Academic literature on the topic 'Fed-batch'

Two series of batch and fed-batch fermentations were carried out using S.cerevisiae in a semi-defined medium containing 200 gl-1 glucose as limiting substrate. Growth rates were calculated and the data used to test the applicability of eight empirical kinetic models. The form proposed by Levenspiel, combining the concept of a limiting ethanol concentration with a power-law form, gave the best results with these data. Glucose concentration was found to have a far smaller, though not negligible, effect on growth rate under these conditions. It was also observed that in fed-batch fermentations the total substrate uptake rate of the broth became constant soon after commencement of feeding, without cessation of growth. It is suggested that ethanol inhibits the synthesis of a rate-controlling enzyme in the glycolyti·c chain, but no previous work could be found to support or refute this explanation. A quasi-mechanistic model of growth under the condition of constant substrate consumption rate is formulated and discussed.

Recently, iterative learning control (ILC) has been used in the run-to-run control of batch processes to directly update the control trajectory. The basic idea of ILC is to update the control trajectory for a new batch run using the information from previous batch runs so that the output trajectory converges asymptotically to the desired reference trajectory. The control policy updating is calculated using linearised models around the nominal reference process input and output trajectories. The linearised models are typically identified using multiple linear regression (MLR), partial least squares (PLS) regression, or principal component regression (PCR). ILC has been shown to be a promising method to address model-plant mismatches and unknown disturbances. This work presents several improvements of batch to batch ILC strategy with applications to a simulated fed-batch fermentation process. In order to enhance the reliability of ILC, model prediction confidence is incorporated in the ILC optimization objective function. As a result of the incorporation, wide model prediction confidence bounds are penalized in order to avoid unreliable control policy updating. This method has been proven to be very effective for selected model prediction confidence bounds penalty factors. In the attempt to further improve the performance of ILC, averaged reference trajectories and sliding window techniques were introduced. To reduce the influence of measurement noise, control policy is updated on the average input and output trajectories of the past a few batches instead of just the immediate previous batch. The linearised models are re-identified using a sliding window of past batches in that the earliest batch is removed with the newest batch added to the model identification data set. The effects of various parameters were investigated for MLR, PCR and PLS method. The technique significantly improves the control performance. In model based ILC the weighting matrices, Q and R, in the objective function have a significant impact on the control performance. Therefore, in the quest to exploit the potential of objective function, adaptive weighting parameters were attempted to study the performance of batch to batch ILC with updated models. Significant improvements in the stability of the performance for all the three methods were noticed. All the three techniques suggested have established improvements either in stability, reliability and/or convergence speed. To further investigate the versatility of ILC, the above mentioned techniques were combined and the results are discussed in this thesis.

Optimisation of a fed-batch fermentation process typically uses the calculus of variations or Pontryagin's maximum principle to determine an optimal feed rate profile. This often results in a singular control problem and an open loop control structure. The singular feed rate is the optimal feed rate during the singular control period and is used to control the substrate concentration in the fermenter at an optimal level. This approach is supported by biological knowledge that biochemical reaction rates are controlled by the environmental conditions in the fermenter; in this case, the substrate concentration. Since an accurate neural net-based on-line estimation of the substrate concentration has recently become available and is currently employed in industry, we are therefore able to propose a method which makes use of this estimation. The proposed method divides the optimisation problem into two parts. First, an optimal substrate concentration profile which governs the biochemical reactions in the fermentation process is determined. Then a controller is designed to track the obtained optimal profile. Since the proposed method determines the optimal substrate concentration profile, the singular control problem is therefore avoided because the substrate concentration appears nonlinearly in the system equations. Also, the process is then operated in closed loop control of the substrate concentration. The proposed method is then called "closed loop optimal control". The proposed closed loop optimal control method is then compared with the open loop optimal feed rate profile method. The comparison simulations from both primary and secondary metabolite production processes show that both methods give similar performance in a case of perfect model while the closed loop optimal control provides better performance than the open loop method in a case of plant/model mismatch. The better performance of the closed loop optimal control is due to an ability to compensate for the modelling errors using feedback.

Le vin est un milieu peu propice à la croissance de la levure mais il est néanmoins possible de la faire croître sur base de vin enrichit en nutriments et dilué pour diminuer la concentration en éthanol. En vue de l’élaboration des vins effervescents par une seconde fermentation, produire la levure Saccharomyces cerevisiae dans ces conditions est indispensable pour l’acclimater mais il s’agit d’un enjeu complexe qui doit prendre en compte de nombreux paramètres physico-chimiques mais aussi économiques. En effet, les paramètres opératoires peuvent induire des conditions de croissance pouvant affecter le développement de la levure. Seule la levure S. cerevisiae (Fizz+) a été utilisée car elle est spécialement sélectionnée pour cette seconde fermentation en vase clos. Le principal enjeu était donc d’obtenir une bonne adaptation de la levure à croître dans un milieu hydro-alcoolique, conditions contraignantes pour elle, mais aussi d’obtenir une production maximale.Nous avons tout d’abord étudié en fioles Erlenmeyer (250 mL) l’influence de divers paramètres : conditions physico-chimiques, concentrations en nutriments, concentration minimale en levure sèche active nécessaire à une bonne activité ainsi que son temps de réhydratation.Dans un deuxième temps, nous avons effectué des propagations en mode batch dans un bioréacteur (5 L) pour valider les conclusions réalisées à la suite de l’étude en Erlenmeyer et ainsi étudier l’influence de différentes aérations sur la production de S. cerevisiae. Les données obtenues ont servi de base pour comparer les améliorations apportées par le procédé développé en mode fed-batch. Les concentrations en levures obtenues suite à l’optimisation des conditions du milieu de culture en cinq litres sont supérieures d’un facteur cinq à celles obtenues dans la pratique en cave.Ensuite l’étude s’est concentrée sur le développement d’un nouveau procédé d’alimentation en nutriments pour cultiver S. cerevisiae en métabolisme respiratoire dans des cuves réalisées par la société partenaire du projet, OEno Concept. La nouveauté réside dans la façon de réguler la température de la culture qui se fait simultanément à l’apport des nutriments suite au dégagement de chaleur lors de la croissance de S. cerevisiae. Un brevet a été déposé sur cette technologie. Ce nouveau procédé a permis une augmentation de la productivité cellulaire, d’un facteur supérieur à quatre, car il a permis aux levures de s’adapter à cet environnement stressant et a favorisé l’oxydation du glucose au détriment de la fermentation<br>Wine is an aggressive/stressful growth medium; it is depleted of micronutrients, rich in ethanol and very poor in assimilable nitrogen. Despite all these difficulties, it is possible to grow yeast in a medium largely based on wine by diluting the ethanol concentration and enriching the medium with micronutrients, a carbon source and assimilable nitrogen. It is, desirable to propagate Saccharomyces cerevisiae in such environment in order to produce a culture of yeast adapted to a second fermentation of alcoholic beverages. Production of microorganism in wine growing environment, is a complex issue that must take into account many, physicochemical and economic parameters. Indeed, the operating parameters can affect the development of yeast in a bioreactor. Therefore, it is important to know the most influential parameters on growth. The strain S. cerevisiae (Fizz+), a commercial strain that has been selected for the second fermentation in bottles, was used during this project. The propagation process served to increase the amount of yeast as well as to adapt the yeast to grow in an alcoholic environment. We first studied in shake-flasks cultures various physicochemical conditions such as nutrients concentration, the rehydration time and the minimum concentration of active dry yeast necessary for good yeast activity.In a second step, we performed batch fermentations in bioreactors (5 L) to confirm the conclusions from the shake-flask cultures and additionally to study the influence of aeration on S. cerevisiae production. The data obtained served as a basis for performing fed-batch cultures. The yeast concentrations obtained as a result of the optimization of the conditions of the culture medium in five liters were five times greater than those obtained in actual industrial production processes. The next step was to develop an automated fed-batch culture to grow S. cerevisiae respiratively in partnership with the industrial partner of the project, OEno Concept. The novelty of the process is the way in which the growth medium feed-rate is linked to the heat produced by the growing S. cerevisiae.This research has allowed an increase in cell productivity, by a factor greater than four, thanks to the novel process in stressful growth environment promoting respiration with regard to fermentation