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

TL;DR

This paper demonstrates that real-time optimization with persistent parameter adaptation (ROPA) can effectively replace traditional RTO by using transient measurements, enabling faster optimization cycles with lower computational costs on an experimental rig.

Contribution

The paper implements ROPA on an experimental rig, compares it with traditional and dynamic RTO, and provides practical guidelines for its implementation.

Findings

ROPA achieves similar performance to dynamic RTO.

ROPA has significantly lower computational cost.

Experimental validation confirms in-silico results.

Abstract

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.