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A comparative study on random search algorithms for biotechnical process optimization
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DOI：10.1016/S0168-1656(96)01649-5.png)
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Abstract
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A comparative study of some random search optimization schemes that can be used to determine sub-optimal open-loop control profiles from given biotechnical process models is presented and compared with the classical optimization approach based on Pontryagin's maximum principle that has often been used in literature. The main objective of this study is to show that the much simpler to use random search procedures lead to essentially the same success as the classical procedure. Surprisingly little effort is required to use these random search techniques in order to determine feed rate, feed concentration, temperature or precursors profiles. Hence, using these techniques, bioengineers can focus their attention much more on creating and improving the underlying process models instead of tuning complicated optimization procedures. Three different techniques are described in derail and their application is illustrated with standard examples from literature. (C) 1997 Elsevier Science B.V.
Keywords:
bioprocess optimization
open loop control
random search techniques
artificial neural networks
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