A comprehensive evaluation method for basalt ore for fiber

Abstract

The present invention relates to the technical field of mineral resource assessment, and specifically to a comprehensive evaluation method for basalt ore for fiber, comprising the following steps: based on an image acquisition device, impact testing is performed on a basalt ore sample, test images are collected and cracks on the ore surface are identified, and in combination with a pressure sensor, stress change data during the impact process is recorded to generate physical property test data. In the present invention, an accurate assessment of the structural integrity and mechanical strength of the ore is achieved through impact testing and image acquisition, and the machinability of the ore is assessed by combining the determination of the expansion coefficient and the analysis of thermal stability, and the fiber output rate in actual processing is predicted, providing a scientific basis for mining decisions. By analyzing the processing parameters required by the target ore sample, energy demand is predicted, and the economic mining potential of the ore is evaluated. In combination with the physicochemical characteristics and processing adaptability of the ore, the market grade is calculated, providing a scientific basis for the market positioning and value assessment of the ore.

Description

Technical Field

[0001] The invention relates to the technical field of mineral resource evaluation, in particular to a comprehensive evaluation method for basalt ore for fiber. Background Art

[0002] The field of mineral resource assessment technology focuses on determining the quality, quantity and exploitability of underground mineral resources by using a variety of technical means, covering exploration, sampling, experimental analysis, resource calculation, quality assessment and multiple links, including the geographical distribution of minerals, geological structure, deposit characteristics, and feasibility analysis of mining technology. It involves geophysical and geochemical methods of geological exploration, the application of geographic information system technology in data management and spatial analysis, and the role of computer simulation and statistical analysis in resource prediction. It aims to ensure the sustainable development of resources, maximize the economic benefits of mineral resource development, and minimize the negative impact on the environment through a variety of scientific and technological means.

[0003] Among them, the comprehensive evaluation method of basalt ore for fiber is focused on evaluating the suitability of basalt ore for the production of fiber materials. Through a comprehensive evaluation of the chemical composition, physical properties, and fiberization potential of basalt ore, it aims to determine whether the ore is suitable for the production of high-performance fibers and ensure that the fiber materials prepared from the ore meet the performance requirements, including high strength and good heat resistance. By predicting the performance of the ore in actual applications, it guides the ore mining and fiber production process, improves resource utilization efficiency and product quality, optimizes product quality, reduces production costs, promotes efficient and environmentally friendly production processes, and helps manufacturers select the most suitable raw materials.

[0004] The traditional fiber basalt ore comprehensive evaluation technology relies on traditional geological exploration and sample analysis, and has deficiencies in real-time data acquisition and comprehensive processing capabilities. It is unable to monitor the formation and expansion of cracks in real time in terms of rapid evaluation of the structural integrity and mechanical strength of the ore, resulting in inaccurate evaluation results and inability to effectively predict the performance of the ore in actual processing. In terms of the evaluation of the thermal stability and machinability of the ore, it ignores the need for simulation of the actual processing environment, and lacks real-time simulation of thermal response and pressure response during processing, which limits the conversion efficiency from theory to practical application, increases uncertainty in the mining and processing process, and reduces the scientific nature of mining decisions. In terms of economic benefits, the relationship between processing parameters and energy consumption cannot be fully considered, and the energy demand for ore processing cannot be predicted, resulting in inaccurate mining cost estimates, affecting the economic feasibility of the project. The lack of a comprehensive evaluation of market dynamics and adaptability to actual applications makes the market positioning and value evaluation of ore resources incomplete, affecting the optimization of resource allocation and market strategies. Summary of the invention

[0005] In order to solve the technical problem of insufficient ore resource value assessment capability in the prior art, the embodiment of the present invention provides a comprehensive evaluation method for basalt ore for fiber. The technical solution is as follows:

[0006] On the one hand, a comprehensive evaluation method for basalt ore for fiber is provided, the method comprising:

[0007] S1: Based on the image acquisition equipment, impact test is performed on basalt ore samples, test images are collected and cracks on the ore surface are identified. Combined with the pressure sensor, stress change data during the impact process is recorded to generate physical property test data;

[0008] S2: Based on the physical property test data, measure the expansion coefficient of the sample at various temperatures and pressures, analyze the thermal stability and decomposition characteristics of the sample during heating, evaluate the machinability of the ore under various conditions, and obtain a processing adaptability score;

[0009] S3: According to the processing adaptability score, by simulating the thermal response and pressure response of the basalt ore sample during processing, predicting the fiber output rate under various processing conditions, and generating fiber conversion rate prediction data;

[0010] S4: using the fiber conversion rate prediction data, testing the physical and chemical properties of the fiber after processing the basalt sample, evaluating the durability and mechanical strength of the target fiber, and generating fiber performance evaluation data;

[0011] S5: Based on the fiber performance evaluation data, by analyzing the processing parameters required by the target ore sample, considering the relationship between energy consumption and processing parameters, predicting the processing energy demand of the target ore sample, and combining the material cost data to generate a processing cost analysis result;

[0012] S6: Based on the processing cost analysis result, combined with the processing adaptability and fiber performance of the target basalt ore, the quality and applicability of the ore are analyzed, the market grade of the ore is calculated, and a basalt ore evaluation list is generated.

[0013] As a further scheme of the present invention, the physical property test data includes crack length measurement data, crack branching pattern, and crack propagation speed; the processing adaptability score includes thermal expansion coefficient, thermal decomposition rate, and stability index under mechanical load; the fiber conversion rate prediction data includes predicted fiber output rate, temperature dependence coefficient of conversion rate, and pressure dependence coefficient of conversion rate; the fiber performance evaluation data includes predicted fiber tensile strength, compression strength prediction data, and durability score; the processing cost analysis results include predicted processing energy consumption, unit fiber processing cost calculation results, and ore cost data; the basalt ore evaluation list includes ore quality grade, market value score, and expected service life.

[0014] As a further solution of the present invention, based on the image acquisition device, the basalt ore sample is impact tested, the test image is collected and the cracks on the ore surface are identified, and the stress change data during the impact process is recorded in combination with the pressure sensor. The steps of generating physical property test data are specifically as follows:

[0015] S101: Based on the image acquisition device, an impact test is performed on the basalt ore sample to collect image data of the crack formation process, record the starting point and expansion path of the crack, and obtain crack morphology image data;

[0016] S102: Based on the crack morphology image data, identifying the crack length and distribution characteristics on the surface of the ore sample, quantifying the distribution and density of the cracks, and obtaining crack characteristic analysis results;

[0017] S103: Based on the crack characteristic analysis results, in combination with a pressure sensor, the stress change process of the ore under the impact force is recorded, the relationship between the stress waveform and the crack development is analyzed, the physical properties of the sample are evaluated, and physical property test data is obtained.

[0018] As a further solution of the present invention, based on the physical property test data, the expansion coefficient of the sample at various temperatures and pressures is measured, the thermal stability and decomposition characteristics of the sample during heating are analyzed, and the processability of the ore under various conditions is evaluated. The steps of obtaining the processing adaptability score are specifically as follows:

[0019] S201: Based on the physical property test data, measuring and recording the expansion and contraction reactions of the ore sample under various temperature and pressure conditions to obtain thermal expansion response data;

[0020] S202: Analyze the thermal stability and chemical decomposition characteristics of the sample during the heating process based on the thermal expansion response data, evaluate the structural changes and mass losses at various temperatures, and obtain thermal stability analysis results;

[0021] S203: Based on the thermal stability analysis results, the machinability of the ore under various mechanical load and temperature conditions is evaluated to obtain a machinability score.

[0022] As a further solution of the present invention, according to the processing adaptability score, by simulating the thermal response and pressure response of the basalt ore sample during processing, the fiber output rate under various processing conditions is predicted, and the steps of generating fiber conversion rate prediction data are specifically as follows:

[0023] S301: Based on the processing adaptability score, multiple simulation environments are set to simulate the heating and compression process of the basalt sample to obtain simulation environment configuration data;

[0024] S302: Based on the simulation environment configuration data, record the physical changes of the sample under various simulation processing conditions, including morphological changes and mass loss, evaluate the changes under various processing parameters, and generate physical change data;

[0025] S303: Calculate and predict the fiber output rate of the target basalt ore sample under various temperature and pressure conditions according to the physical change data, and generate fiber conversion rate prediction data.

[0026] As a further solution of the present invention, the fiber conversion rate prediction data is used to test the physical and chemical properties of the fiber after processing the basalt sample, and the durability and mechanical strength of the target fiber are evaluated. The steps of generating the fiber performance evaluation data are specifically as follows:

[0027] S401: Based on the fiber conversion rate prediction data, by performing fiber processing on the basalt sample, measuring the physical properties of the fiber, including tensile and compressive strength, to obtain fiber physical test data;

[0028] S402: Based on the fiber physical test data, determine the chemical stability and reactivity of the fiber and generate chemical performance analysis data;

[0029] S403: Based on the chemical performance analysis data, the durability and mechanical strength of the fiber after sample processing are evaluated to generate fiber performance evaluation data.

[0030] As a further solution of the present invention, based on the fiber performance evaluation data, by analyzing the processing parameters required by the target ore sample, considering the relationship between energy consumption and processing parameters, predicting the processing energy demand of the target ore sample, and combining the material cost data, the steps of generating the processing cost analysis result are specifically as follows:

[0031] S501: Based on the fiber performance evaluation data, the processing parameters required for processing the target basalt ore sample are analyzed, including temperature, pressure, and time, to obtain processing parameter requirement data;

[0032] S502: Based on the processing parameter requirement data and according to the relationship between various processing parameters and energy consumption, the energy cost required for the target ore sample is evaluated to obtain an energy consumption analysis result;

[0033] S503: Based on the energy consumption analysis result, considering energy efficiency and material utilization rate, combining material cost and operation cost data, calculating the fiber processing cost, and generating a processing cost analysis result.

[0034] As a further solution of the present invention, the specific formula for evaluating the energy cost required for the target ore sample is:

[0035] ;

[0036] in, represents the predicted total energy cost, Represents the energy unit price, which indicates the cost per unit of energy. Represents the processing pressure, represents the processing temperature, Represents the processing time, is the constant term of the regression model, which represents the estimated basic energy consumption without any external processing pressure, temperature or time influence. is the regression coefficient of processing pressure, reflecting the sensitivity of pressure to the increase of energy consumption, is the regression coefficient of processing temperature, reflecting the sensitivity of temperature to the increase of energy consumption, is the regression coefficient of processing time, reflecting the sensitivity of time length to the increase of energy consumption.

[0037] As a further solution of the present invention, based on the processing cost analysis results, combined with the processing adaptability and fiber performance of the target basalt ore, the quality and applicability of the ore are analyzed, the market grade of the ore is calculated, and the steps of generating a basalt ore evaluation list are specifically as follows:

[0038] S601: According to the processing cost analysis result, combined with the physical properties of the target basalt ore, including structural integrity and purity, the quality of the ore is evaluated to obtain quality analysis data;

[0039] S602: Based on the quality analysis data and the performance of the processed fibers, the applicability of basalt fibers in various industrial applications, including building materials, insulation materials, and special applications, is evaluated, the applicable scope and performance are analyzed, and the applicability evaluation results are obtained;

[0040] S603: Utilizing the applicability evaluation results and combining market demand and supply conditions, the value grade of the ore in the market is calculated to obtain a basalt ore evaluation list.

[0041] As a further solution of the present invention, the specific formula for calculating the value grade of the ore in the market is:

[0042] ;

[0043] in, Represents the market value grade of the basalt ore corresponding to the sample, Represents the market demand index, indicating the current market demand intensity for the basalt corresponding to the sample. Represents the supply stability score, reflecting the stability and sustainability of ore supply. A suitability and quality score that evaluates the physical and chemical properties of an ore for its suitability for a specific application. is the weight coefficient of the market demand index, which adjusts the impact of market demand on the total score. is the weight coefficient of the supply stability score, which adjusts the contribution of supply stability to the total score. It is the weight coefficient of the applicability and quality score, which determines the proportion of applicability and quality in the total score.

[0044] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0045] Through impact testing and image acquisition, the structural integrity and mechanical strength of the ore are accurately evaluated. Combined with the determination of the expansion coefficient and the analysis of thermal stability, the machinability of the ore is evaluated, and the fiber output rate in actual processing is predicted, providing a scientific basis for mining decisions. By analyzing the processing parameters required for the target ore samples, energy demand is predicted, and the economic mining potential of the ore is evaluated. Combined with the physicochemical properties and processing adaptability of the ore, the market grade is calculated, providing a scientific basis for the market positioning and value assessment of the ore. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0047] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0048] Figure 2 This is a detailed flow chart of S1 of the present invention;

[0049] Figure 3 This is a detailed flow chart of S2 of the present invention;

[0050] Figure 4 This is a detailed flow chart of S3 of the present invention;

[0051] Figure 5 This is a detailed flow chart of S4 of the present invention;

[0052] Figure 6 This is a detailed flow chart of S5 of the present invention;

[0053] Figure 7 This is a detailed flow chart of S6 of the present invention. DETAILED DESCRIPTION

[0054] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0055] In the embodiment of the present invention, the meaning of "and / or" can be both, or one of the two can be selected.

[0056] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0057] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0058] The embodiment of the present invention provides a comprehensive evaluation method for basalt ore for fiber, such as Figure 1 The flowchart of the comprehensive evaluation method of basalt ore for fiber is shown in the figure. The processing flow of the method may include the following steps:

[0059] S1: Based on the image acquisition equipment, impact test is performed on basalt ore samples, test images are collected and cracks on the ore surface are identified. Combined with the pressure sensor, stress change data during the impact process is recorded to generate physical property test data;

[0060] S2: Based on the physical properties test data, measure the expansion coefficient of the sample at various temperatures and pressures, analyze the thermal stability and decomposition characteristics of the sample during heating, evaluate the machinability of the ore under various conditions, and obtain the processing adaptability score;

[0061] S3: Based on the processing adaptability score, the fiber output rate under various processing conditions is predicted by simulating the thermal response and pressure response of the basalt ore sample during processing, and the fiber conversion rate prediction data is generated;

[0062] S4: Using the fiber conversion rate prediction data, test the physical and chemical properties of the fibers processed from the basalt samples, evaluate the durability and mechanical strength of the target fibers, and generate fiber performance evaluation data;

[0063] S5: Based on the fiber performance evaluation data, by analyzing the processing parameters required by the target ore sample, considering the relationship between energy consumption and processing parameters, predicting the processing energy demand of the target ore sample, and combining with the material cost data, generating the processing cost analysis results;

[0064] S6: Based on the processing cost analysis results, combined with the processing adaptability and fiber performance of the target basalt ore, analyze the quality and applicability of the ore, calculate the market grade of the ore, and generate a basalt ore evaluation list.

[0065] The physical property test data include crack length measurement data, crack branching pattern, and crack propagation rate. The processing adaptability score includes thermal expansion coefficient, thermal decomposition rate, and stability index under mechanical load. The fiber conversion rate prediction data include predicted fiber output rate, temperature dependence coefficient of conversion rate, and pressure dependence coefficient of conversion rate. The fiber performance evaluation data include predicted fiber tensile strength, compression strength prediction data, and durability score. The processing cost analysis results include predicted processing energy consumption, unit fiber processing cost calculation results, and ore cost data. The basalt ore evaluation list includes ore quality grade, market value score, and expected service life.

[0066] See also Figure 2 Based on the image acquisition device, the impact test is carried out on the basalt ore sample, the test image is collected and the cracks on the ore surface are identified. Combined with the pressure sensor, the stress change data during the impact process is recorded. The specific steps for generating physical property test data are as follows:

[0067] S101: Based on the image acquisition device, an impact test is performed on the basalt ore sample to collect image data of the crack formation process, record the starting point and expansion path of the crack, and obtain crack morphology image data;

[0068] In sub-step S101, each stage of the crack formation process is captured by high-speed camera technology, and image analysis software such as OpenCV is used for preliminary image processing, including image grayscale conversion, edge detection and crack tracking. The target operation ensures the accurate extraction of crack information from a large amount of image data. The detection of each crack is accompanied by contrast enhancement and noise filtering steps to improve the accuracy and reliability of crack identification, and obtain crack morphology image data. The generated crack morphology image data is used for subsequent crack characteristic analysis.

[0069] S102: Based on the crack morphology image data, identify the crack length and distribution characteristics on the surface of the ore sample, quantify the distribution and density of the cracks, and obtain crack characteristic analysis results;

[0070] In sub-step S102, image processing and image recognition techniques, such as convolutional neural networks in deep learning, are used to identify and quantify the length and distribution characteristics of cracks on the surface of the basalt sample. The crack area is separated from the non-crack area using image segmentation technology. The length and width of each crack are automatically calculated using a crack recognition algorithm. For calculating the total length of multiple straight line segments in a crack, the Euclidean distance formula is used to calculate the length of each line segment, and the total length is obtained by summing them up. The formula , calculate the length of each crack , through the formula: , calculate the total length;

[0071] In the formula, and Indicates The horizontal and vertical coordinates of the starting point of the crack segment, and Indicates The horizontal and vertical coordinates of the end point of the crack segment, Indicates The length of the crack segment is obtained by calculating the Euclidean distance between the starting point and the end point. It represents the sum of the lengths of all crack segments and is the length of each segment The cumulative result of

[0072] Parameter setting and specific calculation examples:

[0073] Assume there are three cracks, the first one is , paragraph 2 is , paragraph 3 is , substitute the parameters into the formula for calculation:

[0074] Paragraph 1:

[0075] ;

[0076] Paragraph 2:

[0077] ;

[0078] Paragraph 3:

[0079] ;

[0080] Overall length :

[0081] ;

[0082] result The total crack length was shown to be 24.85 units and the calculation process was used to analyze the physical properties of the ore samples.

[0083] S103: Based on the crack characteristic analysis results, combined with the pressure sensor, record the stress change process of the ore under the impact force, analyze the relationship between the stress waveform and the crack development, evaluate the physical properties of the sample, and obtain physical performance test data;

[0084] In sub-step S103, stress analysis software is used for data synchronization and analysis, including time-frequency analysis of stress waveforms and dynamic monitoring of crack development. The signal processing toolbox in MATLAB is used to denoise, filter and analyze the waveform of the data captured by the pressure sensor to ensure the accuracy and efficiency of the stress test. Special attention is paid to the initial stage of crack formation and the peak value of the stress waveform. The comprehensive analysis of the target data helps to reveal the relationship between crack development and stress changes, evaluate the physical properties of the sample under impact load, and predict the behavior pattern in similar environments. The physical property test data obtained provides an important reference for the quality control and application development of the ore.

[0085] See also Figure 3 Based on the physical property test data, the expansion coefficient of the sample is measured at various temperatures and pressures, the thermal stability and decomposition characteristics of the sample during heating are analyzed, and the machinability of the ore under various conditions is evaluated. The specific steps for obtaining the processing adaptability score are as follows:

[0086] S201: Based on the physical property test data, measure and record the expansion and contraction reactions of the ore samples under various temperature and pressure conditions to obtain thermal expansion response data;

[0087] In sub-step S201, the expansion and contraction reactions of basalt samples under different environmental conditions are monitored, and the physical reactions of the samples under changing temperatures and pressures are recorded. Thermodynamic analysis software, such as ANSYS, is used for data analysis. The target data is calculated by measuring the changes in sample dimensions to calculate the thermal expansion coefficient. The measured dimensional changes are correlated with temperature changes using standard thermal expansion formulas to obtain thermal expansion response data. During the process, it is ensured that each measurement is carried out under controlled environmental conditions to reduce the influence of external factors. Repeated tests are used to verify the consistency and reliability of the data. The obtained thermal expansion response data provides a basis for subsequent thermal stability analysis.

[0088] S202: Based on the thermal expansion response data, analyze the thermal stability and chemical decomposition characteristics of the sample during the heating process, evaluate the structural changes and mass loss at various temperatures, and obtain the thermal stability analysis results;

[0089] In sub-step S202, thermogravimetric analysis and differential scanning calorimetry are used to analyze the thermal stability and chemical decomposition characteristics of the sample during the heating process. The target analysis evaluates the structural changes and mass loss of the sample at different temperatures by measuring the mass change and heat flow change of the sample during the heating process. The heating rate and ambient atmosphere are strictly controlled in each experiment to ensure the consistency of the experimental conditions. The application of the target technology makes it possible to accurately observe the physical and chemical behavior of the sample during the heating process, including any phase change or chemical reaction that occurs, and obtain accurate thermal stability analysis results. The target results are crucial to understanding the application potential of ores in high-temperature environments.

[0090] S203: Based on the results of thermal stability analysis, the machinability of the ore under various mechanical loads and temperature conditions is evaluated to obtain a machinability score;

[0091] In sub-step S203, the mechanical properties of the samples are measured using tests such as compression strength test and bending strength test. The influence of temperature on the ore processing performance is considered. Computer simulation software such as COMSOL Multiphysics is used for thermodynamic simulation and structural analysis. The combination of target test and simulation analysis provides a scientific basis for the ore processing adaptability score, ensuring that appropriate processing conditions can be selected in practical applications. The obtained processing adaptability score index helps to formulate more effective ore processing strategies.

[0092] See also Figure 4 According to the processing adaptability score, the fiber output rate under various processing conditions is predicted by simulating the thermal response and pressure response of the basalt ore sample during processing. The steps for generating the fiber conversion rate prediction data are as follows:

[0093] S301: Based on the processing adaptability score, multiple simulation environments are set to simulate the heating and compression process of the basalt sample to obtain simulation environment configuration data;

[0094] In sub-step S301, different simulation environments are set according to the processing adaptability scores to simulate the heating and compression conditions encountered by basalt samples in industrial applications. ANSYS Workbench is used to configure the environment. The settings include a temperature range from room temperature to 1500°C and a pressure from atmospheric pressure to 200 MPa. The target setting reflects the behavior of the sample under extreme conditions. Each simulation experiment records the changes in environmental parameters and the response of the sample. During the process, it is ensured that each parameter can be accurately controlled and monitored to capture tiny physical or chemical changes. Through simulation, the impact of different environmental factors on basalt is understood, and the simulation environment configuration data is obtained. The target data provides an experimental basis for subsequent experimental analysis.

[0095] S302: Based on the simulation environment configuration data, record the physical changes of the sample under various simulation processing conditions, including morphological changes and mass loss, evaluate the changes under various processing parameters, and generate physical change data;

[0096] In sub-step S302, the physical changes of basalt samples under different simulated processing conditions are recorded, and the mass and volume changes of the samples under each condition are recorded. The target change data is analyzed by calculating the density and volume shrinkage rate of the samples, and the changes of the samples under various processing parameters are evaluated. The generated physical change data shows the stability of basalt during the processing and reveals possible optimization points. The target data is the key to designing more effective processing parameters, helping to improve production efficiency and product quality.

[0097] S303: Calculate and predict the fiber output rate of the target basalt ore sample under various temperature and pressure conditions according to the physical change data, and generate fiber conversion rate prediction data;

[0098] In sub-step S303, statistical analysis and prediction models, such as linear regression analysis, are used to calculate and predict the fiber yield of basalt ore samples under different temperature and pressure conditions. By analyzing the relationship between the mass loss of the samples and the processing conditions, data analysis software, such as SPSS, is used for in-depth data processing and model building. The prediction model is generated based on historical data and experimental data to evaluate the fiber conversion rate under different conditions. Through target analysis, the effects of various conditions on the fiber yield in actual production are effectively predicted. The generated fiber conversion rate prediction data provides a scientific basis for parameter setting in the production process, optimizes the production process and improves resource utilization efficiency.

[0099] See also Figure 5 , using the fiber conversion rate prediction data, test the physical and chemical properties of the fibers processed from the basalt samples, evaluate the durability and mechanical strength of the target fibers, and the steps for generating fiber performance evaluation data are as follows:

[0100] S401: Based on the fiber conversion rate prediction data, by processing the basalt sample into fibers, measuring the physical properties of the fibers, including tensile and compressive strengths, to obtain fiber physical test data;

[0101] In sub-step S401, physical property tests are performed using tensile testing machines and compression testing equipment, including the use of a universal material testing machine to determine the tensile strength and compressive strength of fiber samples. The test process is strictly carried out in accordance with international standards ASTM D638 and ASTM D695 to ensure the accuracy and comparability of the data. The data acquisition system records the force and fiber deformation data of each test. The fiber physical test data obtained through the target test reflects the toughness and compressive resistance of the sample fiber. The target data provides important information for subsequent product development and quality control.

[0102] S402: Based on the fiber physical test data, the chemical stability and reactivity of the fiber are determined to generate chemical performance analysis data;

[0103] In sub-step S402, infrared spectroscopy and thermogravimetric analysis are used to determine the chemical stability and reactivity of the fiber. Infrared spectroscopy analyzes the chemical structure by measuring the absorption characteristics of the fiber sample to light of a specific wavelength. Thermogravimetric analysis evaluates the change in mass of the sample during heating and infers the characteristics of chemical decomposition. The analysis simulates the effects of different chemical environments on the sample through environmental control. The generated chemical performance analysis data provides a scientific basis for evaluating the chemical tolerance and application potential of the sample fiber, and guides subsequent product improvement and safe application.

[0104] S403: Based on the chemical performance analysis data, evaluate the durability and mechanical strength of the fiber after sample processing, and generate fiber performance evaluation data;

[0105] In sub-step S403, cyclic load tests and long-term durability tests are used to simulate the performance of the fiber in actual applications. The target test includes placing the fiber sample under various mechanical loads and periodically applying pressure to test its fatigue strength. The environmental aging test chamber is used to simulate the fiber performance under various climatic conditions, such as high humidity and high temperature environments. The fiber performance evaluation data generated by the test results demonstrates the durability and strength of the fiber in coping with complex application environments. The target data is crucial to understanding the actual application limitations and performance potential of the fiber, and provides a basis for manufacturing more reliable and efficient fiber products.

[0106] See also Figure 6 Based on the fiber performance evaluation data, by analyzing the processing parameters required by the target ore sample, considering the relationship between energy consumption and processing parameters, predicting the processing energy demand of the target ore sample, and combining the material cost data, the specific steps of generating the processing cost analysis results are as follows:

[0107] S501: Based on the fiber performance evaluation data, the processing parameters required for processing the target basalt ore sample are analyzed, including temperature, pressure, and time, to obtain processing parameter requirement data;

[0108] In sub-step S501, the optimal processing parameters required for processing basalt ore samples, including temperature, pressure and processing time, are determined. The computational fluid dynamics model is used to simulate the effects of different temperatures and pressures, and the optimal range of temperature and pressure during the processing is determined. The impact of different processing times on sample quality and output rate is evaluated by simulating different processing times. The target simulation is performed using software such as COMSOL Multiphysics to ensure that each parameter is precisely adjusted to achieve the best effect. The obtained processing parameter requirement data will directly affect production efficiency and product quality, providing accurate parameter setting guidance for the actual production process.

[0109] S502: Based on the processing parameter requirement data and according to the relationship between various processing parameters and energy consumption, the energy cost required for the target ore sample is evaluated to obtain an energy consumption analysis result;

[0110] The specific formula for evaluating the energy cost required for a target ore sample is:

[0111] ;

[0112] in, represents the predicted total energy cost, Represents the energy unit price, which indicates the cost per unit of energy. Represents the processing pressure, represents the processing temperature, Represents the processing time, is the constant term of the regression model, which represents the estimated basic energy consumption without any external processing pressure, temperature or time influence. is the regression coefficient of processing pressure, reflecting the sensitivity of pressure to the increase of energy consumption, is the regression coefficient of processing temperature, reflecting the sensitivity of temperature to the increase of energy consumption, is the regression coefficient of processing time, reflecting the sensitivity of time length to the increase of energy consumption.

[0113] formula:

[0114] ;

[0115] Detailed explanation of the formula and the process of formula calculation and derivation:

[0116] The formula is used to calculate the total energy cost in basalt ore processing and the results are used to estimate the economic burden of energy under set processing conditions;

[0117] Parameter meaning and setting value:

[0118] For the base energy consumption, it is assumed to be 50 kWh, reflecting the conservative energy demand when no processing operations are performed;

[0119] The effect of each additional bar of pressure on energy consumption is assumed to be 0.05 kWh / bar, indicating the effect of increased pressure on energy consumption;

[0120] The impact of one degree Celsius increase in temperature on energy consumption is assumed to be 0.1 kWh / °C, showing the direct impact of temperature increase on energy demand;

[0121] The effect of each additional hour of processing time on energy consumption is assumed to be 5 kWh / h, which reflects the energy cost of the extended processing time;

[0122] is the unit energy cost, assumed to be 0.15 yuan / kWh;

[0123] is the processing pressure, assumed to be 20 bar;

[0124] is the processing temperature, assumed to be 1450°C;

[0125] is the processing time, assumed to be 3 hours;

[0126] Substitute the parameters into the formula for calculation:

[0127] ;

[0128] ;

[0129] ;

[0130] ;

[0131] The result 31.65 indicates that under the set processing conditions, the total energy cost is expected to be 31.65 yuan. The result reveals the energy cost under the target operating conditions, which is crucial for the processing plant when conducting a cost-benefit analysis. The calculation results are used to help evaluate the processing cost of the target ore.

[0132] S503: Based on the energy consumption analysis result, considering energy efficiency and material utilization rate, combining material cost and operation cost data, calculating the fiber processing cost, and generating a processing cost analysis result;

[0133] In sub-step S503, comprehensive cost calculation is performed to evaluate the processing cost of basalt fiber. Cost calculation models such as cost-benefit analysis are used to consider material cost, energy cost, labor cost, and equipment depreciation to obtain the cost of each processing step and evaluate the economic feasibility of the entire production process. The process ensures the accuracy and practicality of cost calculation. The generated processing cost analysis results provide decision support for management and help formulate more economical and effective production strategies.

[0134] See also Figure 7 Based on the processing cost analysis results, combined with the processing adaptability and fiber properties of the target basalt ore, the quality and applicability of the ore are analyzed, the market grade of the ore is calculated, and the steps for generating the basalt ore evaluation list are as follows:

[0135] S601: Based on the processing cost analysis results and in combination with the physical properties of the target basalt ore, including structural integrity and purity, the quality of the ore is evaluated to obtain quality analysis data;

[0136] In sub-step S601, X-ray diffraction and scanning electron microscopy are used to evaluate the physical properties of the target basalt ore, including structural integrity and purity. XRD is used to analyze the mineral composition and crystal structure of the ore, and SEM provides microscopic images of the ore surface and cross-section, revealing the integrity of the internal structure and the presence of microscopic defects. The comprehensive use of target technologies allows a comprehensive evaluation of the quality of the ore. The generated quality analysis data shows the physical state of the ore, helps predict its behavior in subsequent processing, and ensures that the selected ore is suitable for the production of high-quality fibers.

[0137] S602: Based on the quality analysis data and the performance of the processed fibers, evaluate the applicability of basalt fibers in various industrial applications, including building materials, insulation materials, and special applications, analyze the scope of application and performance, and obtain the applicability evaluation results;

[0138] In sub-step S602, mechanical property test data is used to evaluate the applicability of processed basalt fiber in different industrial applications, including its potential as a building material, insulation material, and special application. Tensile strength test, compression test, and thermal stability test are used to evaluate the performance of the fiber. By comparing the performance with industry standards and existing market products, the competitive advantages and limitations of the fiber are analyzed. The applicability evaluation results obtained illustrate the performance of basalt fiber in various application fields and provide a scientific basis for market promotion and customer customization.

[0139] S603: using the applicability assessment results, combined with market demand and supply conditions, calculating the value level of the ore in the market, and obtaining a basalt ore evaluation list;

[0140] The specific formula for calculating the value grade of ore in the market is:

[0141] ;

[0142] in, Represents the market value grade of the basalt ore corresponding to the sample, Represents the market demand index, indicating the current market demand intensity for the basalt corresponding to the sample. Represents the supply stability score, reflecting the stability and sustainability of ore supply. A suitability and quality score that evaluates the physical and chemical properties of an ore for its suitability for a specific application. is the weight coefficient of the market demand index, which adjusts the impact of market demand on the total score. is the weight coefficient of the supply stability score, which adjusts the contribution of supply stability to the total score. It is the weight coefficient of the applicability and quality score, which determines the proportion of applicability and quality in the total score.

[0143] Detailed explanation of formula and calculation process

[0144] formula:

[0145] ;

[0146] Detailed explanation of the formula and the process of formula calculation and derivation:

[0147] The formula is used to calculate the market value grade of basalt ore, reflecting the competitiveness and potential value of basalt ore in the market;

[0148] Parameter meaning and setting value

[0149] is the market demand index, which indicates the current market demand intensity for this type of basalt. Assuming the demand index is , reflecting the high demand in the market.

[0150] The supply stability score reflects the stability and sustainability of ore supply. The supply stability score is set as , indicating a relatively stable supply.

[0151] For suitability and quality scores, set the suitability score to , showing the excellent applicability and quality of the ore.

[0152] is the weight coefficient of the corresponding parameter, which is used to adjust the impact of each factor on the total score. , reflecting the importance of market demand over supply stability and applicability;

[0153] Substitute the parameters into the formula for calculation:

[0154] ;

[0155] ;

[0156] ;

[0157] result It shows that basalt ore has a high value grade in the market. The score reflects the strong demand, relatively stable supply and excellent applicability of basalt in the market. The calculation process is used to help decision makers understand the market potential of ore and provide a data basis for ore development and marketing strategies.

[0158] It should be understood that the term "and / or" in this article is merely a description of the association relationship of associated objects, indicating that there may be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural.

[0159] In the present invention, "plurality" means two or more.

[0160] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0161] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0162] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0163] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0164] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0165] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0166] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0167] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A comprehensive evaluation method for basalt ore for fiber, characterized in that: The method comprises: Based on the image acquisition equipment, impact tests are performed on basalt ore samples to collect test images and identify cracks on the ore surface. Combined with pressure sensors, stress change data during the impact process is recorded to generate physical performance test data. Based on the physical property test data, the expansion coefficient of the sample at various temperatures and pressures is measured, the thermal stability and decomposition characteristics of the sample during heating are analyzed, the machinability of the ore under various conditions is evaluated, and a processing adaptability score is obtained; According to the processing adaptability score, by simulating the thermal response and pressure response of the basalt ore sample during processing, the fiber output rate under various processing conditions is predicted to generate fiber conversion rate prediction data; Using the fiber conversion rate prediction data, testing the physical and chemical properties of the fibers processed from the basalt samples, evaluating the durability and mechanical strength of the target fibers, and generating fiber performance evaluation data; Based on the fiber performance evaluation data, by analyzing the processing parameters required by the target ore sample, considering the relationship between energy consumption and processing parameters, predicting the processing energy demand of the target ore sample, and combining with the material cost data, generating a processing cost analysis result; Based on the processing cost analysis results, combined with the processing adaptability and fiber performance of the target basalt ore, the quality and applicability of the ore are analyzed, the market grade of the ore is calculated, and a basalt ore evaluation list is generated.

2. The comprehensive evaluation method of basalt ore for fiber according to claim 1, characterized in that: The physical property test data include crack length measurement data, crack branching pattern, and crack propagation speed; the processing adaptability score includes thermal expansion coefficient, thermal decomposition rate, and stability index under mechanical load; the fiber conversion rate prediction data include predicted fiber output rate, temperature dependence coefficient of conversion rate, and pressure dependence coefficient of conversion rate; the fiber performance evaluation data include predicted fiber tensile strength, compression strength prediction data, and durability score; the processing cost analysis results include predicted processing energy consumption, unit fiber processing cost calculation results, and ore cost data; the basalt ore evaluation list includes ore quality grade, market value score, and expected service life.

3. The comprehensive evaluation method of basalt ore for fiber according to claim 1, characterized in that: Based on the image acquisition equipment, the impact test is carried out on the basalt ore sample, the test image is collected and the cracks on the ore surface are identified. Combined with the pressure sensor, the stress change data during the impact process is recorded. The specific steps for generating physical property test data are as follows: Based on the image acquisition equipment, impact tests are carried out on basalt ore samples to collect image data of the crack formation process, record the starting point and expansion path of the crack, and obtain crack morphology image data; Based on the crack morphology image data, the crack length and distribution characteristics on the surface of the ore sample are identified, the distribution and density of the cracks are quantified, and the crack characteristic analysis results are obtained; Based on the crack characteristic analysis results, combined with the pressure sensor, the stress change process of the ore under the impact force is recorded, the relationship between the stress waveform and the crack development is analyzed, the physical properties of the sample are evaluated, and the physical performance test data is obtained.

4. The comprehensive evaluation method of basalt ore for fiber according to claim 1, characterized in that: Based on the physical property test data, the expansion coefficient of the sample at various temperatures and pressures is measured, the thermal stability and decomposition characteristics of the sample during heating are analyzed, and the processability of the ore under various conditions is evaluated. The specific steps for obtaining the processing adaptability score are as follows: Based on the physical property test data, measuring and recording the expansion and contraction reactions of the ore samples under various temperature and pressure conditions to obtain thermal expansion response data; Based on the thermal expansion response data, the thermal stability and chemical decomposition characteristics of the sample during the heating process are analyzed, the structural changes and mass losses at various temperatures are evaluated, and the thermal stability analysis results are obtained; Based on the thermal stability analysis results, the machinability of the ore under various mechanical load and temperature conditions is evaluated to obtain a machinability score.

5. The comprehensive evaluation method of basalt ore for fiber according to claim 1, characterized in that: According to the processing adaptability score, by simulating the thermal response and pressure response of the basalt ore sample during processing, the fiber output rate under various processing conditions is predicted, and the steps of generating the fiber conversion rate prediction data are specifically as follows: Based on the processing adaptability score, multiple simulation environments are set to simulate the heating and compression process of the basalt sample to obtain simulation environment configuration data; Based on the simulation environment configuration data, recording physical changes of the sample under various simulation processing conditions, including morphological changes and mass loss, evaluating changes under various processing parameters, and generating physical change data; Based on the physical change data, the fiber output rate of the target basalt ore sample under various temperature and pressure conditions is calculated and predicted to generate fiber conversion rate prediction data.

6. The comprehensive evaluation method of basalt ore for fiber according to claim 1, characterized in that: Using the fiber conversion rate prediction data, the physical and chemical properties of the fibers processed from the basalt samples are tested to evaluate the durability and mechanical strength of the target fibers. The steps for generating the fiber performance evaluation data are as follows: Based on the fiber conversion rate prediction data, fiber physical test data is obtained by processing the basalt sample and measuring the physical properties of the fiber, including tensile and compressive strength; Based on the fiber physical test data, the chemical stability and reactivity of the fiber are determined to generate chemical performance analysis data; Based on the chemical performance analysis data, the durability and mechanical strength of the fibers after sample processing are evaluated to generate fiber performance evaluation data.

7. The comprehensive evaluation method of basalt ore for fiber according to claim 1, characterized in that: Based on the fiber performance evaluation data, by analyzing the processing parameters required by the target ore sample, considering the relationship between energy consumption and processing parameters, predicting the processing energy demand of the target ore sample, and combining the material cost data, the steps of generating the processing cost analysis result are specifically as follows: Based on the fiber performance evaluation data, the processing parameters required for processing the target basalt ore sample are analyzed, including temperature, pressure, and time, to obtain processing parameter requirement data; Based on the processing parameter requirement data, according to the relationship between multiple processing parameters and energy consumption, the energy cost required for the target ore sample is evaluated to obtain an energy consumption analysis result; Based on the energy consumption analysis results, considering energy efficiency and material utilization, combined with material cost and operating cost data, the fiber processing cost is calculated to generate a processing cost analysis result.

8. The comprehensive evaluation method of basalt ore for fiber according to claim 7, characterized in that: The specific formula for the energy cost required to evaluate the target ore sample is: ； in, represents the predicted total energy cost, Represents the energy unit price, which indicates the cost per unit of energy. Represents the processing pressure, represents the processing temperature, Represents the processing time, is the constant term of the regression model, which represents the estimated basic energy consumption without any external processing pressure, temperature or time influence. is the regression coefficient of processing pressure, reflecting the sensitivity of pressure to the increase of energy consumption, is the regression coefficient of processing temperature, reflecting the sensitivity of temperature to the increase of energy consumption, is the regression coefficient of processing time, reflecting the sensitivity of time length to the increase of energy consumption.

9. The comprehensive evaluation method of basalt ore for fiber according to claim 1, characterized in that: Based on the processing cost analysis results, combined with the processing adaptability and fiber performance of the target basalt ore, the quality and applicability of the ore are analyzed, the market grade of the ore is calculated, and the steps of generating a basalt ore evaluation list are as follows: According to the processing cost analysis results, combined with the physical properties of the target basalt ore, including structural integrity and purity, the quality of the ore is evaluated to obtain quality analysis data; Based on the quality analysis data and the performance of the processed fibers, the applicability of basalt fibers in various industrial applications, including building materials, insulation materials, and special applications, is evaluated, the scope of application and performance are analyzed, and the applicability evaluation results are obtained; The suitability assessment results are used in combination with market demand and supply to calculate the value grade of the ore in the market and obtain a basalt ore evaluation list.

10. The comprehensive evaluation method of basalt ore for fiber according to claim 9, characterized in that: The specific formula for calculating the value grade of ore in the market is: ； in, Represents the market value grade of the basalt ore corresponding to the sample, Represents the market demand index, indicating the current market demand intensity for the basalt corresponding to the sample. Represents the supply stability score, reflecting the stability and sustainability of ore supply. A suitability and quality score that evaluates the physical and chemical properties of an ore for its suitability for a specific application. is the weight coefficient of the market demand index, which adjusts the impact of market demand on the total score. is the weight coefficient of the supply stability score, which adjusts the contribution of supply stability to the total score. It is the weight coefficient of the applicability and quality score, which determines the proportion of applicability and quality in the total score.

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