Method for detecting impurities in high-purity quartz sand

Abstract

The present application relates to the technical field of quartz sand purification detection, and particularly relates to a high-purity quartz sand impurity removal detection method, which comprises the following steps: dividing the quartz sand into a plurality of detection regions, determining the pretreatment method of each detection region based on the determination result of the impurity removal tendency, determining the impurity occurrence degree of the quartz sand based on the first impurity recessive characteristic deviation value, determining the first impurity removability of the quartz sand based on the collected density heavy phase yield and impurity distribution rate, determining the second impurity removability of the quartz sand based on the collected crystallinity and grain size, determining the third impurity removability of the quartz sand according to the second impurity recessive characteristic deviation value, determining whether the impurity removal detection process of the quartz sand in the preset detection period is qualified based on the fluctuation factor, and adjusting the number of infrared irradiation angles or the impurity dominant characteristic threshold value based on the fluctuation factor difference value. The present application improves the accuracy of the high-purity quartz sand impurity removal detection method.

Description

Technical Field

[0001] This invention relates to the field of quartz sand purification and testing technology, and in particular to a method for detecting impurities in high-purity quartz sand. Background Technology

[0002] High-purity quartz sand is a key raw material widely used in photovoltaics, semiconductors, optical fiber communication, and high-end optical devices. However, natural quartz ore often contains lattice impurities and inclusions. These impurities exist in a variety of complex forms, and their occurrence states vary significantly from surface adsorption and crack filling to lattice substitution. This poses challenges to the targeting and effectiveness of impurity removal processes. Therefore, there is an urgent need for an impurity removal detection method that can comprehensively assess the visible and hidden characteristics of quartz sand, dynamically evaluate the removability of impurities, and calibrate the detection strategy in real time, so as to improve the control accuracy of the quartz sand purification process and the reliability of the detection results.

[0003] Chinese Patent Application Publication No. CN119513559A relates to the field of quartz sand detection technology, specifically a method for detecting impurities in quartz sand. The method includes: acquiring spectral data of a quartz sand sample; determining the spectral interference level of each characteristic peak based on its energy distribution; determining the matrix interference level of each characteristic peak based on its peak shape variation and its offset from the matrix characteristic peaks; further determining the interference intensity of each characteristic peak by combining its anti-interference level with the interference intensity of the characteristic peaks; optimizing the chaos adjustment factor during the initialization of the chaotic mapping population; and finally, obtaining the characteristic band with the least interference using an optimization algorithm. Based on this, the purity and impurity content of the quartz sand are detected. This application can improve the accuracy of quartz sand impurity detection.

[0004] The existing technology still has the following problems: The existing technology only determines the impurity detection results of quartz sand by characteristic peaks, without considering the determination of corresponding detection strategies based on the occurrence state of impurities in quartz sand, and dynamically adjusting the detection angle according to the fluctuation of the detection results to improve the accuracy of the detection results. Summary of the Invention

[0005] To address this, the present invention provides a method for detecting impurities in high-purity quartz sand, which overcomes the problem that existing technologies only determine the impurity detection results of quartz sand through characteristic peaks, without considering the determination of corresponding detection strategies based on the occurrence state of impurities in quartz sand, and dynamically adjusting the detection angle according to the fluctuation of the detection results to improve the accuracy of the detection results.

[0006] To achieve the above objectives, the present invention provides a method for detecting impurities in high-purity quartz sand, comprising: Quartz sand is divided into several detection areas. The quartz sand in each detection area is irradiated with infrared light, and the whiteness and diffuse reflectance of the quartz sand are obtained to generate impurity explicit characteristic values. The impurity removal tendency of each detection area is determined based on the impurity explicit characteristic values. Based on the determination result of the impurity removal tendency, the pretreatment method for each detection area is determined. For any detection area, the first impurity leaching rate and the second impurity leaching rate of the pretreated quartz sand are collected to generate impurity latent characteristic values. Based on the impurity latent characteristic values, it is determined whether the determination result of the impurity removal tendency of the current detection area is accurate. In response to the inaccurate determination result of the impurity removal tendency, the degree of impurity occurrence in the quartz sand is determined based on the first impurity latent characteristic deviation value. In response to the presence of the first impurity, the detection strategy for removing impurities from quartz sand is determined to be the density classification method, and the removability of the first impurity in quartz sand is determined based on the collected density heavy phase yield and impurity distribution rate. In response to the degree of presence of the second impurity, the detection strategy for removing impurities from quartz sand was determined to be the diffraction pattern method, and the removability of the second impurity in quartz sand was determined based on the collected crystallinity and grain size. In response to the accurate determination result of the impurity removal tendency, the removability of the third impurity in the quartz sand is determined based on the deviation value of the latent characteristic of the second impurity. The corresponding fluctuation factor is calculated based on the first impurity removability, the second impurity removability, or the third impurity removability within a preset detection period. Based on the corresponding fluctuation factor, determine whether the impurity removal detection process of quartz sand within the preset detection period is qualified. In response to the failure of the quartz sand impurity removal detection process, adjust the number of infrared irradiation angles or the threshold of impurity manifestation characteristics based on the fluctuation factor difference.

[0007] Furthermore, the process of determining the impurity removal tendency of each detection area includes, The ratio of the whiteness of the quartz sand to the whiteness of the reference quartz sand is determined as the whiteness factor; The ratio of the diffuse reflectance to the reference diffuse reflectance is determined as the reflectance factor; The weighted sum of the whiteness factor and the reflectance factor is determined to be the dominant characteristic value of the impurity; If the impurity dominance characteristic value is greater than or equal to the impurity dominance characteristic threshold, then the impurity removal tendency of the detection area is determined to be easy to remove. If the impurity dominance value is less than the impurity dominance threshold, then the impurity removal tendency of the detection area is determined to be a tendency to leave residues.

[0008] Furthermore, the process of determining the preprocessing method for each detection region includes, In response to the tendency to be easily removed, the pretreatment method for the detection area is determined to be dilution and immersion at room temperature; In response to the tendency to leave residues, the pretreatment method for the detection area is determined to be medium-temperature hot acid leaching.

[0009] Furthermore, the process of determining whether the impurity removal tendency judgment result of the current detection area is accurate includes, The weighted sum of the leaching rates of the first and second impurities is determined to be the latent characteristic value of the impurities. If the latent impurity characteristic value is greater than or equal to the latent impurity characteristic threshold, then the determination result of the impurity removal tendency is accurate. If the value of the latent impurity characteristic is less than the threshold value of the latent impurity characteristic, then the determination result of the impurity removal tendency is inaccurate.

[0010] Furthermore, the process of determining the degree of impurity occurrence in quartz sand based on the first impurity latent characteristic deviation value includes, The difference between the impurity latent characteristic threshold and the impurity latent characteristic value is determined as the first impurity latent characteristic deviation value; If the deviation value of the first impurity latent characteristic is less than or equal to the threshold value of the first impurity latent characteristic, then the degree of impurity occurrence of the quartz sand is determined to be the degree of impurity occurrence of the first impurity. If the deviation value of the first impurity latent characteristic is greater than the threshold value of the first impurity latent characteristic, then the degree of impurity occurrence of the quartz sand is determined to be the degree of impurity occurrence of the second impurity.

[0011] Furthermore, the process of determining the removability of the first impurity in the quartz sand includes, The ratio of the difference between the density heavy phase yield and the reference density heavy phase yield is determined as the density factor; The ratio of the difference between the impurity partition ratio and the reference impurity partition ratio is determined as the partition factor; The weighted sum of the density factor and the allocation factor is determined to be the first impurity removability.

[0012] Furthermore, the process of determining the removability of the second impurity in the quartz sand includes, The ratio of the difference between the crystallinity and the reference crystallinity is determined as the crystallinity factor; The ratio of the difference between the stated grain size and the reference grain size is defined as the grain factor; The weighted sum of the crystallization factor and the grain factor is determined to be the second impurity removability.

[0013] Furthermore, the process of determining the removability of the third impurity in the quartz sand includes, The difference between the impurity latent characteristic value and the impurity latent characteristic threshold is determined as the second impurity latent characteristic deviation value; The ratio of the difference between the latent characteristic deviation value of the second impurity and the latent characteristic deviation threshold value of the second impurity is determined as the removability of the third impurity.

[0014] Furthermore, the process of determining whether the impurity removal test of the quartz sand within the preset testing period is qualified includes, The first fluctuation factor is the ratio of the standard deviation to the mean of the removability of several first impurities. The second fluctuation factor is the ratio of the standard deviation to the mean of the removability of several second impurities. The third fluctuation factor is based on the ratio of the standard deviation to the mean of the removability of several third impurities. If the corresponding fluctuation factor is greater than the corresponding fluctuation factor threshold, the impurity removal test process of the quartz sand is deemed unqualified. The corresponding volatility factor thresholds include the first volatility factor threshold, the second volatility factor threshold, and the third volatility factor threshold.

[0015] Furthermore, the process of adjusting the number of infrared irradiation angles or the threshold for the dominant characteristic of impurities based on the fluctuation factor difference includes, The difference between the corresponding volatility factor and the corresponding volatility factor threshold is defined as the volatility factor difference. If the fluctuation factor difference is greater than or equal to the corresponding fluctuation factor difference threshold, then it is determined that the increase in the infrared illumination angle is based on the fluctuation factor difference. If the fluctuation factor difference is less than the corresponding fluctuation factor difference threshold, it is determined that the impurity dominance characteristic threshold should be reduced based on the fluctuation factor difference. The corresponding volatility factor difference thresholds include the first volatility factor difference threshold, the second volatility factor difference threshold, and the third volatility factor difference threshold.

[0016] Compared with existing technologies, the beneficial effects of this invention are as follows: By dividing the quartz sand raw material into several detection areas and combining infrared irradiation to obtain the whiteness and diffuse reflectance of the quartz sand, and generating explicit impurity characteristic values, this invention can effectively identify the spatial distribution and occurrence differences of impurities in different detection areas; based on the explicit impurity characteristic values ​​and explicit impurity characteristic thresholds, it determines whether the impurity removal tendency is easy to remove or easy to remain, and determines the corresponding room temperature dilution leaching or medium temperature hot acid leaching pretreatment method, thus realizing differentiated treatment of detection areas; by collecting the first impurity leaching rate and the second impurity leaching rate of the pretreated quartz sand and calculating the implicit impurity characteristic values, the accuracy of the previous impurity removal tendency determination results is verified, thereby further improving the accuracy of the high-purity quartz sand impurity removal detection method.

[0017] Furthermore, this invention calculates the first impurity latent characteristic deviation value by using the latent characteristic value and the latent characteristic threshold of impurities to further distinguish the occurrence degree of the first impurity and the second impurity, thus achieving a refined identification of the impurity occurrence degree. It can effectively distinguish the existence form of difficult-to-remove impurities in quartz sand. For the occurrence degree of the first impurity, a density grading method is used to separate different minerals by utilizing the density difference between them, and the removability is directly judged by the density heavy phase yield and impurity distribution rate. For the occurrence degree of the second impurity, a diffraction pattern method is used, which reflects the ease of impurity removal based on crystallinity and grain size. The differentiated matching of the two detection strategies improves the pertinence and effectiveness of the detection method, thereby further improving the accuracy of the high-purity quartz sand impurity removal detection method.

[0018] Furthermore, this invention calculates the removability of the third impurity in quartz sand by using the deviation value of the second impurity latent characteristic and the threshold value of the second impurity latent characteristic when the impurity removal tendency judgment result is accurate, thus forming a complete detection result output system. By calculating the removability of the first impurity, the removability of the second impurity, and the removability of the third impurity within a preset detection period, the corresponding fluctuation factor is obtained, which can reflect the fluctuation degree of impurity removal detection. At the same time, an integrated evaluation mechanism for removability covering multiple detection paths is established, thereby further improving the accuracy of the high-purity quartz sand impurity removal detection method.

[0019] Furthermore, this invention uses a corresponding fluctuation factor to determine the pass / fail status of the impurity removal detection process within a preset detection period. When the detection process is deemed unqualified, the number of infrared irradiation angles or the threshold value of the obvious impurity characteristics are actively adjusted based on the fluctuation factor difference. The number of infrared irradiation angles directly affects the richness and accuracy of the extraction of obvious impurity characteristics, while the threshold value of obvious impurity characteristics determines the sensitivity and specificity of the impurity removal tendency determination. By dynamically adjusting these two core parameters through the fluctuation factor difference, the detection system can adapt to the differences in impurity characteristics of different batches of quartz sand, thereby further improving the accuracy of the high-purity quartz sand impurity removal detection method. Attached Figure Description

[0020] Figure 1 This is a schematic flowchart of the high-purity quartz sand impurity removal and detection method according to an embodiment of the present invention; Figure 2 This is a flowchart illustrating step S3 in an embodiment of the present invention; Figure 3 This is a logic diagram for determining the impurity removal tendency of each detection area in an embodiment of the present invention; Figure 4 This is a logic diagram for determining the accuracy of the impurity removal tendency determination result in the current detection area according to an embodiment of the present invention. Figure 5This is a logic diagram for determining whether the impurity removal detection process of quartz sand within a preset detection period is qualified, according to an embodiment of the present invention. Detailed Implementation

[0021] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0022] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0023] It should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0024] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation" and "connection" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0025] Please see Figure 1 The diagram shown is a flowchart illustrating the high-purity quartz sand impurity removal and detection method according to an embodiment of the present invention. The high-purity quartz sand impurity removal and detection method according to an embodiment of the present invention includes: Step S1: Divide the quartz sand into several detection areas, irradiate the quartz sand in each detection area with infrared light, and generate impurity explicit characteristic values ​​based on the obtained quartz sand whiteness and diffuse reflectance, and determine the impurity removal tendency of each detection area based on the impurity explicit characteristic values. Step S2: Based on the determination result of the impurity removal tendency, determine the pretreatment method for each detection area, collect the first impurity leaching rate and the second impurity leaching rate of the pretreated quartz sand for any detection area to generate the impurity latent characteristic value, and determine whether the determination result of the impurity removal tendency of the current detection area is accurate based on the impurity latent characteristic value. Step S3: In response to the inaccurate determination result of the impurity removal tendency, determine the first impurity removability and the second impurity removability of the quartz sand respectively; in response to the accurate determination result of the impurity removal tendency, determine the third impurity removability of the quartz sand. Step S4: Calculate the corresponding fluctuation factor based on the first impurity removability, the second impurity removability, or the third impurity removability within the preset detection period. Step S5: Determine whether the impurity removal detection process of quartz sand within the preset detection period is qualified based on the corresponding fluctuation factor. If the impurity removal detection process of quartz sand is unqualified, adjust the number of infrared irradiation angles or the threshold of impurity visibility characteristics based on the fluctuation factor difference.

[0026] Please see Figure 2 As shown, the flowchart of step S3 in the embodiment of the present invention is illustrated, wherein S3 further includes: Step S31: In response to the inaccurate determination result of the impurity removal tendency, the degree of impurity occurrence in the quartz sand is determined based on the deviation value of the first impurity latent characteristic. Step S311, wherein, in response to the presence degree of the first impurity, the detection strategy for removing impurities from quartz sand is determined to be the density classification method, and the removability of the first impurity in quartz sand is determined based on the collected density heavy phase yield and impurity distribution rate. Step S312: In response to the degree of presence of the second impurity, the detection strategy for removing impurities from quartz sand is determined to be the diffraction pattern method, and the removability of the second impurity in quartz sand is determined based on the collected crystallinity and grain size. Step S32: In response to the accurate determination result of the impurity removal tendency, the removability of the third impurity in the quartz sand is determined based on the deviation value of the latent characteristic of the second impurity.

[0027] Please see Figure 3 As shown, this is a logic diagram for determining the impurity removal tendency of each detection area according to an embodiment of the present invention. The process for determining the impurity removal tendency of each detection area includes: The ratio of the whiteness of quartz sand to the whiteness of the reference quartz sand is determined as the whiteness factor; The ratio of diffuse reflectance to reference diffuse reflectance is determined as the reflectance factor; The weighted sum of the whiteness factor and reflectance factor is determined to be the dominant characteristic value of impurities; If the impurity dominance characteristic value is greater than or equal to the impurity dominance characteristic threshold, then the impurity removal tendency of the detection area is determined to be easy to remove. If the impurity dominance characteristic value is less than the impurity dominance characteristic threshold, then the impurity removal tendency of the detection area is determined to be a tendency to leave residues.

[0028] Specifically, the baseline quartz sand whiteness is the average of the whiteness of easily removable quartz sand obtained from historical normal data, and the baseline diffuse reflectance is the average diffuse reflectance of easily removable quartz sand obtained from historical normal data.

[0029] Specifically, the sum of the weighting coefficients of the whiteness factor and the reflectance factor is 1. Since whiteness directly reflects visible color impurities and is highly related to acid pickling removability, the weighting coefficient of the whiteness factor is set to 0.6 and the weighting coefficient of the reflectance factor is set to 0.4.

[0030] Specifically, in practice, if the impurity dominant characteristic value is greater than or equal to the impurity dominant characteristic threshold, it indicates a high whiteness factor or a high reflectance factor. A high whiteness factor corresponds to low surface staining, and a high reflectance factor corresponds to weak infrared absorption. Therefore, the overall impurity dominant characteristic value indicates that the impurity is not deeply embedded in the quartz lattice or tightly wrapped, and can be effectively removed at room temperature by gentle methods such as diluted acid leaching, water washing, and wiping. The impurity removal tendency of the detection area is determined to be easy removal. If the impurity dominant characteristic value is less than the impurity dominant characteristic threshold, it indicates a low whiteness factor or a low reflectance factor. A low whiteness factor is due to severe surface iron staining, and a low reflectance factor indicates that a large number of infrared active impurities are located in the shallow and near-surface lattice, and require medium to high temperature, strong acid, or long-term leaching to be removed. The impurity removal tendency of the detection area is determined to be easy residue.

[0031] Specifically, the quartz sand raw material pile is regarded as a plane and divided into several rectangular grid units at a fixed interval of 3m×3m. Each grid unit is an independent detection area, and each detection area is a processing batch. This invention processes only one processing batch of quartz sand at a time. Specifically, in practice, a whiteness meter is used to test the dried quartz sand sample under a standard light source. The whiteness of the quartz sand is obtained by taking the average value of three parallel measurements. An infrared spectrometer with a diffuse reflectance accessory is used to test the quartz sand sample at a 45° illumination angle. The diffuse reflectance is read by selecting the hydroxyl characteristic band. The diffuse reflectance is obtained by taking the average value of three parallel measurements.

[0032] Specifically, the preset threshold for the apparent characteristics of impurities is the product of the reference value for apparent characteristics of impurities and the factor for apparent characteristics of impurities. The reference value for apparent characteristics of impurities is the average value of apparent characteristics of impurities obtained in historical data when the impurity removal tendency of the detection area is easy to remove. The factor for apparent characteristics of impurities can be set by those skilled in the art according to the accuracy requirements of quartz sand detection. The higher the accuracy requirement, the larger the value should be. The value range can be [0.9, 1.0], and preferably, it can be 0.95.

[0033] Understandably, when determining the reference value for the apparent characteristics of impurities, at least 30 test areas with a clearly defined tendency to remove impurities are selected. These areas cover the quartz sand raw materials under normal production conditions. From the areas with an easy tendency to remove impurities, test areas with impurity leaching rates that meet the standards are selected. The arithmetic mean of the apparent characteristics of impurities in several test areas is determined as the reference value for the apparent characteristics of impurities.

[0034] Specifically, the process of determining the preprocessing method for each detection area includes, In response to the tendency of easy removal, the pretreatment method for the detection area was determined to be dilution and immersion at room temperature. In response to the tendency for residues to remain, the pretreatment method for the detection area was determined to be medium-temperature hot acid leaching.

[0035] Understandably, room temperature dilution immersion uses a room temperature of 20℃~30℃ and a 3%~8% mass concentration of dilute acid solution to soak and wash the quartz sand; medium temperature hot acid immersion uses a medium temperature of 60℃~90℃ and a 15%~30% mass concentration of acid solution to intensify the immersion of the quartz sand.

[0036] Please see Figure 4 As shown, this is a logic diagram for determining whether the determination result of the impurity removal tendency of the current detection area is accurate, according to an embodiment of the present invention. The process for determining whether the determination result of the impurity removal tendency of the current detection area is accurate includes: The weighted sum of the leaching rates of the first and second impurities is determined as the latent characteristic value of the impurities. If the latent characteristic value of impurities is greater than or equal to the latent characteristic threshold of impurities, then the determination of the impurity removal tendency is accurate. If the latent characteristic value of the impurity is less than the latent characteristic threshold of the impurity, then the determination of the impurity removal tendency is inaccurate.

[0037] Specifically, the first impurity leaching rate is the iron impurity leaching rate, which reflects the ease of leaching of surface iron, clay iron, and fractured iron. The second impurity leaching rate is the aluminum impurity leaching rate, which reflects the ease of leaching of feldspar, mica-type aluminosilicates, and aluminum inclusions.

[0038] Understandably, iron is the core impurity affecting the whiteness and application performance of quartz. Even trace amounts of iron can cause quartz to yellow and reduce its light transmittance. It is the most obvious and priority impurity to remove. Aluminum mostly exists in the form of lattice substitution or inclusions, which is more difficult to remove than iron. It is a key indicator for judging whether impurities in quartz sand are easy to remain.

[0039] Specifically, in practice, if the latent characteristic value of the impurity is greater than or equal to the latent characteristic threshold, it indicates that the actual leaching effect meets expectations. The tendency to remove or remain impurities, judged based on the apparent characteristic value of the impurity, is consistent with the removability of the standard impurity, thus confirming the accuracy of the impurity removal tendency determination. If the latent characteristic value of the impurity is less than the latent characteristic threshold, it indicates that the actual leaching effect is lower than expected, suggesting a deviation in the apparent characteristic value's reflection of the impurity's removability, thus confirming the inaccuracy of the impurity removal tendency determination.

[0040] Specifically, in the implementation, the quartz sand is acid-leached according to the corresponding pretreatment method. After acid leaching, it is filtered to obtain an acid leaching extract, which is used to determine the leached iron content a and aluminum content b. Another quartz sand sample of the same amount as the acid-leached quartz sand is taken and digested with strong acid at high temperature to determine the total iron content A and total aluminum content B in the quartz sand sample. The leaching rate of the first impurity is determined as a×V / (A×m)×100%, and the leaching rate of the second impurity is determined as b×V / (B×m)×100%, where V is the volume of the acid leaching extract and m is the mass of the quartz sand sample.

[0041] Specifically, the sum of the weighting coefficients of the first impurity leaching rate and the second impurity leaching rate is 1. Since the leaching rates of iron and aluminum have similar influence on the determination of the impurity removal tendency of quartz sand, the weighting coefficient corresponding to the first impurity leaching rate can be set to 0.5 and the weighting coefficient corresponding to the second impurity leaching rate can be set to 0.5, respectively.

[0042] Specifically, the impurity latent feature threshold is the product of the impurity latent feature reference value and the impurity latent feature factor. The impurity latent feature reference value is the average of several impurity latent feature values ​​detected by different preprocessing methods in historical data. Different preprocessing methods correspond to different impurity latent feature factors. The impurity latent feature factor can be set by those skilled in the art according to the accuracy requirements of impurity leaching detection. The higher the accuracy requirement, the larger the set value. The impurity latent feature factor corresponding to room temperature dilution leaching can take the range of [0.9, 1.0], preferably 0.95. The impurity latent feature factor corresponding to medium temperature hot acid leaching can take the range of [0.7, 0.8], preferably 0.75.

[0043] Understandably, when determining the reference value for latent impurity characteristics, at least 30 test areas that have completed the pretreatment and impurity leaching rate testing processes should be selected. The test area samples include test areas treated by the room temperature dilution immersion pretreatment method and test areas treated by the medium temperature hot acid leaching pretreatment method. The latent impurity characteristic value corresponding to each test area should be calculated, and the arithmetic mean of all latent impurity characteristic values ​​should be determined as the reference value for latent impurity characteristics.

[0044] It is understandable that the medium-temperature hot acid leaching pretreatment method is for impurities that are prone to remain. The leaching rate of these impurities fluctuates greatly and is lower than that of impurities treated by the room temperature leaching pretreatment method. Therefore, setting the impurity latent characteristic factor corresponding to medium-temperature hot acid leaching to be smaller than that corresponding to room temperature dilution leaching can balance the sensitivity and specificity of the determination.

[0045] Specifically, this invention divides the quartz sand raw material into several detection areas, combines infrared irradiation to obtain the whiteness and diffuse reflectance of the quartz sand, and generates explicit impurity characteristic values. This effectively identifies the spatial distribution and occurrence differences of impurities in different detection areas. Based on the explicit impurity characteristic values ​​and threshold values, it determines whether the impurity removal tendency is easy to remove or easy to remain, and determines the corresponding room temperature dilution leaching or medium temperature hot acid leaching pretreatment method. This achieves differentiated treatment of the detection areas. By collecting the first impurity leaching rate and the second impurity leaching rate of the pretreated quartz sand and calculating the implicit impurity characteristic values, the accuracy of the previous impurity removal tendency determination results is verified, thereby further improving the accuracy of the high-purity quartz sand impurity removal detection method.

[0046] Specifically, the process of determining the degree of impurity occurrence in quartz sand based on the deviation value of the first impurity latent characteristic includes, The difference between the impurity latent feature threshold and the impurity latent feature value is determined as the first impurity latent feature deviation value, that is, the first impurity latent feature deviation value = impurity latent feature threshold - impurity latent feature value; If the deviation value of the first impurity latent characteristic is less than or equal to the threshold value of the first impurity latent characteristic, then the degree of impurity occurrence of the quartz sand is determined to be the degree of occurrence of the first impurity. If the deviation value of the first impurity latent characteristic is greater than the threshold value of the first impurity latent characteristic, then the degree of impurity occurrence of the quartz sand is determined to be the degree of impurity occurrence of the second impurity.

[0047] Specifically, in practice, under the corresponding pretreatment method, if the deviation value of the first impurity latent characteristic is less than or equal to the first impurity latent characteristic deviation threshold, the impurity dissolution effect is only slightly lower than the standard, indicating that the impurity exists as a physically encapsulated or finely dispersed phase, rather than a lattice impurity with extremely strong chemical bonding. Therefore, the impurity occurrence degree of the quartz sand is determined to be the first impurity occurrence degree. If the deviation value of the first impurity latent characteristic is greater than the first impurity latent characteristic deviation threshold, it indicates that even with the corresponding pretreatment method, the impurity dissolution effect is still lower than the standard. This indicates that the impurity does not exist in the form of simple physical encapsulation or surface adhesion, but is embedded inside the quartz lattice or exists in the form of solid solution or isomorphic substitution. Therefore, the impurity occurrence degree of the quartz sand is determined to be the second impurity occurrence degree.

[0048] Specifically, the purpose of setting the first impurity latent characteristic deviation threshold is to characterize the deviation of the impurity occurrence degree of different quartz sands. Optionally, the value range of the first impurity latent characteristic deviation threshold is [0.1, 0.2], and preferably, it can be 0.15.

[0049] Understandably, when determining the threshold for the latent characteristic deviation of the first impurity, at least 30 detection areas with inaccurate judgment results of impurity removal tendency are selected, the average value of several latent characteristic deviation values ​​of the first impurity is calculated, and the preferred value of the threshold for the latent characteristic deviation of the first impurity is determined to be the average value of several latent characteristic deviation values ​​of the first impurity.

[0050] Specifically, the process of determining the removability of the first impurity in silica sand includes, The density factor is defined as the ratio of the difference between the density heavy phase yield and the baseline density heavy phase yield. The ratio of the difference between the impurity partition ratio and the reference impurity partition ratio is determined as the partition factor; The weighted sum of the density factor and the allocation factor is determined to be the first impurity removability.

[0051] Specifically, density factor = |density heavy phase yield - reference density heavy phase yield| / reference density heavy phase yield, and distribution factor = |impurity distribution rate - reference impurity distribution rate| / reference impurity distribution rate.

[0052] Specifically, the baseline density heavy phase yield is the average of several density heavy phase yields detected in quartz sand samples with the first degree of impurity removability in historical data, and the baseline impurity distribution rate is the average of several impurity distribution rates detected in quartz sand samples with the first degree of impurity removability in historical data.

[0053] Specifically, in practice, the larger the density factor, the more heavy mineral impurities in the sample can be captured by the density grading method, and the greater the total amount of potential impurities that can be removed through grading. The partition factor reflects the enrichment degree of impurities in the heavy phase relative to the baseline level. The first impurity removability is obtained by weighted summation of the density factor and the partition factor to characterize the impurity removal potential and separation efficiency.

[0054] Specifically, in the implementation, a quartz sand sample is taken, dried to constant weight, and the total mass is weighed. The heavy liquid flotation method is used, and the density of the heavy liquid is controlled between 2.65 g / cm³ and 2.68 g / cm³. The sample is thoroughly stirred, allowed to stand and separate, and the lower heavy phase product with a density greater than that of the heavy liquid is collected. The lower heavy phase product is washed, dried to constant weight, and the mass of the heavy phase product is weighed. The density of the heavy phase yield is determined as follows: heavy phase product mass / total mass × 100%.

[0055] Specifically, in the implementation, quartz sand samples and heavy phase product samples are taken separately, microwave digestion is used, and the total impurity content of quartz sand samples and heavy phase product samples is determined separately to obtain the impurity content of quartz sand and the impurity content of heavy phase product. The impurity distribution rate is determined as follows: impurity content of heavy phase product × total mass of heavy phase product / (impurity content of quartz sand × total mass of quartz sand) × 100%.

[0056] Specifically, the sum of the weighting coefficients of the density factor and the distribution factor is 1. Since the impurity distribution rate directly reflects whether impurities are enriched in the heavy phase that can be removed by density sorting, it is the core basis for judging the removability of quartz sand. For example, the weighting coefficient corresponding to the distribution factor can be set to 0.6 and the weighting coefficient corresponding to the density factor can be set to 0.4.

[0057] Specifically, the process of determining the removability of the second impurity in silica sand includes, The ratio of the difference between the crystallinity and the reference crystallinity is determined as the crystallinity factor; The ratio of the difference between the grain size and the reference grain size is defined as the grain factor; The weighted sum of the crystallization factor and the grain factor is determined to be the second impurity removability.

[0058] Specifically, the crystallinity factor = |crystallinity - reference crystallinity| / reference crystallinity, and the grain factor = |grain size - reference grain size| / reference grain size.

[0059] Specifically, the baseline crystallinity is the average of several crystallinities detected in historical data, and the baseline grain size is the average of several grain sizes detected in historical data.

[0060] Specifically, in practice, crystallinity can characterize the reduction in diffraction peak intensity and peak broadening caused by lattice impurities. The crystallization factor is related to the total amount of impurities that must be removed by destroying the original lattice. Under the same historical conditions, pure quartz grains can grow to a large size. When impurities are present, the grain size is significantly refined. The grain size reflects the cumulative effect of lattice distortion and also indicates the difficulty of diffusing impurities out of the lattice through high-temperature treatment. Therefore, the second impurity removability reflects the potential of lattice impurities that can be removed by destroying the quartz crystal structure.

[0061] Specifically, in practice, the quartz sand sample is ground into powder of 200 mesh or higher, pressed into tablets, and diffraction patterns are collected using an X-ray diffractometer to obtain the characteristic diffraction peaks of quartz. The crystallinity is determined by the formula: Crystallinity = (Integrated area of ​​crystalline diffraction peak / (Area of ​​crystalline peak + Area of ​​amorphous diffraction peak)) × 100%, and the grain size is determined accordingly. ,in, The incident wavelength of the X-rays. The full width at half maximum (FWHM) of the diffraction peak. For the Bragg diffraction angle, Let be the Scherrer constant, which is dimensionless and is taken as 0.94.

[0062] Specifically, the sum of the weighting coefficients of the crystallization factor and the grain factor is 1. Since the crystallinity directly determines the integrity of the quartz lattice, it has a significant impact on the degree of impurity solid solution and encapsulation, and has a stronger dominant influence on the removability. For example, the weighting coefficient corresponding to the crystallization factor can be set to 0.6, and the weighting coefficient corresponding to the grain factor can be set to 0.4.

[0063] Specifically, this invention calculates the first impurity latent characteristic deviation value by using the latent characteristic value and the latent characteristic threshold of impurities to further distinguish the occurrence degree of the first impurity and the second impurity, thus achieving a refined identification of the impurity occurrence degree. It can effectively distinguish the existence form of difficult-to-remove impurities in quartz sand. For the occurrence degree of the first impurity, a density grading method is used to separate different minerals by utilizing the density difference between them, and the removability is directly judged by the density heavy phase yield and impurity distribution rate. For the occurrence degree of the second impurity, a diffraction pattern method is used, which reflects the ease of impurity removal based on crystallinity and grain size. The differentiated matching of the two detection strategies improves the pertinence and effectiveness of the detection method, thereby further improving the accuracy of the high-purity quartz sand impurity removal detection method.

[0064] Specifically, the process of determining the removability of the third impurity in silica sand includes, The difference between the latent impurity characteristic value and the latent impurity characteristic threshold is determined as the second latent impurity characteristic deviation value; The ratio of the difference between the latent characteristic deviation value of the second impurity and the latent characteristic deviation threshold of the second impurity is determined as the removability of the third impurity.

[0065] Specifically, the removability of the third impurity = |the latent characteristic deviation value of the second impurity - the latent characteristic deviation threshold of the second impurity| / the latent characteristic deviation threshold of the second impurity.

[0066] Specifically, the removability of the third impurity reflects the incremental increase in actual leaching effect beyond the acceptable line, assuming accurate determination of impurity removal tendency. It is characterized by the ratio of the difference between the latent characteristic deviation value of the second impurity and the latent characteristic deviation threshold of the second impurity, indicating the ease of impurity removal under the corresponding pretreatment method. Please see Figure 5 As shown, this is a logic diagram for determining whether the impurity removal testing process of quartz sand within a preset testing period is qualified according to an embodiment of the present invention. The process for determining whether the impurity removal testing process of quartz sand within a preset testing period is qualified includes, The first fluctuation factor is the ratio of the standard deviation to the mean of the removability of several first impurities. The second fluctuation factor is the ratio of the standard deviation to the mean of the removability of several second impurities. The third fluctuation factor is the ratio of the standard deviation to the mean of the removability of several third impurities. If the corresponding fluctuation factor is greater than the corresponding fluctuation factor threshold, the impurity removal test process of the quartz sand is deemed unqualified. If the corresponding fluctuation factor is less than or equal to the corresponding fluctuation factor threshold, the impurity removal test process of the quartz sand is deemed qualified. The corresponding volatility factors include the first volatility factor, the second volatility factor, and the third volatility factor, and the corresponding volatility factor thresholds include the first volatility factor threshold, the second volatility factor threshold, and the third volatility factor threshold.

[0067] Specifically, the first fluctuation factor reflects the stability of heavy mineral impurity removal under the density grading detection strategy; the second fluctuation factor reflects the stability of lattice impurity removal potential under the diffraction pattern detection strategy; and the third fluctuation factor reflects the stability of impurity leaching when the impurity removal tendency is correct. If the corresponding fluctuation factor is less than or equal to the corresponding fluctuation factor threshold, it indicates that the relative fluctuations of the three removability levels are within acceptable ranges within the preset detection cycle, the impurity removal effect is stable, the consistency of the detection process is good, and no adjustment of detection parameters is required. If the corresponding fluctuation factor is greater than the corresponding fluctuation factor threshold, it indicates that the fluctuation of at least one type of removability exceeds the allowable range, there are significant unstable factors in the detection process, and further analysis of the corresponding fluctuation factor difference is needed. Based on this, the number of infrared irradiation angles or the threshold of impurity explicit characteristics should be adjusted to reduce the fluctuations in subsequent detection cycles and improve the stability and reliability of the detection process.

[0068] Specifically, in order to provide a sufficient number of tests so that the three types of removability can basically meet the statistical sample requirements, the preset testing cycle is set to 24 hours.

[0069] Specifically, this invention calculates the removability of the third impurity in quartz sand by using the deviation value of the second impurity latent characteristic and the threshold value of the second impurity latent characteristic when the impurity removal tendency determination result is accurate, thus forming a complete detection result output system.

[0070] By calculating the removability of the first, second, and third impurities within a preset detection cycle, the corresponding fluctuation factors can be obtained, which can reflect the fluctuation degree of impurity removal detection. At the same time, an integrated evaluation mechanism for removability covering multiple detection paths has been established, thereby further improving the accuracy of the high-purity quartz sand impurity removal detection method.

[0071] Specifically, during the calculation, the selected single volatility factor needs to be normalized separately to eliminate the interference of differences in the volatility ranges of different indicators on the calculation results. Independent normalization is performed using the range standardization method; the formula is as follows: ,in, Let be the normalized value of the i-th type of volatility factor. For the measured or assigned value of the i-th type of volatility factor, Setting it to 0 represents the ideal state with no fluctuations. The maximum allowable fluctuation value for the corresponding process is 0.18, where the maximum allowable fluctuation value for the first fluctuation factor is 0.14, the maximum allowable fluctuation value for the second fluctuation factor is 0.21, and if the normalized value is less than 0, it is directly taken as 0.

[0072] Specifically, the purpose of setting the fluctuation factor threshold is to characterize the consistency of the detection results within a preset period. The first impurity removability is calculated based on density sorting, heavy phase yield, and impurity distribution rate. It is greatly affected by the disturbance of feed uniformity, and the data fluctuation is slightly high. Optionally, the value range of the first fluctuation factor threshold is [0.14, 0.18], preferably 0.16. The second impurity removability is calculated based on the inherent physical property parameters of the material, such as quartz crystallinity and grain size. The raw material itself has stable structural properties and low dispersion. The value range of the second fluctuation factor threshold is [0.10, 0.14], preferably 0.12. The third impurity removability is based on the acid leaching impurity leaching rate. It is affected by the coupling of variables such as acid concentration, leaching temperature, and reaction time, and the overall fluctuation is the largest. The value range of the third fluctuation factor threshold is [0.17, 0.21], preferably 0.19.

[0073] It is understandable that when determining the corresponding volatility factor threshold, no less than 20 preset detection periods are selected, and the arithmetic mean of the corresponding volatility factors of several preset detection periods is determined as the corresponding volatility factor threshold.

[0074] Specifically, the process of adjusting the number of infrared irradiation angles or the threshold for the dominant characteristic of impurities based on the fluctuation factor difference includes, The difference between the corresponding volatility factor and the corresponding volatility factor threshold is defined as the volatility factor difference. If the fluctuation factor difference is greater than or equal to the corresponding fluctuation factor difference threshold, then the increase in the infrared illumination angle is determined based on the fluctuation factor difference. If the fluctuation factor difference is less than the corresponding fluctuation factor difference threshold, then the threshold for reducing the explicit characteristics of impurities is determined based on the fluctuation factor difference. The corresponding volatility factor difference thresholds include the first volatility factor difference threshold, the second volatility factor difference threshold, and the third volatility factor difference threshold.

[0075] Specifically, in practice, if the fluctuation factor difference is greater than or equal to the corresponding fluctuation factor difference threshold, it indicates that the fluctuation of the detection result is severe. In this case, increasing the infrared illumination angle to obtain the diffuse reflectance spectrum of the particle surface at different incident angles improves the ability to identify impurities. If the fluctuation factor difference is less than the corresponding fluctuation factor difference threshold, it indicates that the fluctuation of the detection result is mild. This is due to a systematic deviation caused by setting the impurity explicit characteristic threshold too strictly, resulting in a mismatch in the preprocessing method and thus causing slight fluctuations in the removability.

[0076] Specifically, the purpose of setting the fluctuation factor difference threshold is to characterize the deviation of different impurity removal detection results. For example, the value range of the first fluctuation factor difference threshold, the second fluctuation factor difference threshold, and the third fluctuation factor difference threshold are all [0, 0.08]. In this embodiment, it is preferred to set the first fluctuation factor difference threshold to 0.05, the second fluctuation factor difference threshold to 0.04, and the third fluctuation factor difference threshold to 0.06.

[0077] It is understandable that when determining the volatility factor difference threshold, no less than 20 preset detection periods are selected, and the arithmetic mean of the volatility factor difference values ​​of several preset detection periods is determined as the volatility factor difference threshold.

[0078] Specifically, in implementation, a graded adjustment rule is established based on the degree of deviation between the fluctuation factor difference and the fluctuation factor difference threshold: when the fluctuation factor difference is greater than or equal to the fluctuation factor difference threshold, it is determined that the overall deviation of the detection result is significant, the sampling randomness of the impurity appearance detection is relatively strong, and the increase in the number of infrared irradiation angles is positively correlated with the fluctuation factor difference; the larger the fluctuation factor difference, the higher the degree of dispersion of detection fluctuation, and the number of infrared irradiation angles is increased according to the deviation interval gradient. When the fluctuation factor difference is less than the fluctuation factor difference threshold, the detection system is considered to be operating stably and the detection deviation is within a normal and controllable range, so there is no need to expand the sampling conditions. The threshold for the dominant characteristics of impurities is adjusted according to the fluctuation factor difference gradient. The smaller the fluctuation factor difference, the higher the detection stability. The threshold for the dominant characteristics of impurities is lowered accordingly to improve the recognition accuracy of weak anomalies in impurity presence.

[0079] Specifically, in implementation, when the difference in the first fluctuation factor is greater than or equal to 0.05, the amount of infrared illumination angle to be increased is determined; when the difference in the second fluctuation factor is greater than or equal to 0.04, the amount of infrared illumination angle to be increased is determined; when the difference in the third fluctuation factor is greater than or equal to 0.06, the amount of infrared illumination angle to be increased is determined. If the first fluctuation factor difference is within [0.05, 0.07], determine the amount to increase by 1 infrared illumination angle; if the first fluctuation factor difference is within (0.07, 0.08], determine the amount to increase by 2 infrared illumination angles. If the difference in the second fluctuation factor is within [0.04, 0.07], determine the amount to increase by 1 infrared illumination angle; if the difference in the second fluctuation factor is within (0.07, 0.08], determine the amount to increase by 2 infrared illumination angles. If the difference of the third fluctuation factor is within [0.06, 0.07], determine the amount to increase by 1 infrared illumination angle; if the difference of the third fluctuation factor is within (0.07, 0.08], determine the amount to increase by 2 infrared illumination angles.

[0080] Specifically, in implementation, when the difference in the first fluctuation factor is less than 0.05, the threshold for reducing the dominance of impurities is determined; when the difference in the second fluctuation factor is less than 0.04, the threshold for reducing the dominance of impurities is determined; when the difference in the third fluctuation factor is less than 0.06, the threshold for reducing the dominance of impurities is determined. If the first fluctuation factor difference is within [0, 0.03), the impurity dominance threshold will be reduced by 2% to 3%; if the first fluctuation factor difference is within [0.03, 0.05), the impurity dominance threshold will be reduced by 4% to 6%. If the difference in the second fluctuation factor is within [0, 0.03), the threshold for the dominant impurity characteristic will be reduced by 2% to 3%; if the difference in the second fluctuation factor is within [0.03, 0.04), the threshold for the dominant impurity characteristic will be reduced by 4% to 6%. If the difference of the third fluctuation factor is in the range of [0, 0.04), the threshold for the dominant impurity characteristic will be reduced by 2% to 3%; if the difference of the third fluctuation factor is in the range of [0.04, 0.06), the threshold for the dominant impurity characteristic will be reduced by 4% to 6%.

[0081] Specifically, this invention uses a corresponding fluctuation factor to determine the pass / fail status of the impurity removal detection process within a preset detection period. When the detection process is deemed unqualified, the number of infrared irradiation angles or the threshold value of the obvious impurity characteristics are actively adjusted based on the fluctuation factor difference. The number of infrared irradiation angles directly affects the richness and accuracy of the extraction of obvious impurity characteristics, while the threshold value of obvious impurity characteristics determines the sensitivity and specificity of the impurity removal tendency determination. By dynamically adjusting these two core parameters through the fluctuation factor difference, the detection system can adapt to the differences in impurity characteristics of different batches of quartz sand, thereby further improving the accuracy of the high-purity quartz sand impurity removal detection method.

[0082] All technologies not mentioned in the above embodiments are existing technologies. In actual testing, several thresholds set in this embodiment are dynamically adjusted according to the on-site testing environment and impurity removal requirements.

[0083] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A method for detecting impurities in high-purity quartz sand, characterized in that, include: Quartz sand is divided into several detection areas. The quartz sand in each detection area is irradiated with infrared light, and the whiteness and diffuse reflectance of the quartz sand are obtained to generate impurity explicit characteristic values. The impurity removal tendency of each detection area is determined based on the impurity explicit characteristic values. Based on the determination result of the impurity removal tendency, the pretreatment method for each detection area is determined. For any detection area, the first impurity leaching rate and the second impurity leaching rate of the pretreated quartz sand are collected to generate impurity latent characteristic values. Based on the impurity latent characteristic values, it is determined whether the determination result of the impurity removal tendency of the current detection area is accurate. In response to the inaccurate determination result of the impurity removal tendency, the degree of impurity occurrence in the quartz sand is determined based on the first impurity latent characteristic deviation value. In response to the presence of the first impurity, the detection strategy for removing impurities from quartz sand is determined to be the density classification method, and the removability of the first impurity in quartz sand is determined based on the collected density heavy phase yield and impurity distribution rate. In response to the degree of presence of the second impurity, the detection strategy for removing impurities from quartz sand was determined to be the diffraction pattern method, and the removability of the second impurity in quartz sand was determined based on the collected crystallinity and grain size. In response to the accurate determination result of the impurity removal tendency, the removability of the third impurity in the quartz sand is determined based on the deviation value of the latent characteristic of the second impurity. The corresponding fluctuation factor is calculated based on the first impurity removability, the second impurity removability, or the third impurity removability within a preset detection period. Based on the corresponding fluctuation factor, determine whether the impurity removal detection process of quartz sand within the preset detection period is qualified. In response to the failure of the quartz sand impurity removal detection process, adjust the number of infrared irradiation angles or the threshold of impurity manifestation characteristics based on the fluctuation factor difference.

2. The method for detecting impurities in high-purity quartz sand according to claim 1, characterized in that, The process of determining the impurity removal tendency of each detection area includes: The ratio of the whiteness of the quartz sand to the whiteness of the reference quartz sand is determined as the whiteness factor; The ratio of the diffuse reflectance to the reference diffuse reflectance is determined as the reflectance factor; The weighted sum of the whiteness factor and the reflectance factor is determined to be the dominant characteristic value of the impurity; If the impurity dominance characteristic value is greater than or equal to the impurity dominance characteristic threshold, then the impurity removal tendency of the detection area is determined to be easy to remove. If the impurity dominance value is less than the impurity dominance threshold, then the impurity removal tendency of the detection area is determined to be a tendency to leave residues.

3. The method for detecting impurities in high-purity quartz sand according to claim 2, characterized in that, The process of determining the preprocessing method for each detection area includes: In response to the tendency to be easily removed, the pretreatment method for the detection area is determined to be dilution and immersion at room temperature; In response to the tendency to leave residues, the pretreatment method for the detection area is determined to be medium-temperature hot acid leaching.

4. The method for detecting impurities in high-purity quartz sand according to claim 3, characterized in that, The process of determining whether the impurity removal tendency judgment result of the current detection area is accurate includes: The weighted sum of the leaching rates of the first and second impurities is determined to be the latent characteristic value of the impurities. If the latent impurity characteristic value is greater than or equal to the latent impurity characteristic threshold, then the determination result of the impurity removal tendency is accurate. If the value of the latent impurity characteristic is less than the threshold value of the latent impurity characteristic, then the determination result of the impurity removal tendency is inaccurate.

5. The method for detecting impurities in high-purity quartz sand according to claim 4, characterized in that, The process of determining the degree of impurity occurrence in quartz sand based on the first impurity latent characteristic deviation value includes, The difference between the impurity latent characteristic threshold and the impurity latent characteristic value is determined as the first impurity latent characteristic deviation value; If the deviation value of the first impurity latent characteristic is less than or equal to the threshold value of the first impurity latent characteristic, then the degree of impurity occurrence of the quartz sand is determined to be the degree of impurity occurrence of the first impurity. If the deviation value of the first impurity latent characteristic is greater than the threshold value of the first impurity latent characteristic, then the degree of impurity occurrence of the quartz sand is determined to be the degree of impurity occurrence of the second impurity.

6. The method for detecting impurities in high-purity quartz sand according to claim 5, characterized in that, The process of determining the removability of the first impurity in the quartz sand includes... The ratio of the difference between the density heavy phase yield and the reference density heavy phase yield is determined as the density factor; The ratio of the difference between the impurity partition ratio and the reference impurity partition ratio is determined as the partition factor; The weighted sum of the density factor and the allocation factor is determined to be the first impurity removability.

7. The method for detecting impurities in high-purity quartz sand according to claim 6, characterized in that, The process of determining the removability of the second impurity in the quartz sand includes... The ratio of the difference between the crystallinity and the reference crystallinity is determined as the crystallinity factor; The ratio of the difference between the stated grain size and the reference grain size is defined as the grain factor; The weighted sum of the crystallization factor and the grain factor is determined to be the second impurity removability.

8. The method for detecting impurities in high-purity quartz sand according to claim 7, characterized in that, The process of determining the removability of the third impurity in the quartz sand includes, The difference between the impurity latent characteristic value and the impurity latent characteristic threshold is determined as the second impurity latent characteristic deviation value; The ratio of the difference between the latent characteristic deviation value of the second impurity and the latent characteristic deviation threshold value of the second impurity is determined as the removability of the third impurity.

9. The method for detecting impurities in high-purity quartz sand according to claim 8, characterized in that, The process for determining whether the impurity removal test of quartz sand within the preset testing period is qualified includes: The first fluctuation factor is the ratio of the standard deviation to the mean of the removability of several first impurities. The second fluctuation factor is the ratio of the standard deviation to the mean of the removability of several second impurities. The third fluctuation factor is based on the ratio of the standard deviation to the mean of the removability of several third impurities. If the corresponding fluctuation factor is greater than the corresponding fluctuation factor threshold, the impurity removal test process of the quartz sand is deemed unqualified. The corresponding volatility factor thresholds include the first volatility factor threshold, the second volatility factor threshold, and the third volatility factor threshold.

10. The method for detecting impurities in high-purity quartz sand according to claim 9, characterized in that, The process of adjusting the number of infrared irradiation angles or the threshold for the dominant characteristic of impurities based on the fluctuation factor difference includes: The difference between the corresponding volatility factor and the corresponding volatility factor threshold is defined as the volatility factor difference. If the fluctuation factor difference is greater than or equal to the corresponding fluctuation factor difference threshold, then it is determined that the increase in the infrared illumination angle is based on the fluctuation factor difference. If the fluctuation factor difference is less than the corresponding fluctuation factor difference threshold, it is determined that the impurity dominance characteristic threshold should be reduced based on the fluctuation factor difference. The corresponding volatility factor difference thresholds include the first volatility factor difference threshold, the second volatility factor difference threshold, and the third volatility factor difference threshold.

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