Steady-state real-time optimization using transient measurements on an experimental rig. (July 2022) - Matias, José; Oliveira, Julio P.C.; Le Roux, Galo A.C.; Jäschke, Johannes
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. Additionally, we present some guidelines for ropa 's practical implementation and a theoretical analysis of ropa 's convergence properties. Graphical abstract: Highlights: rto with persistent parameter adaptation (ropa ) is implemented on lab scale plant. Steady-state and dynamic rto are also implemented for comparison. ropa avoided ss detection and showed economic performance comparable to drto . Guidelines for the practical implementation of ropa . An analysis of ropa convergence to the steady-state optimum is carried out.