Multirate state and parameter estimation in an antibiotic fermentation with delayed measurements

This article discusses issues related to estimation and monitoring of fermentation processes that exhibit endogenous metabolism and time‐varying maintenance activity. Such culture‐related activities hamper the use of traditional, software sensor‐based algorithms, such as the extended kalman filter (EKF). In the approach presented here, the individual effects of the endogenous decay and the true maintenance processes have been lumped to represent a modified maintenance coefficient, mc. Model equations that relate measurable process outputs, such as the carbon dioxide evolution rate (CER) and biomass, to the observable process parameters (such as net specific growth rate and the modified maintenance coefficient) are proposed. These model equations are used in an estimator that can formally accommodate delayed, infrequent measurements of the culture states (such as the biomass) as well as frequent, culture‐related secondary measurements (such as the CER). The resulting multirate software sensor‐based estimation strategy is used to monitor biomass profiles as well as profiles of critical fermentation parameters, such as the specific growth for a fed‐batch fermentation of Streptomyces clavuligerus. © 1994 John Wiley & Sons, Inc.

[1]  S. Pirt.  The maintenance energy of bacteria in growing cultures , 1965, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[2]  S. Pirt,et al.  Effect of Growth Rate on the Synthesis of Penicillin by Penicillium chrysogenum in Batch and Chemostat Cultures. , 1967, Applied microbiology.

[3]  D. Southgate.  Determination of food carbohydrates , 1976 .

[4]  J. Sedmak,et al.  A rapid, sensitive, and versatile assay for protein using Coomassie brilliant blue G250. , 1977, Analytical biochemistry.

[5]  A. Demain,et al.  Carbon Catabolite Regulation of Cephalosporin Production in Streptomyces clavuligerus , 1978, Antimicrobial Agents and Chemotherapy.

[6]  Gene F. Franklin,et al.  Digital control of dynamic systems , 1980 .

[7]  N. W. F. Kossen,et al.  Fed‐batch culture: Modeling and application in the study of microbiol energetics , 1981 .

[8]  C L Cooney,et al.  Growth monitoring and control through computer‐aided on‐line mass balancing in a fed‐batch penicillin fermentation , 1983, Biotechnology and bioengineering.

[9]  D. Glasson,et al.  Development and applications of multirate digital control , 1983, IEEE Control Systems Magazine.

[10]  J. A. Roels,et al.  Energetics and Kinetics in Biotechnology , 1983 .

[11]  G Stephanopoulos,et al.  Studies on on‐line bioreactor identification. I. Theory , 1984, Biotechnology and bioengineering.

[12]  Aarne Halme,et al.  A Method to Consider Delayed Laboratory Analysis in State and Parameter Estimation of Bioreactors , 1985 .

[13]  Costas J. Spanos,et al.  Advanced process control , 1989 .

[14]  D. Dochain,et al.  On-Line Estimation and Adaptive Control of Bioreactors , 2013 .

[15]  Nina F. Thornhill,et al.  A Method of using off-line Measurements in an On-line Estimator of Biomass Concentration for a Penicillin Fermentation, and its Effects on the Quality of the Estimates , 1992 .

[16]  L. Lynd,et al.  Bioenergetics and end‐product regulation of Clostridium thermosaccharolyticum in response to nutrient limitation , 1993, Biotechnology and bioengineering.