Data-Driven Adaptive Optimal Control for Flotation Processes With Delayed Feedback and Disturbance

Abstract

Considering the presence of time-delay in actuators and nonvanishing disturbance, a datadriven control technique is developed based on adaptive dynamic programming (ADP) to solve the reagents control problem for flotation processes. First, the reagents control problem is converted into an optimal regulator control problem. Second, a policy iteration (PI) algorithm has been adopted to find desirable adaptive suboptimal controllers. Unlike conventional controllers (PID, MPC) design, the proposed method can achieve the desired control performance by only employing online production data without the complete knowledge of the underlying flotation process dynamics. Specifically, the flotation indexes (tailing grade and concentrate grade) are forced to track the desired values with disturbance rejection and keep reagents consumption to a minimum. The convergence and stability of the proposed data-driven optimal control method are given, and the efficacy is validated in the simulation environment with industrial data. INDEX TERMS Adaptive dynamic programming (ADP), disturbance rejection, flotation processes, inputs time-delay, reagents control.

Decentralized proportional-integral controller based on dynamic decoupling technique using Beckhoff TwinCAT-3.1

“…A graphical representation of column flotation, with a gas bubble generator system utilized for the generation of the bubbles, is presented in Figure 3. Added at the top of the operational tank is a froth washing system, whose purpose is to wash away the infected minerals from the valuable minerals within the froth [16], [24]. The flotation column system as presented by [25], [26] is adopted, as it shows significant interactions…”

Flotation Froth Monitoring Using Unsupervised Multiple Object Tracking Methods

“…The hydrophobic ore particles of valuable minerals are attached to the formed froth bubbles. Bubbles recover upwards, and the remaining part of the ground ore descends (Figure 2), [1]. It is hard to achieve good separation characteristics in the only flotation bank.…”

“…For example, the concentration of copper may already increase from 2% to 4% after the slurry has passed through just one flotation machine. And you can have up to ten flotation machines to increase this concentration several times (Figure 3), [1]. The visual characteristics of the froth can tell us a lot about the physical [2,3] and chemical processes inside the flotation cell, which helps to optimize the overall control of the flotation process [4][5][6][7][8][9][10][11].…”