A novel technique for online slurry grade detection based on EDXRF. (15th January 2019)

• Record Type: Journal Article
• Authors: Li, Hequn; Xu, Zhiqiang; Wang, Weidong; Liu, Yibing; Zhang, Sa
• Is Part Of: Minerals engineering. Volume 131(2019)
• Page Start: 14
• Page End: 22
• Publication Date: 2019-01-15
• Subjects: Slurry grade -- EDXRF -- Intensity correction -- Settling velocity -- Online detection; Mines and mineral resources -- Periodicals; Ressources minérales -- Périodiques; Mines and mineral resources; Periodicals; Electronic journals; 622
• DOI: 10.1016/j.mineng.2018.11.004
• Languages: English
• ISSNs: 0892-6875

Abstract

Highlights: Corrective model for elemental EDXRF intensities in the slurry is built. Slurry's concentration is assessed with the Compton-scattered X-ray intensity. Minimum limitation of slurry velocity is given as the detecting condition. Stability and accuracy of proposed technique are verified and discussed. Real-time monitoring of slurry elemental grade is achieved with this technique. Abstract: The accuracy of energy dispersion X-ray fluorescence spectrometry (EDXRF) decreases via the online detection of slurry grade, which is due to the matrix difference caused by varying slurry components and processes. Among these, the most significant influence is the water content in the slurry, which reduces the X-ray fluorescence intensity of target elements that the detector receives. In this paper, a novel online detection method based on a correction model of X-ray fluorescence intensity is proposed. Using the mineral particle settling model as a foundation, the minimum slurry velocity was first derived to satisfy the condition of pulp content uniformity. Then, the impact of slurry concentration on the X-ray fluorescence intensity was analyzed. An intensity correction model was built, which accounted for the impact of water in slurry on the characteristic X-ray fluorescence intensity of the target elements. The overall methodology was thoroughly validated through several experiments, and was compared with the traditional EDXRF method. The experimental results indicated that the standard deviation of the target's elemental slurry grade dropped by more than 60% for lead and 40% for zinc. Meanwhile, the average relative error of the slurry's elemental grade was reduced by more than 80% for lead and 90% for zinc. The accuracy and stability of online slurry grade detection were therefore significantly improved.