Signal Enhancement Algorithm for On-Line Detection of Multi-Metal Ions Based on Ultraviolet-visible Spectroscopy

In zinc hydrometallurgy, the traditional multi-metal ion detection method is manual off-line detection, which is cumbersome and has a long detection period. In order to realize simple, real-time, and accurate on-line detection, an adaptive signal enhancement algorithm combined with partial least squares regression is proposed for on-line detection of multi-metal ions by Ultraviolet-visible spectroscopy. First, according to the different scale characteristics of signal and noise in wavelet decomposition, the proposed signal enhancement algorithm can adaptively set the initial threshold, step size, and quantization ratio information using the magnitude of the signal and noise wavelet coefficients. Then, by increasing the threshold, the noise wavelet coefficient is set to zero and the signal wavelet coefficient is retained. When the sum of the wavelet coefficients slowly changes and tends to be stable, the optimal threshold parameter of each layer can be determined. Finally, the proposed adaptive algorithm is used for spectral signal preprocessing, and partial least squares regression is used for spectral signal modeling analysis. The simulation results showed that the proposed adaptive algorithm was superior to other denoising algorithms and had a better denoising effect under different noise backgrounds. The experimental results showed that the proposed adaptive algorithm combined with partial least squares regression was suitable for on-line detection of copper and cobalt in zinc hydrometallurgy, allowing it to have many applications.