Systematic modeling for optimization
Optimization usually requires models, which are computationally speaking less expensive than models commonly used for simulations. At the same time, process optimization and model predictive control etc. require dependable accuracies in addition to the fastness. To demystify the art of preparing process models for optimization, a workflow is presented in this contribution, which systematically deduces models based on simplification of existing models and experiment based deduction of computationally inexpensive correlations.
Published in: Proceedings of the 8th International Conference on Foundations of Computer-Aided Process Design, 10.1016/B978-0-444-63433-7.50101-2, Elsevier