Systematic approaches for model derivation for optimization purposes
One of the main problems in process optimization lies in the non-linearity, non-convexity, and sheer size of existing process models. In this contribution, a systematic workflow for process systems engineers developing models suitable for optimization purposes is presented. Hereby, three fundamentally different cases are discussed: the availability of a complex, highly accurate model; the existence of a simplifying, the so-called short-cut model; and the non-existence of a model of any kind. Furthermore, a focus is lain on the systematic model reduction for complex systems by means of linearization and convexification. Afterwards, two case studies are presented showing how this workflow can be applied to a reactive absorption system and to a multi-phase separation process. The presented systematic leads to a successful implementation of process models applicable for optimization, which are both reduced in size, non-linearity, and non-convexity.
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Published in: Chemical Engineering Science, 10.1016/j.ces.2013.11.041, Elsevier