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arXiv · 2109.00795

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

J. Matias·J. P. C. Oliveira·G. A. C. Le Roux·J. Jaschke
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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.

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J. Matias, J. P. C. Oliveira, G. A. C. Le Roux, J. Jaschke. 2021-09-02. Steady-state Real-time Optimization Using Transient Measurements on an Experimental Rig. https://doi.org/10.1016/j.jprocont.2022.04.015

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