PipePatrol E-RTTM (Extended Real-Time Transient Model), developed by KROHNE, is a leak detection system that integrates real-time transient modeling with leak pattern recognition for reliable pipeline monitoring. By creating a virtual pipeline “digital twin” and comparing simulated with measured data, E-RTTM can detect leaks as small as 3?mm within minutes - even under transient conditions such as startup, shutdown, or product changes. Traditional parameter tuning for the RTTM and pattern recognition modules, however, relies on manual adjustments that may limit optimization.

This paper describes the integration of Artificial Intelligence (AI) into the PipePatrol framework to automate and refine the parameter optimization process. AI-driven algorithms adjust RTTM parameters (e.g., wall roughness, leak thresholds) and enhance leak signature analysis, improving the system’s ability to differentiate between true leaks and false alarms caused by linefill variations or sensor errors. Early implementations indicate that this AI-enhanced approach reduces false alarms while increasing sensitivity, as demonstrated in several case studies. In combining E-RTTM’s leak detection methodology with AI-driven optimization, the system presents a promising approach to improving pipeline monitoring practices.