Experimental analysis of wet mill load parameter based on multiple channel mechanical signals under multiple grinding conditions. (1st December 2020)

• Record Type: Journal Article
• Authors: Tang, Jian; Yan, Gaowei; Liu, Zhuo; Liu, Yefeng; Yu, Gang; Sheng, Ning
• Is Part Of: Minerals engineering. Volume 159(2020)
• Publication Date: 2020-12-01
• Subjects: Mineral grinding process -- Mill load parameters -- Multiple channel mechanical signal -- Frequency spectrum data -- Combination estimation index; Mines and mineral resources -- Periodicals; Ressources minérales -- Périodiques; Mines and mineral resources; Periodicals; Electronic journals; 622
• DOI: 10.1016/j.mineng.2020.106609
• Languages: English
• ISSNs: 0892-6875

Abstract

Highlights: Experiments are conducted to investigate the frequency spectrum characteristics. Multiple channel mechanical signals are interpreted for mill load parameter (MLP). A measurement method for the contribution rate of the mechanical signals is proposed. Mechanical channels must be selected to construct an effective MLP prediction model. Abstract: Online monitoring load parameters inside the ball mill is the key to improving the production quality and quantity of the mineral grinding process. In this paper, the experimental analysis of wet mill load parameter (MLPs) based on multiple channel mechanical signals is presented. A series of experiments is conducted to investigate the mechanical frequency spectrum characteristics in terms of different grinding conditions, such as only-ball, -mineral, or –water load change. Based on power spectra density (PowSD), multiple channel mechanical signals are interpreted for different MLPs, i.e., mineral-to-ball volume ratio (MBVR), pulp density (PD), and charge volume ratio (CVR), in detail. Experimental results show that the PowSDs of these mechanical signals are positively correlated with CVR and negatively correlated with MBVR and PD. Further, the generation mechanism of these mechanical signals is qualitatively analyzed, and a new measurement method for the contribution rate of multiple channel mechanical signals, i.e., combination estimation index, is proposed. The results show the different contribution rates of these signals to various MLPs under varied grinding conditions. Appropriate mechanical channels for different MLPs must be selected to construct an effective MLP forecasting model.