Interactive Evolutionary Computation for Model based Optimization of Batch Fermentation

J. Madar, J. Abonyi, B. Balasko, and F. Szeifert (Hungary)


Model Based Optimization, Interactive Evolutionary Com puting, Beer Fermentation


At the optimization of temperature and feeding profiles of batch processes it is often desirable to consider sev eral objectives and constraints into the optimization prob lem. During the beer fermentation a temperature pro file is applied to drive the process so as to obey to cer tain constraints. The design of this temperature profile is an optimization problem where the objective is to mini mize the operation time and optimize the quality of the beer. Similarly to other practical problems, these objec tives and constraints are often non-commensurable and the objective functions are explicitly/mathematically not avail able. In this paper, Interactive Evolutionary Computa tion (IEC) is used to effectively handle such optimization problems. IEC is an evolutionary algorithm whose fitness function is provided by human users. The proposed ap proach has been implemented in MATLAB and applied to design temperature profile for beer fermentation pro cess. The results show that IEC is an efficient and com fortable method to incorporate the priori knowledge of the user into the model based optimization of batch processes. A detailed description of the proposed approach helps the construction of the algorithms; still easier, the developed EAsy-IEC Toolbox and the beer fermentation model writ ten can be downloaded from the website of the authors:

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