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Optimizing Smelting and Refining Equipment Reliability with Prescriptive Analytics
技术
- 分析与建模 - 机器学习
- 分析与建模 - 预测分析
适用行业
- 金属
- 矿业
适用功能
- 离散制造
- 维护
用例
- 机器状态监测
- 预测性维护
服务
- 数据科学服务
挑战
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.
关于客户
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.
解决方案
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.
运营影响
数量效益
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