Research on the connectivity reliability analysis and optimization of natural gas pipeline network based on topology

• Published in: Scientific reports Vol. 15; no. 1; pp. 13442 - 21
• Main Authors: Yang, Xiuxuan, Chen, Kun, Liu, Minghui
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 18.04.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166/4073/4071; 639/4077/4073/4071; Adaptive genetic algorithm; Algorithms; Computer applications; Connectivity; Connectivity reliability; Humanities and Social Sciences; Minimal path; Monte Carlo simulation; multidisciplinary; Mutation rates; Natural gas; Natural gas pipeline network; Optimization; Pipelines; Science; Science (multidisciplinary); Topology; Topology optimization; Topology optimization; Adaptive genetic algorithm; Connectivity reliability; Natural gas pipeline network; Minimal path
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-025-98749-8

The rapid expansion of natural gas pipeline networks in China necessitates robust reliability assessment and optimization frameworks, particularly for large-scale looped configurations where traditional tree-based models fall short. This study proposes an integrated framework combining connectivity reliability evaluation with adaptive topology optimization. First, a minimum path set-based reliability model is developed, leveraging an enhanced depth-first search (DFS) algorithm for efficient path identification and binary decision diagrams (BDD) to eliminate 92% of redundant terms in reliability formulas, reducing computational complexity by 40% compared to Monte Carlo simulations. Second, an adaptive genetic algorithm (AGA) is designed to optimize network topology, dynamically adjusting crossover and mutation rates (0.8≤ ≤0.01, 0.01≤ ≤ 0.8) based on population diversity, while enforcing constraints through penalty functions (node degree =4, pipeline length =120 km). Case studies on a regional pipeline network (89 nodes, 98 segments) demonstrate that loop structures exhibit 25.7% higher average reliability ( = 0.87792) than branch nodes (v79: =0.60933). The AGA-driven optimization increases system-wide connectivity reliability ( ) from 0.03 to 0.247 by strategically adding redundant pipelines (v71–v77), outperforming particle swarm optimization (PSO) by 65%. Key findings reveal that centralized gas source layouts and looped configurations significantly enhance redundancy, with critical segments showing 34% higher D-connectivity importance post-optimization. This work provides a scalable, training-free solution for pipeline network design, balancing computational efficiency (68.7s for 200-node networks) with engineering constraints, and offers actionable insights for infrastructure resilience enhancement.