Steady-state real-time optimization using transient measurements on an experimental rig

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. Additionally, we present some guidelines for ROpA's practical implementation and a theoretical analysis of ROpA's convergence properties.(C) 2022 Published by Elsevier Ltd.