Effect of stirring time on oil agglomeration of fine coal

Wang, Guan; Sha, Jiahao; Liu, P.; Peng, Yaoli; Xie, Guangyuan

doi:10.17159/2411-9717/2018/v118n1a11

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…This is highlighted by the decrease in the LMO entrainment degree in the model black mass between (I) and (II). However, this effect was diminished when kerosene was added during attrition, as shown by our group Vanderbruggen et al [35] and others Guan et al [58], where kerosene enhanced agglomeration, resulting in LMO entrapment. These results suggest that the collector addition step has a strong impact on the entrainment of the impurities in the overflow product.…”

Section: Black Mass and Particle Entrainmentmentioning

confidence: 69%

Improving Separation Efficiency in End-of-Life Lithium-Ion Batteries Flotation Using Attrition Pre-Treatment

et al. 2022

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The comminution of spent lithium-ion batteries (LIBs) produces a powder containing the active cell components, commonly referred to as “black mass.” Recently, froth flotation has been proposed to treat the fine fraction of black mass (<100 µm) as a method to separate anodic graphite particles from cathodic lithium metal oxides (LMOs). So far, pyrolysis has been considered as an effective treatment to remove organic binders in the black mass in preparation for flotation separation. In this work, the flotation performance of a pyrolyzed black mass obtained from an industrial recycling plant was improved by adding a pre-treatment step consisting of mechanical attrition with and without kerosene addition. The LMO recovery in the underflow product increased from 70% to 85% and the graphite recovery remained similar, around 86% recovery in the overflow product. To understand the flotation behavior, the spent black mass from pyrolyzed LIBs was compared to a model black mass, comprising fully liberated LMOs and graphite particles. In addition, ultrafine hydrophilic particles were added to the flotation feed as an entrainment tracer, showing that the LMO recovery in overflow products is a combination of entrainment and true flotation mechanisms. This study highlights that adding kerosene during attrition enhances the emulsification of kerosene, simultaneously increasing its (partial) spread on the LMOs, graphite, and residual binder, with a subsequent reduction in selectivity.

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Section: Black Mass and Particle Entrainmentmentioning

confidence: 69%

Improving Separation Efficiency in End-of-Life Lithium-Ion Batteries Flotation Using Attrition Pre-Treatment

et al. 2022

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“…For this reason, the process can be used for selective separation of one mineral from the mineral waste, e.g. coal purification from inorganic components (Yadav et al, 2018;Guan et al, 2018;Kaya and Ari, 2019;van Netten et al, 2020), coal recovery form tailings (Yasar and Uslu, 2019) or barite separation from carbonaceous minerals (Sadowski, 1995). Despite these advantages of this process, it is not commercially used due to the high costs associated with the use of large amounts of oil (Pietsch, 2005).…”

Section: Introductionmentioning

confidence: 99%

Radial Basis Function Neural Network based on Growing Neural Gas Network applied for evaluation of oil agglomeration process efficiency

Kaminski¹,

Stanislawski²,

Bastrzyk³

2020

Physicochem. Probl. Miner. Process.

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In this study, the neural model for modeling of oil agglomeration of dolomite in the presence of anionic and cationic surfactants (sodium oleate and dodecylammonium hydrochloride) was implemented. The effect of surfactants concentration, oil dosage, time of mixing, pH, and mixing speed of the impeller in the process recovery were investigated using Radial Basis Function Neural Network (RBFNN). A significant problem in this modeling, was the selection of the structure of the neural network. In algorithms based on the RBFNN, the issue mentioned relates to the number of nodes in the determination of the hidden layer. Also, the distribution of functions in data space is significant. In the proposed solution, at this stage of the neural model design, the Growing Neural Gas Network (GNGN) was implemented. Such a procedure introduced automation of the calculation process. The centers were obtained from the GNGN and the structure (number of radial neurons) can be approximated based on a simple searching algorithm. The idea of the data calculations was implemented as an original algorithm that can be easily transferred to Matlab, Python, or Octave software. The values predicted from the neural networks model were in good agreement with the experimental data. Thus, the RBFNN-GNGN model used in this study, can be employed as a reliable and accurate method to predict, and in the future to optimize the performance of oil agglomeration process.

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