Hybrid process models for process optimisation, monitoring and control

Hybrid models aim to describe different components of a process in different ways. This makes sense when the corresponding knowledge to be represented is different as well. In this way, the most efficient representations can be chosen and, thus, the model performance can be increased significantly. From the various possible variants of hybrid model, three are selected which were applied for important biotechnical processes, two of them from existing production processes. The examples show that hybrid models are powerful tools for process optimisation, monitoring and control.

Model-based optimization of biosurfactant production in fed-batch culture Azotobacter vinelandii

Fed-batch cultivation of Azotobacter vinelandii 21was optimized for biosurfactant production. Optimization of feed-rate time profile and concentration of nutrient medium components in feeding solution is based on a hybrid mathematical model consisting of mass-balance equations for biomass, biosurfactant, volume of cultural liquid and substrate components: glucose, ammonia nitrogen and phosphate phosphorus. The rate of cultural liquid emulsification activity growth as well as the rates of ammonia nitrogen and phosphate phosphorus consumption is modelled by means of artificial neural network, while the rates of the other biochemical transformations are modelled by adequate kinetic relationships.

Model-based optimization of viral capsid protein production in fed-batch culture of recombinant Escherichia coli

An optimized fed-batch cultivation process for the production of the polyoma virus capsid protein VP1 in recombinant Escherichia coli BL21 bacteria is presented. The optimization procedure maximizing the amount of desired protein is based on a mathematical model. The model distinguishes an initial cell growth phase from a protein production phase initiated by inducer injection. A new approach to model the target protein formation rate was elaborated, where product formation is primarily dependent on the specific biomass growth rate. Lower growth rates led to higher specific protein concentrations. The model was identified from a series of fed-batch experiments designed for parameter identification purposes and possesses good prediction quality. Then the model was used to determine optimal open-loop control profiles by manipulating the substrate feed rates in both phases as well as the induction time. Feed-rate optimization has been solved using Pontryagin's maximum principle. The solution was validated experimentally. A significant improvement of the process performance index was achieved.