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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www.cementamericas.com • Summer 2019 • CEMENT AMERICAS 33 FEATURE Ideally, your predictive maintenance technique archives data and maps past failure types to understand what the various classes of failures look like. This will help the sys- tem identify when a similar pattern appears in the future. It can then notify the operator that maintenance is needed for the specific issue to help avoid downtime and reduce maintenance costs. Most large-scale moving equipment (conveyors, drives, shakers) can move toward more ideal maintenance cycles (not too early, not too late) with the support of failure-de- tection models. Anomaly Detection: Uncover Poor Performance Anomaly detection learns normal patterns for cement operations, such as grinding, blending, and kiln cooling and preheating. It can then alert operators when something is operating abnormally or something is wrong in the process. For example, imagine your clinker system is not cooling suf- ficiently. Anomaly detection could help you detect the spe- cific problem that is occurring – perhaps the cooler exhaust temperature is high for current loading or distributed unusually. Additionally, the anomaly-detection technology could alert the operator of the issue much earlier than nor- mal alarm conditions. This allows operators to react before downtime, extended periods of poor performance or other issues occur. Anomaly detection can also tell you, specifically, why an issue appears unusual, rather than simply triggering an alarm or alert. Getting to the root of the issue quickly can decrease your downtime and increase your overall equip- ment effectiveness (OEE) by flagging where improve- ments can be made for things like availability, productivity and quality. If you struggle with performance issues in your plant, have significant, fairly frequent issues, but it generally takes too long to troubleshoot, this is a great place to start. Predictive KPIs: Hit Your Goals Predictive KPIs forecast results and estimate what is caus- ing poor performance in areas like product quality, energy efficiency, throughput and yield. This technique utilizes regression models to predict typical process indicators and whether or not they stay within expected ranges. Some questions that you can answer with predictive KPIs include: Is your Blaine or free lime on track? Is the hot-end kiln temperature correct? Are exhaust emissions at accept- able levels? And if not, how can it be improved to get back in line with achievable targets? Predictive KPIs are used to focus on a specific measurement of success for your operations. Anomaly detection may tell you that a problem is occurring in your plant, but a predic- tive KPI will allow you target this specific issue, whether it is yield, energy management or productivity. MPC: Optimize Your Process MPC is complete, closed-loop decision automation. It uses dynamic process models to coordinate and stabilize a cement process at maximum performance levels, while keeping safely within equipment limits. Because MPC is a more complex, multivariate optimizing technology, it can drive multiple KPIs simultaneously to improve and tradeoff overall performance. Imagine being able to wake up in the morning and have your decisions for things like breakfast duration, feasible trip stops or transportation made for you. MPC brings this capability to your cement operations. The plant is constant- ly driving toward the KPIs you prioritize, the right achiev- able balance of throughput, yield, quality and energy man- agement. For example, MPC can drive mills and kilns to their best performance 24/7 by pushing them to the right active constraints. Measuring MPC is fairly simple. The strategy will target a specific set of KPIs. These are usually tons of cement pro- duced, specific energy cost and product quality (interme- diate or final). An MPC solution will look at the sets of KPIs and drive to a targeted balance within equipment and quality limits. MPC is even more effective the better you understand and can specify relative issues and KPIs to target in operations. Start Learning Many machine-learning techniques improve time to val- ue, making it easier for you to implement one or more of the above solutions to assist with operator decision-mak- ing. Before diving into machine learning, work to under- stand the current opportunities and challenges of your operations and what relevant data is available and being archived. S Mike Tay is an advanced analytics product manager for Rockwell Automation. For more information on machine learning, visit www.rockwellautomation.com.

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