Let’s start with a short poem about AI for the petrochemical industry:
Silent sentinels in the industrial night,
AI orchestrates with data in sight.
Guiding processes, unseen yet keen,
Turning raw into refined, a transformation serene.
Cool stuff? Well, that was written by ChatGPT! All it took was the prompt: “write a short poem on AI for the petrochemical industry” and it spit out the poem above. Generative AI (GenAI), which leverages Large Language Models (LLMs) has taken the world by storm since ChatGPT launched not too long ago.
But GenAI won’t help save chemical plants from unforeseen risks.
Times series AI will revolutionize risk mitigation, GenAI won’t
Image of chemical plant produced by generative AI shows it’s capable of a lot, but it can’t help troubleshoot issues in your chemical plant.
For that, a different breakthrough is needed: time-series AI. This type of artificial intelligence (AI) monitors your operational data 24/7 to surface issues much faster than you can today. It then alerts the right teams with the right trends at the right time.
How does time series AI work?
Well, it does not rely on pre-programmed, static rules to identify operational problems. Instead, the AI dynamically learns plant behavior on its own and surfaces issues before humans or alarms can detect them. This breakthrough employs multivariate anomaly detection methods that can determine how groups of variables are behaving with respect to each other. In other words, it surfaces more signal and less noise.
Multivariate anomalies almost always precede alarm storms. Time series AI picks up on these anomalies. When minutes make all the difference, more time gives operators the cushion needed to take remedial actions that stabilize processes before an incident arises.
GenAI has gained popularity, but it’s time-series AI that will unlock better issue detection so operators can troubleshoot faster.
Time series AI in action
For example, a large polymers plant adopted ControlRooms’ time series AI tool for real-time system monitoring and issue detection. Prior to adoption, fouled batches, poor yield, and unplanned downtime kept the plant from running smoothly. Preset rules and limits weren’t working to manage production to plan.
The plant shifted strategies and quickly got time series AI up and running. Immediately, the situation improved. Now, AI-set lImits dynamically model reactor profiles in real-time. ControlRooms continuously learns the plant’s multiple reactors, products, and batch combinations to surface subtle deviations in behavior. The plant has reduced disc ruptures by more than 15% and seen a 10% drop in reaction and process variability – all powered by time series AI.
When minutes matter to stop issues from becoming big problems, GenAI won’t help. Time series AI will save the day.
Learn more about how ControlRooms uses time series AI to help frontline teams detect issues sooner and troubleshoot faster.
