(130a) Enterprise Manufacturing Intelligence (EMI) for Batch Plant Data Systems
AIChE Spring Meeting and Global Congress on Process Safety
2016
2016 AIChE Spring Meeting and 12th Global Congress on Process Safety
2nd Big Data Analytics
Big Data Analytics - Industry Perspective II (Invited Session)
Tuesday, April 12, 2016 - 3:30pm to 4:00pm
Through the development and advancement of new commercially available systems1, key batch process components can be monitored and tracked for a single production run through a unique batch identifier. This software provides easy and fast segmentation of production data into batches, campaigns or other logical groupings for easier analysis and production reporting to optimize process conditions. Because of the defined start and stop time throughout the batch steps, the data associated with each batch can be evaluated either individually or from one batch to another. Historically, this has not been easy to track data for a particular parameter from one batch to another. The isolation of this data provides plants key information about the production process that can help drive continuous improvement across the batch process.
Now that the batch data has been compiled into uniquely identified production runs, plants can take advantage of EMI dashboard technology to quickly understand the performance of their process. EMI allows for continuous monitoring of batches and “flags” when an abnormal process condition has occurred; based on historical batch trends and process targets. This technology also has the capability to merge process data with product analysis to ensure all pertinent information is readily available to plant personnel. This has been extremely difficult to do in the past. This can significantly improve the analysis time and prevent any reoccurrence of incidents prior to the next batch being produced. The implementation of EMI allows for continuous monitoring of long-term batch trends to drive productivity improvements proactively and utilize Dow’s assets more efficiently and effectively.
[1] Marty Moran, “Extending the Value of MES Technology into New Applications – An Industry White Paper,” www.AspenTech.com, 2013