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. Finally, we present some guidelines for ROPA's practical implementation.
Code (1)
Similar Papers 제목 키워드 기반
Optimal Control in Both Steady State and Transient Process with Unknown Disturbances
The scheme of online optimization as a feedback controller is widely used to steer the states of a physical system to the optimal solution of a predefined optimization problem. Such methods focus on regulating the physic…
Bridging Transient and Steady-State Performance in Voltage Control: A Reinforcement Learning Approach with Safe Gradient Flow
Deep reinforcement learning approaches are becoming appealing for the design of nonlinear controllers for voltage control problems, but the lack of stability guarantees hinders their deployment in real-world scenarios. T…
Deep Reinforcement LearningContinuous reset element: Transient and steady-state analysis for precision motion systems
This paper addresses the main goal of using reset control in precision motion control systems, breaking of the well-known "Waterbed effect". A new architecture for reset elements will be introduced which has a continuous…
Leveraging Non-Steady-State Frequency-Domain Data in Willems' Fundamental Lemma
Willems' fundamental lemma enables data-driven analysis and control by characterizing an unknown system's behavior directly in terms of measured data. In this work, we extend a recent frequency-domain variant of this res…
LEMMAAdaptive Event Detection for Representative Load Signature Extraction
Event detection is the first step in event-based non-intrusive load monitoring (NILM) and it can provide useful transient information to identify appliances. However, existing event detection methods with fixed parameter…
Event DetectionNon-Intrusive Load Monitoring