Big Data Analytics for Emission Monitoring and Operational Excellence

The process industries have been facing ever increasing pressure to improve product yield, increase productivity and to effectively control gaseous pollutants such as Volatile Organic Compounds (VOCs) and Hazardous Air Pollutants (HAPs). Existing process design and control strategy may neglect some critical factors that can make a dramatic impact on process performance. On the other hand, with increasingly stringent regulations and laws, emission management may need to go beyond the traditional Leak Detection and Repair (LDAR) and Continuous Emissions Monitoring System (CEMS) approaches to manage potential emission events.

Big data analytics technologies can organize the massive amount of data accumulated throughout years of operation, discover the relationship among variables and Identify the key elements. Explainable AI algorithms can then be developed for significantly improved performance on yield and productivity and prediction of emissions.

Using big data analytics technologies, the process industry can utilize process data and emission data seamlessly to reduce emissions, minimize potential risks and achieve operational excellence. In this presentation, case studies on the production of ethylene derivatives and predictive flaring will be presented to demonstrate the efficacy of technology.

Key words

Big Data Analytics, Process Improvement; Emission Monitoring; Predictive flaring; Operational Excellence