Quartz ore evaluation method

Abstract

The invention relates to the technical field of mineral resources, in particular to a quartz ore evaluation method which comprises the following steps: acquiring the types of impurities on the surface of to-be-evaluated ore, cleaning, and then crushing and screening to obtain small-particle-size ore; performing magnetic separation on the small-particle-size ore, and measuring the mass loss rate; measuring inherent parameters of the small-particle-size ore after magnetic separation, and inputting the mass loss rate, the inherent parameters and the types of the surface impurities into a pre-constructed metal oxide prediction model to obtain the types and contents of the metal oxides; sequentially carrying out flotation, drying and acid pickling on the small-particle-size ore subjected to magnetic separation, and then detecting the content of metal elements; wherein the use amount of strong acid in pickling is determined according to the type and content of metal oxide; through an innovative technical means and a scientific evaluation system, the evaluation period is greatly shortened, a large number of quartz ores can be accurately evaluated in a short time, high-quality ores can be rapidly screened out, and the timeliness of raw material purchasing and production planning is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of mineral resources, and particularly to a method for evaluating quartz ore. Background Art

[0002] As a key raw material for preparing high-purity quartz sand, the quality of quartz ore directly determines the quality and performance of high-purity quartz sand, and thus affects the development of many high-end industries that rely on high-purity quartz sand, such as the semiconductor, optical glass, and photovoltaic industries; in these fields where extremely high purity and quality requirements are imposed on quartz sand, high-quality quartz ore is the cornerstone for ensuring that products meet high-performance standards; therefore, it is particularly important to accurately and efficiently evaluate the production quality of quartz ore and determine whether it has considerable production and economic value.

[0003] However, the existing methods for evaluating quartz ore have significant drawbacks; on the one hand, the evaluation efficiency is low; in the traditional evaluation process, multiple complex and time-consuming procedures are required, such as the cumbersome pretreatment of ore samples, and in the analysis and detection link, the technical means used often take a long time to obtain results, which makes the entire evaluation process drawn out, and a large amount of time and manpower are consumed in this process. From the perspective of enterprise operation, the low evaluation efficiency directly leads to a significant extension of the production cycle, and it is difficult for enterprises to quickly respond to market demands; On the other hand, excessive evaluation error is another major problem of the existing methods. Due to the lack of precision in the technologies used in the evaluation process or the imperfect evaluation index system, incorrect judgments on the value of quartz ore often occur, misjudging low-quality ore as high-quality. Once put into production, the resulting high-purity quartz sand will not meet industry standards, which not only causes waste of precious quartz ore resources but also brings huge economic losses to enterprises, including increased production costs and damage to market reputation due to unqualified product quality.

[0004] The information disclosed in this background art section is only intended to deepen the understanding of the overall background art of the present invention, and should not be regarded as an admission or any form of suggestion that this information constitutes the prior art known to those skilled in the art. Summary of the Invention

[0005] To solve the above technical problems, the present invention provides a method for evaluating quartz ore. Through innovative technical means and a scientific evaluation system, the evaluation cycle is significantly shortened, and accurate evaluations can be made on a large number of quartz ores in a short time, quickly screening out high-quality ores and enhancing the timeliness of raw material procurement and production planning.

[0006] A method for evaluating quartz ore according to the present invention includes: Obtain the types of surface impurities of the ore to be evaluated and clean them, and then crush and screen to obtain small-particle-size ore; Perform magnetic separation on the small-particle-size ore and measure the mass loss rate; Measure the inherent parameters of the small-particle-size ore after magnetic separation, and input the mass loss rate, inherent parameters, and surface impurity types into a pre-constructed metal oxide prediction model to obtain the types and contents of metal oxides; Perform flotation, drying, and pickling on the small-particle-size ore after magnetic separation in sequence, and then detect the metal element content; Among them, the usage amount of strong acid in pickling is determined according to the types and contents of metal oxides.

[0007] As a preferred embodiment of the present invention, the particle size of the small-particle-size ore is 40 - 200 mesh.

[0008] As a preferred embodiment of the present invention, the flotation reagent is a mixture of mixed amine, sodium dodecyl sulfonate, and metal chelating agent.

[0009] As a preferred embodiment of the present invention, the weight of the small-particle-size ore is 40 - 300 g.

[0010] As a preferred embodiment of the present invention, the pickling temperature is 80 - 90 °C.

[0011] As a preferred embodiment of the present invention, by volume, the dosage ratio of strong acid in the pickling step is: HNO3:H2SO4:HCl:HF = 0 - 2:0 - 4:0 - 5:1 - 3.

[0012] As a preferred embodiment of the present invention, the method for determining the usage amount of strong acid in pickling according to the types and contents of metal oxides includes: Based on the chemical reaction equation and the types and contents of metal oxides output by the metal oxide prediction model, calculate the amount of hydrogen ions required to neutralize each metal oxide; Determine the required strong acid and its ratio according to the types and contents of metal oxides; Based on the required amount of hydrogen ions, the required strong acid, and its ratio, determine the usage amount of each strong acid.

[0013] As a preferred embodiment of the present invention, the method for constructing the metal oxide prediction model includes: Collect data on the types of surface impurities of the ore, the mass loss rate, inherent parameters, and metal element content of the corresponding small-particle-size ore, and perform preprocessing to obtain sample data; Select a machine learning model as the basic architecture of the model; Divide the sample data into a training set, a validation set, and a test set, use the training set to train the machine learning model, and adjust the model parameters; Use the validation set to evaluate the model and optimize the model according to the evaluation results; Use the test set to evaluate the accuracy and reliability of the model; Deploy the trained model into practical applications to make predictions and analyses on new data.

[0014] As a preferred embodiment of the present invention, the inherent parameters at least include density, hardness, refractive index, ultraviolet and visible light transmittance, color, and brittleness.

[0015] As a preferred embodiment of the present invention, the preprocessing at least includes: combining the surface impurity types, weight loss rate, and inherent parameters into a feature matrix, performing dimensionality reduction through principal component analysis, and the cumulative variance contribution rate is higher than the set threshold.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: 1) Processing quartz ore into small particle sizes increases the specific surface area, enabling the reagents in subsequent processes to contact the ore more fully, accelerating the reaction process, and the small-particle-size ore can quickly pass through the equipment. For example, it is more susceptible to the magnetic field during magnetic separation and is more evenly mixed with the reagent during flotation. The entire evaluation process of the present invention can be completed within 24 hours, significantly shortening the evaluation cycle; 2) Crushing, magnetic separation, and pickling synergistically improve the evaluation accuracy. Crushing makes the ore particles smaller, facilitating the accurate measurement of inherent parameters, and small-particle-size samples have better representativeness during analysis and detection, being able to truly reflect the overall properties and avoiding errors caused by uneven composition. Magnetic separation effectively separates magnetic impurities, measures the weight loss rate, and combines with the ore characteristics to provide accurate inputs for the metal oxide prediction model. Accurately grasping the change in the content of magnetic impurities is of great significance for evaluating the value of the ore. Pickling precisely controls the dosage of strong acid, removes metal oxides without damaging quartz minerals, and detects the metal element content after pickling to improve the evaluation of the ore purity and quality, avoiding misjudgment caused by improper impurity treatment; 3) The present invention predicts the types and contents of metal oxides based on the surface impurity types, weight loss rate, and inherent parameters of the ore, accurately determines the usage amount of strong acid in pickling, ensures the effective removal of impurities while avoiding the overuse of strong acid, not only reducing the production cost but also reducing the potential harm to the environment caused by the overuse of strong acid, achieving the rational utilization of resources and green evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic flow chart of a method for evaluating quartz ore of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following detailed description of the specific embodiments of the present invention will be made in conjunction with the accompanying drawings of the specification.

[0019] In the following description, numerous specific details are set forth to provide a thorough understanding of the present invention. However, the present invention may be practiced in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the spirit of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0020] Secondly, the "embodiment" referred to herein means a specific feature, structure, or characteristic that may be included in at least one implementation manner of the present invention. The phrase "in an embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it an individual or alternative embodiment that is mutually exclusive with other embodiments.

[0021] Embodiment Refer to Figure 1 , this embodiment provides a method for evaluating quartz ore, including: S1 Obtain the types of surface impurities of the ore to be evaluated and clean them, and then crush and screen to obtain small-sized ore; S2 Perform magnetic separation on the small-sized ore and measure the mass loss rate; S3 Measure the inherent parameters of the small-sized ore after magnetic separation, and input the mass loss rate, inherent parameters, and types of surface impurities into a pre-constructed metal oxide prediction model to obtain the types and contents of metal oxides; S4 Perform flotation, drying, and pickling on the small-sized ore after magnetic separation in sequence, and then detect the metal element content; Among them, the usage amount of strong acid in pickling is determined according to the types and contents of metal oxides; Through steps such as crushing, magnetic separation, and pickling, the present invention demonstrates outstanding advantages of high efficiency, precision, and environmental friendliness. Crushing turns the ore into small-sized particles, significantly increasing the specific surface area, enabling the reagents for subsequent magnetic separation and flotation to contact the ore more fully, accelerating the reaction, and the small-sized ore can also quickly pass through the equipment, significantly improving the processing efficiency; magnetic separation rapidly separates impurities based on magnetic differences, measures the mass loss rate, and directly provides key data for the prediction of metal oxides, streamlining the evaluation process; pickling operates precisely according to the predicted types and contents of metal oxides, avoiding the abuse of strong acids and shortening the pickling and cleaning time; in terms of improving accuracy, crushing, magnetic separation, and pickling work together. Crushing facilitates the precise measurement of the inherent parameters of the ore and improves the representativeness of the sample. Magnetic separation combines with the characteristics of the ore to provide accurate inputs for the prediction model. Pickling precisely controls the usage amount of strong acid, improves the evaluation of the purity and quality of the ore, and effectively avoids misjudgment; in addition, the present invention predicts the types and contents of metal oxides by means of the types of surface impurities, weight loss rate, and inherent parameters of the ore, precisely determines the usage amount of strong acid, not only reduces production costs but also reduces environmental hazards, achieving the rational utilization of resources and green evaluation.

[0022] In some embodiments of the present invention, the small-sized ore particles have a particle size of 40 - 200 mesh. In actual operation, to obtain small-sized ore particles with a particle size of 40 - 200 mesh, first, a jaw crusher is used to coarsely crush the ore to be evaluated after cleaning the surface impurities, initially breaking the large ore pieces into smaller chunks. Subsequently, a cone crusher is used for medium crushing to further reduce the ore particle size. Then, a vibrating screen is employed for screening. The vibrating screen is equipped with 40-mesh and 200-mesh screens to effectively screen out the small-sized ore particles within the target particle size range. The particle size of 40 - 200 mesh enables the small-sized ore particles to come into full contact with the corresponding reagents in subsequent processes such as magnetic separation, flotation, and pickling. For example, during magnetic separation, ore particles of a suitable particle size are more easily affected by the magnetic field and can quickly separate magnetic impurities. During flotation, they are more evenly mixed with the flotation reagents, improving the flotation efficiency. Overall, it ensures the efficient progress of each evaluation step, greatly shortens the evaluation cycle, and at the same time improves the accuracy of the evaluation results, avoiding incomplete reactions or detection errors caused by inappropriate particle sizes.

[0023] In some embodiments of the present invention, the flotation reagent is a mixture of mixed amine, sodium dodecyl sulfonate, and a metal chelating agent. Flotation separates the impurity minerals from the quartz particles in the quartz ore, removing most of the impurities with significantly different surface properties from the quartz, reducing the impurity content in the ore, and providing a purer sample for accurately detecting the metal element content in the quartz ore, thereby more precisely evaluating the quality of the quartz ore. The mixed amine has specific functional groups that can selectively adsorb on the surfaces of certain impurity minerals in the quartz ore, changing their surface wettability and making them easily adhere to the bubbles. Its addition amount is 0.1 - 0.3% of the weight of the small-sized ore particles. Sodium dodecyl sulfonate has good surface activity, which can reduce the surface tension of the solution, enabling reagents such as mixed amine to be more evenly dispersed in the pulp, expanding the contact area between the reagents and the ore. Its addition amount is 0.1 - 0.4% of the weight of the small-sized ore particles. The addition amount of the metal chelating agent is 0.1 - 0.5% of the weight of the small-sized ore particles. The metal chelating agent contains special coordination groups that can chelate with metal impurity ions such as iron and aluminum in the ore to form stable chelates, enhancing the binding force between the impurity minerals and the bubbles. The mixed amine, sodium dodecyl sulfonate, and the metal chelating agent act synergistically to significantly improve the flotation effect. The mixed amine and sodium dodecyl sulfonate improve the dispersion of the reagents in the pulp and their interaction with the ore from different perspectives, while the metal chelating agent specifically binds to metal impurities. The combination of the three not only increases the flotation recovery rate of the impurity minerals but also enhances the selective separation ability of the flotation process for impurities, resulting in a lower impurity content in the ore treated in the subsequent pickling step, laying a solid foundation for accurately detecting the metal element content and precisely evaluating the quality of the quartz ore, effectively avoiding the problem of impurity residues affecting the evaluation accuracy due to incomplete flotation, and improving the reliability and effectiveness of the entire quartz ore evaluation process.

[0024] In some embodiments of the present invention, the weight of the small-particle-size ore is 40-300 g. Samples within this weight range can ensure that in processes such as magnetic separation and flotation, various experimental data are representative. For example, the mass loss rate can be accurately measured, and at the same time, it will not cause the experimental equipment to operate overloaded due to excessive sample volume, affecting the equipment performance and experimental results. Moreover, an appropriate sample weight is also conducive to efficiently completing various detections and analyses within a limited experimental space, ensuring the accuracy and efficiency of the entire quartz ore evaluation method, and achieving good connection and coordinated operation among various steps.

[0025] In some embodiments of the present invention, the pickling temperature is 80-90 °C. This temperature range can effectively enhance the reaction activity of strong acids with metal impurities in the ore, accelerate the reaction rate, prompt the impurities to dissolve more quickly, significantly improve the pickling efficiency, and at the same time, this temperature range can ensure that the impurities are fully removed without damaging the main structure of the quartz ore, maintaining its physical and chemical properties stable, and providing a reliable ore sample for subsequent detections.

[0026] In some embodiments of the present invention, by volume, the dosage ratio of strong acids in the pickling step is: HNO3:H2SO4:HCl:HF = 0-2:0-4:0-5:1-3; in the pickling process of quartz ore, HF plays an indispensable role. Quartz ore often contains some aluminosilicate impurities such as feldspar and mica. These impurities are difficult to be effectively removed by other common strong acids. HF can undergo unique chemical reactions with these aluminosilicates to form water-soluble complexes, thereby dissolving and separating the impurities from the quartz ore. For example, when feldspar (KAlSi3O8) reacts with HF, potassium hexafluoroaluminate (K3AlF6), silicon tetrafluoride (SiF4), and water are formed, removing the feldspar impurities. At the same time, HF also has special dissolving ability for some metal oxide impurities in quartz ore, and can form stable fluoride complexes with metal ions, enhancing the removal effect of metal impurities. Moreover, when combined with strong acids such as HNO3, H2SO4, and HCl in a specific ratio, HF can act synergistically with other strong acids to broaden the dissolving range of different types of impurities, comprehensively improve the pickling effect, and thus provide strong support for accurately detecting the metal element content of quartz ore and accurately evaluating its quality.

[0027] In some embodiments of the present invention, the method for determining the usage amount of strong acids in pickling according to the type and content of metal oxides includes: According to the types and contents of metal oxides output by the chemical reaction equation and the metal oxide prediction model, calculate the amount of hydrogen ions (H⁺) required to neutralize each metal oxide; taking iron oxide as an example of the metal oxide, its chemical equation for reacting with strong acid is: Fe2O3 + 6H⁺ = 2Fe³⁺ + 3H2O; according to the stoichiometric relationship of this equation, every 1 mole of Fe2O3 requires 6 moles of H⁺ to participate in the reaction; using the same method, calculate the amount of H⁺ required to neutralize each metal oxide respectively, and then add up the amounts of H⁺ required for all metal oxides to obtain the total H⁺ demand; Then, according to the characteristics of different metal oxides, determine which strong acids to select and their ratios; for example, for ores containing aluminosilicate impurities, since hydrofluoric acid (HF) can react with aluminosilicate to form water-soluble complexes, HF needs to be selected; for common metal oxides, strong acids such as nitric acid (HNO3), sulfuric acid (H2SO4), hydrochloric acid (HCl), etc. can be selected according to the actual situation. Nitric acid has strong oxidizing properties and is more effective in dealing with some metal oxide impurities with reducibility; sulfuric acid has strong acidity and high boiling point and can play a unique role in some reaction systems; hydrochloric acid has relatively low cost and can also be widely used under suitable reaction conditions; Finally, according to the total amount of hydrogen ions calculated, determine the usage amount of strong acid. Assuming that HNO3, H2SO4, HCl, and HF are selected as the strong acids, and the ionization degree of the strong acid in the solution is known. Taking hydrochloric acid as an example, HCl is completely ionized in water, and 1 mole of HCl can provide 1 mole of H⁺; according to the ionization characteristics of each strong acid and its proportional relationship in the mixed acid, calculate the specific usage amount of each strong acid; When calculating the usage amount of strong acid in pickling, a coefficient needs to be introduced for supplementation. The reason is that the ore composition is complex, and there are trace impurities not included in the model that will react with strong acid and consume hydrogen ions; the reaction conditions are different from the standard conditions. For example, changes in temperature and pressure will cause additional consumption of strong acid or side reactions, and strong acids are not completely ionized in the actual mixed acid system; the determination factors of the coefficient include experimental determination. Pickle representative ore samples according to the theoretical usage amount, monitor the reaction indicators, and compare the actual and theoretical usage amounts to obtain the preliminary coefficient; it can also be determined through big data analysis. Collect pickling data of different ores, and use algorithms to construct a functional relationship between the coefficient and key factors such as the type and quantity of impurities and the content of metal oxides.

[0028] In some embodiments of the present invention, the construction method of the metal oxide prediction model includes: Collect data on the types of surface impurities of the ore, the weight loss rate, inherent parameters, and metal element content of the corresponding small-particle-size ore, and perform preprocessing to obtain sample data; When collecting the types of impurities on the surface of the ore, it is necessary to do so before the ore is broken. Use a professional spectral analyzer to identify the types of impurities on the ore surface and record their composition information. During the formation of the ore, impurities often coexist with metal oxides. Specific types of impurities often imply the existence of associated metal oxides. For example, there is probably copper oxide near copper-containing impurities, which can provide clues for predicting the types of metal oxides. The impurities on the ore surface also represent the environment in which it is located, serving as a certain reference for the types of metal oxides existing inside the ore. At the same time, there may be an inverse or synergistic relationship between the impurities on the ore surface and the metal oxides. By analyzing the types of impurities, the approximate content range of the metal oxides can be inferred, providing a key basis for accurately predicting the types and contents of metal oxides. In the magnetic separation process, since some metal oxides are magnetic and will be screened out together with the magnetic impurities, resulting in a decrease in the weight of the ore. Therefore, the greater the weight loss rate, the more substances are screened out by magnetic separation, which is very likely to indicate that the quartz ore is rich in metal elements. Correspondingly, the content of metal oxides is probably also relatively high. Moreover, different types of metal oxides have different magnetic properties, and the weight loss situations caused during magnetic separation are also different. By analyzing the change characteristics of the weight loss rate, it is possible to provide reference information for inferring the types and contents of metal oxides. Different types of metal oxides will change the inherent parameters of quartz ore, such as density, hardness, refractive index, etc. For example, the density of ore containing heavy metal oxides may be relatively large, and the hardness will also change. By analyzing the values and changes of these inherent parameters, the types and contents of metal oxides can be indirectly inferred, providing strong support for prediction. At the same time, the main body of the inherent parameters being small-particle-size ore after crushing has many advantages. First, the small-particle-size ore is screened after crushing, eliminating the measurement deviation caused by the uneven structure of large pieces of ore, and can more stably and uniformly reflect the overall characteristics of the ore, making the inherent parameters more representative. Second, the small-particle-size ore can better match the analysis instruments. Instruments such as hardness meters and refractometers can accurately act on small-particle-size samples to obtain accurate parameter data, providing a solid and reliable data basis for the metal oxide prediction model.

[0029] Select a machine learning model as the basic architecture of the model. Taking the neural network model as an example, use the machine learning frameworks TensorFlow or PyTorch of Python to build the model architecture. Define the number of input layer nodes to be the same as the number of data features. Set several hidden layers in the middle. The number of hidden layer nodes is determined through multiple experiments. The number of output layer nodes corresponds to the prediction dimension of the metal oxide content. Set the connection methods between layers, such as fully connected layers, and determine the activation functions, such as the ReLU function for the hidden layer and the linear function for the output layer. Divide the sample data into training, validation, and test sets, use the training set to train the machine learning model, and adjust the model parameters. Using the data partitioning tool in Python's scikit-learn library, randomly divide the preprocessed sample data into training, validation, and test sets at a ratio of 70%, 15%, and 15%. During the training phase, input the training set data into the constructed machine learning model. The model continuously adjusts internal parameters, such as weights and biases in the neural network, based on the training data. Use the validation set to evaluate the model and fine-tune the model based on the evaluation results. During the training process, regularly use the validation set data to evaluate the model performance and calculate indicators such as the mean square error. If the mean square error no longer decreases significantly after multiple iterations, adjust the learning rate or increase the number of iterations appropriately and retrain the model. Use the test set to evaluate the accuracy and reliability of the model; when the model performs well on the training set and validation set, input the test set data into the model to calculate the model's root mean square error, mean absolute error and other indicators on the test set. If the root mean square error is less than 0.1 and the mean absolute error is less than 0.05, it indicates that the model has good accuracy and reliability and can accurately predict the type and content of metal oxides; if the indicators do not meet expectations, re-examine the entire modeling process, from the completeness of data collection, the rationality of model selection to the effectiveness of parameter adjustment, etc., to find problems and make improvements.

[0030] Deploy the trained model to real-world applications to make predictions and analyze new data.

[0031] In some embodiments of the present invention, the intrinsic parameters include at least density, hardness, refractive index, UV-visible light transmittance, color, and brittleness; Different metal oxides have different crystal structures, chemical compositions, etc., which will cause changes in the inherent parameters of quartz ore such as density and hardness. For example, the density of ore with a high metal oxide content may be higher due to the larger relative atomic mass of the metal element, and the hardness may also change due to the hardness characteristics of the metal oxide; the refractive index will change due to the different refraction of light by metal oxides, the ultraviolet-visible light transmittance will be affected by the absorption and scattering of light by metal oxides, the color may change due to the characteristic color of the metal oxide, and the brittleness will also vary depending on the distribution and bonding of the metal oxides in the ore.

[0032] In addition to the above fixed parameters, magnetic susceptibility, electrical conductivity, thermal conductivity, etc. will characterize the content of metal oxides to a certain extent.

[0033] In some embodiments of the present invention, preprocessing includes at least: combining the surface impurity types, weight loss rates, and intrinsic parameters into a feature matrix, performing dimensionality reduction through principal component analysis, and the cumulative variance contribution rate is higher than a set threshold; More specifically, first, data collection is carried out. The types of impurities on the ore surface are obtained using professional instruments. The weight loss rate is obtained by measuring the weights before and after magnetic separation. Intrinsic parameters such as density, hardness, and refractive index are measured using various detection devices. Then, these data are integrated. The types of surface impurities are digitally encoded. For example, different numerical tags are assigned to different types of impurities, and the weight loss rate and intrinsic parameters remain in their numerical forms. These data are arranged in rows, with each row representing the data of an ore sample, forming a two-dimensional feature matrix. Subsequently, a principal component analysis dimensionality reduction operation is carried out. With the help of professional data processing software, such as the Scikit-learn library in Python, the principal component analysis algorithm module is called in the software. The constructed feature matrix is input into the algorithm, and the algorithm parameters are set. The threshold of the cumulative variance contribution rate is particularly set, which can generally be set between 90% - 95%. During the operation of the algorithm, the data in the feature matrix are linearly transformed to find the main components in the data. These main components can retain the information of the original data to the greatest extent. After dimensionality reduction, a new matrix with reduced dimensions is obtained, where each column represents a main component. The above method avoids the overfitting phenomenon of the model due to high data dimensions and complex correlations. The main components after dimensionality reduction retain key information, enabling the machine learning model to converge faster, improving the training efficiency and accuracy of the metal oxide prediction model, and providing a precise prediction with more concise data, providing an efficient and reliable data basis for quartz ore evaluation.

[0034] In some embodiments of the present invention, for the mass loss rate in S2, its calculation formula is: weight loss rate = (m1 - m2) / m1 × 100%; where m1 is the weight of the small-particle-size ore before magnetic separation, and m2 is the weight of the small-particle-size ore after magnetic separation.

[0035] In some embodiments of the present invention, the pickling time is 8 - 16h. The sufficient pickling time ensures that the strong acid can fully react with the metal oxide, enabling the impurities to be dissolved and separated from the ore as much as possible.

[0036] In some embodiments of the present invention, the method for detecting the content of metal elements is to use ICP-MS; ICP-MS, namely inductively coupled plasma mass spectrometry, is an advanced analytical technology for detecting the content of metal elements. It ionizes the sample using an inductively coupled plasma and then separates and detects the ions through a mass spectrometer. This technology has extremely high sensitivity and extremely low detection limits, can accurately determine the content of various metal elements in the sample, and can even detect elements at trace levels. At the same time, ICP-MS also has excellent precision and accuracy, as well as the ability to analyze multiple elements simultaneously, and can quickly and efficiently obtain information on various metal elements in the sample. It is widely used in many fields such as environment, geology, biomedicine, and materials science.

[0037] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A method for evaluating quartz ore, characterized in that, Including: Obtain the types of surface impurities of the ore to be evaluated and clean them, and then crush and screen to obtain small-sized ore; Perform magnetic separation on the small-sized ore and measure the mass loss rate; Measure the inherent parameters of the small-sized ore after magnetic separation, and input the mass loss rate, the inherent parameters, and the types of surface impurities into a pre-constructed metal oxide prediction model to obtain the types and contents of metal oxides; Perform flotation, drying, and pickling on the small-sized ore after magnetic separation in sequence, and then detect the metal element content; Among them, the usage amount of strong acid in the pickling is determined according to the types and contents of the metal oxides.

2. The quartz ore evaluation method according to claim 1, wherein The particle size of the small-sized ore is 40 - 200 mesh.

3. The quartz ore evaluation method according to claim 1, characterized in that The flotation reagent is a mixture of mixed amine, sodium dodecyl sulfonate, and metal chelating agent.

4. The quartz ore evaluation method according to claim 1, characterized in that, The weight of the small-sized ore is 40 - 300 g.

5. The quartz ore evaluation method according to claim 1, wherein The pickling temperature is 80 - 90 °C.

6. The quartz ore evaluation method according to claim 5, characterized in that, Calculated by volume, the dosage ratio of strong acids in the pickling step is: HNO3:H2SO4:HCl:HF = 0 - 2:0 - 4:0 - 5:1 - 3.

7. The quartz ore evaluation method according to claim 1, characterized in that The method for determining the usage amount of strong acid in the pickling according to the types and contents of the metal oxides includes: Based on the chemical reaction equation and the types and contents of metal oxides output by the metal oxide prediction model, calculate the amount of hydrogen ions required to neutralize each metal oxide; Determine the required strong acids and their ratios according to the types and contents of the metal oxides; Based on the required amount of hydrogen ions and the required strong acids and their ratios, determine the usage amounts of each strong acid.

8. The quartz ore evaluation method according to claim 1, wherein, The construction method of the metal oxide prediction model includes: Collect data on the types of surface impurities of the ore, the mass loss rate, the inherent parameters, and the metal element content corresponding to the small-sized ore, and perform preprocessing to obtain sample data; Select a machine learning model as the basic architecture of the model; Divide the sample data into a training set, a validation set, and a test set, use the training set to train the machine learning model, and adjust the model parameters; Use the validation set to evaluate the model, and optimize the model according to the evaluation results; Use the test set to evaluate the accuracy and reliability of the model; Deploy the trained model to actual applications to predict and analyze new data.

9. The quartz ore evaluation method according to claim 1, wherein The inherent parameters at least include density, hardness, refractive index, ultraviolet-visible light transmittance, color, and brittleness.

10. The quartz ore evaluation method according to claim 8, characterized in that, The preprocessing at least includes: combining the types of surface impurities, the mass loss rate, and the inherent parameters into a feature matrix, performing dimensionality reduction through principal component analysis, and the cumulative variance contribution rate is higher than the set threshold.

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