Multi-Mode Model Predictive Control and Estimation for Uncertain Biotechnological Processes

Abstract

Biotechnological processes are urged to become more flexible and sustainable in order to meet the challenges of modern society. Using renewable feed stocks for the production of value added products is one strategy to achieve sustainability. The challenges arising from the use of renewables are large compared to the commonly used resources. The concentration of nutrients in renewable feed stocks varies between batches and is not optimal in respect to the demands of the cells. The biological system adapts to this situation by changing the metabolic growth modes in dependence of the availability of nutrients in the media. Consequently, the process can run through multiple modes. Each switch of the mode results in a change of the system dynamics which effects the process performance. To overcome the challenges, we propose a model predictive control approach combined with a moving horizon estimator that takes directly the multi-mode nature of the process into account. It ensures optimal performance while guaranteeing that the constraints are met in each phase of the process. The approach is motivated by and applied to a sustainable biopolymer production from juice waste.