Abstract
Many decisions must be made when determining design and operation of a chromatographic process. Such decisions should optimize the performance, including product purity, recovery, productivity, and desorbent consumption. For this problem, model-based optimization has been shown to be a powerful approach, aided by recent developments in modeling of chromatographic processes. In this article, past studies on model-based optimization for single and multi-column chromatographic processes are reviewed. Focus is placed on applications of mathematical programming techniques. More challenging problems including online optimization and flowsheet design are also discussed.
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