A sequential state and parameter identification technique is applied to batch beverage fermentation. The algorithm uses an extended Kalman filter (EKF) for state estimation and a recursive prediction error method (RPEM) for model parameter identification. This adaptive algorithm is tested using both simulated and real process data. Results show that, even with imperfect models, both states and parameters are estimated well enough to reliably track the dynamics of the true system.

A new beer fermentation model is developed based upon fundamental knowledge of biochemical pathways. The model can be subdivided into a growth model, an amino acid model, and a flavour/aroma model. Experimentation allowed for accurate model parameter identification. The results demonstrate the capability to accurately describe batch beer fermentation dynamics.

For systems such as jacketed vessels with significant dead time, cooling jacket dynamics and unpredictable but measurable load disturbances, advanced control concepts need to be used in order to achieve quality regulatory control. In the current work, optimal regulatory control theory has been applied to this problem. An optimal control algorithm is used to effectively control a batch fermentation reactor. The algorithm is easily implementable and very effective. It is capable of responding to both measurable and unmeasurable process disturbances.

Optimal control theory was applied to the process of batch beer fermentation. The performance functional considered was a weighted sum of maximum ethanol production and minimum time. Calculations were based on the model of Engasser et al. modified to include temperature effects. Model parameters were determined from isothermal batch fermentations. The fermentor cooling duty was the single available control. Temperature state variable constraints as well as control variable constraints were considered. The optimal control law is shown to be bang‐bang control with the existence of a singular arc corresponding to isothermal operation at the maximum temperature constraint. An iterative algorithm is presented for computing appropriate switching times using a penalty‐function‐augmented performance functional.