Online detection of concentrate grade in the antimony flotation process based on in situ Raman spectroscopy combined with a CNN-GRU hybrid model

Abstract

Froth flotation is the most critical process for separating stibnite from raw ore. Concentrate grade is a vital production indicator in the antimony flotation process. It is a direct reflection of the product quality of the flotation process and an essential basis for the dynamic adjustment of its operating parameters. Existing methods of measuring concentrate grades suffer from expensive measurement equipment, difficult maintenance of complex sampling systems, and extended testing times. This paper presents a nondestructive and fast methodology to quantify the concentrate grade in the antimony flotation process based on in situ Raman spectroscopy. A particular Raman spectroscopic measuring system is designed for on-line measurement of the Raman spectra of the mixed minerals from the froth layer during the antimony flotation process. To obtain representative Raman spectra that better characterize the concentrate grades, a traditional Raman spectroscopic system has been redesigned to account for the different interferences during actual flotation field acquisition. A one-dimensional convolutional neural network (1D-CNN) is combined with a gated recurrent unit (GRU) and applied to construct a model for online prediction of concentrate grades based on continuously collected Raman spectra of mixed minerals in the froth layer. With an average prediction error of 4.37% and a maximum prediction deviation of 10.56%, the quantitative analysis of concentrate grade by the model demonstrates that our method is distinguished by high accuracy, low deviation, and in situ analysis, and it essentially satisfies the requirements for online quantitative determination of concentrate grade in the antimony flotation site.