Automatic detection system with efficient and accurate sample preparation for cobalt ion concentration in zinc hydrometallurgy

Abstract

Cobalt ion is an impurity that has a negative effect in zinc hydrometallurgy, resulting in lower zinc yield and quality. Currently, the zinc hydrometallurgy industry typically uses manual sampling followed by manual sample preparation, and single-wavelength spectral analysis to detect cobalt ions in zinc solution, which is inefficient and lacks accuracy. Therefore, a new automatic detection system for cobalt ions concentration is developed. The system effectively overcomes challenges of pipeline blockage and corrosion by innovatively implementing negative pressure extraction, volume quantification, and split pipeline structure. Compared with manual detection, the system has higher detection repeatability and efficiency, specifically reducing reagent consumption by 70 %, and improving the detection speed by 8 times. In addition, to solve the spectral drift and noise problems, a spectral preprocessing algorithm by forced contraction of long-wavelength region spectra and Gaussian filtering is proposed. This algorithm not only improves the local linearity and regression accuracy of the spectrum, but also improves the detection repeatability of the system. Finally, five full-wavelength modeling methods are applied to the system. The best root mean square error for low concentration (0.1 to 1 mg/L) and high concentration (1 to 20 mg/L) cobalt zinc solution is only 0.0027 mg/L and 0.071 mg/L, respectively.