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A simple and highly selective non-extractive spectrophotometric method is presented for the rapid determination of uranium in low grade uranium ores using arsenazo (III). The method is based on the complex formation of uranium (VI) with Arsenazo (III) at pH 2.0 ± 0.1 which showed maximum absorption at 651 nm. Uranium concentration of 10 µg g-1 with a molar absorptivity of 4.45⋅10 4 mol-1 ⋅cm-1 at 296 ± 2 K obeyed Beer's law. Interferences caused by various metallic ions, such as Mn, Fe, Zn, Mo, Cr, Cu, Co, Ni, Zr, Pb, Al and Na were effectively masked by diethylenetriaminepenta-acetic acid (DTPA) and tartaric acid. The proposed technique has been effectively applied to the determination of low levels of uranium in uranium leach liquors. The accuracy of the current method was checked by comparison with the results obtained by inductively coupled plasma-optical emission spectroscopy (ICP-OES).

A quantitative structure-property relationship (QSPR) study based on partial least squares (PLS) and artificial neural network (ANN) was developed for the prediction of ferric iron precipitation in bioleaching process. The leaching temperature, initial pH, oxidation/reduction potential (ORP), ferrous concentration and particle size of ore were used as inputs to the network. The output of the model was ferric iron precipitation. The optimal condition of the neural network was obtained by adjusting various parameters by trial-and-error. After optimization and training of the network according to back-propagation algorithm, a 5-5-1 neural network was generated for prediction of ferric iron precipitation. The root mean square error for the neural network calculated ferric iron precipitation for training, prediction and validation set are 32.860, 40.739 and 35.890, respectively, which are smaller than those obtained by PLS model (180.972, 165.047 and 149.950, respectively). Results obtained reveal the reliability and good predictivity of neural network model for the prediction of ferric iron precipitation in bioleaching process

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