Using the Empirical Method for Elemental Analysis of Barite Ore

Image

Elemental analysis plays a key role in different stages of mining material production in order to ensure proper extraction and process control. Trace elemental analysis, particularly for precious metals, is highly significant to ensure process optimization. Hence, there is a requirement for a simple but robust technique throughout the entire processing line.

The ideal solution to address these industry challenges is the NEX QC energy dispersive X-ray fluorescence (EDXRF) elemental analyzer from Rigaku. The NEX QC analyzer combines a high-performance semiconductor detector and 50 kV direct excitation to deliver unprecedented sensitivity and lower detection limits. It is a robust, yet simple, and affordable instrument to perform elemental analysis of ores and rocks. This article demonstrates the ability of the NEX QC analyzer to measure silver (Ag), lead (Pb), zinc (Zn), and iron (Fe) in barite (BaSO4) concentrates and final tails, using the empirical method.

The Rigaku NEX QC EDXRF Analyzer

The Rigaku NEX QC EDXRF Analyzer

Experimental Setup

  • Model: Rigaku NEX QC
  • Detector: Semiconductor
  • X-ray tube: 50 kV 4 W Ag-anode
  • Sample Type: Ore powders
  • Film: Mylar
  • Environment: Air
  • Analysis Time: 300 seconds
  • Options: Manual Sample Press
  • Optional: 6-position 32mm Autosampler

Sample Preparation

Samples were prepared by grinding the ore material to a homogeneous dry powder of <200 mesh (<75µm particle size), followed by transferring and manually compacting 10 g of the powder into a standard 32 mm sample cup by applying 250 inch-pounds of torque through the manual sample press in order to ensure uniform compaction.

Calibration and Measurement

Individual empirical calibrations were built for each of the supplied matrix types (concentrates and final tails). Empirical calibrations for each main element in the sample matrix were developed utilizing 17 assayed concentrate samples and 20 final tails. The variations in X-ray absorption and enhancement effects within the sample caused by the independent variations in elemental concentration were then automatically compensated by employing ‘alpha corrections.’

Generally, assaying of samples is performed at the mine and processing sites. The assayed samples are then chosen as standards for each element in order to ensure a wide, representative range in concentration. Each suite of standards also incorporates many different samples that differ independently in elemental concentration so as to provide a highly precise model of the ore matrix. The summaries of calibration curves are listed below:

Table 1. Concentrates

Element Concentration Range (ppm) RMS Deviation R2 Confidence
Ag 166–2033 40.7 0.9942
Element Concentration Range (ppm) RMS Deviation R2 Confidence
Fe 29.83–44.71 1.594 0.8401
Zn 2.55–8.34 0.316 0.9756
Pb 0.97–2.72 0.134 0.9048

 

Table 2. Final Tails

Element Concentration Range (ppm) RMS Deviation R2 Confidence
Ag 79–174 8.9 0.9140
Element Concentration Range (ppm) RMS Deviation R2 Confidence
Fe 8.00–22.21 0.498 0.9899
Zn 0.75–3.61 0.113 0.9749
Pb 0.53-1.50 0.082 0.9416

 

Concentrates Ag correlation plot and Final tails Ag correlation plot are shown below:

Table 3. Concentrates Ag Correlation Plot

Element: Ag
Units ppm
RMS Deviation: 40.74
Correlation: 0.994182
Sample STD Calculated
ID Value Value
C1 166 183
C2 188 232
C3 415 365
C4 176 218
C5 480 435
C6 516 518
C7 472 462
C8 547 536
C9 616 580
C10 799 787
C11 757 722
C12 738 783
C13 734 782
C14 646 626
C15 660 654
C16 2033 2018
C17 1250 1292

 

Table 4. Final Tails Ag Correlation Plot

Element: Ag
Units ppm
RMS Deviation: 40.74
Correlation: 0.994182
Sample STD Calculated
ID Value Value
T1 79 90
T2 88 90
T3 92 95
T4 103 97
T5 107 108
T6 108 107
T7 113 113
T8 113 98
T9 121 131
T10 122 121
T11 123 117
T12 125 135
T13 131 134
T14 134 133
T15 141 146
T16 146 152
T17 156 157
T18 159 160
T19 165 146
T20 174 171

Recovery and Repeatability

A concentrated sample and a final tail sample were chosen from the silver concentration mid-range and repeatedly analyzed in a static position for 10 times in order to show recovery and repeatability (precision). The results of the analyses are listed below:

Table 5. Concentrates

Sample ID: C10 Units: ppm
Component Standard Value Average Value Standard Deviation % RSD
Ag 799.3 789.9 9.55 1.24
Sample ID: C10 Units: ppm
Component Standard Value Average Value Standard Deviation % RSD
Fe 34.68 33.74 0.363 1.08
Zn 3.12 3.57 0.014 0.38
Pb 1.73 1.68 0.007 0.42

 

Table 6. Final Tails

Sample ID: C10 Units: Mass %
Component Standard Value Average Value Standard Deviation % RSD
Ag 121.4 127.2 2.11 1.59
Sample ID: C10 Units: Mass %
Component Standard Value Average Value Standard Deviation % RSD
Fe 15.57 15.26 0.054 0.36
Zn 1.18 1.13 0.013 1.14
Pb 0.78 0.72 0.004 0.56

Empirical Detection Limits (LLD)

The empirical approach was employed for determining detection limits in a ’blank’ matrix. The approach involved the 10 repeat analyses of CaO powder to model the X-ray properties of ore. The measurement of the sample was taken in a static position and the standard deviations were calculated. Then, the lower limit of detection (LLD) was defined as three times the standard deviation. The LLDs for the elements, Ag, Fe, Zn and Pb, are shown below:

Table 7. Concentrates

Element Empirical LLD Count Time
Ag 3.0 (ppm) 300 s
Fe 0.0084 (%) 300 s
Zn 0.0001 (%) 300 s
Pb 0.0006 (%) 300 s

 

Table 8. Final Tails

Element Empirical LLD Count Time
Ag 3.0 (ppm) 300 s
Fe 0.0116 (%) 300 s
Zn 0.0004 (%) 300 s
Pb 0.0004 (%) 300 s

Conclusion

The results clearly demonstrate the ability of the Rigaku NEX QC EDXRF analyzer to monitor and quantify silver as well as other major and minor elements present in mining and ore materials rapidly and accurately. The system is capable of measuring elements from sodium and uranium. This capability will enable the system to adapt when existing processes are optimized and new processes are launched.

Image

This information has been sourced, reviewed and adapted from materials provided by Rigaku Corporation.

For more information on this source, please visit Rigaku Corporation.

Citations

Please use one of the following formats to cite this article in your essay, paper or report:

  • APA

    Rigaku Corporation. (2024, June 27). Using the Empirical Method for Elemental Analysis of Barite Ore. AZoMining. Retrieved on December 07, 2024 from https://www.azomining.com/Article.aspx?ArticleID=437.

  • MLA

    Rigaku Corporation. "Using the Empirical Method for Elemental Analysis of Barite Ore". AZoMining. 07 December 2024. <https://www.azomining.com/Article.aspx?ArticleID=437>.

  • Chicago

    Rigaku Corporation. "Using the Empirical Method for Elemental Analysis of Barite Ore". AZoMining. https://www.azomining.com/Article.aspx?ArticleID=437. (accessed December 07, 2024).

  • Harvard

    Rigaku Corporation. 2024. Using the Empirical Method for Elemental Analysis of Barite Ore. AZoMining, viewed 07 December 2024, https://www.azomining.com/Article.aspx?ArticleID=437.

Ask A Question

Do you have a question you'd like to ask regarding this article?

Leave your feedback
Your comment type
Submit

While we only use edited and approved content for Azthena answers, it may on occasions provide incorrect responses. Please confirm any data provided with the related suppliers or authors. We do not provide medical advice, if you search for medical information you must always consult a medical professional before acting on any information provided.

Your questions, but not your email details will be shared with OpenAI and retained for 30 days in accordance with their privacy principles.

Please do not ask questions that use sensitive or confidential information.

Read the full Terms & Conditions.