EMISSIONS MONITORING FOR RECIPROCATING ENGINES THROUGH CLOUD BASED ANALYTICS – A PROGRESS REPORT

摘要

Predictive maintenance and operations optimization are two key emerging technologies in the oil and gas industry that have the potential to reduce unplanned asset downtime and increase productivity for operators. Real-time emissions monitoring, without the use of expensive continuous emissions monitoring systems (CEMS) hardware, provides a significant advantage for reciprocating engines over electric motors from a cost standpoint; cost savings are enabled through increased availability, predictive maintenance and high horsepower without tripping Title V and tonnage limits in NOx, CO, and other volatile organic compounds (VOCs). Real-time monitoring of emissions compliance and plant performance gives operators the data required to ensure the site is continually meeting all state, federal, and local requirements. Additionally, through emissions monitoring, upgrades and maintenance schedules can be optimized to implement when needed, regardless of when the standard schedule would indicate. As a first step in the development process, this paper discusses the development of a chemistry-based, three-way catalyst (TWC) model using first principles, which is key for a reliable post-catalyst emissions monitoring solution for rich burn reciprocating engines. Data from existing assets in the field was used to calibrate and validate the kinetic model. Characteristic curves that could not only be used for model-based air- fuel ratio (A/F) control purposes, but also for emissions monitoring and catalyst diagnostics, are presented. Finally, the application of this model-based approach to lean burn engine emissions monitoring is discussed, along with the next steps required to implement the emissions monitor on the cloud.