(363y) Pipelines Multi-Product Scheduling and Delivering By Model Predictive Control

Pipelines have been extensively utilized for material transportation (such as water, crude and refined oil, and gas product delivery) over the past decades [1]. When it comes to industrial applications, there are various complex operational demands ranging from long-distance pipelines to gathering and distribution pipeline systems[2]. Compared to single-product transportation, more complex batch operations are required to deliver multiple products in pipelines. One fundamental goal in multi-product planning and scheduling is to meet the demands of all delivery stations in time, ensuring the constraints on manipulated control variables are satisfied while maintaining the normal operations of pipeline systems [3]. Therefore, advanced controller design for safely and efficiently delivering and scheduling of multi-product transportation within pipelines is of importance.

This work aims at designing a control strategy for the pipeline systems by optimizing delivery demands while ensuring its operating constraints are satisfied and achieving batch tracking based on volume displacement. An infinite-dimensional transient hydraulic model or the so-called water hammer equation is introduced to describe the complex flow dynamics within a liquid pipeline [4]. In particular, the density parameter is considered to be time-varying accounting for the multiple products transported in a batch mode. Considering that most modern control strategies are often presented in a discrete-time fashion, the Cayley-Tustin transform is utilized to convert the continuous infinite-dimensional system into a discrete one without spatial discretization or model order reduction [5]. The model predictive controller is designed and implemented to achieve satisfactory performance by manipulating the system input while respecting the physical limitations of the pump and the demand constraints of product delivery. Finally, a simulation study is provided on the long-line delivery system 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] Yongtu, L., Ming, L. and Ni, Z., 2012. A study on optimizing delivering scheduling for a multiproduct pipeline. Computers & chemical engineering, 44, pp.127-140.

[4] 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.

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