Software sensing for glucose concentration in industrial antibiotic fedbatch culture using fuzzy neural network View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2002-10

AUTHORS

Toshiaki Imanishi, Taizo Hanai, Ichiro Aoyagi, Jun Uemura, Katsuhiro Araki, Hiroshi Yoshimoto, Takeshi Harima, Hiroyuki Honda, Takeshi Kobayashi

ABSTRACT

In order to control glucose concentration during fed-batch culture for antibiotic production, we applied so called “software sensor” which estimates unmeasured variable of interest from measured process variables using software. All data for analysis were collected from industrial scale cultures in a pharmaceutical company. First, we constructed an estimation model for glucose feed rate to keep glucose concentration at target value. In actual fed-batch culture, glucose concentration, was kept at relatively high and measured once a day, and the glucose feed rate until the next measurement time was determined by an expert worker based on the actual consumption rate. Fuzzy neural network (FNN) was applied to construct the estimation model. From the simulation results using this model, the average error for glucose concentration was 0.88 g/L. The FNN model was also applied for a special culture to keep glucose concentration at low level. Selecting the optimal input variables, it was possible to simulate the culture with a low glucose concentration from the data sets of relatively high glucose concentration. Next, a simulation model to estimate time course of glucose concentration during one day was constructed using the on-line measurable process variables, since glucose concentration was only measured off-line once a day. Here, the recursive fuzzy neural network (RFNN) was applied for the simulation model. As the result of the simulation, average error of RFNN model was 0.91 g/L and this model was found to be useful to supervise the fed-batch culture. More... »

PAGES

275-280

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/bf02932836

DOI

http://dx.doi.org/10.1007/bf02932836

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1039220275


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