Work

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

Year: 2025

Type: article

Abstract: 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≤[Formula: see text]≤0.01, 0.01≤ [Formula: see text]≤ 0.8) based on population diversity, while enforcing constraints through penalty functions (node degree [Formula: see text]=4, pipeline length [Formula: see text]=120 km). Case studies on a regional pipeline network (89 nodes, 98 segments) demonstrate that loop structures exhibit 25.7% higher average reliability ([Formula: see text]= 0.87792) than branch nodes (v79: [Formula: see text]=0.60933). The AGA-driven optimization increases system-wide connectivity reliability ([Formula: see text]) 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.

Source: Scientific Reports

DOI: 10.1038/s41598-025-98749-8

Authors: Xiuxuan Yang, Kun Chen, Minghui Liu

Institution: Chongqing University of Science and Technology

Language: English

Topic: Reliability and Maintenance Optimization

Subfield: Safety, Risk, Reliability and Quality

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Affordable and clean energy

Open Access status: gold

Estimated APC paid: $2,690