Title
A MILP model for the detailed scheduling of multiproduct pipelines with the hydraulic constraints rigorously considered.
Abstract
Multiproduct pipelines are the most effective and important way to transport refined products from refineries to the downstream market. The detailed scheduling of a multiproduct pipeline with the hydraulic constraints considered plays a vital role in the pipeline's safe and economic operation. Aiming at this issue, this paper proposes a continuous-time mixed-integer nonlinear programming (MINLP) model taking the sum of the pumps’ running and start/stop costs as the objective function. Based on a method proposed in a previous study for the rigorous description of the hydraulic loss changes in multiproduct pipelines, the pipeline hydraulic constraints and pump-related functions are considered directly and rigorously in the model. Other actual field processing constraints, such as the pipeline shutdown and contaminated oil flowrate, are also considered. To deal with the nonlinear relationship between the flowrate and pressure/volume, a piecewise linearization method is adopted, and the pipeline flowrate is graded according to the divided intervals. Finally, the application of the established model is successfully conducted on a real-world multiproduct pipeline through two case studies. A comparison with a previous discrete-time model is also performed to verify this model's applicability, accuracy, and superiority.
Year
DOI
Venue
2019
10.1016/j.compchemeng.2019.106543
Computers & Chemical Engineering
Keywords
Field
DocType
Multiproduct pipeline,Detailed scheduling,Hydraulic constraints,Mixed-integer nonlinear programming,Continuous-time representation
Mathematical optimization,Pipeline transport,Nonlinear system,Scheduling (computing),Nonlinear programming,Mathematics,Piecewise linearization,Oil refinery
Journal
Volume
ISSN
Citations 
130
0098-1354
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Xingyuan Zhou101.35
Yong-tu Liang2148.07
Xin Zhang300.34
Qi Liao4107.29
Song Gao500.34
Wan Zhang641.47
Haoran Zhang72612.18