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.

An adaptive control algorithm for fed-batch culture.

The implementation of adaptive control for a fed-batch culture in order to maximize the output of product based on a self-adjusting model is discussed in the present work. Optimization methods were applied to the generalized mathematical model of a fed-batch fermentation process to determine control algorithms that could be used for on-line process control. The efficiency of the proposed adaptive algorithms was investigated by simulating a model system. The model of amylotytic enzyme fermentation that was proposed by the authors was taken from a real process. Dynamic modelling has shown that the main problem of realization is connected with the on-line identification of the adaptive model's parameters. To avoid this problem, we have introduced special limitations on the parameters' time variations that increased the convergence of the identification algorithm. The results of the investigation have shown the efficiency of the proposed adaptive algorithms, and the results of this work should be investigated for real process control.

Optimization of batch fermentation processes by graphical method.

A graphical method is proposed for batch fermentation process optimization. Its objective function is the maximum output of the product when the time of process is fixed or free. The technique of optimization is based on Bellman's dynamic programming concepts and the assumption that the process can be described by mathematical models of generalized structure. An example of the proposed technique's application is presented.