The solution for stabilizing and improving thickening operations
Control stabilization with BrainWave
BrainWave is a patented advanced controller that outperforms traditional Proportional-Integral-Derivative (PID) control because of its two main components: an adaptive model and a predictive controller. BrainWave builds its own live models during normal plant operations, a powerful feature not offered by traditional Model Predictive Control (MPC) systems.
BrainWave’s predictive controller accurately forecasts process responses and accounts for multiple objectives. It adapts to process conditions, such as changes in production rate, keeping the process on target. BrainWave can also accept measured disturbance inputs, like raw materials properties, and takes corrective action before the process is pushed off target (PID, by comparison, must wait for the error to occur, and then react).
BrainWave easily integrates with existing control systems and its patented Laguerre technology means an average implementation time of just a few weeks. Best of all, the plant’s own staff can support and deploy BrainWave.
Control stabilization for thickeners
BrainWave stabilizes the operation of all types of concentrate thickeners, resulting in improved operation and increased production. Concentrate thickeners pose a challenging control problem, as both a nominal bed depth and product density must be maintained for proper operation. Using BrainWave, both of these objectives can be satisfied. Bed depth is controlled by monitoring rake torque and making continual adjustments to the target density, within a preconfigured range. In turn, the product density is maintained by varying the pulling rate from the thickener unit.
Conventional controllers struggle with handling the slow dynamics that are inherent in concentrate thickeners. BrainWave, however, is able to account for these slow dynamics due to its model-based predictive control algorithm. Dynamics may slowly vary over time, due to such factors as build-up in the thickener vessel. BrainWave accounts for these changes by using its built-in model adaptation algorithm. This algorithm enables BrainWave to adjust its internal model of the process based on real-time observations and to maintain tight control, regardless of changing dynamics.