Abstract
Mineral processing plants are inherently complex multivariate interacting system characterized generally by nonlinear dynamics, long dead times, internal and external disturbances, and recirculating loads which is challenging for conventional single input single output control strategies to address. Declining ore grade and increasing variability of raw material, cost of chemical reagents, stringent regulatory requirements and ever increasing competitive global market economy are pushing the limits of processing plants even more.
In spite of these pressing challenges and competing economic and control objectives, most mining plants are still using basic PID controllers. In addition, even though modern mining plants are equipped with sophisticated instrumentation and data acquisition systems, most often the huge amount of data accumulated in their historians is rarely used to advance the economic objectives of plant operation.
How will you achieve your control and economic objectives while meeting operational and regulatory constraints? In this presentation, we will highlight how modern MPC technologies can be leveraged to achieve these competing economic and control objectives and maximize mining plant bottom-line performance to increase their competitive advantage. MPC is a proven practical multivariate predictive/ proactive optimizing controller that is suited for such complex and interactive multivariate systems characterized with long response time and recirculating load with myriads of industrial application that outperform proportional-integral-derivative and expert system control methods.