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Experimental implementation of an economic model predictive control for froth flotation

Paulina Quintanilla, Daniel Navia, Stephen Neethling, Pablo Brito-Parada

Abstract

We present the implementation of a novel economic model predictive control (E-MPC) strategy for froth flotation, the largest tonnage mineral separation process. A previously calibrated and validated dynamic model incorporating froth physics was used, which overcomes the limitations of previous simplified models reported in the literature. The E-MPC's optimal control problem was solved using full discretization with orthogonal collocation over finite elements, employing automatic differentiation via CasADi. This approach was applied in a 30-litre laboratory-scale flotation cell, significantly improving mineral recovery from 9% to 29% under feed flowrate disturbances while maintaining a minimum concentrate grade of 20%.

Experimental implementation of an economic model predictive control for froth flotation

Abstract

We present the implementation of a novel economic model predictive control (E-MPC) strategy for froth flotation, the largest tonnage mineral separation process. A previously calibrated and validated dynamic model incorporating froth physics was used, which overcomes the limitations of previous simplified models reported in the literature. The E-MPC's optimal control problem was solved using full discretization with orthogonal collocation over finite elements, employing automatic differentiation via CasADi. This approach was applied in a 30-litre laboratory-scale flotation cell, significantly improving mineral recovery from 9% to 29% under feed flowrate disturbances while maintaining a minimum concentrate grade of 20%.

Paper Structure

This paper contains 7 sections, 2 equations, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Materials and methods
  3. Model overview and control variables
  4. E-MPC strategy
  5. Experimental setup and implementation
  6. Results and discussions
  7. Conclusions

Figures (4)

  • Figure 1: Implementation framework of the E-MPC strategy at the laboratory scale. The E-MPC determines the optimal control actions, which are sent to the Programmable Logic Controller (PLC). The PLC signals the actuators (valves) to reach the set points.
  • Figure 2: Mineral recovery and concentrate grade for different feed flowrates.
  • Figure 3: Air recovery and superficial air velocity for changes in feed flowrate.
  • Figure 4: Level control using tail flowrates($Q_{tails}$). Red lines are set points from E-MPC optimization, and blue lines are filtered pulp height ($h_p$) in process.