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Article type: Research Article
Authors: Peng, Jinghong | Zhou, Jun; * | Liang, Guangchuan; * | Qin, Can | Peng, Cao | Chen, YuLin | Hu, Chengqiang
Affiliations: Petroleum Engineering School, Southwest Petroleum University, Chengdu, P.R. China
Correspondence: [*] Corresponding author. Jun Zhou and Guangchuan Liang, Petroleum Engineering School, Southwest Petroleum University, Chengdu, 610500, P.R. China. E-mail: zhoujunswpu@163.com (J. Zhou), E-mail: lgcdjr@163.com (G. Liang)
Abstract: Gas gathering pipeline network system is an important process facility for gas field production, which is responsible for collecting, transporting and purifying natural gas produced by wells. In this paper, an optimization model for the layout of star-tree gas gathering pipeline network in discrete space is established to find the most economical design scheme. The decision variables include valve set position, station position and pipeline connection relation. A series of equality and inequality constraints are developed, including node flow balance constraints, pipeline hydraulic constraints and pipeline structure constraints. A global optimization strategy is proposed and an improved genetic algorithm is used to solve the model. To verify the validity of the proposed method, the optimization model is applied to a coalbed methane field gathering pipeline network in China. The results show that the global optimization scheme saves 1489.74×104 RMB (26.36%) in investment cost compared with the original scheme. In addition, the comparison between the global and hierarchical optimization scheme shows that the investment cost of the global optimization scheme is 567.22×104 RMB less than that of the hierarchical optimization scheme, which further proves the superiority of the global optimization method. Finally, the study of this paper can provide theoretical guidance for the design and planning of gas field gathering pipeline network.
Keywords: Natural gas, pipeline network, layout design, global optimization, genetic algorithm
DOI: 10.3233/JIFS-222199
Journal: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2655-2672, 2023
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