Open AccessA Hybrid Meta-Heuristic Solution to Operation Optimization of Hot Oil Pipeline
Author(s) -
Bo Li,
Miao He,
Kang Yang,
Haoyu Shi,
Jiali Zhao,
Yan Liu
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/646/1/012031
Subject(s) - pipeline transport , pipeline (software) , particle swarm optimization , mathematical optimization , nonlinear programming , heuristic , operating cost , integer programming , nonlinear system , process (computing) , computer science , process engineering , engineering , mechanical engineering , mathematics , waste management , physics , quantum mechanics , operating system
In the process of pipeline transportation, the temperature and pressure of waxy and high viscosity crude oil are decreasing. In order to complete the pipeline transportation task, stations are usually set at intervals to overcome the pressure loss caused by friction and impact in the flow and the temperature loss caused by the radial temperature difference. The main equipment of the station is pumps and heaters. The operating fee of pumps and heaters is the core costs of hot oil pipelines, so it is necessary to reasonably optimize the pump and heater operating schemes. Because the hydraulic and thermodynamic of the hot oil pipeline are coupled with each other, and there are a lot of nonlinear constraints and integer variables, this optimization problem is a kind of mixed integer nonlinear programming, which is difficult to solve by using the traditional optimization method. In this paper, a hybrid meta-heuristic algorithm based on particle swarm and pattern search is proposed, which can deal with the problem effectively. The operational cost of the optimized scheme is much lower than that of the actual operating scheme. So this method can provide guidance for the cost reduction and efficiency improvement of the hot oil pipeline.