Modelling and Fuzzy-Decision-Making of Batch Cultivation of Saccharomyces cerevisiae using Different Mixing Systems

Bioreactors which energy in a liquid and gas form is imported are universal in regard to management and establishment a definite mass exchange as mixing. In the mechanical mixing of the environment, the gas reaches its highest dispersion in the liquid as a result of the turbulence received. Having enough gas content, this may cause a huge relative surface of the phases contact; thus allowing the development of cultivation environments having components that differ significantly in respect to density. These advantages of stirred tank bioreactors (have led to their broad usage. They are mostly used in the production of enzymes, amino acids, antibiotics, etc.1,2 However, for optimal cultivation performance, each specific system (culture + bioreactor) requires individual adjustment and optimisation of the cultivation conditions.3 In our study4, an alternative concept of the global models, namely functional state modelling, has been used. With this approach, the whole process is decomposed to functional states, each of which is described with a local model. The general disadvantage of this approach is that there are no clear criteria for understanding the phase of the process and the great number of coefficients in the model. Therefore, it is unsuitable for optimization and optimal control. Multiple objective optimisation is a natural extension of the traditional optimisation of a single objective function. On the one hand, if the multiple objective functions are commensurate, minimizing single objective function, it is possible to minimize all the criteria and the problem could be solved using traditional optimisation techniques. On the other hand, if the objective functions are incommensurate or competing, then the minimization of one objective function requires a compromise in another objective function. The competition between multiple objective functions is a key distinction between multiple objective optimisation and traditional single objective optimisation.5–7 A Pareto optimisation technique has been used in study8 to locate the optimal conditions for an integrated bioprocessing sequence. The benefits of first reducing the feasible space by development provide a smaller search area for the optimisation. Tonnon9 used an interactive procedure to solve multiple objective optimization problems (MOOP). A fuzzy sets theory (FST) has been used to model the engineer’s judgment on each objective function. The properties of the obtained compromise solution Modelling and Fuzzy-Decision-Making of Batch Cultivation of Saccharomyces cerevisiae using Different Mixing Systems