Real-time optimization with persistent parameter adaptation (ROPA) is an RTO approach, where the steady-state model parameters are updated dynamically using transient measurements. Consequently, we avoid waiting for a steady-state before triggering the optimization cycle, and the steady-state economic optimization can be scheduled at any desired rate. The steady-state wait has been recognized as a fundamental limitation of the traditional RTO approach. In this paper, we implement ROPA on an experimental rig that emulates a subsea oil well network. For comparison, we also implement traditional and dynamic RTO. The experimental results confirm the in-silico findings that ROPA's performance is similar to dynamic RTO's performance with a much lower computational cost. Finally, we present some guidelines for ROPA's practical implementation.
Cite
@article{arxiv.2109.00795,
title = {Steady-state Real-time Optimization Using Transient Measurements on an Experimental Rig},
author = {J. Matias and J. P. C. Oliveira and G. A. C. Le Roux and J. Jaschke},
journal= {arXiv preprint arXiv:2109.00795},
year = {2022}
}