Grinding and Flotation Optimization Using Operational Intelligence
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In recent years, metal-producing companies have increased their investment in automation and technological innovation, embracing new opportunities to enable transformational change. Transformation to a digital plant can fundamentally revolutionize how industrial complexes operate. The abundant and growing quantity of real-time data and events collected in the grinding and flotation circuits in a mineral processing plant can be used to solve operational issues and optimize plant performance. A grade recovery model is used to identify the best operating conditions in real time. The strategy for increasing the value of instrumentation in current plants is reviewed. An optimal Gaudin size distribution model provides augmented information from traditional sensors to find the optimal grind cut size to reduce metal losses in the flotation circuits. Sensors in flotation circuits enable an estimate of the recovery and determination of the optimal froth depth and aeration using an air hold up flotation model. A strategy of classifying data for on-line generation of insights to using operational intelligence tools is described. The implementation of a recovery/grind strategy with industrial examples in non-ferrous mineral processing is presented.
KeywordsDynamic performance management Digital plant template Operational intelligence Machine learning Grind cut flotation optimization Particle size distribution shape Flotation bank air hold up profile Invisible losses tracking
The authors acknowledge the support of OSIsoft to publish this technical paper and the participation of many people that have contributed in this over the years.
Compliance with Ethical Standards
Conflict of Interest
The authors declare that they have no conflict of interest.
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