4.1 Article

Optimization of the microwave roasting extraction of palladium and rhodium from spent automobile catalysts using response surface analysis

Journal

INTERNATIONAL JOURNAL OF MINERAL PROCESSING
Volume 143, Issue -, Pages 18-24

Publisher

ELSEVIER SCIENCE BV
DOI: 10.1016/j.minpro.2015.08.007

Keywords

Rh; Pd; Spent automobile catalysts; Microwave roasting extraction; Response surface methodology

Funding

  1. National Natural Science Foundation [51104073]
  2. Yunnan Province Young Academic Technology Leader Reserve Talents [2012HB008]

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The optimum conditions for the extraction of palladium and rhodium from spent automobile catalysts by microwave roasting were determined using response surface methodology (RSM). A central composite design (CCD) was used to investigate the effects of three independent variables (namely proportion (m/m, reagent/material), roasting temperature (degrees C) and holding time (min)) on the leaching rate of palladium and rhodium. The independent variables were coded at three levels and their actual values based on experimental results. The CCD consisted of 20 experimental points and six replications at the center point. Data were analyzed using design expert and statistical analysis system software. A second-order polynomial model was used for predicting the response. The model explained 53% more variation. Canonical analysis of surface responses shows that the stationary surface was a saddle. The optimal conditions for the leaching rate of palladium and rhodium obtained using ridge analysis were 9.75, 550 degrees C, 60 min; 9.00, 540 degrees C, 50 min; and 9.96, 550 degrees C, 53.84 min, respectively. The predicted leaching rate of palladium is more than 99.29% and the predicted leaching rate of rhodium is more than 95.34% under three optimum conditions. In order to obtain the highest leaching rate, the conditions for the extraction of palladium and rhodium were tuned to 10, 550 degrees C, 60 min. The actual leaching rates of palladium and rhodium were 99.33% and 95.48% at the tuned conditions, which are very similar to those predicted by RSM models. (C) 2015 Elsevier B.V. All rights reserved.

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