Study and application of gas quality tracking algorithm for multi-source natural gas pipeline networks

The increasing complexity of multi-source looped natural gas pipeline networks, driven by the global transition to cleaner energy, poses significant challenges for accurate gas composition tracking and operational management. While existing commercial simulation software offers mature hydraulic modeling capabilities, it often lacks integrated, real-time composition tracking and has limited compatibility with field monitoring systems. To bridge this gap, this study develops and validates a novel algorithm for multi-source natural gas composition tracking. The proposed approach integrates the non-dominated sorting genetic algorithm II for flow distribution optimization with an automated engineering data anomaly correction mechanism within a unified computational framework. Validation using operational data from a real pipeline network confirms the system’s compliance with the OIML R140 standard and demonstrates superior performance. Key results include hydraulic calculation errors within 0.4895% for pressure, 0.44% for temperature, and 0.25% for flow velocity. The composition tracking accuracy is also exceptional, with methane errors below 0.4% and errors for other components under 0.03%. Furthermore, calorific value allocation show a maximum error not exceeding 0.2089%. These findings verify the system’s reliability, engineering applicability, and improvement over existing commercial solutions, offering an effective tool for precise gas quality management and energy-based metering in complex pipeline networks.