410:Bacillus megaterium / Q000166:cytology* / Q000201:enzymology*; D019149:Bioreactors; D015169:Colony Count, Microbial / Q000706:statistics & numerical data; D017143:Fuzzy Logic; D008954:Models, Biological; D016571:Neural Networks, Computer; D009862:Online Systems; D010399:Penicillin Amidase / Q000096:biosynthesis*; D049490:Systems Biology
D010399:Penicillin Amidase