Enabling Transfer Line Exchanger Utilization Optimization Based on Advances Data Analytics

The focus of the operator is onto the process itself, for which standard sensors readings are monitored. Those readings up- and downstream the TLE might be used to derive the condition of the heat exchanger. The interpretation of those data strongly depends on the knowledge and experience of the individual operator. In addition regular inspections are conducted which only provide a snap-shot with limited confidence for future condition.

To support the needs of Ethylene plant operators and maintenance team the TLE functionality was further improved by applying Industrial Internet of Things technology. The TLE is equipped with additional sensors, whereas the measurement locations are at the points of interest for the TLE but not for the process. From the sensor readings meaningful values and equipment Key Performance Indicators (KPI’s) are derived and monitored. Additionally the data are used for models which are based on OEM’s domain knowledge with respect to thermal and mechanical design. In addition artificial intelligence (AI) is used to discover abnormalities in operation, to forecast the reliability and performance of the TLE and to provide diagnostics and future recommendations.

The presentation is a follow-up of EPC 2018 1^st session on Big Data Analytics applications in the ethylene industry and discusses in detail TLE health monitoring by smart data derived by domain knowledge and AI. Examples and pilot use cases are presented and further applications are described.