基于瞬态测量的稳态实时优化设计
Steady-state Real-time Optimization Using Transient Measurements on an Experimental Rig
摘要：
具有持续参数自适应的实时优化（ROPA）是一种RTO方法，其中稳态模型参数使用瞬态测量动态更新。因此，我们避免了在触发优化周期之前等待稳态，并且稳态经济优化可以以任何期望的速率被调度。稳态等待已被认为是传统RTO方法的基本限制。在本文中，我们实现ROPA的实验装置，模拟海底油井网络。为了比较，我们还实现了传统的和动态的RTO。实验结果证实了在硅片的发现，ROPA的性能是类似的动态RTO的性能，具有更低的计算成本。最后，我们提出了一些指导原则，ROPA的实际实施。
英文摘要：
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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