Background:

A leading Asian Petroleum Refining major, with multiple refineries and storage & distribution facilities had implemented AVEVA OSIPI stack (Time-series Historian, Asset Framework, PI Vision) for monitoring process performance in real-time. They had also planned to include data from MES applications (such as LIMS, etc.) and from the ERP system to compute advanced performance markers (KPI), which would also be visualized in real-time on PI Vision. This required seamless integration of streaming data from MES and ERP systems to AVEVA OSI PI.

Challenge:

The existing system architecture faced several challenges such as,

1. Complexity of developing individual data interfaces with diverse array of MES systems used in the different refineries of the company. This diversity posed a significant challenge in terms of development efforts/costs and raised questions about the feasibility of maintaining and modifying these interfaces in the future.
2. The mandatory requirement of converting and storing all MES/ERP data as tags (time-series data) in the process data historian.
3. Limited reusability of the data.  

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The need for a comprehensive solution that addresses these challenges prompted the hydrocarbon major to explore the potential of dDriven’s Neutral Data Hub, UNLSH.

Solution:

UNLSH is truly a Neutral Data Hub in every manner, right from being skill-set neutral, i.e., natively no-code and eliminates the need for niche IT expertise. It is also vendor neutral as far as ingestion, processing, storage and delivery of IT/OT data is concerned.

UNLSH integrated seamlessly with different MES/ERP systems across multiple refineries through simple drag and drop configuration. It drastically reduced the data engineering time and effort through its capabilities such as - complete visibility into the source data, graphical data pipeline and Ops building workbenches, drag and drop time-series transformation of the data for writing into AVEVA OSI PI historian, etc. The primary challenge of creating multiple interface and data pipeline was met with less than 30% of efforts and time, which it would otherwise have taken.

Realizing the capabilities of the UNLSH platform, the customer planned reuse of the data for other Visualization and Analytics tools. For example, various operations functions of the company use Microsoft Power BI as their go to DSV (Decision Support Visualization) platform. Same data, that was prepared for streaming to Aveva OSI PI,  was reused to create powerful cross functional dashboards for different operations departments. In this context, it is important to note that, UNLSH also ingests data from the AVEVA OSI PI Historian which were also leveraged in these cross functional dashboards. A few representative examples of Ops dashboards are shown below.

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Flexible reusability of the data shows complete ownership and control of the data by the customer. This also has other wide range of implications, such as flexible choice of hosting of the data, own server, cloud or as hybrid, cost thereof, GDPR compliance, etc.  

Conclusion:

 The Phase-1 deployment across multiple set of refineries already established the strategic and tactical benefits for the customer which are,  

-         Data ownership

-         GDPR compliance

-         Data reuse

-         Long-term predictability of cost

-         Avoid costly and complex, multi-tool data integration.

-         Reduction of complexity

-         Reduction of Time-to-Value

-         Major reduction of Risks

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