Multi-Objective Optimized Overlapping Peak Separation Algorithm for Simultaneous Detection of Copper and Cobalt by Ultraviolet-Visible Spectroscopy

Abstract

In the zinc hydrometallurgy, the excessive impurity ions of copper and cobalt are very harmful to the electrolysis process, which affects product quality and wastes resources. In order to solve the problems of low sensitivity, severe spectral overlap and narrow effective band, an overlapping peak separation algorithm based on multi-objective optimization was proposed for simultaneous detection of copper and cobalt in high concentration zinc solution by ultraviolet-visible spectroscopy. First, according to the characteristics of the spectral signal, the derivative spectroscopy by continuous wavelet transform was used to separate the spectral overlapping peaks of copper and cobalt and shield the zinc interference. Then, taking decomposition scale as decision variable, and the information occupancy and peak separation as the optimization targets, a multi-objective optimization model was established and solved using the particle swarm multi-objective optimization method. Finally, derivative spectroscopy combined with zero-crossing method was used to establish calibration curves for simultaneous detection of copper and cobalt. The experimental results show that the proposed method is suitable for simultaneous detection of trace amounts of copper and cobalt in high concentration zinc solution.

Keywords

• Zinc hydrometallurgy
• overlapping peak separation algorithm
• zero-crossing technique
• continuous wavelet transform
• particle swarm optimization method
• ultraviolet-visible spectroscopy