Modeling and Optimization of Process Engineering Problems Containing Black-box Systems and Noise

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ProQuest, 2008 - 293 pages
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Surrogate data-driven models can be alternatively generated, but many substitute models may need to be built, especially in the case of process synthesis problems. Although model reliability can be improved using additional information, resource constraints can limit the number of additional experiments allowed. Since it may not be possible to a priori estimate the problem cost in terms of the number of experiments required, there is a need for strategies targeted at the generation of sufficiently accurate surrogate models at low resource cost. The problem addressed in this work focuses on the development of model-based optimization algorithms targeted at obtaining the best solutions based on limited sampling. A centroid-based sampling algorithm for global modeling has also been developed to accelerate accurate global model generation and improve subsequent local optimization. The developed algorithms enable the superior local solutions of problems containing black-box models and noisy input-output data to be obtained when the problem contains both continuous and integer variables and is defined by an arbitrary convex feasible region. The proposed algorithms are applied to many numerical examples and industrial case studies to demonstrate the improved optima attained when surrogate models are built prior to optimization.

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