Modelling and adaptive control of fed‐batch penicillin fermentation

Montague, G.A.; Morris, A.J.; Wright, A.R.; Aynsley, M.; Ward, Alan C.

doi:10.1002/cjce.5450640407

Cited by 47 publications

(7 citation statements)

References 39 publications

(38 reference statements)

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“…A segregated model for the antibiotics production can be taken as a first approximation to the inhomogeneities in the population and the effects of morphological differentiation and aging of cells. The most developed models so far of Montague et al [39], Bajpai and Reuss [38,61] and Heijnen et al [41] showed no agreement with the experimental data under these conditions. The model presented here was aimed at the description of cukivations with a high-yielding strain ofPenicillium chrysogenum in a 20-1 reactor.…”

Section: A Segregated Model For Near-production Cultivations In the Pmentioning

confidence: 71%

“…Due to its simplicity, the model served as a basis for a number of further investigations, not only to improve the validity of the model, but also to use it for extensive process optimization studies. Montague et al [39] extended this model for carbon dioxide production and then used it for on-line state estimation and parameter-adaptive control purposes. A further extension and improved parameter estimation of the Baijpal-Reuss model was given by Nicolai et al [40], who also included some ideas of the balancing approach of Heijnen et al [41].…”

Section: Models For Penicillin Productionmentioning

confidence: 99%

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Proces models for production of β-lactam antibiotics

Bellgardt

1998

Relation Between Morphology and Process Performances

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Great progress has been made in the modelling of biotechnical processes using filamentous microorganisms. This paper deals with cultivations of Penicillium chrysogenum for the production of Penicillin and of Acrernonium chrysogenum for the production of Cephalosporin C. The properties of the processes and the existing models are reviewed. Models are presented for both processes that consider aspects which are important for industrial cultivation. The process model for Penicillin production is based on a detailed morphological description of growth of hyphal filaments and pellets. The model allows for simulation of the production process including the preculture and considering the inhomogenous pellet population. It opens new possibilities for understanding the complex kinetics of the process and improvement of its control. The structured segregated model for Cephalosporin C production considers soy oil as second carbon source besides sugar. The application of the model for dynamic optimization of feeding strategies by Iterative Dynamic Programming is demonstrated. As an alternative approach, modelling of the Cephalosporin production by an artificial neural network is discussed.

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Section: A Segregated Model For Near-production Cultivations In the Pmentioning

confidence: 71%

Section: Models For Penicillin Productionmentioning

confidence: 99%

Proces models for production of β-lactam antibiotics

Bellgardt

1998

Relation Between Morphology and Process Performances

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“…Many researchers (Montague et al 1986;Takamatsu et al 1985;Williams et al 1986) have reported various strategies to control glucose or ethanol concentrations in bioprocesses at low values. Aiba et al (1976) reported that the specific growth rate was maintained at a high value by controlling the respiration quotient, RQ, ranging from 1.0 to 1.2 throughout the cultivation.…”

Section: Discussionmentioning

confidence: 99%

Application of fuzzy reasoning to control of glucose and ethanol concentrations in baker's yeast culture

Park

Shi

Shiba

et al. 1993

Appl Microbiol Biotechnol

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Fuzzy reasoning was applied to control both ethanol and glucose concentrations in fed-batch cultures of baker's yeast. This fuzzy controller consisted of three membership functions (concentrations of dissolved oxygen (DO), ethanol and glucose) and 18 production rules. Fuzzy inference was carried out by IF {A is a and B is b . . . . }, THEN {C is c} from the on-line measured concentrations of DO, ethanol and glucose. When "medium" concentrations of ethanol and glucose in fedbatch culture of baker's yeast were set at 2 g/1 and 0.2 g/l, both ethanol and glucose concentrations were controlled at 2.67 + 0.35 g/l and 0.27 + 0.25 g/l, respectively, ethanol production was reduced from 26 g/1 to 3-4 g/l, cell yield increased from 0.38 to 0.53 g dry cell/g consumed glucose and ethanol yield decreased from 0.50 to 0.14g ethanol/g consumed glucose, respectively, as compared with those of the glucose only control at 0.2 g/1.

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“…Instead, they rely on historical data of previous production runs and experience of process experts. Finally, software sensors have been developed based on component balancing and predictive models, often in combination with Extended Kalman Filtering techniques (Acuña et al, 1994;Chattaway and Stephanopoulos, 1989;Liao, 1989;Montague et al, 1986;Pelletier et al, 1994;Shimizu and Takamatsu, 1989;Stephanopoulos and San, 1982;Swiniarski et al, 1982).…”

Section: Data Handling In Isa S88-based Mmi-systemsmentioning

confidence: 99%

Untitled

et al. 1997

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The production of biologicals is subject to strict governmental regulations. These are drawn up in current good manufacturing practices (cGMP), a.o. by the U.S. Food and Drug Administration. To implement cGMP in a production facility, plant automation becomes an essential tool. For this purpose Manufacturing Execution Systems (MES) have been developed that control all operations inside a production facility. The introduction of these recipe-driven control systems that follow ISA S88 standards for batch processes has made it possible to implement cGMP regulations in the control strategy of biological production processes. Next to this, an MES offers additional features such as stock management, planning and routing tools, process-dependent control, implementation of software sensors and predictive models, application of historical data and on-line statistical techniques for trend analysis and detection of instrumentation failures. This paper focuses on the development of new production strategies in which cGMP guidelines are an essential part.

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Modelling and adaptive control of fed‐batch penicillin fermentation

Cited by 47 publications

References 39 publications

Proces models for production of β-lactam antibiotics

Proces models for production of β-lactam antibiotics

Application of fuzzy reasoning to control of glucose and ethanol concentrations in baker's yeast culture

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