This is the institutional Repository of the Helmholtz Centre for Infection Research in Braunschweig/Germany (HZI), the Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), Saarbrücken/Germany, the TWINCORE Zentrum für Exprerimentelle und Klinische Infektionsforschung, Hannover/Germany,Helmholtz-Institut für RNA-basierte Infektionsforschung (HIRI), Würzburg/Germany, Braunschweig Integrated Centre for Systems biology (BRICS), Centre for Structural Systems Biology (CSSB) the Study Centre Hannover, Hannover/Germany and the Centre for Individualised Infection Medicine (CiiM).



    Reuß, M.; Jefferis, Raymond P.; Lehmann, J.; Gesellschaft für Biotechnologische Forschung (GBF) (1976)
    The application of the method of quasilinearization to fermentation modelling is discussed. The text includes a tutorial explanation of the method. Examples include parameter identification in a growth model and both parameter and state estimation in a column reactor. The latter example was performed by an on-line system of coupled process and time-sharing computers.

    Yoshida, T.; Taguchi, H.; Department of Fermentation Technology Faculty of Engineering, Osaka University Yamada-kami, Suita-shi, Osaka 565, Japan (1976)
    Recently there has been a strong interest in the direct digital control of fermentation processes. More effort on identification or modeling of the processes is indispensable to accomplish this highly sophisticated control. This paper was written to present a fundamental view on model construction, process identification, parameter estimation, analysis of model and examples of uses of model. Models in the papers surveyed in the last five years are presented classifying them into three categories: subculture, subcellular and submolecular models. Assessment of model by means of sensitivity analysis and several approaches to modify models for process control are discussed.

    Blachère, Henri T.; Peringer, Paul; Corrieu, Georges V.; Station de Génie Microbiologique, 7 rue Sully, Dijon,France (1976)
    Techniques for estimation of investment and manufacturing costs in fermentation are discussed. A method for optimization of fermentation Plants as whole is described. 5 It necessitates knowledge of a model of the biosynthesis and equations correlating investment and manufacturing costs to the size of equipment. The Minimization of the non-linear objective function in a non-convex field is obtained by a two step method. Optimization in fermentation industry may have numerous aspects. One may envision improving the growth rate, the production rate or the yield in compound biosynthesis. In this case, the main control parameters are : substrate concentrations, temperature, pH, aeration and agitation etc... Since a mathematical model of microbial cell-growth has been formulated by Monod (1), the kinetic pattern of various fermentation systems has been intensively studied. The number of publications in this field is strongly increasing (2,3,4,5,6). The problem concerning the reduction of manufacturing costs such as those of steam, energy of agitation and aeration, consumption of air, water and raw materials belong to another category of optimization. Okabe and Aiba have recently presented solutions to these problems (7,8,9,10,11). The purpose of this paper concerns the approach of a global optimization of a new plan supposing that the annual production of a fermentation product is given, then the optimization consists in determining the size of the fermentor, power of agitation and aeration system, size of heat exchangers, pumps, filters, centrifuges etc... the required condition for temperature, pH, dissolved oxygen, substrate concentration etc... so that the investment capital and annual expenditure for the production be minimized. Since the same control variables and equipment are used in most fermentation industries, we consider that the method proposed, as regards yeast production, will also apply to various other production.

    Jefferis, Raymond P.; Winter, H.; Vogelmann, H.; Gesellschaft für Biotechnologische Forschung (GBF) (1976)
    The application of digital filtering techniques to the data from automatic fermentation process analyzers is discussed. An example illustrates the application of these methods to cell density and growth rate (productivity) estimation from periodic measurements of fermentation broth turbidity. The method of recursive least squares filtering was found best for the cell density estimates. A hybrid technique which combines this method with a model for culture oxygen uptake rate was found to have more rapid response for the cell productivity estimation. The results of on-line use of these digital filtering techniques are included.

    Ribot, D.; Laboratoire d'Automatique et d'Analyse des Systémes du C.N.R.S. Institut National des Sciences Appliquées - TOULOUSE - France (1976)
    The measure of the state variables of a fermentation (biomass and substrate concentration) is always liable to error. It is possible to reconstitute well enough the rates of their variations during a transient state, by a polynomial smoothing. By calculation of the derivative polynomial, the values of the growth rate HM and the global conversion rate R. at each moment can be deduced. The fermentation parameters are then identified through 2 linear smoothings. The method is tested with data obtained by simulation and given purposely noisy (known parameters). It is applied in order to determine the growth parameters of a continuous fermentation. The advantage of this direct method upon the usual sequential techniques is its rapidity and its small occupation of the central memory of the computer. It can be applied to continuous or discontinuous fermentation.

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