A mineral production company faces a common situation: instrumentation and data collection are producing large volumes of operational data, but efforts to turn this data into meaningful improvements in operational efficiency are falling short.
The production line experiences frequent, unexpected downtime events due to variations in raw material that adversely impact a critical process-line machine. These downtime events last anywhere from 2 to 24 hours per occurrence, costing $30,000 per hour and $720,000 a day.
Data, in the form of motor currents, temperatures, valve settings, and stoichiometric measurements are collected from the process line, stored in a process historian (OSIsoft PI), and are made available to the operations team through dashboards and other means. The thresholds, rules, statistical and engineering models that are being used are, however, unable to reliably identify conditions leading to downtime events.
A Falkonry server is installed on-premise, and a Falkonry-supplied integration agent is used to connect it to the customer’s OSIsoft PI System.
Members of the process operations team are given a < 3 hour training on the Falkonry products and then complete the following tasks:
The Assessment stream produced by Falkonry is able to provide early warning of previously-hidden bad raw material conditions. This condition awareness enables the operations team to take corrective actions and avoid many of the costly downtime events that have plagued them previously.
Industrial Predictive Analytics
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All rights reserved. Falkonry Inc