Deployment Optimization of Pressure Sensors in Pipeline Network Via Fusing Shortest Path Planning and Monte Carlo Tree Search

The branched topology of thermal pipeline networks creates multiple propagation paths for leak-induced negative pressure waves (NPWs), causing leak localization algorithms to potentially output “multiple matching solutions,” resulting in erroneous localization. To address this challenge, this paper proposes a novel method that combines shortest path planning (SPP) and Monte Carlo tree search (MCTS) to optimize pressure sensor deployment. Unlike conventional approaches relying on historical network information or simulation software, this method optimizes sensor placement based on actual NPW transmission paths. First, the method discretizes the pipeline network and employs the sum of inter-sensor shortest path lengths as the optimization objective. Then, it utilizes MCTS to iteratively update sensor deployment schemes, ultimately improving the uniqueness of leak localization results obtained through NPW arrival delay matching. In a 12 km×12 km network with 10 sensors, the optimization method increased the total SPP length from 26.1 km to 68.6 km. Across 1,000 simulated leak scenarios, points with unique NPW arrival delay signatures increased from 54.8 to 79.0%, while points located on the SPP rose from 8.6 to 20.3%. We further examined sensor deployment optimization by increasing sensor quantities. Increasing sensors from 10 to 20 led to significant performance improvements in the optimization algorithm. Further increasing sensors to 30, however, yielded negligible performance gains, indicating algorithm saturation. Considering cost constraints, deployment optimization is essential with limited pressure sensors but becomes optional once sensor density reaches sufficient levels. The proposed SPP-MCTS synergistic approach offers practical guidelines for thermal network monitoring system design, especially for cost-constrained leak detection implementations.