Mineral Processing Plant Asset and Energy Optimization: The Calming Cloud over Operations

"Large variability of ore types, escalating variables operating costs and increased throughput require large investment in sensors and systems in mineral processing operations. Mining and mineral processing plants collect and manage more data than a small city with much in real-time. The data is changing rapidly as internal and external conditions evolve. As corporations buy and sell assets, and reengineer their staff, the structure and tools that were used to understand this data is lost. This results in significant deterioration in the ability to maintain a smart operation. When data is recognized as a critical asset and managed as part of an infrastructure, it become a key enabler to help transform the entire operations. In many situations, all partners in business collaboration—such as join ventures, contract manufacturer, expert service providers, and operations and maintenance companies—need access to production and asset data. The emergence and maturation of Cloud technologies make it possible to easily connect the plants to external equipment suppliers and operational support service.This paper will share examples of how mineral processors have adopted new data strategies such as self-service business intelligence, cloud computing and internal/external collaboration.INTRODUCTIONOne of the main challenges in mineral and metallurgical processing plants is effective energy and water management. The lowering of ore grades requires higher throughputs while the ores are getting more complex with variable mineralogical compositions. Electrical energy supply is becoming a restriction in many countries. Rising water management costs has a direct impact on the profitability of the operations. Grade recovery versus energy consumption in copper production for both open pit and underground mines has been described by Fuerstenau (2001). Comminution uses over 55% of the energy to produce nonferrous metals while other operations require much less energy (Nelson, Richins, & Lelinski, 2013)."