KIIO Information

Chemical and other process manufacturing plants operate 24/7, and each hour of unplanned downtime can cost over $100,000. To keep plants running continuously, machines are monitored by sensors. However, studies show that over 80% of equipment failures are caused by abnormal processes rather than equipment end-of-life. By the time such faults are detected, it is often too late for operators to act. Today, process diagnosis is still largely manual: it takes a team of specialists to enumerate, test, and eliminate hypotheses one by one, a time-consuming and labor-intensive procedure that can take weeks or months. This challenge is amplified by the fact that 20% of the chemical industry workforce will retire by 2030, taking decades of operational knowledge with them. New operators urgently require intuitive, explainable diagnostic support to ensure efficient and reliable operations. Currently, no effective solution exists for process-level monitoring and diagnosis to improve reliability. The result is lost productivity: chemical plants run at an average of only 41% overall equipment effectiveness, meaning that half of the potential capacity is lost to downtime, inefficiencies, or off-spec production, translating into hundreds of millions in annual costs for a typical large plant. KIIO has developed technology that shifts monitoring from isolated assets to the full process by treating the plant as a connected network. By learning the relationships among sensor signals and tracking subtle changes as they occur, KIIO detects abnormal processes earlier - before they develop into equipment faults. The technology does not rely on fault libraries (a database of known failures) but learns directly from real-time data. Importantly, KIIO doesn’t just point out deviations from the norm; it also shows how a fault propagates through the process and helps engineers and operators understand where a problem may have started and how it can be mitigated.