Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 363127
Title Control vector parameterization with sensitivity based refinement applied to baking optimization
Author(s) Hadiyanto, M.; Esveld, D.C.; Boom, R.M.; Straten, G. van; Boxtel, A.J.B. van
Event European congress of chemical engineering (ECCE-6), 2007-09-16/2007-09-20
Department(s) Systems and Control Group
Food Process Engineering
AFSG Food Quality
Publication type Contribution in proceedings
Publication year 2007
Abstract Abstract In bakery production product quality attributes as crispness, brownness, crumb and water content are developed by the transformations that occur during baking and which are initiated by heating. A quality driven procedure requires process optimization to improve bakery production and to find operational procedures for new products. Control vector parameterization (CVP) is an effective method for the optimization procedure. However, for accurate optimization with a large number of parameters (representing the control vector), CVP optimization takes a long time for computation. In this work, an improved method for direct dynamic optimization using CVP is presented. The method uses a sensitivity based step size refinement for the selection of control input parameters. The optimization starts with a coarse discretization level for the control input in time. In successive iterations the step size was refined for the parameters for which the performance index has a sensitivity value above a threshold value. With this selection, optimization is continued for a selected group of input parameters while the other non sensitive parameters (below threshold) are kept constant. Increasing the threshold value lowers the computation time, however the obtained performance index becomes less. A threshold value in the range of 10-20% of the mean sensitivity satisfies well. The method gives a better solution for a
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