(492h) Model Predictive Control for Energy Scheduling of Pipeline Networks

One of the most widely utilized infrastructures for material transportation (such as crude and refined oil and gas product delivery) is pipelines that have been laid onshore and offshore over the past decades [1]. During transportation, however, there are various and complex operational demands ranging from long-distance pipeline networks to gathering and distribution pipeline systems, which is a challenging issue for pipeline energy scheduling [2]. To deal with it, this work aims at optimizing liquid-flow pipeline networks to meet different operation demands, taking the realistic actuator and sensor constraints of the systems into account.

In particular, an infinite-dimensional transient hydraulic model is proposed to describe the complex flow dynamics within pipelines, which is governed by the continuity, momentum, and energy equations [3]. Considering the discrete nature in actual implementation and superiority of discrete design, instead of using Euler discretization methods, the Cayley-Tustin transform is utilized to map the continuous infinite-dimensional system to a discrete one without spatial discretization or model order reduction [4]. In this sense, the fully spatial characteristics and the fundamental control theoretical properties of the pipeline systems are preserved. Then, the model predictive controller is designed and implemented to achieve satisfactory performance by manipulating the system input, while respecting the physical limitations of the actuators and sensors. In order to simulate realistic situations, the pipeline networks will be considered along with the different mechanical devices, such as pump stations, valves and etc. Finally, an industrial case study of a liquid-flow pipeline network is provided to demonstrate the feasibility and applicability of the proposed MPC design.

References:

[1] Larock, B.E., Jeppson, R.W. and Watters, G.Z., 1999. Hydraulics of pipeline systems. CRC press.

[2] Van Pham, T., Georges, D. and Besançon, G., 2014. Predictive control with guaranteed stability for water hammer equations. IEEE transactions on automatic control, 59(2), pp.465-470.

[3] Xie, J., Xu, X. and Dubljevic, S., 2019. Long range pipeline leak detection and localization using discrete observer and support vector machine. AIChE Journal, 65(7), p.e16532.

[4] Havu V., Malinen J., 2007. The cayley transform as a time discretization scheme. Numerical Functional Analysis and Optimization 28 (7-8): 825-851.