Model-based automation of baker's yeast production.

An on-line model, estimating key state variables in bioprocesses, is utilized for control of fed-batch baker's yeast production. The state estimates are produced by balances and phenomenological expressions combined with on-line measurements. The goal of the control strategy is to maintain the highest possible glucose flux that can be entirely respiratively assimilated by the cells, resulting in the highest possible yeast growth without formation of metabolic products, such as acetic acid and ethanol. Stepwise improvement of the control algorithm is carried out in order to find a strategy to avoid undesired, irreversible metabolic pathways. In the final algorithm, such undesired changes in metabolism are predicted from an estimate of intracellular storage carbohydrates. A considerable decrease in the estimate indicates future metabolic changes at a time early enough to avoid them. At maximal yield, a growth rate near the highest possible is obtained in laboratory-scale Saccharomyces cerevisiae cultivations with the control strategy developed.

Adaptive on-line model for aerobic Saccharomyces cerevisiae fermentation.

In order to study and control fermentation processes, indirect on-tine measurements and mathematical models can be used. In this article we present a mathematical on-line model for fermentation processes. The model is based on atom and partial mass balances as well as on equations describing the acid-base system. The model is brought into an adaptive form by including transport equations for mass transfer and unstructured expressions for the fermentation kinetics. The state of the process, i.e., the concentrations of biomass, substrate, and products, can be estimated on-line using the balance part of the model completed with measurement equations for the input and output flows of the process. Adaptivity is realized by means of on-line estimation of parameters in the transport and kinetic expressions using recursive regression analysis. These expressions can thus be used in the model as valid equations enabling prediction of the process. This makes model-based automation of the process and testing of the validity of the measurement variables possible. The model and the on-line principles are applied to a 3.5-L laboratory tormentor in which Saccharomyces cerevisiae is cultivated. The experimental results show that the model-based estimation of the state and the predictions of the process correlate closely with high-performance liquid chromatography (HPLC) analyses. (c) 1995 John Wiley & Sons, Inc.