Implementation and Performance Evaluation of a Model Predictive Controller for a Semi-Autogenous Grinding Mill

• Autorzy: Shende Dipali Rohit , Simon Abner

Identyfikatory

DOI

10.12913/22998624/186763

• Języki publikacji: EN

Abstrakty

EN

This paper investigates the implementation of a model-based predictive control (MPC) strategy to improve the performance of a semi-autogenous grinding (SAG) mill in a uranium mineral processing plant. The SAG mill, crucial in crushing and grinding uranium ore to the desired size, is currently managed using conventional proportional-integral-derivative (PID) controllers. However, to enhance production efficiency and control over the SAG mill's variables, this paper suggests the adoption of MPC. The proposed MPC controller is developed using a neural network (NN) model of the SAG mill, created in MATLAB with data collected over 21 days. The effectiveness of the MPC controller is assessed by contrasting its response with that of the real-time operator control. This comparison utilizes tools like MATLAB and the RSlinx remote server for accessing OPC real-time data. Findings reveal that the MPC controller exhibits a quicker reaction to alterations in the SAG mill's process outputs and proficiently regulates crucial outputs such as Mill mass, ensuring that the manipulated variables stay within their designated limits. Unlike operator control, which is slower and adjusts one variable at a time, the MPC approach can maximize the mill's throughput rate without impacting the ore feed rate. This demonstrates the MPC controller's superior ability to optimize SAG mill operations efficiently.

Słowa kluczowe

EN

model predictive control; semi-autogenous grinding mill; uranium ore processing; neural network modeling and operational efficiency optimization

• Wydawca: Lublin University of Technology ; Polish Society of Ecological Engineering (PTIE), Branch of PTIE in Lublin
• Czasopismo: Advances in Science and Technology. Research Journal
• Rocznik: 2024
• Tom: Vol. 18, no 3
• Strony: 257--269
• Opis fizyczny: Bibliogr. 18 poz., fig., tab.

Twórcy

autor

Shende Dipali Rohit

• Engineering Institute of Technology, 6 & 8 Thelma Street, West Perth WA 6005, Australia

• https://orcid.org/0000-0002-1474-2027

autor

Simon Abner

• Engineering Institute of Technology, 6 & 8 Thelma Street, West Perth WA 6005, Australia

Bibliografia

• 1. Salazar J.-L., Valdés-González H., Vyhmesiter E. Vyhmesiter E. Model predictive control of semiautogenous mills (SAG). Minerals Engineering 2014; 64: 92–96.
• 2. Wang X., Yi J., Zhou Z., Yang C. Optimal speed control for a semi-autogenous mill based on discrete element method. Processes 2020; 8: 233.
• 3. Yuwen C., Liu S., Sun B. Dynamic model of semiautogenous (SAG) mill system. In 2019 IEEE 15th International Conference on Control and Automation (ICCA), Edinburgh, Scotland 2019.
• 4. Garrido C.Q., Sbarbaro D.H. Multivariable model predictive control of a simulated SAG plant. IFAC Proceeding Volumes 2009; 42(23): 37–42.
• 5. Hadizadeh M., Farzanegan A., Noaparast M. Supervisory fuzzy logic expert controller for SAG mill grinding circuits: Sungun copper concentrator. Mineral Processing and Extractive Metallurgy Review 2017; (38)3: 168–179.
• 6. Yutronic N.I., Rodrigo T. Design and implementation of advanced automatic control strategy based on dynamic models for high capacity SAG mill 2010. doi: 10.13140/RG.2.1.4345.3206.
• 7. Ruel M. Fuzzy logic control on a SAG Mill. In 16th IFAC Symposium on Automation in Mining, Mineral and Metal Processing, San Diego, California, USA 2013.
• 8. Karelovic P., Razzetto R., Cipriano A. Evaluation of MPC strategies for mineral grinding. In 16th IFAC Symposium on Automation in Mining, Mineral and Metal Processing, San Diego, California, USA 2013.
• 9. Abraham A., Pappa N., Honc D., Sharma R. Reduced order modelling and predictive control of multivariable nonlinear Reduced order modeling and predictive control of multivariable nonlinear. Sadhana 2018; 43(30).
• 10. Le Roux J.D., Padhi R., Craig I.K. Optimal control of grinding mill circuit using model predictive static programming: A new nonlinear MPC paradigm. Journal of Process Control 2014; 24: 29–40.
• 11. Lia H., Evertssonb M., Lindqvista M., Hulthénb E., Asbjörnssonb G. Dynamic modeling and simulation of a SAG mill-pebble crusher circuit by controlling crusher operational parameters. Minerals Engineering 2018; 127: 98–104.
• 12. Bauer M., Brooks K., Burchell J., Coetzee L., Le Roux D., McCoy J., Miskin J. and Winter D. The use of semi-rigorous SAG mill model for hands on workshop. In IFAC PaperOnline 2020.
• 13. Le Rouxa J., Craig I., Hulbert D., Hindec A. Analysis and validation of a run-of-mine ore grinding mill circuit model for process control. Mineral Engineering 2013; 45: 121–134.
• 14. Tano K. Continuous monitoring of mineral processes with a special focus on tumbling mills – a multivariate approach. Agricola Research Centre- Doctoral Thesis 2005; 1402–1544.
• 15. Agarwal S., Ganguli R. Refining automated modeling of operational data by identifying the most important input factors. Mining Engineering 2011; 63(12): 52–54.
• 16. Gough W.A., Eng P. SAG Mill optimization using model predictive control. Andritz Automation Ltd, 13700 International Place, Suite 100, Richmond, BC Canada V6V 2X8.
• 17. Brian P., Fred K., Leigh S. Single Stage SAG/AG Milling Design. Canadian Institute of Mining, Metallurgy and Petroleum 2012; 1: 11.
• 18. Magdziak M. Application of coordinate measuring machines for analysis of a controlled radius based on linear regression. Adv. Sci. Technol. Res. J. 2024: 11–25

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).