Nalazite se na CroRIS probnoj okolini. Ovdje evidentirani podaci neće biti pohranjeni u Informacijskom sustavu znanosti RH. Ako je ovo greška, CroRIS produkcijskoj okolini moguće je pristupi putem poveznice www.croris.hr
izvor podataka: crosbi !

Intelligent control of yeast cultivation (CROSBI ID 474318)

Prilog sa skupa u zborniku | sažetak izlaganja sa skupa | međunarodna recenzija

Beluhan, Damir ; Beluhan, Sunčica Intelligent control of yeast cultivation // BIOTECHNOLOGY 2000. The World Congress on Biotechnology Book of Abstract. Berlin, 2000. str. 224 - 225-x

Podaci o odgovornosti

Beluhan, Damir ; Beluhan, Sunčica

engleski

Intelligent control of yeast cultivation

The recognition of characteristic bioprocess metabolic phases by a neuro-fuzzy-expert system made direct inverse training of system outputs to inputs, by artificial neural networks (ANNs), easier and more realistic. An internal model control structure (IMC)1, further sophisticated this hybrid modeling approach that integrates all available a priori knowledge of the baker's yeast production process. The identification of four bioprocess physiological states (lag/log/O2-limitation/maturation) was based on the empirical operator's fuzzy reasoning (if-then rules), and membership functions automatically tuned by a supervised backpropagation learning procedure2. This neural-fuzzy classification model consisted of four applied linguistic rules on two state variables: oxygen uptake rate and liquid volume. For each phases separate inverse ANN models, trained by the off-line generalized learning approach3, were used for estimation of the current molasses feed rate FEED(t). This mapping was based on measurements of the 'desired' respiratory quotient, RQ(t+1), with sampling period of 1 minute, the past molasses feed rate, FEED(t-1,...t-T), the current and past oxygen uptake rate, OUR(t, t-1,...t-T), carbon dioxide evolution rate, CER(t, t-1,...t-T), concentration of ethanol, EtOH(t, t-1,...t-T) and volume, V(t, t-1,...t-T). The IMC structure was necessary because of inaccuracies of the inverse ANN model, unmeasured process environmental disturbances and internal instability4. Therefore, the negative feedback signal, a difference between the measured process output and noise free prediction estimated by a second feedforward ANN model (applied in parallel connection to a process), was superimposed to the input signal (the desired RQ at the next time step) of the driving inverse ANN controller. The applied neural networks had to capture information from patterns that exist over time and hence, static neural networks were extended to dynamic networks that have short-term memory structures, such as tapped delay line memory structure or a Gamma memory that provides a recursive memory of the input signals past. This hybrid control algorithm of fed-batch yeast cultivation process was successfully realized on a laboratory scale bioreactor under a commercial 'Simatic M7-400' control system.

nije evidentirano

nije evidentirano

nije evidentirano

nije evidentirano

nije evidentirano

nije evidentirano

nije evidentirano

Podaci o prilogu

224 - 225-x.

2000.

objavljeno

Podaci o matičnoj publikaciji

BIOTECHNOLOGY 2000. The World Congress on Biotechnology Book of Abstract

Berlin:

Podaci o skupu

Biotechnology 2000, the World Congress on Biotechnology

poster

03.09.2000-08.09.2000

Berlin, Njemačka

Povezanost rada

Elektrotehnika, Prehrambena tehnologija