On-line monitoring of base metals solutions in flotation using diffuse reflectance spectrophotometry

[摘要] ENGLISH ABSTRACT: This work evaluates the use of inverse least squares (ILS) and classical least squares (CLS) models for calibration of a diffuse reflectance spectrophotometer for on-line monitoring of the aqueous phase in a flotation cells. Both models use a Beer's law for the quantification of the metals. The formulated statistical models are compared to a proprietary Blue Cube model in terms of prediction ability to determine the potential applicability of the models. A diffuse reflectance spectrophotometry was used for simultaneous analysis of copper (Cu), cobalt (Co) and zinc (Zn) in the solutions.The laboratory set-up of Blue Cube instrument was used for the experimental analysis. The concentrations and matrix compositions of the samples are simulated according to Skorpion zinc mine plant conditions. The calibration samples were prepared using a simplex-centroid mixture design with the triplicates of the centroid run. The unknown or test samples were prepared randomly within the same concentration of the calibration samples. The effects of temperature and nickel concentration on absorption of the metals were evaluated in the following range, 20 - 80 °C and 125 - 400 ppm, respectively.The statistical models (ILS and CLS) were calibrated from visible and near infrared (VNIR) spectra data of the calibration samples. A modified Beer's method was used as a preprocessing technique to convert the raw data into absorbance values. The manual wavelength selection procedure was used to select the wavelengths to be used in both models. The quality of the models was evaluated based on Rª and % root mean squared error (RMSE) values with 0.90 and 10% used as the guideline for the respective statistical parameters.Both ILS and CLS models showed good results for all three metals (Cu, Co and Zn) during their calibration steps. It was further shown that both models give worse predictions for Zn as compared to other metals due to its low relative intensity in the mixture. The derivative orders of absorbance spectra that were used to enhance the prediction results of Zn had no positive effect but they rather lowered accuracy of predictions. An increase in temperature was found to increase the intensities of the absorption spectra of all the metals while an increase in nickel concentration decreases the prediction ability of model.The developed statistical models were compared to a Blue Cube model in terms of prediction ability using analysis of variance (ANOVA) test. The ANOVA results revealed that there is no statistical difference between the developed models and Blue Cube model since the F-values for all the metals were below the critical F-value. Furthermore, the partial least squares (PLS) model shows an increased accuracy results for prediction of zinc metal as compared to both the ILS and CLS models. Finally, good comparisons of the statistical models results with atomic absorption spectroscopy (AAS) analyses were establish for the unknown samples.The study demonstrates that chemometric models (ILS and CLS) developed here can be used for quantification of several metals in real hydrometallurgical solutions as samples were simulated according to a plant conditions. However, in order to have confidence in the results of the models, a factorial-mixture design must be used to study the effect of temperature and nickel concentration. Moreover the models must be further tested and validated on the real samples from a plant.

[发布机构] Stellenbosch University