Modeling of Bauxite Ore Wet Milling for the Improvement of Process and Energy Efficiency

Evangelos Petrakis () and Kostas Komnitsas
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Evangelos Petrakis: Technical University of Crete
Kostas Komnitsas: Technical University of Crete

Circular Economy and Sustainability, 2022, vol. 2, issue 2, 633-647

Abstract: Abstract Size reduction is a necessary operation in mineral processing plants and provides the desired size for separation operations and the liberation of the valuable minerals present in ores. Estimations on energy consumption indicate that milling consumes more than 50 % of the total energy used in mining operations. Despite the fact that ball milling is an efficient operation, it is energy intensive, and its modeling is a great challenge. In the present experimental study, efforts are made to model wet milling of bauxite ores and identify the optimum material filling volume in the ball mill. Modeling is based on the characterization of the grinding products obtained after various grinding periods and the description of the particle size distributions using mathematical approaches, i.e., Gates–Gaudin–Schuhmann (GGS), Rosin–Rammler (RR), and logistic distributions. In addition, grinding kinetic models were applied to the experimental data in order to identify if the linear theory of the population balance model is valid during bauxite grinding. The experimental data revealed that the logistic distribution is a model that represents more reliably particle size distributions obtained after grinding and fits the experimental data better than the GGS and RR models. Regarding grinding kinetic analysis, it was found that grinding exhibits non-first-order behavior and the reduction rate of each size is time dependent. This experimental work is in line with the Sustainable Development Goal of UN, SDG12: Responsible Consumption and Production and aims to improve the efficiency of ore grinding and thus reduce energy requirements and the associated CO2 emissions.

Keywords: Bauxite ores; Grinding; Kinetic models; Particle size distributions; Energy efficiency; Non-first-order behavior (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s43615-021-00108-y

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