CETAS Iron sinter process control using XRD. Uwe König 1 & Nicholas Norberg 1 1 PANalytical B.V., Almelo, Netherlands

Transcription

1 Abstract No. 44 Iron sinter process control using XRD Uwe König 1 & Nicholas Norberg 1 1 PANalytical B.V., Almelo, Netherlands The use of high speed detectors made X-ray diffraction (XRD) become an important tool for process control in metal and mining industries. Decreasing ore qualities and increasing prices for raw materials require a better control of processed ore and a more efficient use of energy. Traditionally quality control of iron ore sinter has relied on time consuming wet chemistry. The mineralogical composition that defines the physical properties such as hardness or reducibility is not monitored. XRD analysis in combination with Rietveld quantification and statistical data evaluation using Partial Least-Square Regression (PLSR) has been successfully established to determine the mineralogical composition and the Fe 2+ or FeO content of iron ore sinter within an analysis time of less than 10 minutes per sample. In addition the basicity and Fe 2+ /Fe 3+ ratio were calculated. The results were compared with wet chemistry data. The phase composition gives important information about the sinter strength and the optimization of the return fines rate. Short analysis times and easy sample preparation make the method automatable and ensure a frequent monitoring of the sinter process. 1 of 1

2 Iron sinter process control using XRD Uwe König Nicholas Norberg Detlef Opper Outline 1) Overview iron making process 2) Process control with XRD 3) Examples Rietveld and PLSR 4) Instrumental solution 5) Conclusions 2

3 Iron making process XRD XRD Sinter plant XRD XRD XRD X-ray diffraction techniques Process control iron ore / sinter 3

4 Process control - introduction Common practice in quality control for iron ores and sinters: Chemical analysis with XRF that does not allow the exact determination of Fe 2+. Mineralogy determined by XRD often not considered. Fe 2+ /Fe 3+ ratio (Hematite, Magnetite, Goethite ) affects energy consumption and CO 2 emissions. Fe 2+ as FeO traditionally determined by wet chemistry and/or magnetic methods ( Permagnag ). These approaches for Fe 2+ determination are complex and very time consuming. not suited for day-to-day process control Process control with XRF + XRD XRD is a powerful analytical tool that offers multiple analytical possibilities for iron ore, iron sinter, DRI, and steel applications: X-ray fluorescence (XRF) analysis X-ray diffraction (XRD) analysis elemental phase 6 5

5 Sintering Analytical examples Process control of iron sinters Pre-treatment step in the production of iron Sinter plant Fine particles of XRD iron ores and secondary iron oxide wastes (collected dusts, mill scale) are agglomerated by combustion. Agglomeration of the fines is necessary to enable the passage of hot gases during the subsequent blast furnace operation 8

6 Sintering 6.5 million tons annual production of net sinter Fuel consumption 60 kg coke per tonne Small fuel saving (typically -1 kg / tonne of sinter) already represents a significant saving in energy and costs. Process parameters for common sinter plants: Basicity CaO/SiO 2 Bed height mm Suction mbar Return fines rate % Product quality parameters: ISO-strength (> 6.3 mm) % RDI (Reduction Degradation Index; < 3 mm) % FeO 5-8 % (Fe 2+ ) (further reduction done in the blast furnace) Sinter phases (plus amorphous) Group of iron oxides: (containing the bulk of Fe) Hematite Fe 3+ 2O 3 Magnetite Fe O 4 Wuestite Fe 2+ O Group of silicates or SFCA s (Silico-Ferrites of Calcium and Aluminum, glue for binding of the oxide phases). Stable between 1240 and 1480 C. Larnite (aka Belite) Ca 2 SiO 4 SFCA-a (only Fe 3+ ) SFCA-b (Fe 3+, little Fe 2+ ) - slower reactions + faster reactions - cracking due to phase transitions at high T + stability + stability 9 10

7 Analytical examples Example 1 Rietveld method Overview of the 16 scans Type Step size ( 2θ) Scan range ( 2θ) Total time (hh:mm:ss) Gonio :10:31 Combined scans collected on the 16 analyzed samples. Scan time was 10 minutes for optimal data quality for the subsequent Rietveld full pattern analysis. 12

8 Overview of the 16 scans? Phase Combined scans collected on the 16 analyzed samples. Scan time was 10 minutes for optimal data quality for the subsequent Rietveld full pattern analysis. Scan details? Phase Task Phase composition amorphous content total FeO total FeO Task Phase composition amorphous content total FeO total FeO Type Step size ( 2θ) Scan range ( 2θ) Total time (hh:mm:ss) Gonio :10:31 Detailed view of the range between 30 to 45 2θ showing the differences between the individual scans resulting from significant changes in the phase composition of the various samples

9 Results of Rietveld analysis Sample Results of Rietveld analysis for all samples XRD Phase fractions Sample ID Hematite % Magnetite % C2S - Larnite % SFCA-a % SFCA-b % Wuestite % amorphous % R Profile Sample Sample Sample Sample Sample Sample Sample Sample Sample Sample Sample Sample Sample Sample Sample Sample Combined results as obtained from Rietveld full pattern analysis. 16

10 Comparison XRD Rietveld vs. wet chemical analysis wet chemical analyses XRD vs. chemical results XRD Rietveld Sample ID FeO % FeO % delta FeO Sample Sample Sample Sample Sample Sample Sample Sample Sample Sample Sample Sample Sample Sample Sample Sample Analytical examples Example 2 PLSR method (Partial Least Squares Regression) 17

11 Overview of the 35 Scans Type Step size ( 2θ) Scan range ( 2θ) Total time (hh:mm:ss) Gonio :10:31 Combined scans collected on the 35 analyzed samples. Scan time was 10 minutes for optimal data quality for the subsequent PLSR analysis. Overview of the 35 Scans? total Task Fe2+ Phase composition Basicity amorphous (CaO/SiO content 2 ) Fe 2+ /Fe 3+ ratio total FeO Type Step size ( 2θ) Scan range ( 2θ) Total time (hh:mm:ss) Gonio :10:31 Combined scans collected on the 35 analyzed samples. Scan time was 10 minutes for optimal data quality for the subsequent PLSR analysis

12 Scan details Detailed view of the range between 30 to 45 2θ showing the differences between the individual scans. In this case the variation due to changes in phase composition are not that large. PLS regression calibration The determination of a PLS regression line consists of 3 steps 21 22

13 PLS regression calibration The determination of a PLS regression line consists of 3 steps 1) Optimization: determining the optimum model and number of statistical components to describe a linear relation between the property (Fe 2+, etc.) and the variations between the individual measurements PLS regression calibration The determination of a PLS regression line consists of 3 steps 1) Optimization: determining the optimum model and number of statistical components to describe a linear relation between the property (Fe 2+, etc.) and the variations between the individual measurements 2) Calibration: creation of the regression line based on the model and components determined during the optimization 24 23

14 PLS regression calibration The determination of a PLS regression line consists of 3 steps 1) Optimization: determining the optimum model and number of statistical components to describe a linear relation between the property (Fe 2+, etc.) and the variations between the individual measurements 2) Calibration: creation of the regression line based on the model and components determined during the optimization 3) Cross validation: Using several standards as unknowns (1/4 of the total number of standards) and calculate back the values with the regression line to determine the true RMSEP (Root Mean Square Error of Prediction). PLS regression results Resulting Fe 2+, Fe 2+ /Fe 3+ -ratio and basicity values provided by the PLSR analysis of all the standards, compared with values re-calculated as unknowns. The analysis of the 3 parameters was carried out simultaneously and automatically for all samples Very good agreement between the reference and the newly determined values

15 Automation TWo INstruments One user interface XRF and XRD Belt connections Result via LIMS CubiX 3 Iron Includes : Robot automation Sample via airtube Automatic sample preparation Analytics Result via LIMS HighScore Plus SW Industry SW X Celerator detector Co LFF tube Iron application support available Iron ore Iron sinter Direct reduced Iron Retained Austenite 27 Container laboratory Sample preparation Analytics Result via LIMS

16 Conclusions The presented results demonstrate that: X-ray diffraction with quantitative Rietveld and PLSR analysis provides a powerful solution for the determination of the phase composition as well as other significant parameters (e.g. FeO, Fe 2+ /Fe 3+, basicity, etc.) in Fe-ores and sinters. X-ray diffraction is a fast and reliable technique. Typical measurement time is about 5-10 min using a CubiX 3 Iron instrument equipped with a position sensitive detector. The results obtained by Rietveld analysis and PLSR are stable and repeatable and show a very good agreement with wet chemical results. X-ray diffraction analysis can be automated and easily included in fully automated lines. Thank you very much for your attention! Questions? 30 29