Abstract
The Outokumpu flash smelting process is a very successful technology for copper extraction from sulfide concentrate. Numerical simulation has been used for several decades in the analysis and evaluation of the smelting process. However, significant delay in the particle ignition was found in computations of flash furnaces that had great expansion in their productivity. A study was thereafter carried out to investigate how the gaseous flows influence the particle dispersion and combustion. A momentum ratio was defined to describe the effective portion of the pressure forces caused by the lateral and the vertical gaseous flows. Simulations were carried out with Fluent 6.3 (Fluent Inc. The software package is now known as Ansys Fluent of Ansys Inc.) for cases with different momentum ratios as well as of the same momentum value. A detailed analysis and discussion of influences of the gaseous momentum on the particle dispersion are presented. The result reveals that a large momentum ratio combined with large amount of distribution air is helpful for good particle dispersions and thus quicker combustions. Also the process air is found to perform a constraint influence on the particle dispersions, particularly for those of medium and small sizes.
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Notes
The vertical distance from the reaction shaft roof is referred to as the shaft height H, in this article.
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Acknowledgements
Technical and financial support from the Jinguan Copper and Jinlong Copper of the Tongling Nonferrous Metals Group Co. Ltd. is gratefully acknowledged. Sponsorship from the China Scholarship Council and support from Prof. Sheridan John at Monash University, Australia, are deeply appreciated. The authors also wish to thank to the High Performance Computing Centre of Central South University, China, for providing computing resources for all the numerical simulations carried out for the research.
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Zhou, J., Zhou, J., Chen, Z. et al. Influence Analysis of Air Flow Momentum on Concentrate Dispersion and Combustion in Copper Flash Smelting Furnace by CFD Simulation. JOM 66, 1629–1637 (2014). https://doi.org/10.1007/s11837-014-1115-8
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DOI: https://doi.org/10.1007/s11837-014-1115-8