Leading Pulp & Paper Manufacturer Detects and Avoids Major Fire Using Aspen Mtell
Product
- Aspen Mtell
Tech Stack
- Machine Learning
- Data Analysis
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Cost Savings
- Productivity Improvements
Technology Category
- Analytics & Modeling - Machine Learning
Applicable Industries
- Paper & Pulp
Applicable Functions
- Process Manufacturing
Use Cases
- Predictive Maintenance
Services
- Data Science Services
About The Customer
The customer in this case study is a leading manufacturer in the Pulp & Paper industry. The company operates a wood products processing plant where it faced challenges with process and mechanical events that were causing product quality and throughput interruptions. These interruptions were leading to product losses and potential operational shutdowns. The company needed a solution that could help it detect and prevent these events to maintain product quality and avoid costly shutdowns.
The Challenge
The company faced process and mechanical events at one of its wood products processing plants that had created product quality and throughput interruptions, causing product losses. The challenge was to identify and prevent these events to avoid operational shutdowns and maintain product quality.
The Solution
The company implemented Aspen Mtell, a machine learning solution that scans archived data in the plant historian and correlates it with posted failure incidents in its asset management system. Aspen Mtell was able to identify the failure signature for an overheating situation in a kiln. The solution's industrial machine learning found this same pattern among dozens of sensor signal data streams, where no single sensor could provide a clear indication of impending failure. Live autonomous agents then constantly monitored incoming signals for this failure signature.
Operational Impact
Quantitative Benefit
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