Method for detecting low-grade lithium ore resources

Abstract

The invention relates to the technical field of mineral exploration, and discloses a low-grade lithium ore resource detection method, which comprises the following steps: collecting an ore sample from a lithium ore resource area to be detected, and simultaneously obtaining environmental data when the ore sample is collected; pretreating the ore sample to obtain a sample to be detected; the sample to be detected is subjected to leaching extraction to obtain a leaching extract, the leaching extract is subjected to quantitative analysis, the lithium concentration is obtained, and meanwhile leaching extraction efficiency data is obtained; and performing quality evaluation on the lithium ore resource area to be detected according to the environment data, the leaching extraction efficiency data and the lithium concentration. By comprehensively analyzing the multiple parameters, the quality of the low-grade lithium ore resources can be evaluated more accurately, and a scientific basis is provided for reasonable utilization of the lithium resources. By dynamically optimizing the extraction efficiency, the consumption of energy and chemical reagents is reduced while the recovery rate of lithium resources is improved, so that the production cost and the environmental burden are reduced.

Description

Technical Field

[0001] The present invention relates to the field of mineral exploration technology, and in particular to a method for detecting low-grade lithium ore resources. Background Art

[0002] Lithium, a key strategic resource, plays a vital role in numerous areas of the new energy industry, particularly in the production and manufacturing of lithium batteries. With the explosive growth of global demand for lithium resources, the pressure on the mining of high-grade lithium ores is increasing. At the same time, low-grade lithium ores, due to their low lithium content, complex and diverse composition, and difficulty in extraction, are not ideally utilized using traditional lithium mining technologies. Therefore, there is an urgent need to develop efficient and cost-effective detection and extraction technologies to more fully utilize these low-grade lithium resources.

[0003] At the same time, in current lithium resource assessment practices, most methods rely primarily on a single test of lithium content in the ore, without taking into account key dynamic parameters such as environmental data and leaching efficiency. This makes it difficult for the assessment results to fully and accurately reflect the development potential and economic value of lithium resources. Therefore, innovating and inventing a new low-grade lithium resource detection method is extremely important for improving the development efficiency and economic viability of low-grade lithium resources. Summary of the Invention

[0004] In light of this, this paper proposes a method for detecting low-grade lithium ore resources, aiming to improve the accuracy and applicability of lithium ore quality assessment through comprehensive multi-parameter analysis. This method collects environmental data, performs leaching extraction and quantitative analysis on ore samples, and obtains data on lithium content and leaching efficiency. This allows for a comprehensive assessment of the quality of low-grade lithium ore resources, providing a scientific basis for their development and utilization.

[0005] The present invention proposes a method for detecting low-grade lithium ore resources, comprising:

[0006] Collecting ore samples from the lithium ore resource area to be detected, and simultaneously obtaining environmental data when collecting the ore samples;

[0007] Pre-treating the ore sample to obtain a sample to be tested;

[0008] Leaching the sample to be tested to obtain a leached extract, and quantitatively analyzing the leached extract to obtain lithium concentration and leaching extraction efficiency data;

[0009] A quality assessment of the lithium ore resource area to be detected is performed based on the environmental data, leaching extraction efficiency data and lithium concentration.

[0010] Preferably, the ore sample is pre-processed to obtain a sample to be tested, including:

[0011] The ore sample is crushed, dried and mixed in sequence;

[0012] The particle size of the ore sample after crushing is not higher than 0.5 mm; the humidity of the ore sample after drying is not higher than 2%.

[0013] Preferably, when the ore sample is pre-processed to obtain the sample to be tested, the following steps are further included:

[0014] The uniformity deviation of the ore sample after mixing is no more than 5%; the calculation formula of the uniformity deviation is:

[0015] ;

[0016] Where U represents the uniformity deviation; σ represents the standard deviation of the target element content in the sample; and μ represents the average content of the target element in the sample.

[0017] Preferably, the target element is silicon, iron or calcium.

[0018] Preferably, when the sample to be tested is subjected to leaching extraction to obtain the leaching extract, the process includes:

[0019] In a constant temperature water bath, the sample to be tested is mixed with an acidic leaching agent for leaching extraction; the extraction temperature and extraction time are calculated and set according to the following formula:

[0020] ;

[0021] ;

[0022] Wherein, T represents the extraction temperature; t represents the extraction time; Ea represents the activation energy; A represents the frequency factor; R represents the gas constant; Rp represents the average particle size; k represents the reaction rate constant; ρ represents the ore particle density; Cacid represents the concentration of the acidic leaching agent; and D represents the diffusion coefficient of the target element in the leachate.

[0023] Preferably, when the sample to be tested is subjected to leaching extraction to obtain the leaching extract, the process further comprises:

[0024] The rate constant k of the leaching reaction was measured at at least three different temperatures. The rate constant k was obtained by fitting the extraction efficiency data per unit time to a first-order reaction kinetic model:

[0025] ;

[0026] Wherein, η represents the extraction efficiency per unit time t;

[0027] Using ln(k) as the ordinate and 1 / T as the abscissa, linear regression is performed to obtain a straight line, and the slope and intercept of the straight line are obtained. The activation energy Ea and the frequency factor A are calculated based on the slope and intercept.

[0028] Preferably, when calculating the activation energy Ea and the frequency factor A by the slope and intercept, the following steps are included:

[0029] ;

[0030] ;

[0031] Among them, slope represents the slope; intercept represents the intercept.

[0032] Preferably, before leaching the sample to be tested to obtain the leached extract, the sample to be tested is subjected to ore composition analysis to obtain the mass percentage of lithium element in the sample to be tested;

[0033] The leaching extraction efficiency data is obtained based on the mass percentage of lithium element and lithium concentration in the sample to be tested.

[0034] Preferably, when obtaining the leaching extraction efficiency data according to the mass percentage of lithium element and lithium concentration in the sample to be tested, it includes:

[0035] ;

[0036] Wherein, ηLi represents the leaching extraction efficiency data; CLi represents the lithium concentration; V represents the volume of the leached extract; m represents the mass of the initial ore sample; ωLi represents the mass percentage of the lithium element in the sample to be tested.

[0037] Preferably, when performing quality assessment on the lithium ore resource area to be detected based on the environmental data, leaching extraction efficiency data and lithium concentration, the method includes:

[0038] Calculating an environmental relevance index based on the environmental data; performing a quality assessment of the lithium ore resource area to be tested based on the environmental relevance index, leaching extraction efficiency data, and lithium concentration; wherein the environmental data includes ambient temperature, precipitation, soil and ore surface mineral coverage index;

[0039] The environmental relevance index is calculated according to the following formula:

[0040] ;

[0041] Where EI represents the environmental relevance index; κ1, κ2, κ3, and κ4 represent empirical weight coefficients, respectively; pH represents soil pH; Tc represents ambient temperature; Q represents precipitation; and CI represents the mineral coverage index on the ore surface, which is defined as the spectral reflectance characteristics of the minerals on the sample surface.

[0042] Preferably, when performing quality assessment on the lithium ore resource area to be detected based on the environmental relevance index, leaching extraction efficiency data and lithium concentration, it includes:

[0043] ;

[0044] Where X represents the comprehensive evaluation value; α, β, and ε represent the weight coefficients of environmental relevance index, leaching extraction efficiency data, and lithium concentration, respectively;

[0045] Classify the lithium mineral resource area to be detected according to the relationship between X and a preset first comprehensive evaluation threshold and a second comprehensive evaluation threshold; wherein the first comprehensive evaluation threshold is less than the second comprehensive evaluation threshold;

[0046] When X is greater than the second comprehensive evaluation threshold, the level of the lithium mineral resource area to be detected is high;

[0047] When X is less than the second comprehensive evaluation threshold and X is not less than the first comprehensive evaluation threshold, the level of the lithium mineral resource area to be detected is medium;

[0048] When X is less than the first comprehensive evaluation threshold, the level of the lithium ore resource area to be detected is low.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] Multi-parameter comprehensive analysis: When evaluating lithium ore quality, this method not only considers lithium content alone but also integrates multiple factors, including environmental data and leaching efficiency. This comprehensive analysis method provides a more scientific and accurate quality assessment, contributing to a more comprehensive understanding of the potential value of lithium ore.

[0051] Dynamically optimize extraction efficiency: This method analyzes the specific composition and properties of lithium ore samples to calculate relevant kinetic parameters, such as rate constants and activation energies. Based on these parameters, leaching conditions can be dynamically adjusted and optimized, significantly improving lithium extraction efficiency. This approach ensures maximum resource utilization while reducing production costs.

[0052] Green and environmentally friendly: This invention not only improves extraction efficiency but also prioritizes environmental protection and sustainability. Through precise extraction efficiency analysis and process selection, it effectively reduces acid consumption and waste residue emissions, minimizing negative environmental impacts. This provides a sustainable and efficient path for lithium resource development.

[0053] In summary, the implementation of the present invention not only improves the evaluation and extraction technology of lithium ore, but also provides a new approach for the sustainable development of lithium resources. Through multi-parameter comprehensive analysis, the quality of lithium ore can be more accurately evaluated, providing a scientific basis for the rational utilization and market pricing of lithium resources. Dynamic optimization of extraction efficiency ensures that while improving the lithium resource recovery rate, the consumption of energy and chemical reagents is reduced, reducing production costs and environmental burdens. The process design guided by green environmental protection not only meets the current global requirements for environmental protection, but also lays the foundation for the long-term sustainable development of lithium resources. Therefore, the present invention has significant technological progress and application value in the field of lithium ore evaluation and extraction. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present invention. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0055] Figure 1 The present invention is a flowchart of a method for detecting low-grade lithium ore resources according to an embodiment of the present invention. DETAILED DESCRIPTION

[0056] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, unless there is a conflict, the embodiments of the present disclosure and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.

[0057] like Figure 1 As shown, this embodiment provides a method for detecting low-grade lithium ore resources, including:

[0058] Collecting ore samples from the lithium ore resource area to be detected, and simultaneously obtaining environmental data when collecting the ore samples;

[0059] Pre-treating the ore sample to obtain a sample to be tested;

[0060] Leaching the sample to be tested to obtain a leached extract, and quantitatively analyzing the leached extract to obtain lithium concentration and leaching extraction efficiency data;

[0061] A quality assessment of the lithium ore resource area to be detected is performed based on the environmental data, leaching extraction efficiency data and lithium concentration.

[0062] It can be seen that this embodiment provides a method for detecting low-grade lithium ore resources, which can effectively evaluate the quality of lithium ore resources, thereby providing a scientific basis for the development and utilization of lithium ore resources. The specific steps are as follows:

[0063] Collect ore samples from the lithium resource area to be tested and record the environmental data at the time of collection. This environmental data is crucial for subsequent lithium ore quality assessment.

[0064] The collected ore samples are pretreated to obtain samples suitable for subsequent analysis. The pretreatment step is to convert the ore samples into a form suitable for analysis.

[0065] Leaching extraction is performed on the sample to obtain a leached extract, which is then quantitatively analyzed to determine the lithium concentration. Leaching extraction involves the reaction of chemical reagents with the ore sample to dissolve the lithium element. The lithium concentration is then measured using various analytical techniques (such as atomic absorption spectroscopy and inductively coupled plasma mass spectrometry).

[0066] At the same time, the leaching efficiency data is recorded. The leaching efficiency refers to the efficiency of extracting lithium from the ore sample, which reflects the availability of lithium in the ore.

[0067] The quality assessment of the lithium resource area to be tested is conducted based on environmental data, leaching efficiency data, and lithium concentration. The quality assessment will comprehensively consider environmental factors, extraction efficiency, and lithium content to determine the economic value and development potential of the lithium resource.

[0068] Through the above steps, the method of this embodiment can provide detailed data support for the development of lithium mineral resources, helping decision makers make more scientific and reasonable decisions.

[0069] It is understandable that the advantage of this embodiment is that it can provide a comprehensive and systematic method for lithium ore resource assessment. This method not only focuses on the lithium content of the lithium ore itself, but also takes into account the impact of environmental factors on the efficiency of lithium ore extraction, thereby providing a more accurate and comprehensive assessment of the quality of lithium ore resources. In addition, the method of this embodiment can also provide guidance for the sustainable development of lithium ore resources, because its assessment process covers the collection of environmental data, which helps to take corresponding environmental protection measures during the development process to ensure the rational use of resources and environmental sustainability. The method of this embodiment also has the following advantages:

[0070] Improved detection accuracy: By comprehensively considering environmental data, leaching extraction efficiency and lithium concentration, the quality of lithium mineral resources can be assessed more accurately, avoiding the deviation that may be caused by a single indicator.

[0071] Optimized resource utilization: By evaluating the quality of lithium mineral resources, companies can be guided to choose the most appropriate extraction technology, thereby improving lithium extraction rates and resource utilization.

[0072] Promotes environmental protection: Since environmental factors are taken into consideration, this method helps to take measures to reduce the impact on the environment during the development of lithium mineral resources and achieve green mining.

[0073] Supports scientific decision-making: provides a scientific basis for the development and investment decisions of lithium mineral resources, helping investors and decision makers to evaluate the feasibility and potential value of projects.

[0074] Strong adaptability: This method is applicable to different types of lithium mineral resources, including low-grade lithium minerals, and therefore has a wide range of applicability and market potential.

[0075] In summary, this embodiment provides an innovative method for detecting low-grade lithium mineral resources, which not only improves the accuracy of lithium mineral resource assessment, but also promotes the rational development of lithium mineral resources and environmental protection, and provides strong support for the sustainable development of lithium mineral resources.

[0076] In some embodiments of the present application, the ore sample is pre-processed to obtain a sample to be tested, including:

[0077] The ore sample is crushed, dried and mixed in sequence;

[0078] The particle size of the ore sample after crushing is not higher than 0.5 mm; the humidity of the ore sample after drying is not higher than 2%.

[0079] It will be appreciated that, in order to prepare the sample to be tested, this embodiment first performs a series of pretreatment steps on the ore sample. These steps include crushing, drying and mixing the ore sample in sequence. In the crushing step, the ore sample is crushed to a particle size of no more than 0.5 mm to ensure the uniformity of the sample and facilitate subsequent analysis. After crushing, the sample is dried to reduce its humidity to no more than 2%. Such drying helps to remove moisture from the sample, prevent possible interference during subsequent analysis, and ensure the stability and accuracy of the sample. After drying, the sample will be mixed to ensure the homogeneity of the sample so that any subsample taken from the sample can represent the characteristics of the entire ore sample. Through these pretreatment steps, it can be ensured that the obtained sample to be tested is suitable for further analysis and testing.

[0080] In some embodiments of the present application, when the ore sample is pre-processed to obtain the sample to be tested, the following steps are further included:

[0081] The uniformity deviation of the ore sample after mixing is no more than 5%; the calculation formula of the uniformity deviation is:

[0082] ;

[0083] Where U represents the uniformity deviation; σ represents the standard deviation of the target element content in the sample; and μ represents the average content of the target element in the sample.

[0084] As you can understand, in this example, we further emphasize the importance of post-pretreatment homogeneity. After the sample mixing step, we meticulously assess the homogeneity of the ore sample to ensure that its uniformity deviation does not exceed 5%. This uniformity deviation assessment is based on a specific calculation formula that takes into account the standard deviation (σ) and the average concentration (μ) of the target element in the sample. This detailed assessment ensures that, during subsequent analysis, subsamples taken from any part of the ore sample accurately reflect the distribution of the target element in the entire sample. Such uniformity requirements are crucial for improving the accuracy and reliability of analytical results, providing a solid and reliable scientific basis for further processing and utilization of the ore.

[0085] In some embodiments of the present application, the target element is silicon, iron or calcium.

[0086] It is understandable that in this embodiment, we have particularly emphasized the target elements that require special attention during the pretreatment and analysis of ore samples. Selecting appropriate target elements is crucial for ore evaluation and subsequent processing steps. For example, silicon, iron, and calcium are very common components in ores. Their respective contents and distribution in the ore have an extremely important impact on the classification of the ore, the selection of processing methods, and the quality control of the final product. By focusing on the analysis and research of these key elements, we can more accurately evaluate the quality of the ore, thereby providing a more scientific and accurate basis for the mining, processing, and application of the ore. In addition, silicon is the best choice for the following reasons:

[0087] High universal applicability: Silicon is present in high content in almost all ores and its distribution is stable;

[0088] Detection accuracy: Silicon content is usually high, which makes it easy to detect and the method has high sensitivity;

[0089] Strong representativeness: The distribution of silicon in the mineral lattice directly affects the uniformity of the sample and can accurately reflect its consistency.

[0090] In some embodiments of the present application, when the sample to be tested is subjected to leaching extraction to obtain the leaching extract, the process includes:

[0091] In a constant temperature water bath, the sample to be tested is mixed with an acidic leaching agent for leaching extraction; the extraction temperature and extraction time are calculated and set according to the following formula:

[0092] ;

[0093] ;

[0094] Wherein, T represents the extraction temperature; t represents the extraction time; Ea represents the activation energy; A represents the frequency factor; R represents the gas constant; Rp represents the average particle size; k represents the reaction rate constant; ρ represents the ore particle density; Cacid represents the concentration of the acidic leaching agent; and D represents the diffusion coefficient of the target element in the leachate.

[0095] It will be appreciated that this embodiment describes in detail a leaching extraction process under specific conditions. The sample to be tested is mixed with an acidic leaching agent in a constant temperature water bath to extract the target element. To optimize the extraction process, the extraction temperature (T) and extraction time (t) are set according to specific calculation formulas. These calculation formulas take into account multiple parameters, including activation energy (Ea), frequency factor (A), gas constant (R), average particle size of the ore (Rp), reaction rate constant (k), ore particle density (ρ), and acidic leaching agent concentration (Cacid). By comprehensively considering these parameters, the optimal extraction conditions can be determined to ensure efficient and sufficient extraction of the target element while reducing unnecessary chemical reagent consumption and time costs. This leaching extraction process helps improve the accuracy and efficiency of ore sample analysis.

[0096] In practice, the pH value of the leaching agent must be adjusted to accommodate different types of ores and target elements. Furthermore, the extraction process may need to be performed in the absence of oxygen or in controlled oxygen conditions to prevent sample oxidation, which could affect the accuracy of the extraction results. After extraction, the extract is typically filtered and purified to remove suspended solid particles and impurities, ensuring purity for subsequent analysis. Optimizing the entire extraction process involves not only calculating chemical reaction kinetics but also meticulously controlling experimental equipment and operational procedures to achieve optimal extraction results.

[0097] In some embodiments of the present application, when the sample to be tested is subjected to leaching extraction to obtain the leaching extract, the process further includes:

[0098] The rate constant k of the leaching reaction was measured at at least three different temperatures. The rate constant k was obtained by fitting the extraction efficiency data per unit time to a first-order reaction kinetic model:

[0099] ;

[0100] Wherein, η represents the extraction efficiency per unit time t;

[0101] Using ln(k) as the ordinate and 1 / T as the abscissa, linear regression is performed to obtain a straight line, and the slope and intercept of the straight line are obtained. The activation energy Ea and the frequency factor A are calculated based on the slope and intercept.

[0102] It is understandable that the present embodiment further elaborates how to determine the reaction rate constant k in the leaching extraction process, and calculates activation energy Ea and frequency factor A by experimental data. By measuring the rate constant k of the leaching reaction at at least three different temperatures, a set of data can be obtained, and these data reflect the influence of temperature on the reaction rate. Utilize the first-order reaction kinetic model, the relationship between extraction efficiency η and time t in the fitting unit time can be obtained, thereby obtaining the rate constant k. By plotting ln (k) to 1 / T and performing linear regression, a straight line can be obtained, whose slope and intercept are respectively related to activation energy Ea and frequency factor A. By these calculations, the kinetic characteristics of the leaching reaction can be understood more accurately, for optimizing the extraction process provides a theoretical basis. Such experimental design and data analysis method help to improve the accuracy and reliability of ore sample analysis, ensure the efficiency of the extraction process and the recovery rate of the target element.

[0103] In some embodiments of the present application, when calculating the activation energy Ea and the frequency factor A by the slope and intercept, the calculation includes:

[0104] ;

[0105] ;

[0106] Among them, slope represents the slope; intercept represents the intercept.

[0107] It is understood that this embodiment provides specific mathematical formulas for calculating the activation energy Ea and the frequency factor A. Through these formulas, experimental data can be converted into specific kinetic parameters. The slope and intercept are obtained through linear regression analysis, representing the degree of inclination of the straight line and the intercept position on the y-axis, respectively. These parameters are the key to calculating the activation energy and the frequency factor because they are directly related to the relationship between the reaction rate constant k and the temperature. Specifically, the slope is inversely proportional to the activation energy Ea, while the intercept is directly proportional to the frequency factor A. Through these calculations, the inherent mechanism of the chemical reaction can be more deeply understood, providing a scientific basis for the processing and analysis of ore samples.

[0108] In some embodiments of the present application, before leaching the sample to be tested to obtain the leached extract, the sample to be tested is subjected to ore composition analysis to obtain the mass percentage of lithium element in the sample to be tested;

[0109] The leaching extraction efficiency data is obtained based on the mass percentage of lithium element and lithium concentration in the sample to be tested.

[0110] It is understandable that the present embodiment provides a step of pre-analyzing the sample before the leaching and extraction process to ensure the accuracy of the extraction efficiency data. Through ore composition analysis, the specific content of lithium in the sample to be tested can be determined, and this step is crucial for the subsequent calculation of the leaching and extraction efficiency. The lithium concentration is obtained based on the mass percentage of lithium in the sample, combined with the total mass of the sample and the volume of solvent used in the leaching and extraction process. In this way, a standardized efficiency data can be obtained, which reflects the efficiency of extracting lithium from the sample. Such a pre-analysis step helps to optimize the leaching conditions, improve the efficiency of the extraction process, and ensure that the concentration of lithium in the final extract reaches the expected target. In addition, this also helps to evaluate the economic value of lithium in the sample and provide basic data for subsequent industrial applications or further research.

[0111] In some embodiments of the present application, obtaining the leaching extraction efficiency data according to the mass percentage of lithium element and lithium concentration in the sample to be tested includes:

[0112] ;

[0113] Wherein, ηLi represents the leaching extraction efficiency data; CLi represents the lithium concentration; V represents the volume of the leached extract; m represents the mass of the initial ore sample; ωLi represents the mass percentage of the lithium element in the sample to be tested.

[0114] It will be appreciated that this embodiment provides a mathematical formula for calculating the leaching extraction efficiency. Through this formula, the extraction efficiency of lithium in the ore sample can be converted into a specific numerical value, thereby evaluating the efficiency of the extraction process. The leaching extraction efficiency ηLi is calculated based on the lithium concentration CLi, the volume V of the leached extract, the initial ore sample mass m, and the mass percentage of lithium in the sample to be tested ωLi. Specifically, ηLi reflects the efficiency of extracting lithium from the ore sample and is a key indicator for measuring the success of the extraction process. Through this calculation, the extraction process can be optimized to ensure that the concentration of lithium in the extract reaches the expected target, while providing important data for the economic value assessment of the ore sample.

[0115] In some embodiments of the present application, when quality assessment of the lithium mineral resource area to be detected is performed based on the environmental data, leaching extraction efficiency data and lithium concentration, the following steps are included:

[0116] Calculating an environmental relevance index based on the environmental data; performing a quality assessment of the lithium ore resource area to be tested based on the environmental relevance index, leaching extraction efficiency data, and lithium concentration; wherein the environmental data includes ambient temperature, precipitation, soil and ore surface mineral coverage index;

[0117] The environmental relevance index is calculated according to the following formula:

[0118] ;

[0119] Where EI represents the environmental relevance index; κ1, κ2, κ3, and κ4 represent empirical weight coefficients, respectively; pH represents soil pH; Tc represents ambient temperature; Q represents precipitation; and CI represents the mineral coverage index on the ore surface, which is defined as the spectral reflectance characteristics of the minerals on the sample surface.

[0120] As can be appreciated, this embodiment provides a method for comprehensively assessing lithium mineral resource areas. Environmental data is crucial for assessing the quality of lithium mineral resources because it affects the lithium leaching process and efficiency. By considering ambient temperature, precipitation, soil pH, and mineral cover on the ore surface, the quality and mining potential of lithium mineral resources can be more accurately assessed.

[0121] The calculation of the Environmental Relevance Index (EI) takes into account multiple environmental factors and weights their importance using empirical weight coefficients (κ1, κ2, κ3, and κ4). Soil pH (pH), ambient temperature (Tc), precipitation (Q), and the mineral coverage index (CI) on the ore surface are key environmental parameters affecting the quality of lithium ore resources. Soil pH affects the chemical stability of lithium, ambient temperature and precipitation affect the rate and efficiency of the leaching process, and the mineral coverage index (CI) on the ore surface provides important information about the surface state of the ore, which may affect the efficiency of lithium extraction.

[0122] By calculating the Environmental Relevance Index, we can comprehensively consider the impact of environmental factors on the quality of lithium mineral resources, thereby providing a scientific basis for the development and utilization of lithium mineral resources. This assessment method helps determine the optimal mining location and extraction technology, ensure the efficient utilization of lithium resources, and support the economic value assessment of lithium mineral resources.

[0123] In some embodiments of the present application, when performing a quality assessment of a lithium mineral resource area to be detected based on the environmental relevance index, leaching extraction efficiency data, and lithium concentration, the following steps are included:

[0124] ;

[0125] Where X represents the comprehensive evaluation value; α, β, and ε represent the weight coefficients of environmental relevance index, leaching extraction efficiency data, and lithium concentration, respectively;

[0126] Classify the lithium mineral resource area to be detected according to the relationship between X and a preset first comprehensive evaluation threshold and a second comprehensive evaluation threshold; wherein the first comprehensive evaluation threshold is less than the second comprehensive evaluation threshold;

[0127] When X is greater than the second comprehensive evaluation threshold, the level of the lithium mineral resource area to be detected is high;

[0128] When X is less than the second comprehensive evaluation threshold and X is not less than the first comprehensive evaluation threshold, the level of the lithium mineral resource area to be detected is medium;

[0129] When X is less than the first comprehensive evaluation threshold, the level of the lithium ore resource area to be detected is low.

[0130] As can be appreciated, this embodiment provides a method for grading and assessing lithium resource areas. By comprehensively considering the environmental relevance index (EI), leaching efficiency data, and lithium concentration, a more comprehensive assessment of the quality and mining potential of lithium resources can be achieved. The calculation of the comprehensive assessment value (X) takes these three key factors into account and weights their importance using weight coefficients (α, β, ε). This comprehensive assessment method helps determine the quality grade of lithium resources, thereby providing a scientific basis for resource development and utilization.

[0131] The hierarchical assessment of lithium mineral resource areas helps guide mineral resource development decisions, optimize resource allocation, and improve resource utilization efficiency. By setting the first and second comprehensive assessment thresholds, lithium mineral resource areas can be divided into three levels: high, medium, and low. High-grade lithium mineral resource areas have high mining value and economic potential and are suitable for priority development; medium-grade lithium mineral resource areas have certain mining value but may require further assessment and optimization of mining plans; low-grade lithium mineral resource areas may not be suitable for immediate development due to low quality or high mining costs.

[0132] In summary, the lithium mineral resource assessment method provided in this embodiment can provide a scientific, reasonable and operational solution for the quality assessment and classification of lithium mineral resources, which helps to promote the sustainable development and utilization of lithium mineral resources.

[0133] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0134] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0135] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0136] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0137] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A method for detecting low-grade lithium ore resources, characterized in that: include: Collecting ore samples from the lithium ore resource area to be detected, and simultaneously obtaining environmental data when collecting the ore samples; Pre-treating the ore sample to obtain a sample to be tested; Leaching the sample to be tested to obtain a leached extract, and quantitatively analyzing the leached extract to obtain lithium concentration and leaching extraction efficiency data; A quality assessment of the lithium ore resource area to be detected is performed based on the environmental data, leaching extraction efficiency data and lithium concentration.

2. The method for detecting low-grade lithium ore resources according to claim 1, characterized in that: The ore sample is pre-processed to obtain a sample to be tested, including: The ore sample is crushed, dried and mixed in sequence; The particle size of the ore sample after crushing is not higher than 0.5 mm; the humidity of the ore sample after drying is not higher than 2%.

3. The method for detecting low-grade lithium ore resources according to claim 2, characterized in that: When the ore sample is pre-processed to obtain a sample to be tested, the following steps are also included: The uniformity deviation of the ore sample after mixing is no more than 5%; the calculation formula of the uniformity deviation is: ； Where U represents the uniformity deviation; σ represents the standard deviation of the target element content in the sample; and μ represents the average content of the target element in the sample.

4. The method for detecting low-grade lithium ore resources according to claim 1, characterized in that: The method of leaching the sample to be tested to obtain the leaching extract comprises: In a constant temperature water bath, the sample to be tested is mixed with an acidic leaching agent for leaching extraction; the extraction temperature and extraction time are calculated and set according to the following formula: ； ； Wherein, T represents the extraction temperature; t represents the extraction time; Ea represents the activation energy; A represents the frequency factor; R represents the gas constant; Rp represents the average particle size; k represents the reaction rate constant; ρ represents the ore particle density; Cacid represents the concentration of the acidic leaching agent; and D represents the diffusion coefficient of the target element in the leachate.

5. The method for detecting low-grade lithium ore resources according to claim 4, characterized in that: When the sample to be tested is subjected to leaching extraction to obtain a leaching extract, the method further includes: The rate constant k of the leaching reaction was measured at at least three different temperatures. The rate constant k was obtained by fitting the extraction efficiency data per unit time to a first-order reaction kinetic model: ； Wherein, η represents the extraction efficiency per unit time t; Using ln(k) as the ordinate and 1 / T as the abscissa, linear regression is performed to obtain a straight line, and the slope and intercept of the straight line are obtained. The activation energy Ea and the frequency factor A are calculated based on the slope and intercept.

6. The method for detecting low-grade lithium ore resources according to claim 5, characterized in that: When calculating the activation energy Ea and frequency factor A using the slope and intercept, the following are included: ； ； Among them, slope represents the slope; intercept represents the intercept.

7. The method for detecting low-grade lithium ore resources according to claim 6, characterized in that: Before leaching the sample to be tested to obtain a leached extract, performing an ore composition analysis on the sample to be tested to obtain the mass percentage of lithium element in the sample to be tested; The leaching extraction efficiency data is obtained based on the mass percentage of lithium element and lithium concentration in the sample to be tested.

8. The method for detecting low-grade lithium ore resources according to claim 7, characterized in that: When obtaining the leaching extraction efficiency data according to the mass percentage content of lithium element and lithium concentration in the sample to be tested, it includes: ； Wherein, ηLi represents the leaching extraction efficiency data; CLi represents the lithium concentration; V represents the volume of the leached extract; m represents the mass of the initial ore sample; ωLi represents the mass percentage of the lithium element in the sample to be tested.

9. The method for detecting low-grade lithium ore resources according to claim 8, characterized in that: When conducting a quality assessment of the lithium ore resource area to be tested based on the environmental data, leaching extraction efficiency data and lithium concentration, the following shall be included: Calculating an environmental relevance index based on the environmental data; performing a quality assessment of the lithium ore resource area to be tested based on the environmental relevance index, leaching extraction efficiency data, and lithium concentration; wherein the environmental data includes ambient temperature, precipitation, soil and ore surface mineral coverage index; The environmental relevance index is calculated according to the following formula: ； Where EI represents the environmental relevance index; κ1, κ2, κ3, and κ4 represent empirical weight coefficients, respectively; pH represents soil pH; Tc represents ambient temperature; Q represents precipitation; and CI represents the mineral coverage index on the ore surface, which is defined as the spectral reflectance characteristics of the minerals on the sample surface.

10. The method for detecting low-grade lithium ore resources according to claim 9, characterized in that: When conducting a quality assessment of the lithium ore resource area to be tested based on the environmental relevance index, leaching extraction efficiency data and lithium concentration, it includes: ； Where X represents the comprehensive evaluation value; α, β, and ε represent the weight coefficients of environmental relevance index, leaching extraction efficiency data, and lithium concentration, respectively; Classify the lithium mineral resource area to be detected according to the relationship between X and a preset first comprehensive evaluation threshold and a second comprehensive evaluation threshold; wherein the first comprehensive evaluation threshold is less than the second comprehensive evaluation threshold; When X is greater than the second comprehensive evaluation threshold, the level of the lithium mineral resource area to be detected is high; When X is less than the second comprehensive evaluation threshold and X is not less than the first comprehensive evaluation threshold, the level of the lithium mineral resource area to be detected is medium; When X is less than the first comprehensive evaluation threshold, the level of the lithium ore resource area to be detected is low.

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