Cement Americas

SUM 2019

Cement Americas provides comprehensive coverage of the North and South American cement markets from raw material extraction to delivery and tranportation to end user.

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32 CEMENT AMERICAS • Summer 2019 • www.cementamericas.com FEATURE M ore stringent environmental regulations are pushing the cement industry to find ways to be competitive while improving energy and capacity management. New capabilities, like machine learning, are expanding what's possible. It can help you learn what's happening in your cement operations and also predict what's going to happen. This can then lead to improved production and maintenance strategies, so you can perform better and consume less energy per produced ton. Machine learning using adaptive algorithms is making suc- cessful predictive performance and maintenance strategies ever-more achievable for global cement producers. Before you implement machine learning, however, you will want to examine your business goals and the best ways to bring them to life. There are four principal machine learning strategies in cement: predictive maintenance, anomaly detection, pre- dictive KPIs and model predictive control (MPC). Below, we explore the varying techniques and their unique benefits. Predictive Maintenance: Get Ahead of Failures Predictive maintenance can forecast and identify when major equipment or systems will fail earlier. This allows maintenance teams to address issues before they lead to downtime. Predictive maintenance can also reduce your maintenance spend, because it helps you catch issues earlier. Rather than needing larger amounts of money to repair or replace dam- aged equipment, you can make targeted repairs and avoid threatening issues, altogether. Machine Learning The Right Strategy Can Improve Cement Plant Operational Performance and Energy Management. By Mike Tay

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