Imperial College London
Portrait Picture

ProfessorStephenNeethling

Faculty of Engineering, Department of Earth Science & Engineering

Professor of Minerals Processing
 
 
 
//

Contact

 

+44 (0)20 7594 9341s.neethling

 
 
//

Location

 

RSM 2.35Royal School of MinesSouth Kensington Campus

//

Summary

 

Publications

Citation

BibTex format

@article{Quintanilla:2023:10.1016/j.mineng.2023.108050,
author = {Quintanilla, P and Navia, D and Neethling, SJ and Brito-Parada, PR},
doi = {10.1016/j.mineng.2023.108050},
journal = {Minerals Engineering},
pages = {1--16},
title = {Economic model predictive control for a rougher froth flotation cell using physics-based models},
url = {http://dx.doi.org/10.1016/j.mineng.2023.108050},
volume = {196},
year = {2023}
}
Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - The development of an economic model predictive control (E-MPC) strategy is presented. The strategy uses a novel dynamic flotation model that incorporates the physics of the froth phase in a flotation cell. The dynamic model was previously calibrated and validated using experimental data.Sensitivity analyses were conducted to select a suitable objective function that accounted for both process economics and control variable sensitivities. While the ultimate goal of a rougher flotation cell is to maximise the metallurgical recovery at a steady state for a specified minimum grade, it was evident that the incorporation of air recovery dynamics (which can be measured in real-time) and concentrate grade dynamics (calculated through first-principle models) led to the best results. The addition of a dynamic variable that can be easily measured online, i.e. air recovery, offers great potential to improve plant performance in existing froth flotation systems. Furthermore, a minimum concentrate grade was imposed in the E-MPC strategy. This acts as an economic constraint as it allows the metallurgical recovery to be optimised while ensuring that concentrate grade requirements are met.The dynamic optimisation problem for the E-MPC strategy was discretised using orthogonal collocations, and was implemented in Matlab using automatic differentiation via CasADi. Two typical manipulated variables were considered: air flowrate and pulp height setpoints. Based on laboratory-scale data, the implementation of the E-MPC strategy resulted in improvements ranging from +8 to +22 % in metallurgical recovery, while maintaining the specified grade. This is therefore an encouraging control strategy to explore in larger flotation systems.
AU  - Quintanilla,P
AU  - Navia,D
AU  - Neethling,SJ
AU  - Brito-Parada,PR
DO  - 10.1016/j.mineng.2023.108050
EP  - 16
PY  - 2023///
SN  - 0892-6875
SP  - 1
TI  - Economic model predictive control for a rougher froth flotation cell using physics-based models
T2  - Minerals Engineering
UR  - http://dx.doi.org/10.1016/j.mineng.2023.108050
UR  - http://hdl.handle.net/10044/1/103268
VL  - 196
ER  -
Download