Issue |
E3S Web Conf.
Volume 261, 2021
2021 7th International Conference on Energy Materials and Environment Engineering (ICEMEE 2021)
|
|
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Article Number | 01068 | |
Number of page(s) | 6 | |
Section | Energy Development and Energy Storage Technology Research and Development | |
DOI | https://doi.org/10.1051/e3sconf/202126101068 | |
Published online | 21 May 2021 |
Research on Oil and Gas Pipeline Operation Optimization Based on Improved Newton-Raphson Method
1
Pipeline R & D Center, PipeChina North Pipeline Company, Langfang, 065000, China
2
PipeChina North Pipeline Company, Langfang, 065000, China
3
Xi’an Oil and Gas Transportation Branch of PipeChina North Pipeline Company, Xi’an, 710000, China
4
PipeChina Pipeline Changchun oil and gas transportation sub-Company, changchun, 130062, China
* Corresponding author’s e-mail: duanlj@pipechina.com.cn
Oil and gas pipelines are the main channel to ensure national energy security and national economic development due to the safety and efficiency of the transportation coast. To achieve an optimal state of pipeline operation in terms of safety and efficiency is the crucial important issue throughout the life cycle of a pipeline system. However, the optimization problem of the pipeline network system is a typical Mixed Integer Non-Linear Problem (MINLP) which are extremely difficult to solve. An optimal solution to keep pipeline operated in most efficient state under the premise of safe operation is given in the paper by using the dynamical programming method. Firstly, the improved Newton-Raphson method is used to solve the discrete pipeline system, and the operating parameters such as temperature, pressure and flow of any section surface in the pipeline are obtained. The fluid parameter values of the each discrete nodes can ensure the safety of the pipeline. Based on this, the total energy consumption cost is set as the objective function, and the oil and gas pipeline operation optimization model is then established, and the dynamic programming method is used to solve it, so that it can obtain the optimal solution of the current working conditions in a reasonable computational cost. The actual example shows that the energy cost of the optimized operation scheme can be reduced by 6.8% compared with the pre-optimization scheme.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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