FIELD OF THE INVENTION
The present invention relates to a method and apparatus to enhance the
analytical capability of prior art automated mineralogy systems to identify in a
sinter phases other than minerals.
BACKGROUND OF THE INVENTION
Automated mineral analysis is an integrated technology which is used mainly to
identify mineral phases followed by other information like liberation, modal
percentage, grain sizes, chemical proportion etc. The automated mineralogy
system is based on a Scanning Electron Microscope with additional components
for example, an Energy Dispersive Spectrometer (henceforth EDS) and a Back
Scattered Electron Detector (henceforth BSE). This system is primarily based on
characteristic input spectrum from the EDS and characteristic input signal
intensity from the BSE to identify mineral phase. These input signals are
captured and stored in the system against particular respective mineral phases.
The capability of the system to identify mineral phases is dependent on the
resolution of the signals stored in the computer from EDS and the BSE, which is
treated as a hardware tool for evaluating the system's performance. However,
the prior art systems are unable to identify new mineral phases, since the system
does not possess the characteristic input spectrum and characteristic input signal
intensity for new mineral phases respectively.
Non-patent literature entitle "Optical and SEM-based microcopy integration for
optimiszation of geometallurgical modeling and ore deposit characterisation",
Hartner, R et al published in October 10, 2011, discloses Digital optical
microscopy (DOM) and automated SEM-based (ASEM) mineralogy systems (MLA,
QEMSCAN). Major hardware and software advances in DOM in the last few years
have provided important new capabilities with potential applications to
automated mineralogy. But these technological advances have been largely
driven by sectors outside mining (eg medical pathol.) and have not yet been
widely adopted within the minerals industry. The advent of DOM offers
significantly more automated mineralogy capabilities than traditional expert-
mineralogist driven optical microscopy. This is based on advanced automated
image acquisition, high resolution cameras for digital imaging, imaging of large
areas through mosaic options, integration of multiple layers and application of
advanced image analysis techniques. This research involves combination of the
outputs of DOM and ASEM-based microscopy to create new capabilities for
integrated microscopy based on development of advanced cross-platform image
fusion and data integration between DOM and ASEM (exploiting the benefits of
both analysis platforms). This requires non-linear image registration and transfer
of mineralogical identification from ASEM to DOM systems using sophisticated
image manipulation and data analysis software. Examples will be given of image
fusion and data registration for a range of different ore types. Image fusion
techniques are demonstrated using a porphyry copper deposit sample where
sulfides and precious metals are classified using the MLA and gangue mineralogy
obtained from DOM images. Data integration enables creation of a library
containing optical property variability information for minerals identified by the
MLA; thus reducing the reliance on skilled mineral identification by
supplementing human interpretation.
The non-patent literature entitle ''Use of QEMSCAN for the characterization of Ni-
rich and Ni-poor goethite in laterite ores", by Andersen, Jenes et al published in
the year 2009, teaches accurate characterization of mineralogy texture and
grade of nickel laterites. As spatially resolved mineralogical techniques,
automated SEM-based analysis systems (such as QEMSCAN) offer significant
advantages over traditional bulk compositional and mineralogical methods. This
paper provides procedure as to how QEMSCAN can be employed to characterize
goethite and potentially interfering mineral compounds in nickel laterites. This
involves the development and testing of a Species Identification Protocol (SIP)
that discriminates goethite on the basis of nickel, chromium, and manganese
content. The SIP is calibrated to quantified phase compositional data obtained by
electron-probe microanalysis (EPMA). The SIP is tested on described ore
specimens. The project demonstrates the advantages of increased X-ray
acquisition rate for the characterization of low-Ni concentrations, and the
significance of EPMA analysis for the quant, validation of mineral identifications
in the SIP.
The non-patent literature entitled "Advance characterization of diverse natural
materials using the newly developed QEMSCAN technique", by Azenkeng,
Alexander published in August, 2009, teaches that QEMSCAN technique is a
surface analysis tool that allows for quantity evaluation of minerals by matching
spectra collected on the sample with a pre-defined took-up table, called species
identification profile (SIP), and then assigns and quantifies the mineral phases in
user-customized reports. In addition, it creates digital images that provide a
visual map of the mineral phase common. Equipped with a maximum of four
light element nitrogen-free silicon-drift EDS detectors, BSE and SE analysis
functions and limited SEM capabilities, all linked to a proprietary software
platform, this system can provide a quick turn over on samples.
The non-patent literature entitled "Use of scanning electron microscopy-based
automated quantitative mineralogy for the characterisation of Ni-poor goethite in
laterites, by Anderse, 3 et al published in the year 2008, discusses on
investigation of oxide and hydroxide species in laterites using QEMSCAN
EDS/SEM-based automated quantity mineralogy. Particular emphasis was placed
on the development of SIP for quantification for Ni-poor and Ni-rich goethite. A
successful species identification profile was developed on the basis of wavelength
dispersive electron microprobe analysis to discriminate goethite on the basis of
Ni content. In all of the studied samples, the Mn concentrations are minor.
The non-patent literature entitled "study on the reproducibility of sample
preparation and QEMSCAN measurements for heavy mineral sands samples", by
Hrstka, T et al published in August 2008, suggests that QEMSCAN technology is
a widely accepted automated SEM technique providing quantitative and
statistically robust mineralogical data to the mining industry. It uses X-ray
mapping combined with backscattered electron (BSE) brightness to identify
minerals in almost all types of geological material. Besides, the modal mineralogy
along with the liberation, average grain size and other textural information can
be obtained. The aim of this study is to test reproducibility of this technique by
setting up and running std. tests on different QEMSCAN systems. Multiple sample
blocks were prepared according to standard operating procedure as epoxy-
mounted, carbon-coated, polished blocks. The replicates were measured under
the same conditions and measurement parameter settings on different QEMSCAN
instruments. The relative standard deviation (henceforth RSD) for the set of
measurements was calculated. Generally the reproducibility of the systems
themselves was found to be excellent, ranging between 0.4% and 1.5% RSD.
The more significant change in the reproducibility was found to be caused
potentially by the sample preparation and variation in the mineralogy of the
replicates. The measurement parameter settings together with these variations
can account for almost 15% RSD for minerals of low abundance. For analyzed
samples good results for the main phases present in quantities higher than 2-5
wt.% of the total mass of the sample were confirmed with RSD generally below
6%. Following the experiments, the parameters having the biggest influence on
the performance of the QEMSCAN and its precision are discussed in detail.
The non-patent literature entitled "SEM-EDS based automated mineral analysis
solution for PGM-bearing ores and flotation products", by Bunshell, charles et al
published in 2011, discusses the problems of SEM-EDS based automated
platinum group mineral analysis systems. The main reasons for this is the size of
the PGM grains, which are often smaller than 3 "m in diam., particularly in
chromitite ores of the Bushveld Complex. This leads to "mixed" x-ray (EDS)
spectra, in which the relative PGM: gangue elemental contribution is variable.
This is further complicated by the fact that PGM species often occur in the form
of a solid solution series, and ideal chem. Formulas of these minerals cannot be
relief upon for automated mineral identification. These conditions make it difficult
to automatically identify PGM species based on spectral matching or "windowed"
elemental EDS x-ray analysis schemes. An algorithm that can identify PGM
species from raw EDS spectral data, is provided to overcome the problem of
mixed spectra. This PGM identification algorithm is being integrated as a plug-in
into the Carl Zeiss SmartPITM particle analysis software to provide a reliable
automated PGM analysis system.
The non-patent literature entitled a scanning electron microscope method for
automated, quantitative analysis of mineral matter in coal, by Creelman et al
published in 1996, discloses the image analysis system (QEMSCAN) gathers x-ray
spectra (EDS) and backscattered electron (BSE) data from a number of points on
a conventional grain-mount polished section under the SEM, and interprets the
data from each point in mineralogical terms. The cumulative data in each case
was integrated to provide a volumetric modal analysis of the species present in
the coal sample, expressed as percentages of the respective coal mineral matter.
Comparison was made of the QEMSCAN results to data obtained from the same
samples using other methods of quantity mineralogical analysis namely x-ray
diffraction of the low-temperature O-plasma ash and normative calculation from
the (high-temperature) ash analysis and carbonate C02 data. Good agreement
was obtained from all 3 methods for quartz in the coals, and also for most of the
Fe-bearing minerals. However, the correlation between results from the different
methods was less strong, for individual clay minerals, or for minerals such as
calcite, dolomite and phosphate species that made up only relatively small
proportions of the mineral matter. The image analysis approach using the
electron microscope for mineralogical studies, has significant potential as a
supplement to optical microcopy in quantative coal characterization.
OBJECT OF THE INVENTION
It is therefore an object of the invention to propose a method and an apparatus
to enhance analytical capability of automated mineralogy systems to identify in a
sinter the phases other than minerals.
SUMMARY OF THE INVENTION
The automated mineralogy system as modified adapts QEM*SEM technology.
The automated mineralogy technique has flexible opportunity to store relevant
data (BSE, EDS) in a protocol named "Species Identification Protocol"
(henceforth SIP).
According to the invention, the modified system is enabled to identify phases
other than minerals, since the corresponding BSE and EDS data is stored through
the SIP in the system. The composition of the phases under consideration is
obtained through SEM-EDS system. The EDS and BSE signals are captured
through the SEM. The identification of a particular phase in the sample through
the SEM is based on combination of signal intensity received from the BSE
detector, and the spectrum profile obtained from the EDS. Thus, a particular
phase has unique combination of BSE value as well as EDS spectrum. Therefore,
for different phases different sets of BSE values and EDS spectrums are
incorporated. A new SIP in the prior art automated mineralogy system is either
created according to the invention, alternatively already existed SIP are
upgraded to store new data against a particular phase. When the automated
mineralogy system scans a set of phases. BSE value and EDS spectrum for all the
phases present in that sample is captured. If a particular set of BSE value and
EDS spectrum is matched with the BSE values and EDS spectrums stored in the
system through the SIP, the system is able to identify that particular phase
related to corresponding BSE values and EDS spectrums. If BSE value and EDS
spectrum are not matched system is unable to identify that phase. So it is
essential to store corresponding BSE and EDS value in the system through SIP
for identifying that phase.
According to the invention, a new SIP is developed in a hierarchical manner. At
the top of the hierarchy, a base SIP itself is present. In this hierarchy, the BSE
value and EDS spectrum is stored to identify the phases. Primary SIP is the next
member of the hierarchy where chemical composition and density of particular
phase are stored which can be obtained from other system (e.g. EPMA). Last of
the hierarchy is a secondary SIP which is descended from the primary SIP. This
hierarchy is used for easy interaction with the modified system. It is observed
that same phase, with minor difference in composition, projects different BSE
values. So a range of BSE and EDS data are stored for a particular phase. In the
base SIP and the primary SIP, this individual sub-phase can be identified
separately. However, as the sub-phases belong to same phase those can be
clubbed together in the secondary SIP to identify as the same name.
BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGS
Figure 1 - shows a prior art automated mineralogy system
Figure 2 - shows a process flow chart according to the invention depicting
enhancement of analytical ability of the prior art
Figure 3 - shows the modified system of the invention in an analytical mode
of a sinter.
DETAILED DESCRIPTION OF THE INVENTION
Figure-1 shows different component of a prior art system and their mutual
relationship to adhere the enhancement of the system. The amplified electronic
pulse is generated from an electronic source which hits on the sample to
generate the signals required by the BSE and the EDS. Subsequently those
signals are captured by the BSE and the EDS and stored in the system through
the SIP. If there is a matching with the already stored values, the system
identifies the phases thus enhancing its capability.
Different sinter samples were collected and prepared through a polishing step for
using in SEM and later in the automated mineralogy system. At the initial stage,
an analysis was carried out to capture through EDS spectrum and BSE values.
New and separate SIP for sinter was created and named as "Sinter" to store
those data in the existing system for further processing. Several number of
grains for individual phases were analysed to get the average composition of
those individual phases like Calcium ferrite, Iron oxide, Al oxide, Al-Fe silicate,
AL-Fe oxide, Fe-Mg oxide, Fe-Mg silicate, Quartz, Ca-Mg silicate, Ca-Mg-Fe
silicate. Any variation of elemental concentration in particular phase is captured
by giving a range of BSE and EDS values in the existing system through the SIP.
Likewise required data (BSE value and EDS spectrum) for every phase were
captured and stored in the automated mineralogy system after a full scan of the
same sample. The acquired data were compared with the already stored data.
The chemical data of phase arising out from the analysis was compared with
results obtained from chemical analysis of the same sample. At the first instance,
the results were not matched with each other. Accordingly more number of data
were captured to match the bulk chemistry with modal phase analysis through
the system. This step had been repeated until the phases within the permissible
range are identified by the existing system. Figure-2 shows a generalized flow
chart to enhance the capability of the existing system. When the data were fully
matched, the SIP was assigned the status of final protocol for enhancement of
the system. Primary SIP was created by entering the composition obtained from
the EDS system.
A series of analysis were made on the basis of this SIP. Figure-3 shows an of
analysis of a sinter by the modified system. Whenever the analysis is performed,
the BSE and EDS signals were matched with the value stored in the existing
system to enhance the analysis capability of a prior art.
WE CLAIM
1. An improved automated mineralogy system operating under QEM*SEM
technology to identify and analyse sinter phases of calcium ferrite, iron
oxide, Al oxide, Al-Fe silicate, Al-Fe oxide, Fe-Mg oxide, Fe-Mg silicate, Ca-
Mg silicate, and Ca-Mg-Fe silicate, the system comprising a scanning
electron microscope (SEM) a back scattered electron detector (BSE); and
an energy dispersive spectrometer (EDS), the improvement is
characterized in that a species identification protocol (SIP) tool is provided
to the system comprising a base SIP configured on top followed by a
primary SIP, and a secondary SIP, the primary SIP is enabled to store
data on chemical composition and phase density, the secondary SIP
interacting with the system, and the base SIP having BSE value, and EDS
spectrum.
2. The system as claimed in claim 1, wherein said SIP tool is enabled to
store EDS spectrum value and BSE values of Calcium ferrite, Iron oxide, Al
oxide, Al-Fe silicate, Al-Fe oxide, Fe-Mg oxide, Fe-Mg silicate, Ca-Mg
silicate, Ca-Mg-Fe silicate.
3. The system as claimed in claim 2, wherein said EDS spectrum value of
Calcium ferrite are AI=0-80, Fe=17-480, O=0-133, C=0-510, F=0-62,
Mg=0-145, P=0-35, Si=0-220, Ti=0-30, CI=0-55, S=0-40, K=0-29,
Ca=10-482, Na=0-45, Ni=0-101 with range of BSE value from 25 to 92.
4. The system as claimed in claim 1 or 2, wherein said EDS spectrum value
of Iron oxide are 0=10-180, C=0-500, F=0-90, Mg=0-30, Fe= 10-552,
P=0-200, Mn=0-52, Ti=0-50, Ci=0-72, S=0-92, Ca=0-62, Ce=0-50,
Sb=0-60, Ni=0-80, with a range of BSE value from 20 to 195.
5. The system as claimed in claim 1 or 2, wherein said EDS spectrum value
of Al-oxide are Al-oxide are Al=125-502, 0=35-65, C=0-70, with a range
of BSE value from 20 to 60.
6. The system as claimed in claim 1 or 2, wherein said EDS spectrum value
of Al-Fe silicate are Al=30-501, 0=20-112, C=0-176, F=0-50, Mg=0-60,
Fe=20-392, Cr=0-100, Si=20-166, Mn=0-100, cl=0-40, S=0-42 with a
range of BSE value from 25 to 80.
7. The system as claimed in claim 1 or 2, wherein said EDS spectrum value
of Al-Fe oxide are Al=18-522, 0=20-116, C=0-148, F=0-50, Mg=0-42,
Fe=15-450 with a range of BSE value from 25 to 80.
8. The system as claimed in claim 1 or 2, wherein said SIP EDS spectrum
value of Fe-Mg oxide are 0=20-120, C=0-126, F=0-65, Mg=20-285,
Fe= 150-376, Cr=0-25, with a range of BSE value from 25 to 84.
9. The system as claimed in claim 1 or 2, wherein said EDS spectrum value
of Fe-Mg silicate are AI=0-60, 0=20-118, C=0-168, F=0-60, Mg=20-278,
Fe=75-376, Si= 130-282, with a range of BSE value from 25 to 80.
10.The system as claimed in claim 1 or 2, wherein said EDS spectrum value
of Ca-Mg silicate are AI=0-89, 0=0-104, C=0-60, Mg=20-85, Fe=0-140,
Cr=0-153, Si-75-200, Ca=145-280, Na=0-30 with a range of BSE value
from 30 to 70.
11. The system as claimed in claim 1, wherein said EDS spectrum value of Ca-
Mg-Fe silicate are Al=0-130, 0=0-87, C=0-93, F=0-42, Mg=20-195,
Fe=10-324, Cr=0-53, Si=55-232, K=0-45, Ca=20-232, Na=0-98, Ni=0-87,
with a range of BSE value from 35 to 85.

ABSTRACT

The invention relate to an improved automated mineralogy system operating
under QEM*SEM technology to identify and analysis sinter phases of calcium
ferrite, iron oxide, Al oxide, Al-Fe silicate, Al-Fe oxide, Fe-Mg oxide, Fe-Mg
silicate, Ca-Mg silicate, and Ca-Mg-Fe silicate the system comprising a scanning
electron microscope (SEM) a back scattered electron detector (BSE); and an
energy dispersive spectrometer (EDS), the improvement is a species
identification protocol (SIP) tool is provided to the system comprising a base SIP
configured on top followed by a primary SIP, and a secondary SIP, the primary
SIP is enabled to store data on chemical composition and phase density, the
secondary SIP interacting with the system, and the base SIP having BSE value,
and EDS spectrum.