Data: Finding What's Hiding Gets Easier

Sophisticated predictive analysis software can reveal valuable insights concealed in disparate data

By Seán Ottewell, Editor at Large

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The ongoing rise of the Industrial Internet of Things (IIOT) is spurring the generation of ever-increasing amounts of data at plants. However, as many chemical makers can attest, more data doesn’t necessarily lead to more insights about operations. Uncovering underlying relationships among data to identify trends that affect a plant hasn’t been easy and often has demanded the skills of data scientists. Fortunately, developments in predictive analytics software are enabling engineers on site to make more effective use of data to improve operations. A number of vendors and even an operating company now offer innovative technologies that are producing impressive results.

For instance, TrendMiner, Houston, and Hasselt, Belgium, points to its work with Total Refining and Chemical. This began with a pilot project at Total’s plant in Antwerp, Belgium, to use TrendMiner’s predictive analytics to optimize process performance.

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“The pilot OSIsoft real-time process data infrastructure with the self-service process and asset analytics software of TrendMiner showed significant benefits in all four industrial priorities for Total: reduce safety risks, improve asset availability, reduce operational costs and increase energy efficiency. The 45 experts involved gave it the highest average rate for any software tool ever assessed by Total on user friendliness and capabilities to improve overall plant performance,” says TrendMiner CEO Bert Baeck.

The TrendMiner support team involved in the project reported a three-to-five times return on investment in the first year. Based on this success, Total has started to roll out the software to its entire refining and chemicals business unit.

The analysis software relies on a patent-pending pattern recognition technology designed to find in seconds similar (multivariate) process behavior in up to a decade’s worth of data. This speed of analyzing process behavior is just the starting point for identifying root causes; a range of tags can be instantly assessed to find influencing factors for anomalies.

TrendMiner is unique, according to Baeck, because of its second patent-pending technology to predict process behavior based on behavior in the past. Rather than creating predictive analytics models, the process engineer can create fingerprints to monitor the real-time process performance. These fingerprints are used to send early warnings, based on user-defined rules, in case of deviations but also allow comparing actual and ideal behavior. In the case of a deviation, the notifications enable field operators or control room personnel to take appropriate, recommended action. Fingerprints also can serve to capture interesting events for future analysis to further optimize plant performance.

The self-service approach has many benefits over traditional data modeling, too, especially in terms of time demands, contends Baeck: “Subject matter experts can easily verify process anomalies based on their expertise via graphical representation of the process behavior for a specific tag, so avoiding the need for complex data modeling.”

He cites another process example at a plant that was having problems with the cooling phase in a reactor during polymer production due to heat exchanger fouling. Monitoring the fouling was extremely difficult because the reactor produces a variety of grades of polymer, each requiring a different recipe. The plant faced the classic dilemma: scheduling maintenance too early leads to unwarranted reactor downtime while leaving it too late degrades performance and poses potential risks such as insufficient cooling capacity in emergency situations.

So, the plant deployed a monitor to check on the cooling time for the most frequent products. The start of an increase in the duration of the cooling phase prompts a warning to its engineers, who then can schedule timely maintenance. This has resulted in extended asset availability, reduced operational and maintenance costs, and decreased safety risk.

No Data Movement

Total’s experience with predictive analytics comes as no surprise to Michael Risse, vice president of Seeq Corp., Seattle. Chemical and pharmaceutical companies seem to appreciate and value improvements they can get more than other firms and so have a greater willingness to adopt the technology, he says.

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