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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MINE & PLANT DESIGN tools needed to achieve those goals has generally involved tailored, often proprietary datacom solutions that sometimes don't play well, or at all, with others, leading to data management inefficiencies. However, where once these solutions may have been addressed individually, during various phases of project development or commercial production, or by different teams or business groups within the mining organization, the pressures of today's business environment and the opportunities offered by advanced technology and broader interconnectivity are now pushing mine operators to look more closely at planning their projects from the start to include allinclusive, well-integrated strategies and arrangements for managing data effectively. Meanwhile, in recent years, the lingering financial burdens of the Great Recession, weakening investor interest and rising competitive intensity also have forced operators to pay more attention to asset management and optimization solutions for mine and plant projects as well, in order to improve capital efficiency and gain better overall economic performance. The producer that puts these puzzle pieces into place most quickly—efficiently applying data collection and analysis to support and inform operational and longterm management decisions—may be the first to grab the competitive edge offered by real-time control of the entire mineralproduction process. That achievement likely begins by developing or adopting the platforms needed to connect existing disparate systems in one location together, moving on to wider integration of systems at multiple locations as dictated by a company's business strategy. That, in turn, would require acceptance and understanding of the "Internet of Things (IoT)," a concept that, right now, has many definitions but few standards. One of the more cogent definitions of IoT is offered by SAP, the German multinational software corporation that makes enterprise software. According to SAP, IoT is: "A world where physical objects are seamlessly integrated into the information network, and where the physical objects can become active participants in business processes. Services are available to interact with these 'smart objects' over the Internet, query and change their state and any information associated with them, taking into account security and privacy issues." The Road to Interoperability Schneider Electric, for example, considers the trends of Big Data and modernization emerging within the mining segment as ways to address some of the challenges. "Big Data for Mining" under its integrated mine planning and optimization philosophy implements data collection and analytics across mining operations, emphasizing the importance of integration across disciplines, open architectures, and asset and resources management within mine sites. Big Data extracts value by leveraging data gathering and process analytics onto existing embedded capabilities to identify abnormalities and pinpoint waste. Schneider Electric also offers "Intelligent Modernization for Mining" as a related approach when consulting with its customers on modernization strategies, focusing on creating efficiency through operations and supply chain that use less energy and water, increase productivity, reduce maintenance costs and increase cost effectiveness. Intelligent modernization focuses on ensuring that capital investments are offset by an increase in efficiency to streamline operations to keep costs low. E&MJ; spoke recently with Greg Magdanz, director of the Mining, Minerals and Metals Competency Center at Schneider Electric, about the opportunities and challenges Big Data and modernization present for the mining industry. Magdanz said, "Schneider Electric sees Big Data Analytics as a tool to assist producers in handling crucial business issues such as skilled labor shortages, rising consumables and energy costs, and problems associated with developing orebodies that are likely to be deeper in the earth, located in more remote locations, and quite probably lower in grade than before." "If operators can't find ways to get ahead of these issues, they'll likely be facing constantly lower margins and operational problems. Our goal is to provide solutions that easily integrate across the mine and provide the kind of information that leads to better business decisions," Magdanz explained. He noted that solutions can be implemented as early as the feasibility or even prefeasibility stages of a project, enabling, for example, project team members to simulate and define the scope and components of mine-site logistics—size of haulage fleet, conveyance and storage needs, rail or ship loading capacity, etc. But according to Magdanz, it's really never too late to incorporate modernization into a project or operating mine: "Customers don't have to go full-bore into a complete modernization program. All of our solutions are based on modular concepts, and because we design them on openarchitecture principles, it doesn't matter so much what specific type or brand of equipment is installed in the mine or plant. The modules can be implemented on a progressive basis to reach the level of data communications and collection required." Implementing Intelligence Bentley Systems, a provider of "information mobility" systems to improve asset performance, also offers a roadmap to "intelligent mining," a concept that was explained in a presentation by Bentley's Leslie McHattie, a reliability practitioner, at the 23rd World Mining Congress in Montreal, Canada. Data Mining vs. Mining's Big Data: What's the Difference? When compared with data in other sectors (e.g., government, financial services and retail), industrial data (such as that derived in a mining and processing environment) is different. It's creation and use are faster; safety considerations are more critical; and security environments are more restrictive. Computation requirements are also different. Industrial analytics need to be deployed on machines—sometimes in remote locations—as well as run on massive cloud-based computing environments. As a result, the integration and synchronization of data and analytics, often in real time, are needed more than in other sectors. Industrial busiwww.e-mj.com nesses require a Big Data platform optimized for these unique characteristics. The need for a new industrial Big Data platform is also driven by the advent of new and cheaper forms of computing, storage, and sensor technology, as well as the growing complexity of industrial companies themselves. Furthermore, industrial operators are looking for more consumer-like experiences in their workplaces, especially as the current generation retires. — The Case for an Industrial Big Data Platform: Laying the Groundwork for the New Industrial Age, GE Software, 2013. JANUARY 2014 • E&MJ; 43