Operation Scheduling Optimization of a Transfer Hub with Multiple Tank Farms Based on a New Continuous Time Representation

Abstract

This study addresses the operation-scheduling problem of a transfer hub with multiple tank farms (M-TH) connected to a multiproduct pipeline network. As part of the pipeline network, the THs serve as links that receive, store, and deliver oil products. Previous scheduling optimization works on pipeline networks with oil products largely focused on the batch plans of each pipeline, without considering the operation schedule of the THs. In this study, the main task is to optimize the loading and unloading scheduling operations of the tanks in the M-TH, which are subjected to given schedules of upstream/downstream pipelines. The schedules of pipelines that carry different types of products can be decomposed into several independent single product scenarios. A mixed-integer linear programming (MILP) formulation is presented. The proposed formulation takes into account the capacities of the tanks, operational rules of the tanks and tank farms, structural constraints, and settling time after the loading operation, while minimizing the switch operations between the tanks and switchovers between the tank farms. Moreover, the MILP formulation can be solved for one separate products at one time as there is no interference among the single-product scheduling problems within the transfer hub. The formulation is based on a new continuous time representation with static and dynamic time slots. The scheduling horizon is partitioned into several static time slots on the basis of the time points of batches arriving at and leaving from the M-TH or the flow rate changed. Each static time slot is divided into several dynamic time slots, whose duration and starting/ending points are determined by the optimization process. The proposed formulation is validated using the optimal results of a real-world case. A time horizon partitioning strategy is employed to deal with the long-term horizon scenario. The results help verify that we can substantially save computational time and obtain a satisfactory solution; however, the optimality is compromised.

Crude-Oil Scheduling Network in Smart Field Under Cyber-Physical System

“…Similarly, the work [15] uses dynamic programming algorithm to solve the optimal operation scheduling between a pumping station and multiple pumps for the reduction for energy and maintenance cost. The recent paper [16] studies the operation schedule of the multiple tank farms with continuous time representation for a multiproduct pipeline network to receive and deliver oil products. In the proposed pump and compressor scheduling research, the most widely adopted performance metrics have been merely focus on minimizing the operational cost.…”