Optimizing Smelting and Refining Equipment Reliability with Prescriptive Analytics
Company Size
1,000+
Product
- Aspen Mtell
Tech Stack
- Machine Learning
- Predictive Analytics
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Cost Savings
- Customer Satisfaction
- Productivity Improvements
Technology Category
- Analytics & Modeling - Machine Learning
- Analytics & Modeling - Predictive Analytics
Applicable Industries
- Metals
- Mining
Applicable Functions
- Discrete Manufacturing
- Maintenance
Use Cases
- Machine Condition Monitoring
- Predictive Maintenance
Services
- Data Science Services
About The Customer
The customer is one of the world’s largest fully integrated zinc and lead smelting and refining complexes. As a producer of refined zinc and lead, a variety of precious and specialty metals, chemicals and fertilizer products, their team’s success is based on improving best practices, optimizing efficient processes, reducing failures and increasing the bottom line. They wanted to improve their metallurgical operations and recognized an opportunity to improve preventative maintenance by using information from their process signal historian. They also wanted a solution that could help as the company developed a comprehensive approach to strengthen environmental, employee and community safeguards.
The Challenge
One of the world’s largest fully integrated zinc and lead smelting and refining complexes wanted to improve their metallurgical operations. The team recognized they had an opportunity to improve preventative maintenance by using information from their process signal historian. In addition, they wanted a solution that could help as the company developed a comprehensive approach to strengthen environmental, employee and community safeguards. The operations group’s reliability team needed a technology to track, detect and prevent equipment failures.
The Solution
The customer utilized Aspen Mtell machine learning to track and predict equipment failure as well as determine the precise process signature leading to a failure. Mtell has the ability to read process signals and calculate how much runtime a piece of equipment has left, and even automatically file a work order. The agent within Mtell provided guidance of a time-to-failure of roughly 40 days on a process crucial pump. The maintenance and reliability team acted and performed a detailed SWOT analysis to determine the best course of action based not only on the tool’s guidance, but on the site’s production forecast as well.
Operational Impact
Quantitative Benefit
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