The world’s largest paper and pulp company leverages Camgian AI technologies to overcome equipment disruptions and limit operational downtime.
In today's industrial landscape, accurate process control is crucial for efficient operations and optimal productivity. International Paper (IP)'s Columbus, MS-based mill faced a unique challenge - untimely sensor disruptions and failures that resulted in a lack of real-time awareness in key processes leading to unexpected shutdowns and delays. Shutting down the entire production line to replace faulty sensors was not an option due to the time required and its impact on throughput. To solve this issue, IP partnered with Camgian. Camgian’s engineers introduced the concept of "virtual signals" to predict measurements from broken or faulty sensors, bridging the gap until scheduled downtimes for sensor replacement.
Camgian's solution hinged on the implementation of predictive analytics to overcome the limitations posed by sensor failures. Through leveraging data from other sensors within the production process, Camgian created algorithms that could accurately predict the measurements of broken sensors.
The virtual signals filled the gaps left by broken sensors, enabling uninterrupted process control and minimizing disruptions in production. This innovative approach not only improved operational effectiveness but also optimized resource allocation by ensuring that sensor replacements were strategically planned during scheduled maintenance periods.
Next, by leveraging real-time data analysis and predictive modeling, the solution provided IP with valuable insights into their production environment, as well as the ability to troubleshoot the root cause of potential equipment failures. This allowed the mill’s team to make informed decisions, optimize operational resources, and enhance overall operational efficiency.
In conclusion, the collaboration between Camgian and IP exemplifies how innovative solutions can address complex challenges in process control. Through the implementation of predictive analytics and virtual signals, Camgian enabled the mill to bridge the gap between sensor failures and replacement downtimes. The result was improved process control, reduced downtime, and enhanced operational effectiveness and efficiency. This success story underscores the potential of utilizing big data and machine learning in transforming industrial operations, paving the way for a more productive and resilient future.