Case Studies Leading Pulp & Paper Manufacturer Detects and Avoids Major Fire Using Aspen Mtell

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	Leading Pulp & Paper Manufacturer Detects and Avoids Major Fire Using Aspen Mtell

Leading Pulp & Paper Manufacturer Detects and Avoids Major Fire Using Aspen Mtell

Technology Category	Analytics & Modeling - Machine Learning
Applicable Industries	Paper & Pulp
Applicable Functions	Process Manufacturing
Use Cases	Predictive Maintenance
Services	Data Science Services
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. Read More
About 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. Read More
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. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Machine Learning

Applicable Industries

Paper & Pulp

Applicable Functions

Process Manufacturing

Use Cases

Predictive Maintenance

Services

Data Science Services

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.

About 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.

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.

Impact #1	Aspen Mtell provided a nine-day advance warning of imminent overheating, allowing the plant to change operating conditions and avoid an operational shutdown.
Impact #2	Autonomous agents successfully detected other impending equipment failures weeks in advance, avoiding costly shutdowns.

Impact #1

Aspen Mtell provided a nine-day advance warning of imminent overheating, allowing the plant to change operating conditions and avoid an operational shutdown.

Impact #2

Autonomous agents successfully detected other impending equipment failures weeks in advance, avoiding costly shutdowns.

Benefit #1	9 days advance warning of imminent overheating
Benefit #2	Avoided operational shutdown

Benefit #1

9 days advance warning of imminent overheating

Benefit #2

Avoided operational shutdown

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Overview

Leading Pulp & Paper Manufacturer Detects and Avoids Major Fire Using Aspen Mtell

Operational Impact

Quantitative Benefit