  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.
