Online model maintenance in real-time optimization methods. (February 2021)

• Record Type: Journal Article
• Authors: Matias, José; Jäschke, Johannes
• Is Part Of: Computers & chemical engineering. Volume 145(2021)
• Publication Date: 2021-02
• Subjects: Model-based optimization -- Modifier adaptation -- Model-plant mismatch -- Model correction -- Symbolic regression; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2020.107141
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: An online method for identifying the model structure and estimating its parameters. The method is developed for Real-time Optimization (RTO) implementations. Plant measurements and gradient are used for updating the model structure. Problem is presented in a superstructure framework and solved using MINLP solvers. A gas lift oil well network case study. Abstract: The performance of model-based optimization methods, like Real-time Optimization (RTO), relies on the model accuracy and adequacy. However, features of the process may be unknown and/or the system behavior can drastically change with time (e.g. system degradation). Therefore, even if we have a perfect model in the beginning, we may end up making decisions based on a poor model. This paper proposes a method that adapts the model structure online, based on an available model component set, while simultaneously estimating the model parameters. The problem is presented in a superstructure framework and solved using a mixed-integer nonlinear formulation. Then, the updated model is combined with Output Modifier Adaptation, an RTO variant for economic optimization. Our method is tested in case studies considering a continuous stirred-tank reactor and a gas lifted oil well network. The results show that we can select the correct model structure, update its parameters, and simultaneously converge to the plant optimum.