Robust adaptive control of yeast fed-batch cultures

Model-based control of bioprocesses is a difficult task due to the challenges associated with biological system modeling and the lack of on-line measurements. In this study, two robust controllers using minimal a priori process knowledge and minimal measurement information are designed to maximize biomass productivity in aerobic cultures of Saccharomyces cerevisiae. This latter objective can be achieved through the regulation of the ethanol concentration at a low constant value. The linearization of Sonnleitner’s model allows simple transfer function models to be derived, which describe the relation between the ethanol concentration, the substrate feed and an exponential disturbance – image of the substrate demand for cell growth – in the different operating (respirative and respiro-fermentative) regimes. The two controllers are based on these linear models and use a RST structure, but differ in the way the exponential growth disturbance is handled. In the first controller, the disturbance is represented by a linear model, whereas in the second controller, the disturbance is measured on-line via the oxygen transfer rate signal and a feedforward control action is used to cancel the disturbance effect on the ethanol concentration. Particular attention is paid to the robustification of the controllers to measurement noise, neglected high frequency dynamics and uncertain stoichiometry coefficients using the observer polynomial. Tests in simulation show the controller performance.