JP2016017194A - Data processing apparatus, data-processing program, data processing method, processing condition determining method, and output data structure of mineral analysis result - Google Patents

Abstract

PROBLEM TO BE SOLVED: To easily and appropriately obtain a surface condition in an outer periphery of a mineral particle, as mineral properties of an ore containing a target mineral.SOLUTION: A data processing apparatus is configured by including: data acquisition means 11 which acquires an analysis result data obtained with a mineral analyzer about each mineral particle which constitutes an ore sample; determination means 12 for extracting mineral particles containing a valuable mineral, as a valuable mineral-containing particle out of each mineral particle to determine whether the valuable mineral-containing particle is a simple mineral which consists only of the valuable mineral, an exposed mineral formed by exposing a part of valuable mineral to an outer peripheral surface of the particle, or an inclusion mineral in which the valuable mineral is not exposed to the outer peripheral surface thereof at all; data editing means 13 which summarizes distribution states sorted by size in the ore sample about the valuable mineral-containing particles, in a list form capable of discriminating the simple mineral, the exposed mineral, and the inclusion mineral from one another; and data output means 14 which performs data output about the distribution states according to the list form.SELECTED DRAWING: Figure 2

Description

  The present invention relates to a data processing apparatus, a data processing program and a data processing method for processing analysis result data as a result of mineral analysis on an ore sample, a processing condition determination method using the data processing method, and an output of a mineral analysis result Regarding data structure.

When smelting non-ferrous metals such as copper, lead, zinc, nickel and molybdenum or smelting precious metals such as gold and silver, qualitative analysis (identification) and content to specify the type of mineral contained in the ore It is necessary to know in advance what the mineral properties in the ore are by performing a specific quantitative analysis or the like. This is because the quality and the actual yield of the resultant product obtained by smelting or beneficiation performed as a pre-process thereof largely depend on the mineral properties in the ore.
Therefore, conventionally, for ores and metal smelting intermediates, etc., mineral analysis is performed based on the results of observation with an optical microscope to identify mineral species and obtain quantitative analysis values, and quantitative analysis of minerals contained in the ores It has been proposed to group values by size (see, for example, Patent Document 1). In such a quantitative analysis method, since the mineral in the ore exists as a single ore or combined ore, the mineral analysis of the ore (for example, minerals) It is possible to easily grasp the distribution state by size and existence state). The single ore means a state in which one mineral is present alone and constitutes one particle. Bonded ore means a state in which a plurality of minerals are combined to form one particle.

  In recent years, automatic mineral analyzers using scanning electron microscopes with energy dispersive X-ray analyzers have been developed, called MLA (Mineral Liberation Analyzer) and QEMSCAN (Quantitative Evaluation of Materials by Scanning Electron Microscop). They are used for quantitative analysis of minerals in mines and research laboratories around the world (for example, see Non-Patent Document 1).

JP 2004-347330 A

`` Modern SEM-based mineral liberation analysis '' International Journal of Mineral Processing vol.84 2007 p310-320

  However, the conventional mineral analysis techniques described above have the following disadvantages.

For example, in precious metal smelting, blue leaching may occur during the smelting process. More specifically, in precious metal smelting that recovers gold from gold-containing ore, the gold component in the ore is obtained by crushing and grinding the gold-containing ore, followed by ore concentrate and flotation. Is concentrated, and the gold in the concentrated product is leached in a solvent such as cyan solution (blue leaching), and gold is recovered from the obtained leachate using electrowinning (dry smelting). . When blue leaching is performed during the smelting process, it is possible to evaluate the leaching property of the target mineral in the solvent as the mineral properties of the ore containing the target mineral. It is very important for the purpose.
However, in the analysis method disclosed in Patent Document 1, single ore and combined ore are divided and evaluated, but it is not possible to appropriately evaluate the leachability of the target mineral into the solvent. In other words, in order to evaluate the leachability of the target mineral in the solvent, it is not enough to simply separate the single ore and combined ore, if the target mineral exists as a single ore or is combined with other minerals Is based on information that distinguishes whether part of the target mineral is exposed on the outer peripheral surface of the mineral particle or whether it is completely contained in the mineral particle, and information on its size and content ratio. It is necessary to grasp the surface condition of the outer periphery.
Therefore, in the analysis method disclosed in Patent Document 1, it is difficult to grasp the surface state of the outer periphery of the mineral particles and it cannot be said that it is suitable for evaluating the leachability of the target mineral into the solvent. There is a risk that the quality of the product and the actual yield cannot be improved sufficiently.

  In recent years, since an automatic mineral analyzer as disclosed in Non-Patent Document 1 is used, the surface condition of the outer periphery of the mineral particles is grasped based on the collected data obtained by this automatic mineral analyzer and the target mineral solvent is used. It is desirable to be able to evaluate the leachability of the. However, since the data collected by the automatic mineral analyzer is not intended to grasp the surface condition of the outer periphery of the mineral particles, the leachability of the target mineral in the solvent cannot be immediately evaluated as it is, and useful evaluation results are obtained. It is necessary to perform some appropriate data processing to obtain

  Therefore, the present invention provides a data processing apparatus, a data processing program, a data processing method, a processing condition determination method, and a mineral analysis result that make it possible to grasp the surface state of the outer periphery of the mineral particles as the mineral properties of the ore containing the target mineral. The purpose is to provide an output data structure.

The present invention has been devised to achieve the above object.
The first aspect of the present invention is:
Data acquisition means for acquiring analysis result data obtained by the mineral analyzer for each mineral particle constituting the ore sample;
Based on the analysis result data, mineral particles containing useful ore are extracted from the respective mineral particles as useful ore containing particles, and the useful ore containing particles are simple ores consisting of only the useful ore or the useful ore A discriminating means for discriminating whether a part of the ore is an exposed ore exposed on the outer peripheral surface of the particle, or whether the useful ore is an inclusion ore not exposed at all on the outer peripheral surface of the particle;
Based on the analysis result data, the distribution state according to size in the ore sample of at least the useful ore-containing particles is summarized in a list form of a mode in which the distinction between the simple ore, the exposed ore and the inclusion ore can be identified. Data editing means,
Data output means for outputting data about the distribution state in the list format;
A data processing apparatus comprising:

According to a second aspect of the present invention, in the data processing device according to the first aspect,
The data acquisition means acquires, as the analysis result data, at least mineral species data related to the type of mineral contained in each mineral particle and size data related to size,
The discrimination means obtains a proportion of the size occupied by the useful mineral in the useful mineral-containing particles from the size data based on the mineral species data and the size data for the useful mineral-containing particles, and determines the proportion as a predetermined threshold value. To determine whether the useful ore-containing particles are the simple ore, the exposed ore, or the inclusion ore.

According to a third aspect of the present invention, in the data processing device according to the second aspect,
The discrimination means uses the outer circumference length data of the useful ore-containing particles as the size data and the outer circumference appearance length data of the useful ore contained in the useful ore-containing particles, and the outer circumference appearance length data for the outer circumference length data. And determining the single ore, the exposed ore and the inclusion ore.

According to a fourth aspect of the present invention, in the data processing device according to any one of the first to third aspects,
The mineral analyzer is an automatic mineral analyzer using X-ray analysis.

According to a fifth aspect of the present invention,
Computer
Data acquisition means for acquiring analysis result data obtained by the mineral analyzer for each mineral particle constituting the ore sample;
Based on the mineral species data, mineral particles containing useful minerals are extracted from the mineral particles as useful mineral-containing particles, and the useful mineral-containing particles are simple ores consisting of only the useful minerals or the useful minerals. A discriminating means for discriminating whether a part of the ore is an exposed ore exposed on the outer peripheral surface of the particle, or whether the useful ore is an inclusion ore not exposed at all on the outer peripheral surface of the particle,
Based on the analysis result data, the distribution state according to size in the ore sample of at least the useful ore-containing particles is summarized in a list form of a mode in which the distinction between the simple ore, the exposed ore and the inclusion ore can be identified. Data editing means, and
Data output means for outputting data on the distribution state in the list format;
It is a data processing program characterized by functioning as

The sixth aspect of the present invention is:
A data acquisition step of acquiring analysis result data obtained by the mineral analyzer for each mineral particle constituting the ore sample;
Based on the analysis result data, mineral particles containing useful ore are extracted from the respective mineral particles as useful ore containing particles, and the useful ore containing particles are simple ores consisting of only the useful ore or the useful ore A determination step of determining whether a part of the ore is an exposed ore exposed on the outer peripheral surface of the particle, or whether the useful ore is an inclusion ore that is not exposed at all on the outer peripheral surface of the particle;
Based on the analysis result data, the distribution state according to size in the ore sample of at least the useful ore-containing particles is summarized in a list form of a mode in which the distinction between the simple ore, the exposed ore and the inclusion ore can be identified. A data editing step;
A data processing method characterized by comprising:

The seventh aspect of the present invention is
Based on the distribution state summarized in the list form by the data processing method according to the sixth aspect, the processing conditions for performing the beneficiation or smelting on the processing object having the same component as the ore sample are determined. A processing condition determination method comprising: a processing condition determination step.

According to an eighth aspect of the present invention, in the processing condition determination method according to the seventh aspect,
Prior to performing leaching with a solvent on the object to be treated and recovering the target mineral contained in the object to be treated, the degree of exposure of the useful mineral to the outer peripheral surface of the particles is the solvent of the object to be treated. It is used as an index for evaluating the leachability of the useful ore against

The ninth aspect of the present invention provides
Based on the analysis result data obtained by the mineral analyzer for each mineral particle constituting the ore sample, the mineral analysis used when outputting data on the distribution state of the useful ore contained in each mineral particle in the ore sample The resulting output data structure,
Regarding the useful ore-containing particles extracted from the respective mineral particles as mineral particles containing at least the useful ore, the useful ore-containing particles are simple ores that consist only of the useful ore, or a part of the useful ore Is an exposed ore exposed on the outer peripheral surface of the particle, or an inclusion ore that is not exposed on the outer peripheral surface of the particle at all, and the distribution state is in a list format according to size. This is the output data structure of the mineral analysis results characterized by being expressed.

  ADVANTAGE OF THE INVENTION According to this invention, it becomes possible to grasp | ascertain easily and appropriately the surface state of mineral particle outer periphery as the mineral property of the ore containing a target mineral.

It is explanatory drawing which shows typically the relationship between a mineral particle and the mineral contained therein. It is a block diagram which shows the schematic structural example of the mineral analysis system used in one Embodiment of this invention. It is a flowchart which illustrates the procedure of the data processing performed with respect to the analysis result data which is a result of mineral analysis in one Embodiment of this invention. It is explanatory drawing which shows a specific example of the grain data about the attention grain in one Embodiment of this invention. It is explanatory drawing which shows a specific example of the exposure ore distribution process in one Embodiment of this invention. It is explanatory drawing which shows an example of the format of the output data structure of the mineral analysis result in one Embodiment of this invention. It is explanatory drawing which shows the other example of the format of the output data structure of the mineral analysis result in one Embodiment of this invention. It is explanatory drawing which shows a specific example of the output data structure of the mineral analysis result in one Embodiment of this invention.

Hereinafter, embodiments of the present invention will be described with reference to the drawings.
Here, description will be made with the following categories.
1. Analysis object System configuration 2-1. Automatic mineral analyzer 2-2. 2. Data processing device 3. Procedure of data processing method Output data structure of mineral analysis results 4-1. Outline of output data structure 4-2. Specific example of output data structure 4-3. Usage form of output data structure (processing condition determination method)
5). Effects of the present embodiment 6. Modifications etc.

<1. Analytical object>
First, the analysis object in this embodiment is demonstrated.
In the present embodiment, when smelting a non-ferrous metal such as copper, lead, zinc, nickel, molybdenum or a noble metal such as gold or silver, it is obtained in the process of ore mined as containing the target element or in the smelting process. Samples such as metal smelting intermediates (hereinafter, these samples are collectively referred to as “ore samples”) are the objects of analysis processing.

  The ore sample is composed of a collection of mineral particles. More specifically, the ore sample is constructed by embedding mineral particles in a resin, and after passing through each polishing step of rough polishing, intermediate polishing, final polishing, and final polishing in order, the smooth polished surface is the surface to be analyzed. It is said.

The mineral particles that make up the ore sample include simple ores, which are mineral particles composed of a single mineral species, and ores (single blade ore), which are mineral particles composed of multiple mineral species. There is.
In both simple and combined ores, the mineral species that make up the mineral particles should be classified into useful ores that are useful for obtaining the target element and unnecessary ores that are unnecessary for obtaining the target element. Can do. Examples of useful ores in non-ferrous metal smelting include chalcopyrite (CuFeS ₂ ), chalcopyrite (Chalcocite: Cu ₂ S), porphyry (Bornite: Cu ₅ FeS ₄ ), and molybdenite (MoS ₂ ). Etc. Examples of useful ores in precious metal smelting include natural gold (Au), electrum (Electrum: Au / Ag solid solution), and the like. Examples of waste ore include pyrite (Pyrite: FeS ₂ ), gangue, silicate mineral, oxide mineral, and the like. More specifically, for example, when gold smelting is performed, natural gold is a representative useful ore.
The mineral particles containing such useful minerals are referred to as “useful mineral-containing particles” in the following description. That is, useful mineral-containing particles and other mineral particles exist as the mineral particles constituting the ore sample.

Here, the relationship between the mineral particles and the minerals constituting the mineral particles will be briefly described.
FIG. 1 is an explanatory diagram schematically showing the relationship between mineral particles and minerals contained therein. In addition, the example of a figure has shown one specific example in case a mineral particle is a bond ore.
For example, as for the ore, one mineral particle (particle) 50 is constituted by a plurality of types of minerals (grains) 51, 52, 53, and 54. Further, each of the minerals 51, 52, 53, and 54 constituting the mineral particle 50 is exposed to the exposed ore 51 and 52, part of which is exposed on the outer peripheral surface of the mineral particle 50, and completely exposed to the outer peripheral surface of the mineral particle 50. It is distinguished from inclusion ores 53 and 54 that are completely included.
In addition, about a single ore, since one particle is comprised with a single mineral seed | species, the mineral (grain) contained there will comprise a mineral particle (particle) as it is.

For ore samples composed of the mineral particles described above, the qualitative analysis (identification) that identifies the type of mineral contained in each mineral particle, the quantitative analysis that identifies the content, etc. It is necessary to know what it is like. This is because the mineral properties of each mineral particle have a great influence on the quality and actual yield of the resultant product obtained by the beneficiation or smelting. Specifically, the mineral species contained in each mineral particle and its content greatly affect the quality of the resulting product, and the particle size and unit separation degree of each mineral particle (ratio of single ore to the whole mineral species) is actually obtained. This greatly affects the rate (the recovery rate of useful minerals to concentrate).
Moreover, especially in precious metal smelting, as the mineral properties of useful ore-containing particles, it is possible to evaluate the leachability of the target mineral into the solvent by grasping the surface condition of the outer periphery of the mineral particles and improving the quality of the smelting result. This is very important for improving the actual yield. This is because precious metal smelting often undergoes blue leaching during the smelting process.
Therefore, in this embodiment, the mineral property of each mineral particle which comprises the said ore sample can be grasped | ascertained by making an ore sample into the object of an analysis process and performing the analysis process.

<2. System configuration>
Next, an analysis system that performs an analysis process on an ore sample in the present embodiment will be described.
FIG. 2 is a block diagram illustrating a schematic configuration example of the mineral analysis system used in the present embodiment.
The mineral analysis system used in this embodiment includes an automatic mineral analysis device 1 and a data processing device 2, which are configured to be communicably connected. In such a mineral analysis system, the data processing device 2 corresponds to the data processing device according to the present invention, executes the data processing program according to the present invention, and uses the data processing method according to the present invention. The processing condition determination method according to the present invention is used to output data according to the output data structure of the mineral analysis result according to the present invention.

<2-1. Automatic mineral analyzer>
The automatic mineral analyzer 1 performs an analysis process (qualitative analysis and quantitative analysis) on an ore sample as an analysis object, and outputs analysis result data as a result thereof. Specifically, an automatic mineral analyzer called an MLA or QEMSCAN, which uses a scanning electron microscope with an energy dispersive X-ray analyzer, is used.

  The automatic mineral analyzer 1 automatically performs an analysis process on an ore sample, for example, according to the procedure described below. First, the automatic mineral analyzer 1 obtains a backscattered electron (BSE) image of an analyzed surface of an ore sample by a scanning electron microscope, and analyzes each BSE image to analyze each mineral constituting the ore sample. The particles are separated and identified, and an EDS spectrum is obtained from each mineral particle by energy dispersive X-ray spectrometry (EDS). Then, the acquired EDS spectrum is compared with the spectrum pattern of the mineral type registered in the database in advance, and the mineral type included in each mineral particle is identified. Furthermore, by analyzing the BSE image, the size of each mineral particle, the size of the mineral contained in the mineral particle, and the like are quantitatively specified. Thereafter, the automatic mineral analyzer 1 outputs the results of the analysis processing (qualitative analysis and quantitative analysis) on the ore sample obtained in this way to the data processing device 2 as analysis result data.

The analysis result data output by the automatic mineral analyzer 1 is composed of particle data that is data in units of mineral particles related to each mineral particle, and grain data that is data in units of minerals related to minerals contained in the mineral particles. ing.
Each grain data includes the type of mineral specified by the grain data, mineral size (for example, the equivalent circle diameter (the diameter of a circle having the same area as the projected area of the particle)), the weight ratio, and the outer circumference of the mineral particle. Each piece of information includes the size of the appearing part (for example, the outer circumference appearing length), the bonding ratio of different minerals in the same particle, and the like.
Each particle data includes the particle size of the mineral particle specified by the particle data (for example, the equivalent circle diameter), the appearance (exposure) ratio of each mineral contained in the mineral particle to the outer peripheral surface of the mineral, Each information such as the weight ratio of each mineral contained in the mineral particles is included.
In other words, analysis result data composed of grain data and particle data includes at least mineral species data regarding the type of mineral contained in each mineral particle, size data regarding each size such as weight, size, and length, Will be included. In addition to the size of the mineral particles and the size of each mineral itself, the size data includes the appearance size (for example, the outer appearance length) of each mineral on the outer periphery of the mineral particles and the appearance (exposure) ratio. It will be.

  With regard to the analysis result data having such a configuration, when a certain sample is analyzed by the automatic mineral analyzer 1, thousands to tens of thousands of grain data and particle data can be obtained. It is necessary to perform appropriate data processing so that it can be accurately grasped.

<2-2. Data processing device>
The data processing device 2 connected to the automatic mineral analyzer 1 receives the analysis result data output from the automatic mineral analyzer 1 and performs data processing on the analysis result data. Specifically, the data processing device 2 includes a CPU (Central Processing Unit), a RAM (Random Access Memory), a ROM (Read Only Memory), an HDD (Hard disk drive), a communication I / F (interface) unit, and the like. A computer configured by combining hardware resources such as an information processing unit 10, an information output unit 20 such as a display device or a printer device, and an information input unit 30 such as a keyboard or a mouse is used.

  In the data processing device 2, the CPU of the information processing unit 10 reads and executes a predetermined program from the HDD, so that the information processing unit 10 performs data acquisition unit 11, determination unit 12, data editing unit 13, and data output unit. 14 functions.

  The data acquisition means 11 acquires the analysis result data by receiving the analysis result data output from the automatic mineral analyzer 1. The analysis result data to be acquired specifies the result of mineral analysis by the automatic mineral analyzer 1, that is, the result of qualitative analysis and quantitative analysis for each mineral particle constituting the ore sample. It is composed of data and particle data, and includes at least mineral type data relating to the type of mineral contained in each mineral particle and size data relating to size. In addition, if the specific aspect (data format, communication format, etc.) of the data acquisition from the automatic mineral analyzer 1 is predetermined, it will not be specifically limited.

  For each mineral particle whose analysis result is specified by the analysis result data acquired by the data acquisition unit 11, the determination unit 12 extracts a mineral particle containing a useful mineral as a useful mineral-containing particle from the mineral species data. Furthermore, the extracted useful ore-containing particles are simple ores composed of only useful ores, or are exposed ore in which a part of the useful ore is exposed on the outer peripheral surface of the particles, or the useful ores are particles. Whether or not the inclusion ore is not exposed at all on the outer peripheral surface is determined using a predetermined function based on the mineral species data and the size data. The “predetermined function” used by the discriminating means 12 is a series of instructions that execute processing as defined for mineral species data and size data and return the result, and details thereof will be described later in “3”. ”Data processing method procedure”.

  Based on the analysis result data acquired by the data acquisition unit 11, the data editing unit 13 determines the distribution state of at least the useful ore-containing particles in the ore sample according to size (for example, the equivalent circle diameter), the single ore and the exposure. It summarizes in the form of a list in which the distinction between ore and inclusion ore can be identified. The data editing means 13 only needs to summarize at least the distribution state of useful mineral-containing particles, and therefore may summarize the distribution state of mineral particles other than the useful mineral-containing particles in addition to the useful mineral-containing particles. . The “list format” compiled by the data editing means 13 is a table format in which the outline of a certain matter can be understood at a glance. More specifically, the distribution state of mineral particles in an ore sample (which This is an example in which the amount of the size is known at a glance, and a specific example thereof will be described later in “4. Output data structure of mineral analysis result”.

  The data output means 14 outputs data about the distribution state in the list form compiled by the data editing means 13. Data output is performed, for example, by transmitting image data created in a list format to the information output unit 20, but the present invention is not limited to this, and as another example, writing to a computer-readable recording medium It is possible to do it.

Each of these means 11 to 14 is realized by the information processing unit 10 executing a predetermined program. In other words, the predetermined program causes the data processing device 2 having hardware resources as a computer to function as the respective units 11 to 14, and corresponds to an embodiment of the data processing program according to the present invention. .
Such a data processing program is installed in the information processing unit 10 of the data processing device 2 and used. In this case, the data processing program may be provided by being stored in a recording medium readable by the data processing device 2 prior to installation in the information processing unit 10, or with the data processing device 2. It may be provided to the data processing apparatus 2 through a connected communication line.
The data processing program for realizing the functions as each of the means 11 to 14 may be developed exclusively for the data processing program, but general general software such as a relational database management system and spreadsheet software. It is preferable that the software program is constructed by utilizing at least a part in terms of improving usability, suppressing development costs, and the like.

<3. Procedure of data processing method>
Next, analysis processing performed using the analysis system configured as described above will be described in detail with particular attention paid to the procedure of the data processing method executed by the data processing device 2. In the following description, when gold smelting is performed, an ore sample is analyzed by an automatic mineral analyzer 1 called MLA, and the data processing apparatus 2 performs the analysis result data that is the analysis result. Take an example.
FIG. 3 is a flowchart illustrating the procedure of data processing performed on analysis result data in the present embodiment.

  When the data acquisition unit 11 acquires the analysis result data output from the automatic mineral analysis device 1 (step 101, the following step is abbreviated as “S”), the data processing device 2 starts data processing on the analysis result data. To do. At the start of data processing, first, the data acquisition means 11 sends the acquired analysis result data to a relational database management system (RDBMS) such as “MICROSOFT ACCESS (registered trademark)” of Microsoft Corporation, USA. Data is exported so that the data format can be processed (S102). Then, the data acquisition unit 11 combines the associated particle data and grain data included in the acquired analysis result data (S103). Specifically, using identification information such as a particle data ID, the particle data of a certain mineral particle and the grain data of each mineral contained in the mineral particle are associated with each other. Further, the data acquisition means 11 is a data format that can be processed by spreadsheet software such as “MICROSOFT EXCEL (registered trademark)” of Microsoft Corporation, USA, for example, for the analysis result data after combining the particle data and the grain data. Data export is performed so that As a result, each data element (data value, etc.) constituting the analysis result data can be subjected to arithmetic processing or summarized in a table format as necessary.

  Thereafter, in the data processing device 2, the discrimination means 12 discriminates the single ore, the exposed ore and the inclusion ore using the analysis result data after the data export. For this purpose, the discriminating means 12 first extracts one piece of grain data as the grain data for the target grain from the analysis result data after the data export, for example, using the filtering function of the spreadsheet software (S105). .

Here, a specific example of the grain data of the target grain will be briefly described.
FIG. 4 is an explanatory diagram showing a specific example of grain data for the grain of interest in the present embodiment.
As shown in the figure, the grain data of the target grain is the grain identification information “Grain ID”, the grain mineral type “Group Name”, and the weight percentage of grains in the ore sample (ie, quantitative analysis value). “Wt%”, “Grain EC Diameter (micron)”, which is the equivalent circle diameter of the grain, “Free Surface%”, which is the ratio of the outer circumference of the grain to the outer circumference of the particle, and the particle containing the grain It consists of “Particle ID” that is identification information and “Particle EC Diameter (micron)” that is the equivalent circle diameter of the particle.
In other words, the grain data of the grain of interest is at least “Group Name”, which is the grain mineral data, and “Wt%”, “Grain EC Diameter (micron)”, and “Free Surface%”, which are the grain size data. It is configured to include.

  When the grain data having such a configuration is extracted, as shown in FIG. 3, the discriminating unit 12 first uses the grain (mineral) specified by the grain data based on the “Group Name” of the grain data. It is determined whether or not it is a mineral (S106). This determination may be made based on whether or not the mineral species specified by the “Group Name” of the grain data corresponds to a useful ore. For example, in the case of gold smelting, “Gold” is a useful ore. In addition, about which mineral kind corresponds to a useful ore, as long as it is preset, you may set suitably. Therefore, in the case of gold smelting, minerals other than the above may be judged as useful ores.

  As a result of this determination, if the target grain does not correspond to a useful ore, each processing step described below is passed until it is determined whether there is an unprocessed target grain to be processed next (S114).

  If the target grain corresponds to a useful ore, the discriminating means 12 then uses a predetermined function set in advance for the particles (mineral particles) containing the grains (minerals) specified by the grain data. It is determined whether it is an ore, exposed ore or inclusion. Specifically, the discriminating means 12 is a simple or exposed ore ore by comparing the size ratio of the grains with respect to the particles with a predetermined threshold mainly based on the grain size data while following a series of instructions by a predetermined function. Or whether it is an inclusion ore.

  More specifically, the discriminating means 12 is based on the “Free Surface%” of the grain data of the target grain, and is present on the particle circumference of the grain (mineral) specified by the grain data with respect to the outer circumference length of the particle. After recognizing the ratio of the length to be output, the ratio is set to an upper limit value (hereinafter simply referred to as “upper limit threshold”) and a lower limit value (hereinafter simply referred to as “lower limit”). It is determined whether the ratio is greater than or equal to the upper threshold, within a range smaller than the upper threshold and greater than the lower threshold, or less than or equal to the lower threshold (S107). For example, the upper threshold value may be set to “100%” and the lower threshold value may be set to “0%”, for example. Any material can be used as long as it is appropriately set according to the ore sample.

  As a result of this determination, if the ratio of the outer circumferential length of the grain to the outer circumferential length of the particle is equal to or greater than the upper limit threshold (specifically, “ratio value = 100%”), the entire outer circumferential surface of the particle is caused by the grain. The determining unit 12 determines that the particle including the target grain is a single ore composed of only the target grain (S108).

  Further, if the ratio of the grain outer peripheral appearance length to the particle outer peripheral length is within a range smaller than the upper limit threshold and larger than the lower limit threshold (specifically, “0% <ratio value <100%”), the outer periphery of the particle Since only a part of the surface is occupied by the grains, the discriminating means 12 is configured such that the particles including the target grain are formed by combining the target grain and the other grains, It is determined that a portion of the exposed ore is exposed on the outer peripheral surface of the particle (S109).

  Also, if the ratio of the grain outer appearance length to the particle outer circumference length is equal to or smaller than the lower limit threshold (specifically, “ratio value = 0%”), the grain does not appear at all on the outer circumferential surface of the particle. Or the equivalent means, the discriminating means 12 includes the inclusion mineral in which the particle including the target grain is formed by combining the target grain and other grains, and the target grain is not exposed to the outer peripheral surface of the particle at all. (S110).

By the way, although the exposed ore and the inclusion ore are both formed by combining the grain of interest and other grains, there may be cases where the other grains as the binding partner extend to a plurality of mineral species. In such a case, rather than treating particles containing multiple types of binding ore as a single exposed or included ore, it is handled as a collection of multiple exposed or included ores of each type of combined ore. However, it may be more effective for subsequent beneficiation and smelting.
From this, when it is determined that the particle including the target grain is an exposed or included ore, the determining unit 12 determines whether or not the binding partner ore in the particle includes a plurality of types (S111). If it is determined that the number of species is more than one, a distribution process of the binding partner ore as described later is performed (S112). In addition, the judgment of whether or not there are multiple types of binding partner ore is based on the “Particle ID” of the grain data of the target grain and the “Group Name” of the grain data of the partner mineral existing in the same particle as the target grain. , Based on the above.

It is conceivable that the distribution processing (S112) of the exposed or included ore is performed according to the size ratio of various binding partner ores in the particles based on the size data of the binding partner ore.
Here, the distribution process according to the size ratio of the binding partner ore will be described in detail by taking the particles including the target grain shown in FIG. 4 as an example. Such particles have a particle diameter of “46.1 μm”, the grain diameter of the gold grain of interest is “4.7 μm”, and the “Free Surface%” of the gold grain is “4.1%”, which is 100%. Since it is smaller and larger than 0%, it is determined by the determining means 12 as an exposed ore.
FIG. 5 is an explanatory diagram showing a specific example of the exposed ore distribution process in the present embodiment.
In the exposure ore distribution process, the determination unit 12 first extracts “Free Surface%” as the size data for all the grains in the particles including the target grain. In the figure, besides “Gold” which is a remarkable grain, “Free Surface%” of “arsenopyrite” which is one of its binding partner ores is “6.7%”, and one of the other partnering ores When "Free Surface%" of sulfide mineral (Sulphides) is "11.3%", and "Free Surface%" of gangue (Gangue), which is yet another partner ore, is "77.8%" Is shown.
As described above, when all the “Free Surface%” are extracted for the various binding partner ores, the discriminating means 12 calculates the size ratio between them. That is, the discriminating means 12 converts the size ratio of the particles into a size ratio (distribution ratio) between only the binding partner ores. In the example, “arsenopyrite”, sulfide mineral (Sulphides), and gangue exist as binding partner ores, so “6.7 / (6.7 + 11.3 + 77.8) = 0.07 for arsenopyrite. ”,“ 11.3 / (6.7 + 11.3 + 77.8) = 0.12 ”for sulfide minerals (Sulphides), and“ 77.8 / (6.7 + 11.3 + 77.8) = 0.81 ”for gangue Is shown.
When the distribution rate is obtained in this way, the determination unit 12 treats the exposed ore subjected to the distribution process as being distributed according to the calculated distribution rate. Specifically, in the case of the example of the figure, particles that contain gold (Gold) that is the grain of interest and that are identified as exposed ore from the value of “Free Surface%” , 7.0% for exposed ore combined with gold and arsenopyrite, 12.0% for exposed ore combined with gold and sulfide (Sulphides), 81.0% for gold Subsequent data processing is performed as an exposed ore in which grains and gangue are combined.

  The discriminating means 12 discriminates whether the particle including the target grain is a single ore, exposed ore or inclusion ore while using a predetermined function that is a group of instructions for instructing a series of processes as described above. It is.

  When the discriminating means 12 discriminates the single ore, exposed ore and inclusion ore, then in the data processing device 2, as shown in FIG. 3, the data editing means 13 performs the particle size extraction / recording process (S113). . This particle size extraction / recording process is to collect at least the distribution status by size in the ore sample of useful ore-containing particles in a list form of modes in which distinction between simple ore and exposed ore can be distinguished. In addition, the data editing means 13 performs this. Specifically, the data editing unit 13 determines “Particle EC Diameter (micron)” for the determined particles and each of the included grains based on the discrimination results of the single ore, exposed ore and inclusion ore by the discrimination unit 12. ”,“ Wt% ”,“ Grain EC Diameter (micron) ”, etc., are extracted, and the extraction result is recorded (plotted) in the corresponding part of the data table in the preset list format. . The “list format” here is synonymous with the “list format” compiled by the data editing means 13, and a specific example thereof will be described later in “4. Mineral analysis result output data structure”. .

  Then, in the data processing device 2, the determination unit 12 and the data editing unit 13 perform a series of processes (S105 to S113) for the grain of interest as described above until there is no unprocessed grain in the analysis result data (S114). ) Repeat. If there is no unprocessed grain, the analysis result data obtained from the automatic mineral analyzer 1 will show at least the distribution status according to size in the ore sample for useful ore-containing particles by the distinction between simple ore, exposed ore and inclusion ore. The data processing for collecting the data in a list form in a form that can be identified is completed.

  Thereafter, in the data processing device 2, the data output unit 14 outputs data about the distribution state in the list form compiled by the data editing unit 13 (S115). By referring to the data output result, the user of the data processing apparatus 2 can identify at least the useful ore-containing particles while distinguishing between the single ore, the exposed ore and the inclusion ore, and the distribution according to size in the ore sample. The state can be easily grasped.

<4. Output data structure of mineral analysis results>
Next, an output result in a list format output through the data processing method of the above procedure, that is, an output data structure of a mineral analysis result output by the data processing device 2 will be described.

<4-1. Outline of output data structure>
The output data structure of the mineral analysis results is for outputting the results of the qualitative analysis and quantitative analysis of the ore sample by the automatic mineral analyzer 1, and more specifically, the minerals contained in each mineral particle constituting the ore sample Is used when outputting the distribution state of the ore in the ore sample. The “distributed state” here is a state indicating what kind of mineral is present in the ore sample in what weight ratio, and more specifically, the mineral contained in each mineral particle. It is the relationship between mineral species and weight percentage.
However, in the present embodiment, the distribution state of minerals contained in each mineral particle in the ore sample is classified according to the size of the mineral particles (for example, the size of the particle diameter) so that the user can easily output the data. And at least a useful ore-containing particle in a list form of a mode that can identify whether the useful ore-containing particle is a simple or exposed or included ore. Output. The distinction between the single ore, exposed ore and inclusion ore in this list form corresponds to the result determined using a predetermined function, as already described.

FIG. 6 is an explanatory diagram showing an example of the format of the output data structure of the mineral analysis result in the present embodiment.
As shown in the figure, in the output data structure of the mineral analysis result in this embodiment, each row (vertical direction) in the list format is classified according to the size of the mineral particles (for example, the size of the particle diameter [unit: μm]). In addition, the list form is formed in a two-dimensional matrix table structure in which the column direction (horizontal direction) is classified according to the state of mineral particles. More specifically, the row direction (lateral direction) is classified according to whether the mineral particles are “single ore” or “bond ore” combined with other grains as the state of the mineral particles. And for “bond ore”. For the mineral type of the binding partner ore (for example, “Arsenite”, “Brassite”, “Pyrite”, “Other sulfide minerals”, and “Portal”), “Exposed ore” and “Included ore” respectively. Another is classified. That is, the column direction (horizontal direction) of the list form is classified so that the distinction between “single ore”, “exposed ore”, and “inclusion ore” can be identified.

  In the output data structure formed in such a list format, the column (field) specified by each item in the row direction and the column direction has a weight ratio (that is, quantitative analysis) of the mineral (grain) corresponding to the column. Value) is recorded (plotted) in an aggregated form.

Specifically, for example, if the grain is determined as a single ore in the determination step of S107 described in “3. Procedure of data processing method”, the particle size of the grain in the output data structure in the format shown in FIG. In the column where the corresponding row and the “single ore” column intersect, the weight ratio of the grain is added and recorded. For example, it is determined that the ore is exposed in the determination step of S107, and the determination step of S111. If the grain is determined to be one type of combined ore, in the output data structure in the format shown in FIG. 6, the column in which the grain size of the grain corresponds and the column “opposite ore-exposed” intersect In addition, the weight ratio of the grains is added and recorded.
Further, for example, if the grain is determined to be an exposed ore in the determination step of S107 and the combination partner ore is determined to be a plurality of types in the determination step of S111, the output data structure in the format shown in FIG. A column in which the product of the weight ratio of the grain and the distribution ratio for each binding partner mineral obtained in the distribution processing step of S112 intersects the row corresponding to the grain size of the grain and the column of “exposure” of each binding partner ore Are added and recorded.
Further, for example, if the grain is determined to be an inclusion ore in the determination step of S107 and is determined to be one type of the binding partner ore in the determination step of S111, the output data structure in the format shown in FIG. The weight ratio of the grain is added and recorded in the column where the row corresponding to the grain size of the grain intersects the column of “opposite ore-inclusion”.
Further, for example, if the grain is determined to be an inclusion ore in the determination step of S107 and the combination partner ore is determined to be plural types in the determination step of S111, the output data structure in the format shown in FIG. A column in which the product of the weight ratio of grain and the distribution rate for each binding partner mineral obtained in the distribution processing step of S112 intersects the row in which the grain size of the grain corresponds and the “inclusion” column for each binding partner ore. Are added and recorded.

FIG. 7 is an explanatory diagram showing another example of the format of the output data structure of the mineral analysis result in the present embodiment.
The output data structure of the mineral analysis results in the example is attached to the above-described output data structure (see, for example, FIG. 6) as necessary, and is one of the useful ore containing particles determined as “exposed ore”. This type is used when outputting the data of the distribution of useful ores contained in the exposed ore. The “distribution state” here is a distribution state of effective ores in the exposed ore, unlike the case of the output data structure described above. In this example, the distribution status of “exposed ore” is summarized, but the data structure of the accompanying output is configured in exactly the same way for “inclusion ore” formed by combining other grains as well as exposed ore. It is possible to do.
The figure shows an exposed ore composed of “Gold” and “Gangue” combined, and what size percentage of “Gold” is in the exposed ore. It is for showing whether it exists. More specifically, in the output data structure of the mineral analysis results in the figure, each row (vertical direction) in the list format is classified according to the size of the exposed ore (particle) (for example, the size of particle diameter [unit: μm]). It is formed into a two-dimensional matrix table structure in which the row direction (lateral direction) of the list format is classified according to the size of useful ore (for example, the size of mineral diameter [unit: μm]). .

  In the output data structure formed in such a list format, the column (field) specified by each item in the row direction and the column direction has a weight ratio (that is, quantitative) of the useful ore (grain) corresponding to the column. Analysis value) “Wt%” is recorded (plotted) in a tabulated form.

Specifically, for example, in the case of an exposed ore composed only of “Gold” and “Gangue”, in the output data structure in the format shown in FIG. In the column where the size rows intersect, the weight percentage of the gold grains is added and recorded.
Further, for example, in the case of an exposed ore composed of “Gold” and “Gangue” and other minerals, the size of the gold grain in the output data structure in the format shown in FIG. The product of the weight ratio of the gold grain and the distribution rate obtained in the distribution processing step of S112 described in “3. Procedure of data processing” is added and recorded in the column where the column of particle size and the row of particle size intersect. The

  Note that the output data structure of the mineral analysis result described with reference to FIGS. 6 and 7 is only an example format in the present embodiment, and the output data structure of the mineral analysis result output by the data processing device 2 is the same. It is not limited. That is, the output data structure of the mineral analysis result output by the data processing device 2 can identify whether the useful mineral-containing particles are simple or exposed or inclusion ores, at least for the useful mineral-containing particles. The distribution state of the useful ore-containing particles in the ore sample may be expressed in a list format according to size, for example, with respect to the format example of FIG. 6 or FIG. May be replaced, or the number of items such as the binding partner mineral type and size in the row direction or the column direction may be increased or decreased.

<4-2. Specific example of output data structure>
Here, the output data structure of the mineral analysis result will be described with a specific example.
Here, a case where gold-containing ore is used as an ore sample and the ore sample is subjected to an analysis process using the analysis system having the configuration described in “2. System configuration” will be described as an example. More specifically, the analysis processing was performed using “MLA650 FEG” manufactured by FEI of the United States as an automatic mineral analyzer 1 constituting the mineral analysis system. Then, the analysis result data that is the analysis result is subjected to the data processing of the procedure described in “3. Procedure of data processing method” while using the analysis system having the configuration described in “2. System configuration”. The data processing result was output in the output data structure of the format example described in “4-1. Outline of output data structure”.

FIG. 8 is an explanatory diagram showing a specific example of the output data structure of the mineral analysis result in the present embodiment.
According to this specific example, for “Gold”, the “weight” existing as “single ore” in the ore sample is “19.5%” in total, and “exposed ore” out of those combined with “arsenite” ”Is the total weight ratio of“ 6.8% ”, the“ inclusion ”is the total weight ratio of“ 4.4% ”, and the combined weight ratio of“ Brassite ”is the“ exposed ore ”weight ratio. Is “0.0%” in total, and the weight ratio existing as “inclusions” is “0.2%” in total, and among those combined with “pyrite”, the weight ratio existing as “exposed ore” is “14.5%” The total weight ratio of “inclusions” is “6.2%”, and the weight ratio of “exposed ores” combined with “other sulfide minerals” is “10.9%”. The weight that exists as The total proportion of "0.1%" and the combination of "gangue" that exists as "exposed ore" is "36.7%" in total, and the proportion of weight that exists as "inclusion ore" is "0.7" % ”, And adding up all of these results in“ 100% ”.

<4-3. Use form of output data structure (processing condition determination method)>
Next, a usage form of the output data structure of the mineral analysis result as described above will be specifically described.

  It is conceivable that the output data structure of the mineral analysis result in the present embodiment is used to determine the processing conditions when performing ore dressing or smelting on an ore sample, for example. This is because the mineral property of each mineral particle constituting the ore sample has a great influence on the quality and actual yield of the resultant product obtained by the beneficiation or smelting.

  For example, after performing an analysis process for grasping the mineral properties of a certain ore sample, the ore of the same component as the ore sample or a metal smelting intermediate (hereinafter, these are collectively referred to as “processing object”). On the other hand, consider the case where smelting is performed through blue leaching to obtain the target mineral. In this case, the data processing of the procedure described in “3. Procedure of data processing method” is performed using the analysis system having the configuration described in “2. System configuration”, and the result of the data processing is expressed as “4-1. After output in the output data structure described in “Overview of output data structure”, before starting to smelt the processing object, the contents of the output data structure (ie, the mineral distribution state summarized in a list form) Based on the above, the processing conditions for performing the smelting are determined. That is, after the mineral analysis result is output by the output data structure, a processing condition determination step based on the contents of the output data structure is performed before smelting is started. The “treatment conditions” determined here include, for example, the conditions of pulverization (particle diameter after pulverization, etc.) performed prior to blue leaching, but are not limited to this, and when performing smelting Other useful processing conditions may be applicable. The “treatment conditions” are not necessarily limited to those related to smelting, but may be related to beneficiation performed prior to smelting. For flotation, etc., the surface condition of mineral particles (whether the target mineral exists as a single ore, or if the target mineral is combined with other minerals, is a part of the target mineral exposed on the outer surface of the mineral particle? This is because whether it is completely contained in mineral particles or the like) has a great effect on improving the quality of the resultant product and improving the actual yield.

This point will be described more specifically. For example, the case where the data processing result of the content shown in FIG. 8 is obtained is given as an example.
As a result of such data processing, at least for useful ore containing the target mineral, whether or not the useful ore is a simple ore, and if it is not a simple ore, is it an exposed or included ore? The output data structure is summarized in the form of a list that identifies these distinctions. In other words, it is not limited to simply separating the single ore and combined ore. If the target mineral exists as a single ore or is combined with other minerals, part of the target mineral is the outer peripheral surface of the mineral particles. It has an output data structure capable of grasping information on whether it is exposed to the surface or completely contained in mineral particles, and information on the size and content ratio.
Therefore, based on the results of such data processing, for example, for “Gold” which is the target mineral, even if the target mineral is extracted by blue leaching and subsequent dry smelting, each particle Since the ratio of single ore, exposed ore and inclusion ore by size is known, the surface condition of the outer periphery of the mineral particles can be easily and correctly grasped, and the leachability of the target mineral into the solvent can be appropriately determined. Can be evaluated. In other words, when it is assumed that leaching with a predetermined solvent will be performed in a later step in order to recover a target mineral (valuable metal, etc.) contained in a certain processing target, Prior to the recovery of the target mineral by leaching with a solvent, the data processing result is an index for evaluating the leaching property of the useful ore with respect to the solvent of the processing object based on the degree of exposure of the useful ore to the outer peripheral surface of the particle. As a result, it is possible to appropriately evaluate the leachability of the target mineral into the solvent.

Specifically, from the results shown in FIG. 8, about 20% of the gold grains are present as a single ore in the processing object, and the others are present in gangue, pyrite, and arsenite. I understand. Furthermore, it can be seen that the inclusion ore has a high proportion of particle sizes of 50 μm or more.
If such a result is obtained, it is judged that it is necessary to polish the inclusion ore with a particle diameter of 50 μm or more to make it an exposed or simple ore in order to efficiently leach gold into the solvent in the subsequent process. can do. In other words, based on the data processing results described above, the processing conditions for blue leaching and dry smelting are determined based on the data processing results so that blue leaching and subsequent dry smelting quality improvement can be expected. To do.

  It is very difficult to determine the processing conditions as described above based only on the data output result from the automatic mineral analyzer 1 such as MLA or QEMSCAN. However, using the analysis system having the configuration described in “2. System configuration” of the present embodiment, the data processing of the procedure described in “3. Procedure of data processing method” is performed, and the result of the data processing is expressed as “4. If the output data structure described in “1.Outline of output data structure” is output, the output data structure is not limited to simple ore and combined ore. Thus, for example, the leachability of the target mineral into the solvent can be evaluated, so that the processing conditions as described above can be determined appropriately and easily.

  In addition, although the case where it reflected in the processing condition determination of the refining performed with respect to a process target object was mentioned as an example here as a utilization form of an output data structure, in addition to this, ore processing and refining performed after that It can be used to determine both processing conditions, or to determine only the processing conditions of the beneficiation. That is, the processing condition determination step in the present embodiment may be anything that determines the processing conditions when performing the beneficiation or smelting.

<5. Effects of this embodiment>
The data processing device 2 described in the present embodiment, a data processing program for realizing the characteristic functions of the data processing device 2, a data processing method and a processing condition determination method executed using the data processing device 2, and the According to the output data structure of the mineral analysis result by the data processor 2, the following effects can be obtained.

  According to the present embodiment, in the data processing for the analysis result data of the mineral analysis, the useful mineral-containing particles are extracted from each mineral particle constituting the ore sample, and the useful mineral-containing particles are composed of only the useful minerals. It is discriminated whether it is an ore, an exposed ore in which a part of the useful ore is exposed on the outer peripheral surface of the particle, or an inclusion ore in which the useful ore is not exposed at all on the outer peripheral surface of the particle. Then, the discrimination results are collected in a list form of identifiable forms, and at least data regarding the distribution state by size in the ore sample of useful ore containing particles is output. In other words, it is not limited to simply separating the single ore and combined ore, but if it is combined with other minerals, part of the target mineral is exposed on the outer peripheral surface of the mineral particles or completely on the mineral particles. Whether it is included or not is also identifiable so that the surface condition of the outer periphery of the mineral particles can be easily grasped. Therefore, based on the result of the data output, for example, even when dry smelting is performed through blue leaching to extract the target mineral, the leachability of the target mineral into the solvent is appropriately evaluated. As a result, it is possible to appropriately and easily improve the quality and the actual yield of the resulting product obtained by blue leaching and dry smelting.

  Moreover, according to the present embodiment, the mineral contained in each mineral particle while determining whether the useful ore-containing particle is a single ore, an exposed ore, or an inclusion ore according to a series of commands according to a predetermined function. Based on mineral species data and size data. In other words, the distinction between simple ore, exposed ore and inclusion ore will be objectively and quantitatively based mainly on numerical data such as size data, as in an optical microscope observer There is no room for the subjectivity of the observer. Therefore, the discriminant criterion does not vary depending on the observer (difference whether or not it is a skilled observer). For example, in a bond ore where a large number of minerals are combined or a part of the combined minerals is a trace amount Even if it is, a certain analysis result can be obtained, so that it is possible to improve the accuracy and reliability of the mineral analysis result.

  Moreover, according to the present embodiment, the distinction between simple ore, exposed ore and inclusion ore is objectively and quantitatively based mainly on numerical data such as size data, while following a series of instructions by a predetermined function. Therefore, even if the analysis result data from the automatic mineral analyzer 1 to be processed (that is, the collected data in the automatic mineral analyzer 1) is mainly composed of numerical data, the analysis result data (collected) Data) can be sufficiently consistent.

  In addition, according to the present embodiment, in distinguishing between the single ore, the exposed ore and the inclusion ore, the ratio of the size occupied by the useful ore in the useful ore containing particles is obtained, and the size ratio is compared with a predetermined threshold. Thus, this determination is performed. That is, since the distinction between the single ore, the exposed ore and the inclusion ore is performed according to a predetermined uniform discrimination criterion called a predetermined threshold, the discrimination result becomes objective and quantitative.

  In particular, in this embodiment, “Free Surface%” of grain data is used as size data for discrimination processing between a single ore, exposed ore and inclusion ore. That is, the ratio of the ore appearance length data of useful ore to the outer periphery length data of useful ore containing particles is determined, and the ratio is compared with a predetermined threshold value to discriminate the single ore, exposed ore and inclusion ore. Therefore, since the discrimination process can be performed using the analysis result data output from the automatic mineral analyzer 1 as it is, it can be expected to reduce the processing load of the discrimination process and to increase the speed associated therewith.

<6. Modified example>
While one embodiment of the present invention has been described above, the above disclosure is an exemplary embodiment of the present invention. That is, the technical scope of the present invention is not limited to the above exemplary embodiment.

  For example, in this embodiment, in the noble metal smelting that recovers gold from the gold-containing ore, a case where the gold which is the target mineral is taken out by performing dry smelting after the blue leaching is taken as an example. However, the present invention is not limited to this, and can be applied to non-ferrous metal smelting. For example, wet smelting such as the SX-EW method may be employed for copper smelting. In such a case, the surface state of the outer periphery of the mineral particles may be grasped as the mineral properties of the ore containing the target mineral. This is because it is very important for improving the quality of the smelted product and improving the actual yield.

  Further, in the present embodiment, an example is given in which “Free Surface%” of grain data is used as size data for the discrimination process between single ore, exposed ore and inclusion ore. However, the size data used for the discrimination process is not limited to this, and other types of size data can be used. Other types of size data include, for example, the weight ratio (Wt%) of each mineral, the area ratio, the binding ratio (boundary) of different minerals in the same particle, and the like. Even if one of these size data is used, numerical data is used as a reference in the same manner as in the present embodiment, so the discrimination process between single ore, exposed ore and inclusion ore is objective. And it becomes possible to carry out quantitatively. It should be noted that what type of size data is used may be appropriately selected depending on the purpose of data processing, the type of ore sample as an analysis object, and the like.

  Further, in the present embodiment, the ratio of the size occupied by the useful ore in the useful ore-containing particles is obtained, and the size ratio is compared with a predetermined threshold value to distinguish between the simple ore, the exposed ore and the inclusion ore. An example is given. However, the discrimination process between the single ore, the exposed ore and the inclusion ore is not limited to this as long as it is performed mainly based on the size data of the mineral. Specifically, for example, instead of the size ratio, the difference between the size of the useful ore-containing particles and the size of the mineral contained therein is obtained, and by comparing the size difference with a predetermined threshold, the single ore and the exposed ore It is also possible to discriminate from inclusion ore.

  Moreover, in this embodiment, the format example shown in FIG. 6 or FIG. 7 is mentioned as an output data structure of the mineral analysis result which the data processor 2 outputs, for example. However, the output data structure of the mineral analysis results shows the distribution state of the useful ore-containing particles in the ore sample in such a manner that the useful ore-containing particles can be identified as simple or exposed or included ore. Is not limited to this as long as it is expressed in a list format according to size. In other words, the output data structure of the mineral analysis results is not limited to a specific format, and includes other information not related to useful ore-containing particles, for example, information on waste ore simplex ore. It may be. Furthermore, regarding the notation by size in the output data structure of the mineral analysis results, in this embodiment, the case of notation by the particle size of the mineral particles (particles) is taken as an example, but it is indicated by the size of the mineral (grain). It doesn't matter.

  In this embodiment, the case where the data processing device 2 uses RDBMS, spreadsheet software, or the like is described as an example in order to summarize the mineral analysis results in the output data structure described above. In this way, it is possible to construct a data processing program executed by the data processing device 2 while utilizing a general general-purpose software program. However, the present invention is not necessarily limited to this, and the data processing program executed by the data processing apparatus 2 may be developed exclusively for the data processing program.

  In the present embodiment, the data processing device 2 receives the analysis result data from the automatic mineral analysis device 1, performs data processing on the analysis result data, and outputs data according to the above-described output data structure of the mineral analysis result. An example is given in the case of performing. However, the analysis result data acquired by the data processing device 2 is not necessarily received directly from the automatic mineral analyzer 1. For example, the analysis result data obtained by the automatic mineral analyzer 1 such as MLA or QEMSCAN may be input to the data processing device 2 using the information input unit 30 in the data processing device 2. . Furthermore, a type of mineral analysis different from that of the automatic mineral analyzer 1 such as a scanning electron microscope (SEM) or an X-ray analyzer that is not categorized by the automatic mineral analyzer 1 such as MLA or QEMSCAN. It is also conceivable that analysis result data obtained by the apparatus is input and used as a data processing target in the data processing apparatus 2.

  DESCRIPTION OF SYMBOLS 1 ... Automatic mineral analyzer, 2 ... Data processing apparatus, 10 ... Information processing part, 11 ... Data processing means, 12 ... Discrimination means, 13 ... Data editing means, 14 ... Data output means, 20 ... Information output part, 30 ... Information input section

Claims (9)

Data acquisition means for acquiring analysis result data obtained by the mineral analyzer for each mineral particle constituting the ore sample;
Based on the analysis result data, mineral particles containing useful ore are extracted from the respective mineral particles as useful ore containing particles, and the useful ore containing particles are simple ores consisting of only the useful ore or the useful ore A discriminating means for discriminating whether a part of the ore is an exposed ore exposed on the outer peripheral surface of the particle, or whether the useful ore is an inclusion ore not exposed at all on the outer peripheral surface of the particle;
Based on the analysis result data, the distribution state according to size in the ore sample of at least the useful ore-containing particles is summarized in a list form of a mode in which the distinction between the simple ore, the exposed ore and the inclusion ore can be identified. Data editing means,
Data output means for outputting data about the distribution state in the list format;
A data processing apparatus comprising: The data acquisition means acquires, as the analysis result data, at least mineral species data related to the type of mineral contained in each mineral particle and size data related to size,
The discrimination means obtains a proportion of the size occupied by the useful mineral in the useful mineral-containing particles from the size data based on the mineral species data and the size data for the useful mineral-containing particles, and determines the proportion as a predetermined threshold value. The data processing apparatus according to claim 1, wherein it is determined whether the useful ore-containing particles are the simple ore, the exposed ore, or the inclusion ore. The discrimination means uses the outer circumference length data of the useful ore-containing particles as the size data and the outer circumference appearance length data of the useful ore contained in the useful ore-containing particles, and the outer circumference appearance length data for the outer circumference length data. The data processing device according to claim 2, wherein the ratio of the single ore, the exposed ore, and the inclusion ore is determined. The data processing device according to any one of claims 1 to 3, wherein the mineral analyzer is an automatic mineral analyzer using X-ray analysis. Computer
Data acquisition means for acquiring analysis result data obtained by the mineral analyzer for each mineral particle constituting the ore sample;
Based on the mineral species data, mineral particles containing useful minerals are extracted from the mineral particles as useful mineral-containing particles, and the useful mineral-containing particles are simple ores consisting of only the useful minerals or the useful minerals. A discriminating means for discriminating whether a part of the ore is an exposed ore exposed on the outer peripheral surface of the particle, or whether the useful ore is an inclusion ore not exposed at all on the outer peripheral surface of the particle,
Based on the analysis result data, the distribution state according to size in the ore sample of at least the useful ore-containing particles is summarized in a list form of a mode in which the distinction between the simple ore, the exposed ore and the inclusion ore can be identified. Data editing means, and
Data output means for outputting data on the distribution state in the list format;
A data processing program characterized by functioning as A data acquisition step of acquiring analysis result data obtained by the mineral analyzer for each mineral particle constituting the ore sample;
Based on the analysis result data, mineral particles containing useful ore are extracted from the respective mineral particles as useful ore containing particles, and the useful ore containing particles are simple ores consisting of only the useful ore or the useful ore A determination step of determining whether a part of the ore is an exposed ore exposed on the outer peripheral surface of the particle, or whether the useful ore is an inclusion ore that is not exposed at all on the outer peripheral surface of the particle;
Based on the analysis result data, the distribution state according to size in the ore sample of at least the useful ore-containing particles is summarized in a list form of a mode in which the distinction between the simple ore, the exposed ore and the inclusion ore can be identified. A data editing step;
A data processing method comprising: The process which determines the process condition at the time of performing a beneficiation or smelting with respect to the process target object of the same component as the said ore sample based on the said distribution state put together in the said list format by the data processing method of Claim 6 A processing condition determining method comprising: a condition determining step. Prior to performing leaching with a solvent on the object to be treated and recovering the target mineral contained in the object to be treated, the degree of exposure of the useful mineral to the outer peripheral surface of the particles is the solvent of the object to be treated. The processing condition determination method according to claim 7, wherein the processing condition determination method is used as an index for evaluating the leaching property of the useful ore with respect to. Based on the analysis result data obtained by the mineral analyzer for each mineral particle constituting the ore sample, the mineral analysis used when outputting data on the distribution state of the useful ore contained in each mineral particle in the ore sample The resulting output data structure,
Regarding the useful ore-containing particles extracted from the respective mineral particles as mineral particles containing at least the useful ore, the useful ore-containing particles are simple ores that consist only of the useful ore, or a part of the useful ore Is an exposed ore exposed on the outer peripheral surface of the particle, or an inclusion ore that is not exposed on the outer peripheral surface of the particle at all, and the distribution state is in a list format according to size. An output data structure of mineral analysis results characterized by being represented.

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