(169d) Incorporating Model-Based Technology with Big Data Concepts for Monitoring Ethylene Production

Data is becoming abundant and supplied in many different forms through mobile devices, wireless communications and big storage servers. With rich available data production facilities are applying “Big Data” concepts to analyze, identify and correlate parameters that indicate asset performance and health. Many model based solutions have been applied to Ethylene production providing significant benefits in monitoring process performance and optimizing production. Model based solutions feature intrinsic relationships characterized by first principle concepts or determined empirically that have been qualified for accuracy. Incorporating existing model based solutions and big data analysis offers potential for discovering process indicators that improve understanding of process and equipment performance. The resulting awareness advances process knowledge beyond “what is happening to the process” to “how did the process gets to this point” and “what other paths for the process to take” . Evaluating information generated by model based solutions and stored in large data sources can be cumbersome. Collaboration with analyzing large amounts of information and displaying discoveries in digestible form to operations and managers such as dashboards and high performance graphics often requires support from information technologist. Reliance on IT to provide a forum for data analysis and presentation of findings may restrain advances in process knowledge discoveries. Solutions that incorporate model based applications, big data analysis and provides engineers with a forum for easily analyzing information and displaying findings provides an opportunity for advancing asset performance and health.