There is a lot of talk these days about the rise of AI. It’s not only that the technology is so new and exciting, with so much potential to change the world. AI is also a hot topic because the global environment is right for the changes it can bring. A workforce shortage has left many organizations struggling to find skilled personnel who can operate their plants just as operations have become more complex to meet a changing marketplace.
In his recent article in InTech magazine, Rick Kephart explains that automation has always been the solution to increasing complexity and workforce shortages, but evolving operations require evolving automation solutions. That’s where AI comes in.
Modern problems require modern solutions
Teams have long used failure mode and effects analysis to help design tools to guide plant processes safely through aberrations. The rules stemming from that analysis were typically created manually by human analysts in a time-consuming process that required deep expertise. However, today’s operations are more complex than ever before. That means the old ways of designing automation to solve problems are no longer as effective as they need to be. Rick explains,
“It is difficult, if not impossible, to build explicit logic and rules to handle all the possible abnormal conditions that can show up in a plant. Many of a plant’s most challenging aberrations are those that are entirely unexpected—often caused by a wide array of factors that seem minimal on their own but together create turmoil.”
At the heart of the problem is the many variables an analyst must consider when designing automation to take the plant safely through a problem situation. This is where AI has a massive advantage. Rick shares,
“AI tools can take vast amounts of multivariate data and compare it and trend different process variables and states to gain a higher-level understanding of the process itself. Most importantly, AI solutions can perform this comparison and calculation much faster than a human engineer or operator.”
Tracking and trending performance over time with AI tools empowers teams to see far more possibilities far faster than manual analysis. Users can cover a wider range of failure modes and can detect problems far earlier than they could have without AI assistance.
A deluge of data
The most critical element of operational efficiency is good data. Unfortunately, many organizations have so much data, they struggle to identify which data is valuable and which is extraneous. Further complicating data use is that most plants have stored years or decades of historical data. Having humans comb through that data takes time that most plants don’t have—especially when finding people just to run the plant is so difficult.
Once again, Rick explains, AI tools can step in to fill the gap,
“Today’s plants typically have massive amounts of historical operations and maintenance data, and a well-designed AI system can consume that data in very little time. Once an AI engine has consumed a plant’s historical data, it can build a picture of baseline operations and develop a conceptual framework for how and why processes diverge from specification.”
From there, humans step back in again to feed data from AI tools into higher-level systems like digital twin simulations for training and forecasting. Some of the most advanced groups are even considering AI copilots to work alongside human technicians, providing decision support in the field backed by years or decades of plant and equipment history.
Preparing for change
AI tools are developing and have not reached their full capabilities yet. So, why does all of this matter? It matters because AI is coming, and in many industries will be a deciding factor for competitive advantage. Preparing for that change means putting the right infrastructure in place, and that work can start today. Rick expands upon these ideas, and even shares practical examples in his full article over at InTech. Check it out to see how you can begin getting ready for the big shift in automation fueled by AI.
