Electrical Trace Heating Data Generation: Toward Building Intelligence into Real Time Circuit Health and Performance Monitoring Solutions

Electrical trace heating (ETH) is an established thermal management technology for maintaining process fluids at desired temperatures. Basic successful industrial ETH applications require (1) a custom design specific to the customer’s piping and equipment infrastructure, and (2) an installation that implements design features and accommodates the reality of the environments. Advanced control and monitoring would additionally require (3) real-time monitoring that validates the ETH design and the quality of installation, and (4) intelligence that understands and responds to the monitoring data in/near real time to ensure ETH works as designed. However, there remains a gap on how to quantitively map design features to time series of theoretical ETH metrics (e.g., pipe temperature and heater power consumption), which are critical for comparative analysis with the actual monitoring data so the user can validate heater/insulation performance and then act upon alarming signals related to unexpected process changes. To resolve this issue, we present an IEEE 515-based technique to generate time series using ETH design features over a required time period and at a desired sampling granularity. We test the technique in a case study in which we generated trend time series using ETH design features and historical weather (i.e., ambient temperature) data. We demonstrate consistency between the generated data and the customer monitoring data. This technique is further affirmed by a comparative analysis with results from transient computational fluid dynamic modelling. We anticipate this work will establish the foundation and standard procedures for ETH health and performance assessment and predictive analytics.