4.7 Article

Roasted zinc concentrate leaching: Population balance modeling and validation

期刊

HYDROMETALLURGY
卷 175, 期 -, 页码 208-217

出版社

ELSEVIER SCIENCE BV
DOI: 10.1016/j.hydromet.2017.11.013

关键词

Roasted zinc concentrate; Population balance; Leaching; Modelling; Hydrometallurgy

资金

  1. PROEX/CAPES (Programa de Excelencia Academica/Coordenagelo de Aperfeigoamento de Pessoal de Nivel Superior)
  2. FAPEMIG (Fundaceio de Amparo a Pesquisa do Estado de Minas Gerais)
  3. CNPq (Conselho Nacional de Desenvolvimento Cientifico e Tecnologico)

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A Population Balance (PB) model was developed for the sulfuric acid leaching of a roasted zinc concentrate (RZC). Considering that no previous work has validated with experimental data a PB model for this system, leaching experiments were done in order to validate and refine the proposed model. Initially, the chemical and mineralogical composition of the RZC, determined by XRF, XRD, and AAS, indicated the predominance of zinc (65% w/w), mainly as zincite. Next, leaching experiments were carried out using typical conditions of industrial processes. The parameters investigated were the stirring speed, the acid concentration, the reaction time, and the molar ratio, which is the molar ratio between the acid and the zincite present in the RZC loaded to the reactor. It was observed that the acid concentration affected had the major influence on the initial leaching rate, while the molar ratio determined the maximum zinc extraction achieved. It was also found that the model correctly described this behavior, which suggests that the model accounted for the major phenomena occurring during the leaching. Then, using statistical analysis, it was found an excellent agreement between the experimental and calculated data, which verified the validity and accuracy of the proposed model. A potential application of this model would be predicting the effect of changes in operational parameters (e.g., particle size, distribution polydispersity, and acid concentration) on the reactor performance. Thus, the model provides a robust technique for plant design and control, which may lead to better decision-making and costs reduction.

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