Adaptive nonlinear predictive control and estimation of microaerobic processes

Abstract

Compared to other sectors, industrial biotechnology only employs a limited number of automatic control strategies. The applied control strategies are mainly recipe-based and often do not take directly the high variability and flexibility of biological systems into account. While this provides sharply defined conditions and quality by design guarantees, process are operated significantly below the optimum, with inefficient product yields and limited reproducibility. The information that is available online, the data from previous batches and the additional biological knowledge are not considered, usually because the integration of this information requires the use of control strategies with an adaptive character, which increases the the design complexity and the implementation cost.We present an approach which allows to optimize biotechnological production process and the corresponding growth model at the same time with reasonable computational cost. Employing a basal model with unknown parameters, the combination of moving horizon estimation and adaptive nonlinear model predictive control enables the identification of parameters for the whole growth range of the respective organism online. The adapted model is then used for prediction and control of the process, leading to an optimized process performance within one experiment. Such an adaptive strategy accelerates the design, characterization and optimization of new production processes. It furthermore reduces time and cost intensive traditional process development.