Online model maintenance in real-time optimization methods

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. (C) 2020 Published by Elsevier Ltd.

Automated summary

This paper introduces a new method that can adapt the model structure online, estimate parameters, and optimize economically in the face of uncertain system changes.