Predictive Control applied to a mathematical model of a Flotation Column

Abstract

As the flotation process is multivariable, this work investigates the implementation of a predictive multivariable controller for operation a typical flotation column. This controller was tested using a model with delays of a prototype column mounted on Nuclear Technology Development Center (CDTN). Taking as input signals the flushing wash water, air feeding, and non floated fraction flow rates, the controller determines the froth layer height and air holdup in the recovery zone. This control maintains stability. The operation of the controller is based on the optimization of a cost function. The conducted tests were based on the change of setpoint of the controlled variables. It was intended to analyze the system behavior for different operation conditions, considering the constraints of the process and the response speed.

Design of a Decentralized PID Controller Using the Relative Gain Array Technique for a Coupled Flotation Process

“…This was achieved through the application of Adaptive Reinforcement Learning (ARL). The flotation column system is one of the multivariable processes with an objective of metallurgical performance control to ensure compliance with the process operation as shown by the grade and recovery of valuable minerals [10,[12][13][14][15][16][17]]. An essential industrial procedure for extracting rich minerals from gangue minerals is froth flotation [17].…”