Dynamic Modeling and Real-Time Monitoring of Froth Flotation

Minerals

Khushaal Popli²  Masih Sekhavat²  Artin Afacan²  Stevan Dubljevic²  Qi Liu²  Vinay Prasad¹ 

[1] Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB T6G 3G6, Canada;

关键词: froth flotation;    fundamental model;    extended Kalman filter;    image analysis;    principal component analysis;    partial least squares regression;    fault detection;   

DOI  :  10.3390/min5030510

来源: mdpi

【 摘 要 】

A dynamic fundamental model was developed linking processes from the microscopic scale to the equipment scale for batch froth flotation. State estimation, fault detection, and disturbance identification were implemented using the extended Kalman filter (EKF), which reconciles real-time measurements with dynamic models. The online measurements for the EKF were obtained through image analysis of froth images that were captured and analyzed using the commercial package VisioFroth (Metso^® Minerals). The extracted image features were then correlated to recovery using principal component analysis and partial least squares regression. The performance of real-time state estimation and fault detection was validated using batch flotation of pure galena at various operating conditions. The image features that were strongly representative of recovery were identified, and calibration and validation were performed against off-line measurements of recovery. The EKF successfully captured the dynamics of the process by updating the model states and parameters using the online measurements. Finally, disturbances in the air flow rate and impeller speed were introduced into the system, and the dynamic behavior of the flotation process was successfully tracked and the disturbances were identified using state estimation.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.