For the first time ever, the customer was able to view a complete set of refining data in ways previously unavailable. By using Element Unify, not only did they significantly reduce the time to design, aggregate, contextualize, and share all refining data together, the engineering and data science teams enjoyed an up-to-date, 360-degree view using their visualization / BI tool of choice. As an example, the customer was able to rapidly model 6,200 tags, uncovering 27,845 new data relationships that were captured within the Element Graph. These relationships translate to thousands of new ways to view process unit data, offering additional unit monitoring insights.
In addition, project time and costs were reduced by 75% (for example the first 3 PI AFs were built in 3 weeks vs. their previously successful project of over 4 months) delivering value at rates previously unseen.
With Element Unify in place, engineers were freed up from the frustrating and burdensome tasks of collecting and preparing data. Tasks that typically consumed 80% of their day, now only consume 20%. This allows them to spend more time delivering their expert guidance, a 4X increase in engineer productivity. They were also able to relate production data with safety data. This had two benefits; It increased the timeliness and quality of their integrity reports, and also gave them a way to visualize production data alongside safe operating limits, informing them as to the consequences of exceeding those constraints. Engineering calculations can now be developed with changes tracked in a central location, not buried within desktop applications. Asset Templates can be encoded with variable operating envelopes, excursion limits, or anomalies. Physical relationships can be defined between attributes (e.g. Power = Current * Voltage).
With Element Unify in place as an efficient, cost-effective solution, they can not only continue to improve refining unit monitoring, but also expand their scope to help optimize a broader set of Refinery operations and processes.
- Export metadata in the form of PI Asset Frameworks (AFs) to import back into the OSIsoft PI System
- Export various hierarchies and raw data to the customers Azure storage for use with Microsoft PowerBI, R Studio, and Microsoft Azure Databricks