Engineering & Mining Journal

JUL 2018

Engineering and Mining Journal - Whether the market is copper, gold, nickel, iron ore, lead/zinc, PGM, diamonds or other commodities, E&MJ takes the lead in projecting trends, following development and reporting on the most efficient operating pr

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JULY 2018 • E&MJ 65 PROCESSING SOLUTIONS running for a year already at an African operation, and "… the results are quite promising. By analyzing data from three connected Metso Nordberg MP crushers, we have been able to identify certain failure modes and predict some failures ahead of time. I believe it will not be long before we can start predictively maintain- ing these crushers based on actual and forecast component wear. "Going forward, we will be able to col- lect and analyze data from an increasingly wide variety of machines within the com- minution circuit," he said. "We will also be able to predict — and prevent — a lot more of the different equipment failure modes. The machine designs will improve at a radically faster pace when the engi- neers who design and build the machines can see data from the machines and how they perform in the fi eld in real-world conditions. Furthermore, we will be able to increase our customers' crushing cir- cuit performance by optimizing crushing effi ciency and by retaining a more consis- tent particle-size distribution." He also noted that although predic- tive maintenance is a proven concept in mobile mining fl eets, it has taken a sur- prisingly longer time for this concept to fi nd its way to processing. "One of the major roadblocks has been the non-stan- dardized nature of the processing plants. While mobile fl eets are often standard- ized around one vendor and type, virtually every mining company in the world runs a 'mixed fl eet' in their processing plant. The plants consist of machines from mul- tiple suppliers and are of starkly differ- ent ages and models, built according to a custom fl owsheet design. This has slowed the adoption of data, AI and IoT in the fi xed plant. But all of that is about to change now," he said. Picking Up the Pace It's clear the pace of preventive mainte- nance development for plants is quicken- ing, as suppliers weigh in with new design and maintenance solutions on an almost monthly basis. Rockwell Automation, for example, recently announced it will offer predictive maintenance as a service to help prevent unwanted plant downtime. The data-driven service, to be ap- plied on critical assets identifi ed by the customer, analyzes data from connected technologies, such as sensors, control systems and smart machines. Leveraging FactoryTalk Analytics and applying ma- chine learning technology, engineers from Rockwell Automation can identify normal operations and build out data models to help predict, monitor for, and mitigate future failures or issues as part of a pre- ventive maintenance strategy. "By building up that backlog of histor- ical data, we can start to see when there might be issues down the road," said Phil Bush, remote monitoring and analytics product manager, Rockwell Automation. "This helps end-users focus on solving the problem proactively and minimizes the impact on production, rather than waiting for something to fail and trying to troubleshoot on the fl y." Prior to deploying a predictive mainte- nance solution, one Rockwell Automation customer experienced a bearing failure that resulted in more than $3 million in costs related to maintenance and lost productivity. After working with Rockwell Automation to review over a year's worth of historical data on the asset that failed, engineers found that the bearing cooling system had not been correctly operating for six months. If a predictive mainte- nance service had been deployed, the or- ganization would have been able to iden- tify the bearing failure and its root cause before the failure occurred. According to Rockwell Automation, OEMs can implement the service on an asset and use the predictive capability to provide better uptime performance scaled across all similar customer assets. Using this service, said the company, provides customers the ability to moni- tor predictions and analyze details of an alert without needing to build their own data models or engineer their own solu- tion. Rockwell Automation delivers the data collection, machine learning and engineering support to build the models, validate and monitor patterns and predic- tions, and keep those models up to date as data sets evolve. Taking a slightly different tack, Out- otec reported it is optimizing the way in which it undertakes and stores the infor- mation from its process-equipment fi eld service site inspection reports. It is mov- ing from a pen and paper method to a dig- ital on-site inspection and cloud-based data solution, according to an article ap- pearing recently in its Minerva newsletter. The article explained that develop- ment of a mobile app and web service to replace pen and paper meets its needs not only now, but in the future as well. A cloud-based mobile app can make it easier to analyze data, then create dash- boards and reports that can be easily shared with others, allowing access from anywhere. Outotec noted that although there are many inspection applications on the mar- ket today that offer off-the-shelf usability, they are not always easy to adapt to suit highly specifi c requirements. In order to achieve the fl exibility and adaptability it requires with a digital inspection appli- cation, the company decided to develop an application based on a generic plat- form that suits its internal and custom- er requirements. Consequently, Outotec initiated a Proof of Concept program with a number of applications that have been built based on its requirements, with the fi nal stage of PoC involving fi eld testing at Boliden's Kevitsa site. According to the company, the fi eld test was successful, with fi nalization and implementation of the solution to follow shortly. Teaching Technology Approaching process-plant digitalization from a big-picture perspective, Siemens' Process Industries and Drives (PD) and Digital Factory (DF) groups are joining with Bentley Systems and the Bentley In- stitute to establish a Process Industries Academy. The academy's aim, according to the companies, is to share best prac- tice for plant engineering and operations. Facilities will be located in Karlsruhe, Germany (Siemens Process Automation World), Houston, Texas/USA (Bentley's Digital Advancement Academy) and Shanghai, China (Siemens Process Indus- try Centre for Excellence) to support the global process industry. The fi rst event will be held in the second half of 2018 at the Siemens Process Automation World in Karlsruhe. In making the announcement, Sie- mens and Bentley noted that in light of recent developments in the process industries, and the subsequent effects on plant production, established work- ing methods are under constant review across the whole plant life cycle. Less investment in green fi eld projects and an increased focus on optimizing productiv- ity, performance and utilization of exist- ing plants are causing plant operators to search for new ways in which to increase

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