Predictive Maintenance: Plants Hit the Data Jackpot

More and better information is opening up new opportunities

By Seán Ottewell, Editor at Large

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Chemical makers will see plant operating staff spending far less time collecting and managing data and far more time focusing on critical value-added work and decision-making, says Mark Granger, reliability solutions manager at Emerson Process Management, Knoxville, Tenn. In five years, the continued technology advancements that Emerson and other vendors are driving will result in more smart assets and systems that monitor themselves and their processes, he believes. Moreover, multiple groups within a company will take advantage of the embedded smart information from the plant. Predictive maintenance (PdM), condition monitoring, and asset performance details will become more closely integrated with control optimization and, thus, will play an increasing role in improving availability, throughput, health/safety/environmental (HSE) efforts, operations and maintenance, and plant life. “The boundaries between control, reliability, PdM, operations, safety and production are getting more blurry for the chemical plant of the future,” he stresses.

Efforts both by chemical makers and vendors are fostering this future.

For instance, Evonik, Essen, Germany, is focusing on using big data as a new digital process optimization tool — especially in terms of PdM.

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“The importance of big data simply cannot be emphasized enough. Most companies are fully aware of the significance and potential of digitization. The actual challenge here is to make selective use of this wealth of data for the classic purposes of a manufacturing organization: for increasing productivity, optimizing processes, and becoming more efficient,” explains Kai Dadhe, who heads the manufacturing intelligence group in the company’s performance intermediates business line.

The tools traditionally used for optimizing processes are reaching their limits — with the challenge compounded because linking and networking isolated data silos around the chemical plant remains difficult and fraught with obstacles, believes Dadhe.

“The result? Our view of the overall production process is obscured, preventing us from recognizing optimization potential and capitalizing on improved efficiency,” he says.

Big data combined with existing process experience and expertise in interdisciplinary optimization programs is the way forward, Dadhe reckons.

In practice, this means collecting, storing and strategically using the continuous flow of data from modern instruments, equipment, etc., in a way that allows staff to identify operating conditions with greater precision, and that makes error analyses simpler and more accurate. This fosters reliability and transparency that, in turn, enable tailoring repairs and maintenance for each individual piece of equipment.

“What is clear about big data is that it changes the way individual parts of the company deal with data, makes digital information more important, incorporates this information in every decision-making process, and blurs the boundaries between disciplines and divisions within the company — it’s a challenge for everyone, in other words, and one that requires professional moderation and support.”

Pilot Project

In a new development that builds on this, Evonik’s intermediates business line has teamed up with its process technology and engineering business line and its global information technology and processes department to pursue a pilot project at its Marl, Germany, complex (Figure 1) aimed at taking a detailed look at the potential that digital information holds for defined production processes.

Described as a “tremendous leap” by Dadhe, the project explores how to use big data for optimizing processes from many different perspectives — including technical and business — while incorporating expertise from throughout the entire company. The objective of the project is two-fold: implementing PdM, and addressing concrete process issues.

A key PdM issue is whether digital intelligence can enable creating more efficient and flexible maintenance and repair routines for specific machines that are particularly susceptible to material deposits. Such deposits negatively impact process efficiency and lead to increased repair work, he says.

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