Environment & Safety Gas Processing/LNG Maintenance & Reliability Petrochemicals Process Control Process Optimization Project Management Refining

2020 AFPM Summit: Advanced analytics drive IIoT success

Advanced analytics drive IIoT success

JOANNA ZINSLI, Seeq Corporation


Refineries and petrochemical plants face similar challenges in daily operations as each type of facility must mitigate risk, anticipate maintenance, optimize operations and minimize expenses. To achieve these goals, plant personnel rely heavily on data to drive decisions.

The excitement around the Industrial Intranet/Internet of Things (IIoT) is its ability to deliver this data at lower cost and in greater volumes than ever before. This is accomplished when IIoT implementations take advantage of advancements in the areas of wired and wireless sensors, networking and data storage technologies.

Enabling data access. The driving forces for these innovations are declining costs and increased connectivity options, which are enabling plants to measure far more parameters of interest, many of which were too expensive to monitor with older solutions.

Access to this data by engineers and plant management may be enabled worldwide to anyone with intranet or internet connectivity and proper security credentials. The proliferation of high-speed Wi-Fi and cellular networks has made this type of connectivity ubiquitous and reliable.

However, collecting, storing and disseminating data is just the starting point for IIoT implementations, as process and reliability engineers—often referred to as subject matter experts (SMEs)—must analyze this data and derive results to provide operational guidance.

This is best done using advanced analytics software to connect to the myriad of data sources. The right software is an effective tool in the hands of SMEs, enabling them to create and share insights using a workflow process.

The following example shows how advanced analytics software can be used in refineries and petrochemical plants to improve operations.

Preventing vessel brittle fracture. Vessels in refineries and petrochemical plants require scheduled inspection and maintenance; many different factors can cause degradation of integrity. A leading risk requiring particular attention during transient operations is brittle fracture.

Brittle fracture occurs when a vessel experiences high stress, such as elevated pressure, without being sufficiently preheated. The minimum temperature at which a vessel can withstand pressure is often represented by a minimum allowable temperature (MAT) curve plotted on an X-Y diagram, with pressure on the X-axis and temperature on the Y-axis. Awareness and careful monitoring of pressure and temperature during vessel heat up and cool down can mitigate risks of brittle fracture.

Plant personnel often use the MAT diagram appropriate for a piece of equipment based on its metallurgy, and transpose these as limits, especially for startup and shutdown procedures.

A better approach is to use advanced analytics software to convert the relationship between pressure and temperature as depicted on the MAT diagram to a new signal based on vessel pressure. This allows a desirable operating range to be established, and then used to closely monitor operations based on live pressure data.

MAT diagrams differ based on a vessel’s metallurgy and other factors, and the new signals created for comparison will also need to be unique to each vessel to align with operating pressures and temperatures.

The left side of FIG. 1 shows an MAT vs. pressure diagram applicable for a vessel.1 This diagram represents operating conditions as one focused point on the trend. However, during startup or shutdown conditions, that point may vary significantly, and to maintain integrity it is important to ensure this point will always remain above the MAT line.

FIG. 1. Advanced analytics software was used to create an inferred variable (blue line on chart on the right) based on pressure, which can be tracked to ensure it stays within acceptable limits. Courtesy: Seeq.

As shown on the right side of FIG. 1, an SME can use advanced analytics software to create a piecewise function for this line, which now becomes an inferred variable calculated as a function of the vessel’s pressure. This allows plant personnel to track operations in real time by observing—and alarming, as required—this inferred signal, which varies based on the measured pressure variable.

Takeaway. For this example, traditional tools (e.g., spreadsheets) can be used, but these general-purpose tools require much more effort than using advanced analytics software specifically designed to create insights from time-series process data.

Many of these applications involve advanced modeling. An important aspect of effective predictive models is the incorporation of subject matter knowledge, but this expertise must also be shared to produce maximum benefit throughout a company. Advanced analytics software simplifies sharing and collaboration, allowing engineers to invest their time and knowledge in activities driving their organizations to higher profitability.

As data provided by IIoT implementations continues to grow, corresponding demands on SMEs will only increase, driving the need for advanced analytics to find more insights faster.

For more information, visit www.seeq.com


1 Benac, D. J., N. Cherolis and D. Wood, “Managing cold temperature and brittle fracture hazards in pressure vessels,” Journal of Failure Analysis and Prevention, (2016), online: https://doi.org/10.1007/s11668-015-0052-3


JOANNA ZINSLI is a Principal Analytics Engineer at Seeq Corp. She enjoys helping engineers across verticals discover value in their process data and simplify their routine workflows using Seeq. She received her BS degree in chemical engineering from the University of Arizona, and then began her career as a process engineer in petroleum refining with Valero Energy Corp. After supporting several units, she transitioned to refinery economics as an optimization engineer. Prior to joining Seeq, she managed the refinery's planning and economics team.

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