# Steady-state Real-time Optimization Using Transient Measurements on an Experimental Rig

arXiv: https://arxiv.org/abs/2109.00795 · HTML: https://mlsanity.com/paper/2109.00795/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2021.

## Built on by (most cited first, 1 of 1)

- [Digital Twin Framework for Optimal and Autonomous Decision-Making in Cyber-Physical Systems: Enhancing Reliability and Adaptability in the Oil and Gas Industry](https://mlsanity.com/paper/2311.12755/) (2023)

## Abstract (arXiv)

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.