Model Parameterization Tailored to Real-time Optimization

Challenges in real-time process optimization mainly arise from the inability to build and adapt accurate models for complex physico-chemical processes. This paper surveys different ways of using measurements to compensate for model uncertainty in the context of process optimization. A distinction is made between model-adaptation methods that use the measurements to update the parameters of the process model before repeating the optimization, modifier-adaptation methods that adapt constraint and gradient modifiers, and direct-input-adaptation methods that convert the optimization problem into a feedback control problem. This paper argues in favor of modifier-adaptation methods, since it uses a model parameterization, measurements, and an update criterion that are tailored to the tracking of the necessary conditions of optimality.