Bioprocess Modelling and Control by Constraint-based Optimization Methods
An important part of the biotechnological industry focuses on the production of high added value products (e.g., pharmaceuticals, proteins, precursor molecules for other chemicals) by micro-organisms or cell cultures. In contrast with the chemical industry, advanced estimation and control of bioprocesses is not yet common practice. Control of chemical and biotechnological processes uses process measurements (product concentrations, biomass concentration, temperature, etc.) to adapt the process parameters to achieve higher productivities. Unfortunately, not all process variables can be accurately measured due to the lack of an (affordable) sensor. Model-based software is then capable of estimating the non-measured process variables based on available measurements.
In the last decade, new and more advanced systems biology models have been developed that allow a more detailed description of fermentations. Within this project, these models are exploited for the development of new estimation and control strategies to achieve improved fermentation control.
At first, the newly developed estimation and control methods result in prototype software applicable to different fermentations for which a model of the employed micro-organism is available. This prototype software is a versatile tool for future advanced control of bioprocesses. It provides information about essential measurements and assesses the feasibility of the production goals.
Secondly, the constructed software will be applied on a process where methane consuming bacteria produce lactic acid. Lactic acid is a precursor for poly-lactic acid, an economically important biopolymer with 5.1% world market share in biopolymers and a projected 10% annual growth rate.
Besides the clear potential of the envisioned control methods in the production of lactic acid, the prototype software offers a development platform to improve control of fermentations in any bioprocess. The more detailed process information, provided to the operators, allows them to observe deviations from normal operation earlier, and helps to reduce the number of failed batches. Another application is the control of bioreactors in continuous operation, which improves productivity compared to state-of-the-art operating modes.