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A model for estimating the PFD80 transportable moisture limit of iron ore fines

Ferreira, Rodrigo Fina, Pereira, Tiago Martins, Lima, Rosa Malena Fernandes
Powder technology 2019 v.345 pp. 329-337
empirical models, goethite, iron, laboratory experimentation, liquefaction, mining, powders, risk, shipping, transportation, water content
Shipping is an essential link in the mining industry production chain. Seaborne ore cargo transportation is internationally regulated by the International Maritime Organization (IMO), whose regulatory framework includes laws that aim to ensure the safety and security of shipping. Some wet mineral cargoes may liquefy during passage under certain conditions. This phenomenon can shift the cargo and put the vessel and its crew at risk. According to the IMO regulations, in order to ship these cargoes, the moisture content shall be lower than the so-called Transportable Moisture Limit (TML), a regulatory parameter determined by laboratory tests. Iron ore fines with goethite content <35% are susceptible to liquefaction, and its TML can be obtained through the Modified Proctor/Fagerberg Test for Iron Ore Fines (PFD80), a compaction test that consists in compacting ore samples at several different moisture contents, the TML being the moisture content at which the material reaches 80% saturation. Since 2017, iron ore fines shippers from IMO Member States shall determine the TML of their cargoes preferably using this method, when applicable, which is obviously being included in the scope of ore characterization laboratories. This paper presents a novel empirical model that allows estimating the iron ore fines TML from a single PFD80 compaction point, the first prediction model in the literature related to this test. The method is a useful auxiliary tool for research and control of this parameter, which reduces the response time and the amount of sample required for testing. A performance evaluation conducted for 62 new samples, including other authors' data, showed good fit between observed and predicted TML, validating the proposed model.