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Schneider Announces New AI Patent For Process Safety

admin by admin
February 22, 2025
in Uncategorized
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The announcement is part of an initiative to answer a growing interest in combining AI and human ingenuity in functional safety analysis.

Schneider Electric has announced a new patent to leverage artificial intelligence (AI) to help reduce the likelihood process safety hazards.

The company says its new system automatically or semi-automatically analyzes potential process hazards and validates protection mechanisms in an industrial process. Users can then work to prevent hazards using an analysis tool to help identify protective mechanisms to the process.

This patent is a part of Schneider’s strategic initiative to enhance functional safety using AI. It is now possible to simulate hazards with varying conditions and then attempt to prevent dangerous conditions by using a process hazard analysis tool to generate protective actions.

As more industries embrace digital transformation and generate high-quality data, the advantages of implementing AI in day-to-day operations increases. This latest patent from Schneider’s EcoStruxure Triconex Safety team has the potential to identify hazards and safeguards in a process.

Process safety management can then take advantage of industrial, real-time data to revalidate hazard and operability (HAZOP) studies to prevent industrial hazards and save lives.

“We are the first to push this boundary of automating the hazard process analysis with artificial intelligence,” said Chris Stogner, Schneider Electric’s senior director of offer management. “Bringing AI to functional safety has the potential to create a more rigorous and robust HAZOP study, generating more combinations of scenarios and deviations then what was humanly possible before.”

Three other Schneider Electric patents incorporating AI into functional safety lifecycle are currently pending. The company is developing these initiatives to answer a growing interest in combining human ingenuity in functional safety analysis with strategic implementation of reenforced learning to prevent hazardous scenarios in industrial automation.

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