444734 Enterprise Manufacturing Intelligence (EMI) for Batch Plant Data Systems

Tuesday, April 12, 2016: 3:30 PM
335A (Hilton Americas - Houston)
Bradley J. David, Manufacturing Analytics, Dow Chemical Canada ULC, Sarnia, ON, Canada, Chaitanya Khare, Manufacturing Analytics, The Dow Chemical Company, Freeport, TX and Mukund Patel, Dow AgroSciences Tech Center, The Dow Chemical Company, Midland, MI

Dow collects and stores millions of data across its global manufacturing sites every day using a wide variety of data historians to monitor product and process variables during production runs. Because of the complexity of these database systems, extracting and analyzing this vast amount of data into meaningful information for the plant can be complex and time consuming, thus reducing opportunities to improve certain areas of the process. In order to extract the most significant and immediate value from that data, process design knowledge and operational experience can be used to summarize and aggregate plant data to expose the most relevant information in the right context. The goal of Enterprise Manufacturing Intelligence (EMI) is to make these high value relevant data available in real time to all levels of production management (Operations to Plant and Business Management). Therefore, plants can take advantage of this information to make improvements in raw material usage, asset utilization by reducing unnecessary process wait times and customer satisfaction through improved product quality by reducing product and process variation. This applies to both batch and continuous processes. A Dow case study will be discussed in the presentation to show the benefits of EMI in a batch process.

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

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