A HPGR pseudo-dynamic model approach integrated with real-time information for pressing iron ore concentrates in industrial-scale
T M Campos1, H A Petit2, R Olympio3 and L M Tavares4
1. Research assistant, Department of Metallurgical and Materials Engineering, Universidade Federal do Rio de Janeiro, 21941–972. Email: This email address is being protected from spambots. You need JavaScript enabled to view it.
2. Post-doctoral fellow, D.Sc, Department of Metallurgical and Materials Engineering, Universidade Federal do Rio de Janeiro, 21941–972. Email: This email address is being protected from spambots. You need JavaScript enabled to view it.
3. Engineer, M.Sc, Vale S.A, Complexo de Tubarão, 29090–860. Email: This email address is being protected from spambots. You need JavaScript enabled to view it.
4. Professor, Ph.D., Department of Metallurgical and Materials Engineering, Universidade Federal do Rio de Janeiro, 21941–972. Email: This email address is being protected from spambots. You need JavaScript enabled to view it..br
ABSTRACT
Much practical experience has been gathered in the last 30 years of application of high-pressure grinding rolls (HPGR) integrated with ball milling in size reduction of fine iron ore concentrates. The company Vale S.A, in Complexo de Tubarão (Brazil), was one of the pioneers applying the technology prior to pelletisation with an outstanding size reduction energy efficiency in the circuit and a significant increment in the specific surface area of the product. Recent studies by the authors demonstrated benefits of modelling and simulation to improve the performance of HPGRs in this particular application, with the model being able to describe HPGR performance under different operating conditions and under variations of feed size distribution. Despite these important
advances, this modelling approach has only been used offline and under steady-state conditions. The present work applies the modified Torres and Casali model proposed by the authors in pseudodynamic simulations. The ability of the model to predict the characteristics of the product in real-time is evaluated using data available online for the pellet feed preparation circuit. Results demonstrated the model capabilities to map the physical operation and give a realistic representation of the process. Additionally, the model is demonstrated to be able to support the pellet feed production by providing extended real-time information of the process, making it a useful tool for improvement of the operational strategies and process stability