Honeywell announced the commercial launch of Experion Operations Assistant, an AI-powered solution designed to transform how industrial operators monitor plant performance, make critical decisions and respond to alarm incidents before they happen.
Built on Honeywell’s flagship distributed control system, Experion PKS, Experion Operations Assistant merges historical data with real-time operational insights to allow operators to forecast and respond to potential critical scenarios associated with unsafe operations and production losses. The solution aims to bridge the gap between autonomous technologies and control room operators.
The commercial launch follows the recent pilot program during which Chevron and TotalEnergies were among the partners who leveraged Honeywell’s Experion Operations Assistant in their operations to help minimize unplanned downtime. In its pilot phase, the AI-powered assistant made predictions an average of 5-10 minutes before alarm incidents would have happened, enabling operators to quickly implement corrective actions and avoid potential events.
“Industrials are under pressure to avoid unplanned downtime and maximize the productivity of existing assets, while navigating the massive knowledge transfer of a retiring workforce,” said Mr. Jim Masso, president and CEO of Honeywell Process Automation. “By combining more than 50 years of process automation expertise with site-specific knowledge, data and advanced AI, Experion Operations Assistant helps operators anticipate issues earlier, respond with confidence and operate more safely and effectively. Throughout our pilot phase, Experion Operations Assistant delivered tangible, real-world results across multiple customer sites, highlighting the immense opportunity as the solution scales.” As part of Honeywell’s Experion PKS distributed control system network, the Honeywell Experion Operations Assistant is designed to seamlessly integrate into existing control room environments, leveraging the systems already in place and building on the legacy site-specific data. The solution is centered upon its capability to capture site-specific knowledge and then use language models to analyze this data in real-time in order to proactively advise operators of impending issues.
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