داده های کانی شناسی خودکار
ترجمه نشده

داده های کانی شناسی خودکار

عنوان فارسی مقاله: انتخاب حسگر بهینه برای مرتب سازی مبتنی بر حسگر بر اساس داده های کانی شناسی خودکار
عنوان انگلیسی مقاله: Optimal sensor selection for sensor-based sorting based on automated mineralogy data
مجله/کنفرانس: مجله تولید پاک – Journal of Cleaner Production
رشته های تحصیلی مرتبط: مهندسی معدن، شیمی
گرایش های تحصیلی مرتبط: استخراج معدن، شیمی معدنی
کلمات کلیدی فارسی: مرتب سازی مبتنی بر حسگر، انرژی دوگانه، انتقال اشعه X، طیف سنجی فروسرخ موج کوتاه، کانی شناسی خودکار، ژئومتالورژی سنگ قلع
کلمات کلیدی انگلیسی: Sensor-based sorting، Dual energy، X-ray transmission، Short-wave infrared spectroscopy، Automated mineralogy، Cassiterite Geometallurgy
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.jclepro.2019.06.259
دانشگاه: Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Chemnitzer Straße 40, 09599, Freiberg, Germany
صفحات مقاله انگلیسی: 9
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2019
ایمپکت فاکتور: 7.096 در سال 2018
شاخص H_index: 150 در سال 2019
شاخص SJR: 1.620 در سال 2018
شناسه ISSN: 0959-6526
شاخص Quartile (چارک): Q1 در سال 2018
فرمت مقاله انگلیسی: PDF
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E13232
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست مطالب (انگلیسی)

Abstract

Graphical abstract

1. Introduction

2. Materials and methods

3. Results

4. Discussion

5. Conclusions

Declarations of interest

Acknowledgements

Appendix A. Supplementary data

References

 

بخشی از مقاله (انگلیسی)

Abstract

Assessing the success of sensor-based sorting in the raw materials industry currently requires timeconsuming and expensive empirical test work. In this contribution we illustrate the prospects of successful sensor selection based on data acquired by scanning electron microscopy-based image analysis. Quantitative mineralogical and textural data from more than 100 thin sections were taken to capture mineralogical and textural variability of two different ore types from the H€ ammerlein SneIneZn deposit, Germany. Parameters such as mineral grain sizes distribution, modal mineralogy, mineral area and mineral density distribution were used to simulate the prospects of sensor-based sorting using different sensors. The results illustrate that the abundance of rock-forming chlorite and/or density anomalies may well be used as proxies for the abundance of cassiterite, the main ore mineral. This suggests that sorting of the Hammerlein ore may well be achieved by either using a short-wavelength infrared detector € d to quantify the abundance of chlorite d or a dual-energy X-ray transmission detector to determine the abundance of cassiterite. Empirical tests conducted using commercially available short-wave infrared and dual-energy X-ray transmission sensor systems are in excellent agreement with simulation-based predictions and confirm the potential of the novel approach introduced here.

Introduction

Sensor-based sorting is a technology applied by the mining industry to reject coarse barren particles (in a size range between 10 and 100 mm) of a heterogeneous ore at an early stage of a beneficiation process (Knapp et al., 2014; Wills and Finch, 2016; Wotruba and Harbeck, 2010). The sorting process can be subdivided into four basic steps, as illustrated in Fig. 1 by the example of a chute sorter (Nienhaus et al., 2014). Before sensor-based sorting can be implemented into a flow sheet it must be the first objective to identify the sensor, which is able to detect d or even quantify d a suitable property that allows classifying the particles of the investigated ore type into concentrate and waste. Most commonly, direct identification of the mineral of interest is not possible because suitable sensors are not available. In such cases, a measureable property that correlates well with the mineral/element of interest d a so-called proxy d can be selected for sensor-based sorting. Examples of commonly-detected properties/proxies are atomic density with a (dual-energy) X-ray transmission sensor, (DE-)XRT for short (e.g. Neubert and Wotruba (2016); Robben et al. (2013); Walker (2017)), color, fluorescence, or transparency (optical sensor), shortwavelength infrared or near-infrared radiation (SWIR/NIR; e.g. Dalm et al. (2017); Phiri et al. (2018); Iyakwari et al. (2013)), or X-ray fluorescence (XRF; e.g. Nadolski et al. (2018)).