Sampling test evaluation method for tailings, electronic device, and storage medium

Abstract

The application provides a tailing sampling test evaluation method, an electronic device and a storage medium, different tailing discharge types are classified and sampled, and the sampled tailing is highly representative in time and space. The evaluation error caused by sampling deviation is fundamentally reduced, and the accuracy of the evaluation result is improved. The phase factor influence coefficient based on phase analysis can quantify the proportion of recoverable elements and is used for evaluating recoverability. The dissociation degree factor influence coefficient based on dissociation degree analysis can quantify the restriction of mineral intergrowth state on target element recovery and is used for evaluating easy selection. The particle size factor influence coefficient based on particle size analysis can quantify the influence of particle size on beneficiation efficiency and is used for evaluating recoverability. The three-dimensional constraint relationship is used to determine the concentrate recovery rate, and the tailing evaluation result is determined according to the concentrate recovery rate and the tailing comprehensive grade, thereby providing a direct basis for formulating a targeted technical scheme for tailing recovery.

Description

Technical Field

[0001] This application relates to the field of mineral assessment technology, and in particular to a tailings sampling, testing and assessment method, electronic equipment and storage medium. Background Technology

[0002] In production practice, there are three main methods for predicting mineral processing indicators such as recovery rate, grade, yield, and comprehensive utilization rate. Mineral processing experiments, primarily using gravity separation, flotation, magnetic separation, and electrostatic separation, can yield relatively accurate indicators, but they are costly and have relatively long testing cycles. They are particularly significant for minerals with fluctuating metal prices, hindering rapid decision-making by production units. Parameter prediction methods using statistical methods or machine learning models can establish relationships between various mineral processing indicators and make inferences, but they are highly dependent on production data and cannot calculate indicators for tailings without production lines. Ore gene characteristic prediction methods, based on information such as ore mineral composition, structure, and chemical composition, are mainly for raw ore and have significant limitations for tailings with extremely fine particle size and no obvious structure, as well as other mineral types. These methods cannot accurately and efficiently assess tailings. Summary of the Invention

[0003] In view of this, the purpose of this application is to propose a sampling, testing and evaluation method, electronic equipment and storage medium for tailings, which measures and evaluates tailings through multi-dimensional influence coefficients, and quickly predicts information such as tailings recovery rate, grade and output value, so as to provide an economic reference for tailings reprocessing.

[0004] To achieve the above objectives, this application provides a method for sampling, testing, and evaluating tailings, comprising:

[0005] Tailings are classified and sampled according to the tailings discharge type to obtain sample tailings, and the comprehensive grade of tailings is determined according to the tailings grade and tailings quality of the sample tailings.

[0006] According to the preset sample mass, multiple initial sample tailings are taken from the sample tailings. Each initial sample tailings is dried and reduced to obtain multiple sample tailings.

[0007] The influence coefficient of phase factor is determined based on the occurrence phase in the tailings of the subsample corresponding to the preset target element;

[0008] The influence coefficient of dissociation factor is determined based on the relative proportion of intergrowths in the hosted phase;

[0009] The particle size factor influence coefficient is determined based on the particle size distribution of the tailings sample.

[0010] The concentrate recovery rate is determined based on the phase factor influence coefficient, the degree of dissociation factor influence coefficient, and the particle size factor influence coefficient, and the tailings assessment result is determined based on the concentrate recovery rate and the comprehensive grade of the tailings.

[0011] Optionally, the step of classifying and sampling tailings according to their discharge type to obtain sample tailings includes:

[0012] In response to the tailings discharge type being wet tailings, tailings slurry from multiple shifts is collected in batches according to preset sampling batches and minimum sampling weights to obtain the sample tailings; wherein, the sampling batches cover at least one actual production cycle;

[0013] In response to the tailings discharge type being dry tailings, sampling points are evenly spaced in the tailings pond according to a preset sampling distance and minimum sampling number to obtain multiple sampling point locations; tailings are sampled at the sampling point locations according to the minimum sampling weight, preset longitudinal sampling depth, and longitudinal sampling distance to obtain the sample tailings.

[0014] Optionally, determining the overall tailings grade based on the tailings grade and tailings quality of the sample tailings includes:

[0015] The tailings grade is calculated by weighting the tailings mass of a single sample as a weighting factor.

[0016] Optionally, determining the phase factor influence coefficient based on the occurrence phase in the tailings sample corresponding to the preset target element includes:

[0017] The elemental distribution rate and phase recovery coefficient of each occurrence phase are determined, and the product of the elemental distribution rate and the phase recovery coefficient is determined as the single-item influence coefficient.

[0018] The sum of the individual influence coefficients of different present phases is determined as the influence coefficient of the phase factor.

[0019] Optionally, determining the dissociation degree factor influence coefficient based on the relative proportion of intergrowths in the hosted phase includes:

[0020] Determine the mineral percentage of the target mineral in the intergrowth, and classify the intergrowth according to the mineral percentage to obtain the intergrowth type;

[0021] Determine the type recovery coefficient for each interspecies type, and determine the type proportion of each interspecies type in the interspecies based on the relative proportion. The product of the type recovery coefficient and the type proportion is determined as the single-type influence coefficient.

[0022] The sum of the single-category influence coefficients of different intergrowth types is determined as the dissociation degree factor influence coefficient.

[0023] Optionally, determining the particle size factor influence coefficient based on the particle size distribution of the tailings sample includes:

[0024] The tailings sample was classified by particle size distribution to obtain particle size grades in different particle size ranges.

[0025] Determine the grade proportion and grade recovery coefficient for each particle size grade, and determine the grade influence coefficient by multiplying the grade recovery coefficient and the grade proportion.

[0026] The sum of the influence coefficients of different particle size levels is determined as the particle size factor influence coefficient.

[0027] Optionally, determining the tailings assessment result based on the concentrate recovery rate and the overall tailings grade includes:

[0028] The concentrate yield is determined based on the original concentrate yield corresponding to the tailings and a preset ratio threshold.

[0029] The product of the concentrate recovery rate and the overall grade of the tailings is used as the ratio of the concentrate yield to the concentrate grade.

[0030] The equivalent average unit price of the target element is determined based on the pricing coefficient of the target element and the historical price data of the target element within a preset historical time period.

[0031] The expected output value of the concentrate is determined based on the equivalent average unit price and the concentrate grade, and the tailings assessment result is determined based on the expected output value of the concentrate and the preset processing cost per ton of ore.

[0032] Optionally, determining the tailings assessment result based on the expected output value of the concentrate and the preset processing cost per ton of ore includes:

[0033] The product of the expected output value of the concentrate and the concentrate yield is determined as the expected output value of the tailings;

[0034] In response to the tailings' expected output value being greater than the processing cost per ton of tailings, the profit is determined as the tailings assessment result;

[0035] In response to the tailings' expected output value being less than or equal to the processing cost per ton of tailings, a loss is determined as the tailings assessment result.

[0036] Based on the same inventive concept, this application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable by the processor, wherein the processor implements the method described above when executing the computer program.

[0037] Based on the same inventive concept, this application also provides a non-transitory computer-readable storage medium that stores computer instructions for causing a computer to perform the method described above.

[0038] As can be seen from the above, the tailings sampling, testing, and evaluation method, electronic equipment, and storage medium provided in this application can classify and sample tailings according to their discharge type to obtain sample tailings, and determine the overall tailings grade based on the tailings grade and mass of the sample tailings. Multiple initial sample tailings are taken from the sample tailings according to a preset sample mass, and each initial sample tailings is dried and reduced to obtain multiple sample tailings. The influence coefficient of phase factors is determined based on the occurrence phases in the sample tailings corresponding to the preset target element. The influence coefficient of liberation degree factors is determined based on the relative proportion of intergrowths in the occurrence phases. The influence coefficient of particle size factors is determined based on the particle size distribution of the sample tailings. The concentrate recovery rate is determined based on the influence coefficients of phase factors, liberation degree factors, and particle size factors, and the tailings evaluation result is determined based on the concentrate recovery rate and the overall tailings grade. Classifying and sampling according to the different characteristics of different tailings discharge types ensures that the collected sample tailings are highly representative in both time and space. This approach fundamentally reduces assessment errors caused by sampling bias, improving the accuracy of assessment results. Phase factor influence coefficients based on phase analysis quantify the proportion of recoverable elements, used to assess recyclability, excluding non-recoverable components at the source, ensuring the assessment focuses only on economically valuable phases, and avoiding inflated resource potential. Liberation degree factor influence coefficients based on liberation degree analysis quantify the constraints of mineral intergrowth state on target element recovery, used to assess beneficiation ease, and evaluate the additional grinding input required for tailings recovery, avoiding excessive costs. Particle size factor influence coefficients based on particle size analysis quantify the impact of particle size on beneficiation efficiency, used to assess recyclability, and quantify the adaptability of particle size distribution to existing beneficiation processes, avoiding tailings particles that are not suitable for re-beneficiation. The three coefficients represent the key dimensions affecting the recovery rate. The constraints of the three dimensions determine the concentrate recovery rate, and the tailings assessment result is determined based on the concentrate recovery rate and the comprehensive grade of the tailings. The shortcomings of any dimension will have a decisive impact on the tailings assessment result, thus making the tailings assessment result conservative and reliable. This effectively realizes the accurate estimation of tailings value, provides an economic reference for tailings reprocessing, and provides a direct basis for formulating targeted technical solutions for tailings recovery. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in this application or related technologies, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is a flowchart of the tailings sampling, testing, and evaluation method according to an embodiment of this application;

[0041] Figure 2a This is a schematic diagram of the horizontal layout of points in an embodiment of this application;

[0042] Figure 2b This is a schematic diagram of the vertical layout of points in an embodiment of this application;

[0043] Figure 2c This is a schematic diagram illustrating sampling based on sampling points in an embodiment of this application;

[0044] Figure 3 This is a schematic diagram of a tailings sampling, testing, and evaluation device according to an embodiment of this application;

[0045] Figure 4 This is a schematic diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with specific embodiments and the accompanying drawings.

[0047] It should be noted that, unless otherwise defined, the technical or scientific terms used in the embodiments of this application should have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms "first," "second," and similar terms used in the embodiments of this application do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are only used to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0048] In this article, it is important to understand that any number of elements in the accompanying figures is for illustrative purposes and not for limitation, and any naming is for distinction only and has no limiting meaning.

[0049] Based on the above background description, the following situations also exist in the related technologies:

[0050] New mineral reserves are primarily determined through geological prospecting. However, extensive exploration has already largely deciphered outcrops, shallow deposits, and easily accessible minerals. Breakthroughs in deep prospecting are difficult, risky, and require consideration of environmental impact. Without significant methodological innovation and technological advancements, these problems will become increasingly prominent in the future. Therefore, while vigorously promoting breakthroughs in prospecting technology, it is still necessary to find another promising path. With changing economic and technological conditions, when the development and utilization of tailings can generate economic profits, tailings ponds also become "mineral deposits," offering the possibility of further recycling. In production practice, there are three main methods for predicting mineral processing indicators such as recovery rate, grade, yield, and comprehensive utilization rate. Mineral processing experiments, primarily using gravity separation, flotation, magnetic separation, and electrostatic separation, can yield relatively accurate beneficiation indicators. However, they are costly and have relatively long testing cycles. Their impact is particularly significant for minerals with fluctuating metal prices, hindering rapid decision-making by production units. Parameter prediction methods using statistical methods or machine learning models can establish relationships between various beneficiation indicators and make inferences, but they are highly dependent on production data and cannot calculate indicators for tailings before production lines are established. Ore gene characteristic prediction methods based on ore mineral composition, structure, and chemical composition are mainly applicable to raw ore and have significant limitations for extremely fine-grained tailings and other minerals with no obvious structural structure. These methods fail to accurately and efficiently assess tailings.

[0051] The tailings sampling, testing, and evaluation method, electronic equipment, and storage medium provided in this application can classify and sample tailings according to their discharge type to obtain sample tailings, and determine the overall tailings grade based on the tailings grade and mass of the sample tailings. Multiple initial sample tailings are taken from the sample tailings according to a preset sample mass, and each initial sample tailings is dried and reduced to obtain multiple sample tailings. The influence coefficient of phase factors is determined based on the occurrence phases in the sample tailings corresponding to the preset target element; the influence coefficient of liberation degree factors is determined based on the relative proportion of intergrowths in the occurrence phases; the influence coefficient of particle size factors is determined based on the particle size distribution of the sample tailings; the concentrate recovery rate is determined based on the influence coefficients of phase factors, liberation degree factors, and particle size factors; and the tailings evaluation result is determined based on the concentrate recovery rate and the overall tailings grade. Classifying and sampling according to the different characteristics of different tailings discharge types ensures that the collected sample tailings are highly representative in both time and space. This approach fundamentally reduces assessment errors caused by sampling bias, improving the accuracy of assessment results. Phase factor influence coefficients based on phase analysis quantify the proportion of recoverable elements, used to assess recyclability, excluding non-recoverable components at the source, ensuring the assessment focuses only on economically valuable phases, and avoiding inflated resource potential. Liberation degree factor influence coefficients based on liberation degree analysis quantify the constraints of mineral intergrowth state on target element recovery, used to assess beneficiation ease, and evaluate the additional grinding input required for tailings recovery, avoiding excessive costs. Particle size factor influence coefficients based on particle size analysis quantify the impact of particle size on beneficiation efficiency, used to assess recyclability, and quantify the adaptability of particle size distribution to existing beneficiation processes, avoiding tailings particles that are not suitable for re-beneficiation. The three coefficients represent the key dimensions affecting the recovery rate. The constraints of the three dimensions determine the concentrate recovery rate, and the tailings assessment result is determined based on the concentrate recovery rate and the comprehensive grade of the tailings. The shortcomings of any dimension will have a decisive impact on the tailings assessment result, thus making the tailings assessment result conservative and reliable. This effectively realizes the accurate estimation of tailings value, provides an economic reference for tailings reprocessing, and provides a direct basis for formulating targeted technical solutions for tailings recovery.

[0052] The following describes in detail, with reference to the accompanying drawings, the tailings sampling, testing and evaluation method provided by the embodiments of this application.

[0053] In some embodiments, such as Figure 1 As shown, a method for sampling, testing, and evaluating tailings is characterized by comprising:

[0054] Step 101: Classify and sample the tailings according to their discharge type to obtain sample tailings, and determine the overall grade of the tailings based on the grade and quality of the sample tailings.

[0055] In practice, to ensure that the sample tailings accurately and comprehensively reflect the chemical and physical characteristics of the entire tailings dam and provide a reliable data foundation for subsequent assessments, the key is to guarantee the representativeness of the sample tailings and avoid distorted assessment results due to sampling bias. Differentiated sampling strategies were developed based on the tailings discharge method (wet / dry), demonstrating a high degree of systematicity and adaptability.

[0056] In some embodiments, tailings are classified and sampled according to the tailings discharge type to obtain sample tailings, including:

[0057] In response to the tailings discharge type being wet tailings, tailings slurry from multiple shifts is collected in batches according to the preset sampling batch and minimum sampling weight to obtain sample tailings; wherein, the sampling batch covers at least one actual production cycle.

[0058] In response to the tailings discharge type being dry tailings, multiple sampling points are obtained by arranging sampling points at equal intervals in the tailings pond according to the preset sampling spacing and minimum sampling number. Tailings samples are then taken at the sampling points according to the minimum sampling weight, preset longitudinal sampling depth, and longitudinal sampling interval to obtain sample tailings.

[0059] In practice, wet tailings sampling involves a core strategy: for wet tailings discharged as slurry, the sampling strategy must cover the fluctuations over time. The composition of wet tailings may vary between production shifts due to changes in the raw ore and fluctuations in the beneficiation process. Therefore, slurry samples must be collected in batches from multiple shifts (at least one complete production cycle, such as 8 hours or 24 hours), and mixed to obtain a comprehensive sample that represents the "average" state. Simultaneously, the dry weight of the sample must be at least 2 kg to ensure sufficient material for subsequent multiple and varied tests, improving the accuracy of the evaluation.

[0060] Dry tailings sampling: For tailings ponds where dry tailings are stored, the core sampling strategy is to capture spatial inhomogeneities. During sedimentation, tailings often exhibit particle stratification (coarse and fine particles aggregate separately) and component segregation. Before sampling, both lateral and vertical sampling points need to be established. Lateral sampling is as follows... Figure 2a As shown, a grid-like sampling method is used to cover the entire tailings dam area at equal intervals, ensuring no omissions. Based on statistical principles, it is necessary to ensure that the number of sampling points is no less than 20 and the spacing d between the points is no less than 3 meters, striking a balance between feasible workload and sufficient representativeness. More points and a denser grid result in better representativeness, but also a greater workload. Figure 2a The red dots in the diagram represent sampling points, the blue areas represent water puddles, the black dots represent no sampling, and the outer circle represents the actual area of ​​the tailings dam.

[0061] Vertical layout as follows Figure 2bAs shown, this method is used to address the vertical stratification of tailings ponds. The initial sampling depth (0.5–2 m) for the first layer in the longitudinal layout takes into account operational convenience and the characteristics of surface tailings. Subsequent sampling is conducted every h meters (h ≥ 3 m), meaning the initial sampling depth is 0.5–2 m, followed by sampling every 3 meters to ensure sample representativeness. This longitudinal sampling layout reveals the grade variations of tailings along the vertical direction. This is crucial for assessing the economic value of tailings at different depths and developing phased mining plans.

[0062] For dry tailings disposal, the sampling volume at each sampling point must be no less than 2 kg to ensure sufficient material for subsequent multiple and various types of tests, improving the accuracy of the assessment. In practice, the GPS coordinates, depth, and surrounding environment (such as proximity to drainage outlets or slopes) of each sampling point should be recorded in detail. This helps to establish a spatial distribution model of tailings grade, allowing for precise location and priority mining if samples from a particular area show extremely high value.

[0063] The sampling tools and methods differ depending on the type of tailings discharge. For dry tailings, shallow sampling can be done with a shovel; borehole sampling requires specialized equipment such as a core drill to ensure that a columnar sample of uncontaminated depth is obtained. For wet tailings, a specialized slurry sampler is required.

[0064] After sampling is completed at the sampling point, the tailings sample is obtained. The comprehensive grade of the tailings is determined based on the tailings grade and quality of the sample tailings, providing data support for the subsequent economic assessment of the tailings.

[0065] In some embodiments, determining the overall tailings grade based on the tailings grade and tailings quality of the sample tailings includes:

[0066] The tailings grade is calculated by weighting the tailings mass of a single sample as a weighting factor.

[0067] In practice, the tailings grade and tailings quality of the sample tailings are shown in Table 1:

[0068] Table 1. Tailings Grade and Tailings Quality

[0069]

[0070] In this context, the first letter 'n' in the subscript indicates that the sampling point is located in the nth layer of the vertical layout, and the second letter 't' in the subscript indicates the tth sampling point within the corresponding vertical layer.

[0071] The overall grade of the first layer is the weighted average of the tailings mass and grade of the tailings samples from multiple sampling points located in the first layer. Therefore, the overall grade of the first layer is... θ 1 is:

[0072] .

[0073] Similarly, the second level of comprehensive quality is θ 2 is:

[0074] .

[0075] The overall quality of the nth layer is θ n for:

[0076] .

[0077] The overall grade of tailings in the tailings dam θ 0 is:

[0078] .

[0079] The overall grade of tailings in a tailings dam can also be the average of the overall grades of n layers.

[0080] For example Figure 2c Taking the dry tailings as an example, the horizontal distribution points are as follows: Figure 2c As shown, due to environmental and safety reasons, this tailings dam is not suitable for borehole sampling. Therefore, shallow sampling was used, taking samples from only the first layer, meaning only one layer of sampling points was used vertically. Sampling points were placed throughout the tailings dam at 40 m intervals and a sampling depth of 1 m. Figure 2c The circles in the image represent sampling points. Figure 2c The tailings mass and grade of the samples taken at the sampling points are shown in Table 2.

[0081] Table 2. Tailings mass and grade at sampling points.

[0082]

[0083] Then θ0=θ1=(0.30+0.33+0.33+0.35+0.22+0.30+0.26+0.35+0.35+0.28+0. 28+0.26+0.28+0.35+0.28+0.64+0.86+0.28+0.30+0.28+0.48+0.26+0. 41+0.39+0.41+0.35+0.31+0.31+0.27+0.28+0.43+0.35+0.24+0.33+0. 35+0.35+0.65+0.44+0.32+0.47+0.41+0.39+0.37+0.4)×3÷(3×43)=0.36 g / t.

[0084] Step 102: Take out multiple initial sample tailings from the sample tailings according to the preset sample mass, dry and reduce each initial sample tailings to obtain multiple sample tailings.

[0085] In practice, the sample tailings are directly obtained raw samples that may be uneven. Through a series of standardized processes, they are transformed into secondary sample tailings that are uniform in quantity, dry, have fine particles, and are chemically representative. Key process mineralogical analyses are then performed.

[0086] The purpose of identifying byproduct tailings is to obtain a smaller, more easily processed laboratory sample from the total sample, which has the same chemical composition as the total sample. First, an equal amount of initial byproduct tailings is separated from the sample tailings at each sampling point (the weight of each initial byproduct tailings is the byproduct mass, ranging from 50 to 100 g, for example, 80 g). Then, all initial byproduct tailings are combined into a single composite sample. Combining them into a composite sample is a homogenization process, further enhancing the overall representativeness of the sample. For example, taking four samples from the sample tailings with an byproduct mass of 80 g yields four initial byproduct tailings. These four initial byproduct tailings are then combined to obtain a homogeneous composite sample.

[0087] Among these methods, the quartering method or the divisor method are commonly used standard methods for sample reduction. The principle is to gradually reduce the sample size through multiple mixing and division, while ensuring that the sample retained after each reduction represents the characteristics of the parent sample, avoiding errors caused by human bias. Alternatively, the cone quartering method, the nine-point method, and mechanical reduction methods can also be used for sample reduction.

[0088] Before fraction reduction, the tailings sample needs to be dried to remove moisture. The presence of moisture will affect the quality of the sample (leading to inaccurate grade calculations), hinder fraction reduction, and may cause splashing or result deviation during chemical analysis. Drying methods can include natural air drying (suitable for situations where testing is not urgent and the ambient temperature is suitable) or oven drying (e.g., drying at 105℃ for 2 hours, which is fast and standard).

[0089] After drying and reducing each initial tailings sample, four or more tailings samples were obtained. Three of these samples were used to analyze the influence coefficients of three dimensions, and one was kept as a backup.

[0090] Step 103: Determine the influence coefficient of phase factors based on the occurrence phases in the tailings sample corresponding to the preset target elements.

[0091] In practice, one of the three tailings samples is taken for chemical phase analysis to determine the mineral form in which the target element (e.g., gold) exists. This is crucial for determining whether the material is recyclable. For example, exposed native gold is easily recovered, while gold encased in sulfide or silicate minerals is difficult to recover directly. Direct data is provided by calculating the phase factor influence coefficient α. The phase factor influence coefficient α quantifies the distribution proportion of the target element in the recyclable phase. It excludes the unrecyclable portion at the source, ensuring that predictions are based only on the actual recyclable portion, excluding unrecyclable phases, thus avoiding overestimation.

[0092] In some embodiments, determining the phase factor influence coefficient based on the occurrence phase in the tailings sample corresponding to the preset target element includes:

[0093] Determine the elemental distribution rate and phase recovery coefficient for each occurrence phase, and determine the product of the elemental distribution rate and phase recovery coefficient as the single-item influence coefficient;

[0094] The sum of the individual influence coefficients of different occurrence phases is determined as the phase factor influence coefficient.

[0095] In practice, at least one target element (such as gold) is identified, and its occurrence phase (such as exposed gold, sulfide-encased gold, etc.) is analyzed to determine the elemental distribution rate 'a' of each occurrence phase. i and phase recovery coefficient m i The product of elemental distribution rate and phase recovery coefficient is determined as the single-item influence coefficient A. i =a i ×m i Finally, the sum of the individual influence coefficients of different occurrence phases is determined as the phase factor influence coefficient α:

[0096] .

[0097] The elemental distribution rate refers to the percentage of the target element (such as gold) present in a specific phase in the tailings relative to the total content of that element. Through chemical phase analysis, the total amount of the target element in the tailings is distributed according to its different mineral carriers or chemical forms. For example, gold may exist as exposed native gold, gold encased in sulfides, or gold contained within the crystal lattice of iron minerals. The phase recovery coefficient is used to assess the technical feasibility and economic rationality of recovering the target element from a specific phase. Its value is typically set to 0 or 1. The phase recovery coefficient is assigned a value based on currently mature mineral processing technologies (such as gravity separation, flotation, and leaching) and economic benefits.

[0098] A recovery factor of 1 indicates that the target element in the phase can be recovered effectively and economically using existing technologies. For example, exposed gold particles are easily recovered through gravity separation or flotation. A recovery factor of 0 indicates that the target element in the phase is difficult to recover or is too costly under current technological and economic conditions. For example, gold dispersed isomorphously in the lattice of iron minerals requires extremely high energy consumption (such as ultrafine grinding or high-temperature smelting) to release, which is uneconomical for the reprocessing of low-value tailings. Tailings have low economic value and are not suitable for high-energy-consuming grinding operations. When their content is low, this part can be disregarded, and its phase recovery factor can be set to 0.

[0099] Elemental distribution rate and phase recovery coefficient are used in calculating the single-factor influence coefficient A of a single phase. i =a i ×m i Synergistic effect, element distribution rate (a) i This provides a theoretical upper limit to the resource potential. Phase recovery coefficient (m) i This acts as a "filter," only identifying those parts that have actual recycling value.

[0100] The overall influence coefficient of phase factors α = ΣA i The essence is to recycle all recyclable phases (i.e., (m) i The elemental distribution rates of the phases with a density of 1 are summed. This quantifies the proportion of the target element in the tailings that truly has economic recovery value.

[0101] The elemental distribution rate reveals the "state of existence" of the target element, while the recovery coefficient assesses its "availability" based on techno-economic feasibility. The combination of these two factors allows the phase factor influence coefficient (α) to objectively and practically evaluate the potential of tailings reprocessing in the "phase" dimension, avoiding the risk of overestimating resource value due to the inclusion of non-recoverable components, and providing a reliable basis for subsequent economic decisions.

[0102] For example, the chemical phases of gold in tailings are shown in Table 3.

[0103] Table 3 Chemical phases of gold in tailings

[0104]

[0105] The elemental distribution of exposed gold as the chemical phase was 52.78%, with a phase recovery coefficient of 1. The elemental distribution of sulfide-encapsulated gold as the chemical phase was 30.56%, with a phase recovery coefficient of 1. The uniform distribution of gold in iron minerals as the chemical phase was 8.33%, with a phase recovery coefficient of 0. The uniform distribution of other chemical phases was 8.33%, with a phase recovery coefficient of 0.

[0106] The influence coefficient of phase factors, α, is calculated as (52.78% × 1) + (30.56% × 1) + (8.33% × 0) + (8.33% × 0) = 83.34%. This coefficient determines the upper limit of the recovery rate; a low α value limits the potential for further selection. It directly determines the baseline level of the recovery rate. If the influence coefficient of phase factors is small, even with superior other factors, the concentrate recovery rate ε will be limited. This highlights the crucial impact of phase screening on economics—helping users or companies prioritize the selection of high-value phase tailings.

[0107] Step 104: Determine the influence coefficient of dissociation factor based on the relative proportion of intergrowths in the occurrence phase.

[0108] In practice, one of the three tailings samples is taken for microscopic analysis (such as AMICS). Microscopic analysis is an automated mineral analysis system that can accurately calculate the degree of liberation of the target mineral—that is, the ratio of the mass of individual mineral particles to the total mass of the minerals. The degree of liberation directly determines the ease of separation and the grade of the concentrate. The liberation factor influence coefficient based on the degree of liberation analysis can quantify the constraint of the mineral intergrowth state on the recovery of the target element, and is used to assess the ease of separation and the additional grinding input required for tailings recovery, avoiding excessive costs. The degree of liberation C = [A / (A + B)] × 100%, where A is the mass of the target mineral individual; B is the mass of the intergrowth particles containing the target mineral.

[0109] In some embodiments, determining the dissociation degree factor influence coefficient based on the relative proportion of intergrowths in the stored phase includes:

[0110] Determine the mineral percentage of the target mineral in the intergrowth, classify the intergrowth according to the mineral percentage, and obtain the intergrowth type;

[0111] Determine the type recovery coefficient for each interspecies type, and determine the type proportion of each interspecies type in the interspecies based on the relative proportion. The product of the type recovery coefficient and the type proportion is determined as the single-type influence coefficient.

[0112] The sum of the individual influence coefficients of different intercropping types is determined as the dissociation degree factor influence coefficient.

[0113] In practice, the intergrowths are classified according to the mineral percentage of the target mineral in the intergrowth, resulting in the following types of intergrowths: Type I intergrowths with a target mineral percentage of [3 / 4, 1]; Type II intergrowths with a target mineral percentage of [1 / 2, 3 / 4); Type III intergrowths with a target mineral percentage of [1 / 4, 1 / 2); and Type IV intergrowths with a target mineral percentage of [0, 1 / 4).

[0114] The type recovery factor is used to assess the ease and efficiency of recovering a target mineral from particles of a specific intergrowth type. Its value ranges from 0 to 1. The type recovery factor answers the question, "How much of the target mineral can be recovered from particles of a certain degree of intergrowth?" The type recovery factor is assigned based on mineral processing theory and practice. A higher recovery factor (closer to 1) indicates higher efficiency and less loss in recovering the target mineral from that type of particle. For example, the type recovery factor for a first-type intergrowth with a target mineral content of [3 / 4, 1] is typically set to 1 or close to 1 because these particles can almost be considered as single entities and are extremely easy to recover.

[0115] A lower recovery coefficient (closer to 0) indicates lower recovery efficiency. For example, the fourth type of intergrowth with a target mineral content of [0, 1 / 4) has a recovery coefficient of 0.5 because the target mineral content in these particles is extremely low, and most of it will be lost in the tailings. Even if it can be recovered, the concentrate grade will be very low.

[0116] The type recovery factor, as an "efficiency discount factor," converts the theoretical mineral content into the expected value of actual recyclability.

[0117] Type percentage refers to the percentage of different intergrowth types among all grains of a target mineral (such as a gold-bearing mineral). It describes the distribution of the mineral's liberation state. Type percentage answers the question, "What proportion of mineral grains are in what liberation state?" Each intergrowth type may include multiple intergrowths; the sum of the relative proportions of each intergrowth is the type percentage. For example, if the relative proportion of intergrowths with a mineral percentage of 0.9 is 23.60%, and the relative proportion of intergrowths with a mineral percentage of 0.8 is 23.07%, then the type percentage of the first type of intergrowth is 46.67%.

[0118] Type recovery coefficient N j and type proportion b j When calculating the influence coefficient β of the dissociation degree factor, they work together. The formula for calculating β is:

[0119] β = ΣB j = b1N1+ b2N2+ b3N3+ b4N4.

[0120] Where b1 is the type proportion of the first type of interspecies, N1 is the type recovery coefficient of the first type of interspecies; b2 is the type proportion of the second type of interspecies, N2 is the type recovery coefficient of the second type of interspecies; b3 is the type proportion of the third type of interspecies, N3 is the type recovery coefficient of the third type of interspecies; b4 is the type proportion of the fourth type of interspecies, N4 is the type recovery coefficient of the fourth type of interspecies.

[0121] For each type of co-occurrence, first calculate its individual single-category influence coefficient B. j =b j ×N j Multiplying the "proportion of this type of particle" by the "efficiency of recovery from this type of particle" yields the contribution of this type of particle to the overall recovery rate. Adding the contributions of all four intergrowth types (B1, B2, B3, B4) gives the total liberation factor influence coefficient β. The β value can be understood as the expected proportion of the target mineral that can be recovered in the "liberation degree" dimension, after comprehensively considering the distribution of liberation states and the recovery efficiency of different states.

[0122] For example, the type recovery coefficient and type proportion of conjoined organisms are shown in Table 4.

[0123] Table 4. Type recovery coefficients and type percentages of conjoint organism types.

[0124]

[0125] The calculation process of the dissociation factor influence coefficient β clearly demonstrates the synergistic effect of type proportion and type recovery coefficient:

[0126] The influence coefficient of the degree of dissociation factor β = (46.67% × 1) + (6.67% × 0.98) + (26.66% × 0.86) + (20.00% × 0.5) = 86.13%. Since the degree of dissociation is not perfect (there are a large number of intergrowths, especially the 20% of particles with extremely low degree of dissociation), the recovery potential of the target mineral in the dimension of "degree of dissociation" is 86.13% of its total content, and 13.87% of the potential loss is due to insufficient dissociation.

[0127] The type proportion reveals the "quantitative distribution" of mineral liberation states, while the type recovery coefficient, based on mineral processing engineering practice, defines the "quality efficiency" of different liberation states. The combination of these two factors allows the liberation degree factor influence coefficient β to objectively and practically predict the impact of the degree of liberation on the final recovery effect, providing a crucial basis for assessing the difficulty and potential of tailings reprocessing.

[0128] Step 105: Determine the particle size factor influence coefficient based on the particle size distribution of the auxiliary tailings sample.

[0129] In practice, one of the three tailings samples is taken for particle size analysis to determine its particle size distribution. Excessively fine particles (sludge) can interfere with the beneficiation process, increasing reagent consumption and reducing separation efficiency. The particle size factor influence coefficient γ is calculated to measure process adaptability. Based on particle size analysis, the particle size factor influence coefficient can quantitatively assess the impact of particle size on beneficiation efficiency, evaluate recoverability, quantify the adaptability of particle size distribution to existing beneficiation processes, and avoid tailings particles that are not suitable for reprocessing.

[0130] In some embodiments, determining the particle size factor influence coefficient based on the particle size distribution of the by-sample tailings includes:

[0131] The tailings samples were classified by particle size distribution to obtain particle size grades in different particle size ranges.

[0132] Determine the grade proportion and grade recovery coefficient for each particle size grade, and determine the grade influence coefficient by multiplying the grade recovery coefficient and grade proportion.

[0133] The sum of the influence coefficients of different particle size levels is determined as the particle size factor influence coefficient.

[0134] In practice, the tailings samples are classified by particle size distribution to obtain the following particle size grades: a first particle size grade with a particle size range of (-∞, 1]; a second particle size grade with a particle size range of (1, 10]; a third particle size grade with a particle size range of (10, 74]; and a fourth particle size grade with a particle size range of (74, ∞). The unit of the boundary values ​​in the particle size range is micrometers (μm).

[0135] Grade recovery coefficient p k The grade recovery factor is used to assess the ease and efficiency of recovering a target mineral from particles within a specific particle size range. Its value ranges from 0 to 1. The grade recovery factor answers the question, "How much of the target mineral can be effectively recovered from particles within a certain particle size range?" The grade recovery factor is assigned based on mineral processing engineering practices and particle behavior.

[0136] A higher grade recovery factor (closer to 1) indicates that particles in that size range are more easily recovered by existing mineral processing technologies. For example, the grade recovery factor p3 for the third size class is typically set to 0.99, as this is the efficient particle size range for many mineral processing methods, such as flotation.

[0137] The lower the grade recovery coefficient (closer to 0), the lower the recovery efficiency. For example, the grade recovery coefficient p1 (corresponding to particles <1μm) for the first particle size grade is set to 0.5 because these extremely fine particles (often called "sludge") have a large specific surface area and high surface energy, which not only makes them difficult to recover, but also interferes with the entire sorting process (such as consuming a large amount of reagents and covering the surface of other minerals to reduce selectivity).

[0138] Grain size distribution refers to the percentage of the mass (or quantity) of particles within a specific grain size range out of all particles in a target mineral (such as a gold-bearing mineral). It describes the grain size distribution of the target mineral. Grain size distribution answers the question, "How is the target mineral distributed across different grain size ranges?"

[0139] The grade percentage and grade recovery coefficient work together when calculating the particle size factor influence coefficient, and their calculation formula is as follows:

[0140] γ = ΣC k = c1p1 + c2p2 + c3p3 + c4p4

[0141] Where c1 represents the percentage of the first particle size class, p1 represents the recovery coefficient of the first particle size class; c2 represents the percentage of the second particle size class, p2 represents the recovery coefficient of the second particle size class; c3 represents the percentage of the third particle size class, p3 represents the recovery coefficient of the third particle size class; c4 represents the percentage of the fourth particle size class, p4 represents the recovery coefficient of the fourth particle size class.

[0142] For each particle size class, calculate its individual class influence coefficient C. k = c×p k This is equivalent to multiplying the "proportion of particles in this size range" by the "efficiency of recovery from particles in this range" to obtain the contribution of this size range to the overall recovery rate. The total particle size factor influence coefficient γ is obtained by summing the grade influence coefficients (C1, C2, C3, C4) of all four particle size ranges. The γ value can be understood as the expected proportion of the target mineral that can be recovered in the "particle size" dimension after comprehensively considering particle size distribution and the recovery efficiency of different particle sizes.

[0143] For example, the particle size distribution of the target mineral is shown in Table 5.

[0144] Table 5. Grain size distribution of the target mineral.

[0145]

[0146] The calculation process of the particle size factor influence coefficient γ clearly demonstrates the synergistic effect of grade proportion and grade recovery coefficient: γ = (0% × 0.5) + (83.33% × 0.98) + (16.67% × 0.99) + (0% × 1) = 98.17%. Since the particle size distribution of this tailings is very ideal (100% of the particles are concentrated in the high-efficiency recovery range of 1-74μm), the recovery potential of the target mineral in the dimension of "particle size" is as high as 98.17% of its total content. Only about 1.83% of the potential loss is due to the particle size not being in the optimal range (such as p2 and p3 being slightly less than 1).

[0147] Grade proportions reveal the "size distribution" of the target mineral particles, while the recovery coefficient, based on the limitations of the beneficiation process, defines the "floatability / selectivity" of particles of different sizes. The combination of these two factors allows the particle size factor γ to objectively predict the impact of particle size on the final recovery effect. A higher γ value generally indicates that the inherent particle size characteristics of the tailings are favorable for re-concentration, reducing grinding costs and operational difficulty; while a lower γ value warns of problems such as slime interference or excessively coarse particles, requiring careful consideration during economic evaluation.

[0148] Step 106: Determine the concentrate recovery rate based on the influence coefficients of phase factors, degree of liberation factors, and particle size factors, and determine the tailings assessment result based on the concentrate recovery rate and the comprehensive grade of tailings.

[0149] In practice, the product of the influence coefficients of phase factors, degree of liberation factors, and particle size factors is determined as the concentrate recovery rate, i.e., the concentrate recovery rate. The multiplicative method used to determine concentrate recovery rate indicates that the three dimensions of factors are interdependent and interconnected. The concentrate recovery rate can be imagined as passing through three sieves. The first sieve (achieved through the phase factor influence coefficient α) removes all unrecoverable portions. The second sieve (achieved through the dissociation degree factor influence coefficient β) removes insufficiently dissociated portions. The third sieve (achieved through the particle size factor influence coefficient γ) finally removes portions with unsuitable particle sizes. Only the portion that passes through all sieves is expected to be recovered. A low pass rate at any sieve will directly lead to a sharp decrease in the final recovery amount.

[0150] The calculation model for actual concentrate recovery implies that any weakness in any dimension will have a decisive impact on the final recovery rate. For example, even if the tailings grade is very high (large α), if the liberation degree is extremely poor (small β), the final predicted recovery rate ε will be very low. This forces assessors to pay comprehensive attention to the process mineralogical properties of the tailings, rather than just looking at the grade.

[0151] For example, α = 83.34%, β = 86.13%, and γ = 98.17%. The predicted concentrate recovery ε is then 83.34% × 86.13% × 98.17% ≈ 70.47%. The maximum experimentally measured recovery of gold is 70.18%. The predicted and experimental values ​​are in excellent agreement, strongly demonstrating the high concentrate recovery rate. The computational model is accurate and practical. It successfully decomposes the complex mineral processing process into three quantifiable and testable key factors, thereby enabling reliable prediction of tailings reprocessing potential at low cost and in a short time.

[0152] The calculation of concentrate recovery rate ε is a rapid, quantitative prediction process based on multi-dimensional mineralogical characteristics. Calculating concentrate recovery rate allows companies to scientifically assess the economic value of tailings without conducting expensive beneficiation tests, and provides clear guidance for optimizing beneficiation processes (such as determining whether grinding is needed to improve the β value).

[0153] After determining the concentrate recovery rate, it needs to be further transformed into a simpler and more understandable economic forecast.

[0154] In some embodiments, the tailings assessment results are determined based on the concentrate recovery rate and the overall tailings grade, including:

[0155] The concentrate yield is determined based on the original concentrate yield corresponding to the tailings and a preset proportional threshold.

[0156] The product of concentrate recovery rate and tailings comprehensive grade is used as the ratio of concentrate yield to concentrate grade.

[0157] The equivalent average unit price of the target element is determined based on the pricing coefficient of the target element and the historical price data of the target element within a preset historical time period.

[0158] The expected output value of the concentrate is determined based on the equivalent average unit price and concentrate grade, and the tailings assessment result is determined based on the expected output value of the concentrate and the preset processing cost per ton of ore.

[0159] The tailings assessment results are determined based on the expected output value of the concentrate and the pre-set processing cost per ton of ore, including:

[0160] The expected output value of tailings is determined by multiplying the expected output value of concentrate by the concentrate yield.

[0161] In response to the fact that the expected output value of tailings is greater than the processing cost per ton of tailings, the profit is determined as the tailings assessment result;

[0162] In response to the expected output value of tailings being less than or equal to the processing cost per ton of tailings, the loss is determined as the tailings assessment result.

[0163] In practice, the concentrate yield is taken as "the average value of this type of tailings reprocessing under current economic and technological conditions" or "50% of the concentrate yield of the original process." Optionally, the concentrate yield is determined based on the original concentrate yield corresponding to the tailings and a preset proportion threshold. For example, if the original concentrate yield in the original mining process is 7.33% and the preset proportion threshold is 50%, then the concentrate yield = 7.33% × 0.5 = 3.67%.

[0164] The concentrate grade is determined by the ratio of the product of concentrate recovery rate and tailings overall grade to concentrate yield. Therefore, the concentrate grade θ = (θ0 × ε) / λ = 6.91 g / t

[0165] Then, the equivalent average unit price of the target element is determined based on the pricing coefficient of the target element and the historical price data of the target element within a preset historical time period. For example, the historical price data is shown in Table 6.

[0166] Table 6 Historical Price Data

[0167]

[0168] Then the equivalent average unit price =(1004.43 + 978.00 + 934.27 + 929.22 + 840.09 + 776.93+ 770.66 + 774.36 + 763.72 + 757.16 + 696.16 + 677.06 + 634.66 + 617.74 + 614.30 + 613.82 + 585.20 + 567.91 + 558.57+540.83 + 542.45 + 543.52 + 502.06+ 466.51 + 465.03 + 466.21 + 466.62 + 449.02 + 449.04 + 456.71 + (440.57 + 447.15 + 447.63 + 443.29 + 424.41 + 407.67 + 414.44) ÷ 37 = 607.07 yuan / g.

[0169] With a concentrate grade of 6.91 g / t and a pricing coefficient of 0.45, the expected output value of producing 1 ton of concentrate is ω = 6.91 × 1 × 0.45 × 607.07 = 1887.68 yuan. The expected output value of the concentrate means that if 1 ton of gold concentrate with a grade of 6.91 g / t is produced, it is expected to be sold for about 1887.68 yuan.

[0170] The expected output value of tailings is then determined by multiplying the expected output value of the concentrate by the concentrate yield. Therefore, the expected output value of tailings ω0 = ω × λ = 1887.68 × 3.67% = 69.28 yuan / ton. λ is the yield, representing how many tons of concentrate can be obtained by processing 1 ton of tailings. ω0 represents the expected revenue obtained from processing 1 ton of tailings. The expected output value of tailings is a crucial indicator. If the expected output value of tailings is greater than the cost per ton of tailings, a profit is determined as the tailings assessment result; this means that if the cost of processing 1 ton of tailings (including all expenses such as mining, transportation, beneficiation, and management) is less than the expected output value of 69.28 yuan, then the project is economically feasible. If the expected output value of tailings is less than or equal to the cost per ton of tailings, a loss is determined as the tailings assessment result; this means that if the cost of processing 1 ton of tailings (including all expenses such as mining, transportation, beneficiation, and management) is greater than the expected output value of 69.28 yuan, then the project is economically infeasible.

[0171] In summary, based on the phase composition, degree of liberation, and particle size information of tailings, the concentrate recovery rate, grade, and output value can be rapidly predicted, resulting in an economic feasibility assessment and providing an economic reference for tailings reprocessing. Vertical regional hierarchical division of tailings with different spatial distributions can be performed, prioritizing the separation of target areas with higher economic value. The entire assessment process does not require expensive and time-consuming actual mineral processing tests; through systematic sampling and advanced mineralogical analysis, a reliable and scientific prediction of the economic feasibility of tailings reprocessing can be made quickly and scientifically, reducing assessment costs and making the process easier to understand.

[0172] It should be noted that the method in this embodiment can be executed by a single device, such as a computer or server. The method can also be applied in a distributed scenario, where multiple devices cooperate to complete the task. In such a distributed scenario, one of these devices may execute only one or more steps of the method in this embodiment, and the multiple devices will interact with each other to complete the method described.

[0173] It should be noted that the above description describes some embodiments of this application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in a different order than that shown in the above embodiments and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0174] Based on the same inventive concept, corresponding to any of the above embodiments, this application also provides a tailings sampling, testing and evaluation device.

[0175] refer to Figure 3 The tailings sampling, testing, and evaluation device includes:

[0176] The classification and sampling module 10 is configured to: classify and sample tailings according to the tailings discharge type, obtain sample tailings, and determine the comprehensive grade of tailings based on the tailings grade and tailings quality of the sample tailings.

[0177] The sample processing module 20 is configured to: extract multiple initial sample tailings from the sample tailings according to the preset sample mass, and dry and reduce each initial sample tailings to obtain multiple sample tailings.

[0178] Phase analysis module 30 is configured to: determine the phase factor influence coefficient based on the occurrence phases in the tailings sample corresponding to the preset target element;

[0179] The dissociation analysis module 40 is configured to: determine the influence coefficient of dissociation degree factor based on the relative proportion of intergrowths in the stored phase;

[0180] The particle size analysis module 50 is configured to: determine the particle size factor influence coefficient based on the particle size distribution of the tailings sample;

[0181] The economic assessment module 60 is configured to: determine the concentrate recovery rate based on the phase factor influence coefficient, the degree of liberation factor influence coefficient, and the particle size factor influence coefficient, and determine the tailings assessment result based on the concentrate recovery rate and the comprehensive grade of the tailings.

[0182] For ease of description, the above devices are described in terms of function, divided into various modules. Of course, in implementing this application, the functions of each module can be implemented in one or more software and / or hardware.

[0183] The apparatus described above is used to implement the corresponding tailings sampling, testing and evaluation method in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0184] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, this application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the tailings sampling, testing and evaluation method described in any of the above embodiments.

[0185] Figure 4This embodiment illustrates a more specific hardware structure of an electronic device. The device may include a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, memory 1020, input / output interface 1030, and communication interface 1040 are interconnected internally via the bus 1050.

[0186] The processor 1010 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0187] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

[0188] The input / output interface 1030 is used to connect input / output modules to realize information input and output. Input / output modules can be configured as components within the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touchscreens, microphones, various sensors, etc., while output devices may include displays, speakers, vibrators, indicator lights, etc.

[0189] The communication interface 1040 is used to connect a communication module (not shown in the figure) to enable communication between this device and other devices. The communication module can communicate via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0190] Bus 1050 includes a pathway for transmitting information between various components of the device, such as processor 1010, memory 1020, input / output interface 1030, and communication interface 1040.

[0191] It should be noted that although the above-described device only shows the processor 1010, memory 1020, input / output interface 1030, communication interface 1040, and bus 1050, in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the embodiments of this specification, and not necessarily all the components shown in the figures.

[0192] The electronic devices described above are used to implement the corresponding tailings sampling, testing and evaluation methods in any of the foregoing embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0193] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, this application also provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the tailings sampling test and evaluation method as described in any of the above embodiments.

[0194] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0195] The computer instructions stored in the storage medium of the above embodiments are used to cause the computer to execute the tailings sampling, testing and evaluation method as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0196] It is understood that before using the technical solutions of the various embodiments in this application, users will be informed of the type, scope of use, and usage scenarios of the personal information involved in an appropriate manner, and user authorization will be obtained.

[0197] For example, upon receiving a user's active request, a prompt message is sent to the user to explicitly inform them that the requested operation will require the acquisition and use of the user's personal information. This allows the user to independently choose, based on the prompt message, whether to provide personal information to the software or hardware such as electronic devices, applications, servers, or storage media performing the operations described in this application.

[0198] As an optional but not limited implementation, in response to a user's active request, sending a prompt message to the user can be done via a pop-up window, where the prompt message can be presented in text format. Furthermore, the pop-up window can also include a selection control allowing the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0199] It is understood that the above notification and user authorization process is merely illustrative and does not limit the implementation of this application. Other methods that comply with relevant laws and regulations may also be applied to the implementation of this application.

[0200] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of this application is limited to these examples; under the concept of this application, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of the embodiments of this application as described above, which are not provided in detail for the sake of brevity.

[0201] Additionally, to simplify the description and discussion, and to avoid obscuring the embodiments of this application, the well-known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided drawings. Furthermore, the apparatus may be shown in block diagram form to avoid obscuring the embodiments of this application, and this also takes into account the fact that the details of the implementation of these block diagram apparatuses are highly dependent on the platform on which the embodiments of this application will be implemented (i.e., these details should be fully understood by those skilled in the art). While specific details (e.g., circuits) have been set forth to describe exemplary embodiments of this application, it will be apparent to those skilled in the art that the embodiments of this application can be implemented without these specific details or with variations thereof. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0202] Although this application has been described in conjunction with specific embodiments thereof, many substitutions, modifications, and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed.

[0203] The embodiments of this application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the claims of this application. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the embodiments of this application should be included within the protection scope of this application.

Claims

1. A method for sampling, testing, and evaluating tailings, characterized in that, include: Tailings are classified and sampled according to the tailings discharge type to obtain sample tailings, and the comprehensive grade of tailings is determined according to the tailings grade and tailings quality of the sample tailings. According to the preset sample mass, multiple initial sample tailings are taken from the sample tailings. Each initial sample tailings is dried and reduced to obtain multiple sample tailings. The influence coefficient of phase factors is determined based on the occurrence phases in the tailings sample corresponding to the preset target element; wherein, the determination of the influence coefficient of phase factors based on the occurrence phases in the tailings sample corresponding to the preset target element includes: determining the element distribution rate and phase recovery coefficient of each occurrence phase, determining the product of the element distribution rate and the phase recovery coefficient as the single influence coefficient; and determining the sum of the single influence coefficients of different occurrence phases as the influence coefficient of phase factors. The degree of dissociation factor influence coefficient is determined based on the relative proportion of intergrowths in the host phase; wherein, determining the degree of dissociation factor influence coefficient based on the relative proportion of intergrowths in the host phase includes: determining the mineral proportion of the target mineral in the intergrowth; classifying the intergrowths according to the mineral proportion to obtain intergrowth types; determining the type recovery coefficient of each intergrowth type; determining the type proportion of each intergrowth type in the intergrowth according to the relative proportion; determining the product of the type recovery coefficient and the type proportion as the single-type influence coefficient; and determining the sum of the single-type influence coefficients of different intergrowth types as the degree of dissociation factor influence coefficient. The particle size factor influence coefficient is determined based on the particle size distribution of the tailings sample; wherein, determining the particle size factor influence coefficient based on the particle size distribution of the tailings sample includes: classifying the tailings sample according to the particle size distribution to obtain particle size grades of different particle size ranges; determining the grade proportion and grade recovery coefficient of each particle size grade, and determining the grade influence coefficient by multiplying the grade recovery coefficient and the grade proportion; and determining the particle size factor influence coefficient by summing the grade influence coefficients of different particle size grades. The concentrate recovery rate is determined based on the phase factor influence coefficient, the degree of dissociation factor influence coefficient, and the particle size factor influence coefficient, and the tailings assessment result is determined based on the concentrate recovery rate and the comprehensive grade of the tailings.

2. The tailings sampling, testing, and evaluation method according to claim 1, characterized in that, The tailings are classified and sampled according to the tailings discharge type to obtain sample tailings, including: In response to the tailings discharge type being wet tailings, tailings slurry from multiple shifts is collected in batches according to preset sampling batches and minimum sampling weights to obtain the sample tailings; wherein, the sampling batches cover at least one actual production cycle; In response to the tailings discharge type being dry tailings, multiple sampling points are obtained by equally spaced sampling points in the tailings pond according to the preset sampling point spacing and minimum sampling number; tailings are sampled at the sampling point locations according to the minimum sampling weight, preset longitudinal sampling depth and longitudinal sampling spacing to obtain the sample tailings.

3. The tailings sampling, testing, and evaluation method according to claim 1, characterized in that, The determination of the overall tailings grade based on the tailings grade and tailings quality of the sample tailings includes: The tailings grade is calculated by weighting the tailings mass of a single sample as a weighting factor.

4. The tailings sampling, testing, and evaluation method according to claim 1, characterized in that, The determination of tailings assessment results based on the concentrate recovery rate and the overall tailings grade includes: The concentrate yield is determined based on the original concentrate yield corresponding to the tailings and a preset ratio threshold. The product of the concentrate recovery rate and the overall grade of the tailings is used as the ratio of the concentrate yield to the concentrate grade. The equivalent average unit price of the target element is determined based on the pricing coefficient of the target element and the historical price data of the target element within a preset historical time period. The expected output value of the concentrate is determined based on the equivalent average unit price and the concentrate grade, and the tailings assessment result is determined based on the expected output value of the concentrate and the preset processing cost per ton of ore.

5. The tailings sampling, testing, and evaluation method according to claim 4, characterized in that, The determination of the tailings assessment result based on the expected output value of the concentrate and the preset processing cost per ton of ore includes: The product of the expected output value of the concentrate and the concentrate yield is determined as the expected output value of the tailings; In response to the tailings' expected output value being greater than the processing cost per ton of tailings, the profit is determined as the tailings assessment result; In response to the tailings' expected output value being less than or equal to the processing cost per ton of tailings, a loss is determined as the tailings assessment result.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1 to 5.

7. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1 to 5.

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