Fly ash activity index detection method and system based on microelement analysis
By setting the fly ash particle size and moisture thresholds for preprocessing, the problem of insufficient accuracy of trace element analysis caused by uneven particle size and moisture fluctuation in fly ash activity index detection is solved, and efficient and accurate activity index detection is achieved.
Patent Information
- Application Number
- CN202511005690.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-22
- Publication Date
- 2025-09-30
AI Technical Summary
The existing fly ash activity index detection method has insufficient accuracy in trace element analysis due to uneven particle size and fluctuations in moisture content, making it difficult to meet the reliability requirements of engineering applications.
By analyzing the interference effects of fly ash particle size and moisture content on trace element detection, setting preset particle size and moisture content thresholds, and performing crushing, screening and drying processes, suitable pretreated samples are generated. X-ray fluorescence spectrometry is used for trace element analysis to generate activity index test results.
It effectively eliminates the interference of particle size and moisture on trace element detection, improves the accuracy and reliability of fly ash activity index detection, and ensures the accuracy and consistency of test results.
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Figure CN120721769A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of trace element analysis, and in particular to a method and system for detecting fly ash activity index based on trace element analysis. Background Art
[0002] With the continuous advancement of industrialization, coal-fired power plants and other industrial processes generate large quantities of fly ash. Fly ash is widely used in building materials, road construction, and other fields. However, due to its complex composition and unstable quality, accurate measurement of its activity index (I) has become a key issue. The I I of fly ash is an important indicator of its potential for application in engineering materials such as concrete and is closely correlated with factors such as the trace element content, particle size distribution, and moisture content of the fly ash.
[0003] However, the accuracy of traditional fly ash activity index testing using X-ray fluorescence spectroscopy (XRF) is significantly affected by the sample's physical state. Fly ash, a porous, inhomogeneous powder material, has an uneven particle size distribution, which can lead to differences in the X-ray penetration path within the sample, resulting in enhanced scattering effects and attenuation of the characteristic X-ray signal, leading to deviations in the detection of trace element content. Furthermore, residual moisture in fly ash not only absorbs X-ray energy (especially affecting low-energy X-rays), but can also be accompanied by the release of volatile substances, interfering with the stability of the spectral signal and further reducing the accuracy of trace element analysis. These interfering factors cause the activity index analysis results based on XRF testing to fluctuate significantly, making it difficult to meet the requirements for detection reliability in engineering applications. Summary of the Invention
[0004] This application provides a fly ash activity index detection method and system based on trace element analysis, aiming to solve the technical problem of insufficient trace element analysis accuracy caused by uneven particle size and fluctuation of moisture content in existing fly ash activity index detection methods.
[0005] The first aspect disclosed in the present application provides a fly ash activity index detection method based on trace element analysis, the method comprising: sampling a fly ash batch to be inspected to obtain a fly ash sample; analyzing the interference effects of the fly ash particle size and moisture content on the detection of the trace element content using an X-ray fluorescence spectrometer detection instrument, and generating a preset particle size threshold and a preset moisture content threshold; crushing and screening the fly ash sample with the preset particle size threshold as a target to generate a first pretreated sample; drying the first pretreated sample and pressing it into a thin sheet with the preset moisture content threshold as a target to generate a second pretreated sample; performing trace element detection on the second pretreated sample using the X-ray fluorescence spectrometer detection instrument to generate trace element content information; performing fly ash activity index analysis based on the microelement content information to generate an activity index detection result.
[0006] Another aspect disclosed in the present application provides a fly ash activity index detection system based on trace element analysis, the system comprising: a sample sampling module for sampling fly ash of a batch to be inspected to obtain a fly ash sample; an interference impact analysis module for analyzing the interference impact of fly ash particle size and moisture content on trace element content detection with respect to an X-ray fluorescence spectrum detection instrument, and generating a preset particle size threshold and a preset moisture content threshold; a crushing and screening module for crushing and screening the fly ash sample with the preset particle size threshold as a target to generate a first pretreated sample; a drying and pressing module for drying the first pretreated sample and then pressing it into a thin sheet with the preset moisture content threshold as a target to generate a second pretreated sample; a trace element detection module for performing trace element detection on the second pretreated sample using the X-ray fluorescence spectrum detection instrument to generate trace element content information; an activity analysis module for performing fly ash activity index analysis based on the trace element content information to generate an activity index detection result.
[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:
[0008] The aforementioned fly ash activity index detection method based on microelement analysis first samples the fly ash to be tested, then analyzes the effects of the fly ash's particle size and moisture content on X-ray fluorescence spectrometry detection to determine appropriate particle size and moisture content thresholds. Subsequently, the sample is crushed and sieved according to the set particle size threshold to obtain a first pretreated sample. This sample is then dried and pressed into thin sheets according to the moisture content threshold to produce a second pretreated sample. The second pretreated sample is then subjected to microelement analysis using an X-ray fluorescence spectrometer to obtain microelement content information. Finally, the fly ash activity index is analyzed based on this microelement data to obtain the final activity index test result, thereby achieving efficient and accurate evaluation of fly ash activity.
[0009] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS
[0010] 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 paying any creative work.
[0011] Figure 1The figure is a flow chart of a method for detecting fly ash activity index based on trace element analysis in one embodiment.
[0012] Figure 2 This is a diagram of the architecture of a fly ash activity index detection system based on trace element analysis in one embodiment.
[0013] Explanation of the reference numerals: sample sampling module 11 , interference impact analysis module 12 , crushing and screening module 13 , drying and pressing module 14 , trace element detection module 15 , activity analysis module 16 . DETAILED DESCRIPTION
[0014] The embodiments of the present application provide a fly ash activity index detection method and system based on trace element analysis to solve the technical problem of insufficient trace element analysis accuracy caused by uneven particle size and fluctuation of moisture content in existing fly ash activity index detection methods.
[0015] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only some of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.
[0016] It should be noted that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products or devices.
[0017] Example 1, as Figure 1 As shown, the present application provides a method for detecting fly ash activity index based on trace element analysis, the method comprising:
[0018] Sampling is performed on the fly ash of the batch to be inspected to obtain a fly ash sample.
[0019] In the examples of this application, large quantities of fly ash are generated during the production process. To ensure representative and randomized test results, a certain number of fly ash samples are randomly selected from these batches for analysis. During sampling, attention should be paid to the uniformity of the fly ash, avoiding sampling from only a specific area. This ensures that the sample fully reflects the characteristics of the entire batch, providing a foundation for subsequent testing and analysis.
[0020] For X-ray fluorescence spectrometry detection instruments, the interference effects of fly ash particle size and moisture content on trace element content detection are analyzed, and preset particle size thresholds and preset moisture content thresholds are generated.
[0021] In one embodiment, an X-ray fluorescence spectrometer used for microelement detection first analyzes the impact of fly ash particle size and moisture content on microelement detection results. Typically, excessively large or small fly ash particles may cause changes in the scattering effect of X-rays as they propagate through the sample, thereby affecting the accuracy of microelement detection. Excessively high or low moisture content may also cause changes in the absorption or scattering of X-ray signals, affecting measurement accuracy. A simulation model is established using the historical spectral detection data from the X-ray fluorescence spectrometer. Through multiple iterations of the simulation model, an appropriate particle size threshold and moisture content threshold can be determined as the preset particle size threshold and moisture content threshold. These two preset thresholds can be used to control these influencing factors during sample preprocessing, ensuring the accuracy and consistency of detection results.
[0022] Furthermore, the present application provides an X-ray fluorescence spectrometry detection instrument for analyzing the interference effects of fly ash particle size and moisture content on trace element content detection, generating a preset particle size threshold and a preset moisture content threshold, including:
[0023] Collect historical spectral detection data of the X-ray fluorescence spectrometer, perform digital simulation modeling, and generate an X-ray penetration model; determine preset X-ray emission intensity information; load the X-ray penetration model with the X-ray emission intensity information, perform granularity iterative simulation, and determine the preset granularity threshold at which the granularity interference influence threshold satisfies the preset threshold; load the X-ray penetration model with the X-ray emission intensity information, perform water content iterative simulation, and determine the preset water content threshold at which the water content interference influence threshold satisfies the preset threshold.
[0024] Preferably, first, historical spectral detection data accumulated from previous fly ash detection by an X-ray fluorescence spectrometer is collected. This historical data reflects the spectral response, elemental measured values, and background noise values of trace elements detected at different particle sizes and moisture contents. Based on this historical spectral detection data, digital simulation modeling is performed through Monte Carlo simulation or finite element analysis to simulate the propagation path and signal attenuation of X-rays in fly ash particles, and an X-ray penetration model is constructed. This model can reflect the influence of the physical properties of the fly ash sample (such as particle size and moisture content) on X-ray penetration. Subsequently, the preset X-ray emission intensity information used during the detection (such as fixed tube voltage and tube current parameters) is obtained as a simulation benchmark condition. The preset X-ray emission intensity information is then loaded into the X-ray penetration model for particle size iteration simulation. This simulation process simulates fly ash samples of different particle sizes and analyzes the scattering and absorption effects of X-rays when propagating between particles. Through multiple iterations, the interference effects at different particle sizes are determined, and a preset particle size threshold that meets the corresponding preset threshold is calculated. This preset particle size threshold can meet the preset interference effect standard, thereby ensuring the accuracy of the detection. Similar to particle size simulation, X-ray emission intensity information is also loaded into the X-ray penetration model for iterative moisture content simulation. During the simulation, fly ash samples with varying moisture contents are simulated to evaluate the impact of moisture on X-ray absorption and signal intensity. Through repeated iterations, the interference effects of varying moisture contents are determined, and a pre-set moisture threshold is calculated to meet the preset threshold. This pre-set moisture threshold helps control the interference of moisture in the sample on the test results, ensuring test reliability.
[0025] Furthermore, the present application provides a method of loading the X-ray penetration model with the X-ray emission intensity information, performing granularity iterative simulation, and determining the preset granularity threshold at which the granularity interference impact threshold satisfies the preset threshold, including:
[0026] The X-ray penetration model is loaded, and by adjusting the fly ash particle size, the particle size interference influence simulation is iterated to determine a first particle size distribution range in which the particle size interference influence threshold satisfies a preset threshold; multiple groups of particle size distribution difference samples with different particle size distribution uniformities are constructed using the first particle size distribution range; the particle size interference influence simulation of the multiple groups of particle size distribution difference samples is executed using the X-ray penetration model to determine a particle size uniformity parameter that satisfies a preset threshold; and the preset particle size threshold is generated using the first particle size distribution range and the particle size uniformity parameter.
[0027] Optionally, when performing iterative particle size simulation, the X-ray penetration model is first loaded into the simulation environment, and the particle size interference effect simulation is performed by continuously adjusting the fly ash particle size. In this process, the particle size distribution is gradually adjusted (such as increasing or decreasing the average particle size, changing the particle size span), simulating the X-ray scattering effect and signal intensity change under different particle size conditions. After each adjustment, the effect of particle size interference on microelement detection (such as the characteristic X-ray signal attenuation rate and background noise level) is obtained and compared with the preset allowable interference threshold. The particle size range that meets the threshold requirement is obtained as the first particle size distribution range. This range defines the upper and lower limits of the fly ash particle size, ensuring that within this particle size range, the interference effect of X-ray fluorescence spectroscopy detection is minimized and meets the accuracy requirements. Subsequently, based on the first particle size distribution range, multiple different particle size distribution samples are constructed. These samples have different particle size uniformity, that is, different degrees of uniformity in the particle size distribution. For example, some samples have a relatively uniform particle size distribution, while some samples have large particle size differences. These samples are constructed to test the effect of different particle size distributions on X-ray fluorescence spectroscopy detection. Afterwards, the multiple groups of samples with different particle size distributions are input into the X-ray penetration model to simulate the influence of particle size interference and analyze the influence of different particle size distributions on the scattering effect and signal intensity of X-rays when propagating in fly ash particles. Through simulation, the model will evaluate how the uniformity of the particle size distribution in each group of samples affects the intensity and stability of the X-ray signal, thereby determining the particle size parameter that meets the preset interference influence threshold, and use this particle size parameter as the particle size uniformity parameter, which describes the uniformity requirement of the particle size distribution (such as the particle size standard deviation). Finally, based on the first particle size distribution range and the particle size uniformity parameter, a preset particle size threshold is generated. This threshold defines the range in which the particle size should be controlled when processing the fly ash sample to minimize the interference caused by the particle size and ensure the accuracy and reliability of the X-ray fluorescence spectrum detection.
[0028] Furthermore, the present application provides simulation of particle size interference effects including scattering effects and signal intensity simulation when X-rays propagate in fly ash particles.
[0029] Optionally, the established X-ray penetration model is loaded into the simulation software. By using different particle sizes, the changes in the scattering path of X-rays between particles are simulated, and the scattering angle distribution and scattered X-ray intensity are recorded. In this process, the characteristic X-ray signals of heavy elements (such as iron and aluminum) are also analyzed. When the particles are large (such as >50μm), the X-ray penetration depth is insufficient, the proportion of scattered photons increases, and the characteristic peak intensity attenuates. When the particles are too small (such as <10μm), the randomness of the scattered photon direction increases and the background noise increases. Through continuous simulation, the critical particle size of the scattering effect on the detection accuracy can be determined. Subsequently, for each group of samples, the spatial distribution of trace elements is simulated, and the deviation between the apparent trace element content (i.e., the test results) and the actual content of each group of samples is calculated through the X-ray penetration model to identify the impact of particle uniformity on the measurement results. After that, the particle size is adjusted and the contact area between the X-rays and the particles is calculated for each particle size group. The larger the contact area, the more fluorescence photons excited by the X-rays and the higher the characteristic peak intensity. At the same time, the signal stability is analyzed. Large particles typically have insufficient penetration depth, leading to uneven excitation of elements at different depths in the sample and fluctuations in characteristic peak intensity. Small particles, due to uneven stacking, have random variations in contact area and decreased signal stability. Through continuous simulation, it is possible to determine at which particle size the signal intensity reaches its peak (maximum contact area) and achieves optimal signal stability. Finally, the simulation results for different particle sizes are summarized to determine a preset particle size threshold. Even when X-rays propagate through samples of different particle sizes, the scattering effect, signal intensity, and particle uniformity are effectively controlled. The threshold determined by the simulation will help select the appropriate particle size range, avoiding the influence of overly large or undersized particles on the test results, thereby optimizing the pretreatment conditions of fly ash samples.
[0030] Furthermore, the present application provides a method for loading the X-ray penetration model with the X-ray emission intensity information, performing iterative simulation of water content, and determining that the water content interference impact threshold satisfies the preset water content threshold, including:
[0031] Construct a moisture content simulation sample; load the X-ray penetration model, use the preset particle size threshold as a simulation constraint, simulate the moisture content interference effect on the moisture content simulation sample, determine the moisture content range that meets the preset threshold, and generate the preset moisture content threshold.
[0032] Optionally, when performing iterative simulation of moisture content, the X-ray penetration model will be loaded first, and the simulation of moisture content interference effect will be iterated by adjusting the moisture content. The specific process is similar to the aforementioned iterative simulation of particle size interference effect, so as to obtain moisture content simulation samples that meet the interference threshold. These samples contain fly ash with different moisture contents, which are used to evaluate the propagation of X-rays and the detection effect of trace elements under different moisture conditions. Subsequently, the preset particle size threshold is used as a constraint in the simulation process to ensure that the sample particle size range in the simulation meets the requirements, thereby making the interference simulation of moisture content more realistic. After setting the preset particle size threshold, the interference effect simulation of the moisture content simulation sample is started. This simulation process simulates fly ash samples at different moisture contents and analyzes the impact of moisture on X-ray penetration depth, absorption, volatile matter interference, and the accuracy of the final trace element content measurement results. Through continuous simulation, the interference effects under different moisture content conditions can be evaluated, thereby determining a moisture content range that meets the preset interference threshold and using this range as the preset moisture content threshold. This preset moisture content threshold defines the upper and lower limits of the moisture content to ensure that when the sample is within this range, the X-ray signal can stably and accurately reflect the trace element content in the sample, thereby improving the accuracy of the trace element detection results.
[0033] Furthermore, the present application provides simulation of the interference effect of water content, including simulation of water absorption, interference of volatile substances and analysis accuracy.
[0034] Optionally, by using samples with different water contents for simulation, the absorption coefficients of hydrogen (H) and oxygen (O) elements in the water for low-energy X-rays (such as 1.74keV for Si and 1.49keV for Al) are calculated (based on the cross-section data of the interaction between X-rays and matter). The propagation path of X-ray photons in the sample is then simulated using the Monte Carlo method, the proportion of photons absorbed by water is counted, and the attenuation ratio of the characteristic peak intensity is calculated. Subsequently, the evaporation of water is simulated. Some volatile substances in the sample (such as oxides or hydrates of certain chemical elements) may be taken away along with the water, changing the actual composition of the sample. The loss of these volatile substances will cause the chemical composition of the fly ash to change, thereby affecting the accuracy of the trace element analysis. In the simulation, by simulating the process of water evaporation, the degree of loss of volatile substances at different water contents is evaluated, and the impact of water evaporation on the composition of the fly ash (such as the content of lost trace elements) is determined. Afterwards, the presence of moisture not only affects the penetration depth of X-rays, but may also lead to errors in the measurement of elements, especially when the sample is not dried enough. Excessive moisture will increase the wet weight of the sample, resulting in deviations in the determination of element content due to the presence of moisture in subsequent analysis. During the simulation process, the measurement accuracy of samples under different moisture conditions will be simulated, the impact of moisture content on the quantitative results of elements will be evaluated, and it will be determined whether the actual content of the sample is underestimated or overestimated. Finally, the simulation results under different moisture contents are summarized to determine the preset moisture content threshold. This threshold ensures that the moisture content of the sample is not too high or too low, so that the X-ray fluorescence spectroscopy detection results are not excessively affected by moisture absorption, loss of volatile substances and analytical precision errors, ensuring the accuracy and reliability of the final test results.
[0035] The fly ash sample is crushed and sieved with the preset particle size threshold as a target to generate a first pretreated sample.
[0036] In one embodiment, the desired particle size range of the fly ash sample is first determined based on a predetermined particle size threshold. The fly ash sample is then pulverized using a pulverizing device to reduce larger particles to an appropriate size. Subsequently, screening is performed to ensure that the sample's particle size distribution falls within the predetermined particle size range, removing oversized and undersized particles. This results in a first pre-processed sample for subsequent testing and analysis.
[0037] Furthermore, the present application provides a method for crushing and screening the fly ash sample with the preset particle size threshold as a target to generate a first pretreated sample, comprising:
[0038] Collect initial particle size information of the fly ash sample; compare the initial particle size information with the preset particle size threshold, make a crushing parameter decision based on the comparison result, perform crushing control on the fly ash sample, and then perform sample screening according to the preset particle size threshold to generate the first pretreated sample.
[0039] Preferably, a preliminary measurement is first performed on the fly ash sample to be inspected, the particle size distribution information of the particles is collected, and these size ranges and distribution characteristics are recorded to form the initial particle size data of the fly ash sample. Subsequently, the collected initial particle size information is compared with the preset particle size threshold value set previously to determine whether the particle size of the sample meets the requirements. If not, it means that there are too large particles in the fly ash sample. At this time, a crushing parameter decision is made, that is, based on the deviation between the initial particle size information and the first particle size distribution range in the preset particle size threshold, the type of crushing equipment is determined (if the deviation is greater than the threshold, a ball mill or other equipment with high grinding efficiency is selected; if it is less than the threshold, a vibration mill or other equipment for fine grinding is selected). Then, based on the deviation size, the recommended crushing time and recommended speed of the current crushing equipment type under the deviation size are extracted from the parameter mapping table. After the crushing parameters are determined, the fly ash sample will be crushed using the selected crushing equipment, thereby crushing larger particles into smaller particles that meet the requirements, ensuring that the particle size of the sample meets the preset threshold range. After pulverization, the sample enters the screening stage, where a sieve is used to remove undersized particles, ensuring that the final fly ash sample particle size distribution falls within a predetermined threshold. The resulting sample, after pulverization and screening, is the first pre-processed sample, which is then used for subsequent trace element analysis and testing.
[0040] Taking the preset moisture content threshold as a target, the first pretreated sample is dried and then pressed into a thin sheet to generate a second pretreated sample.
[0041] In one embodiment, the ideal moisture content range for the fly ash sample is first determined based on a preset moisture content threshold. Subsequently, the first pretreated sample is dried to remove excess moisture and ensure that the sample's moisture content meets the preset moisture content standard. The drying process typically utilizes an oven or other drying equipment to remove moisture from the sample by heating it and continuously ventilating it. After drying, to make the sample more uniform and facilitate subsequent testing, the fly ash sample is pressed into thin sheets using a tablet press, forming a second pretreated sample. These sheets are more stable in subsequent analysis and are easier to detect using an X-ray fluorescence spectrometer.
[0042] The second pretreated sample is subjected to trace element detection by the X-ray fluorescence spectrometer to generate trace element content information.
[0043] In one embodiment, the second pretreated sample, dried and pressed into thin sheets, is tested for trace elements using an X-ray fluorescence spectrometer. This instrument irradiates the sample with X-rays, stimulating elements within the sample to produce fluorescence, which is then analyzed for fluorescence intensity. Based on the intensity of the fluorescence signal, the X-ray fluorescence spectrometer can quantitatively determine the content of various trace elements in the sample, generating trace element content information. This information records the concentration of each element in the fly ash sample, providing essential data for subsequent activity index analysis.
[0044] The fly ash activity index is analyzed according to the trace element content information to generate an activity index test result.
[0045] In one embodiment, after obtaining trace element content information, this information is input into an activity evaluation model to analyze the fly ash activity index. This activity index reflects the reactivity and activity of the fly ash. Through model analysis, the fly ash activity index can be calculated, generating an activity index test result. This provides a reference for evaluating the quality of fly ash in practical applications and helps determine its suitability for use in building materials such as concrete.
[0046] Furthermore, the present application provides a method for performing fly ash activity index analysis based on the trace element content information to generate an activity index test result, including:
[0047] Collect historical trace element content data and corresponding application environment and activity index samples to train an activity evaluation model; analyze the trace element content information using the activity evaluation model, output the change trend of the activity index with the application environment, and generate the activity index detection result.
[0048] Preferably, first, a large amount of historical trace element content data and corresponding fly ash activity index samples are collected. These data usually come from multiple experiments or fly ash samples from different sources. These historical data will provide a basis for model training. Subsequently, an activity evaluation model is constructed based on a multi-layer perceptron, including an input layer, a hidden layer, an output layer, etc., and the weights of the activity evaluation model are initialized using random numbers or other methods. The training data is input into the initialized activity evaluation model for forward propagation, and is passed layer by layer through the input layer, hidden layer, output layer, etc. to calculate the prediction results including the activity index. Afterwards, the mean square error function is used to calculate the loss value between the prediction result and the real data, and the gradient of the loss to the weight of each layer is calculated layer by layer through back propagation, and then the Adam optimizer is used to optimize the model parameters and adjust the weights to minimize the value of the loss function. Repeat the above process until the maximum number of iterations is reached. After the training is completed, the model performance is tested using data not used for training to evaluate the accuracy of the model in the activity index prediction task. If the accuracy meets the expected accuracy, the current activity evaluation model is output. Otherwise, hyperparameters such as the learning rate and number of training batches are adjusted to further improve the predictive performance of the activity evaluation model. Then, the new trace element content information is input into the trained activity evaluation model. Through model analysis, the activity performance of fly ash under different trace element contents can be predicted and the fly ash activity index under the current application environment can be calculated. By summarizing all predicted activity indices, the activity index trend that varies with the application environment can be obtained. This trend reflects how the activity performance of fly ash changes under different conditions and helps evaluate the potential and performance of fly ash in practical applications. Finally, based on the activity index trend, an activity index test result is generated. This result can provide guidance for the application of fly ash and help determine its applicability in different application environments, thereby providing data support for the resource utilization and quality control of fly ash.
[0049] Furthermore, after providing a training activity evaluation model, the present application also includes:
[0050] The specific surface area of the fly ash sample is measured; based on the relationship between the specific surface area and the trace element content, the influence weight of the specific surface area on the activity index is generated through regression analysis; and the optimization unit of the activity evaluation model is constructed based on the influence weight of the specific surface area on the activity index.
[0051] Optionally, the specific surface area of the fly ash sample can be measured first. The specific surface area is the total surface area per unit mass of powder, which directly affects the reactivity of fly ash. A common measurement method is a specific surface area meter (such as the BET method). The fly ash sample is degassed under vacuum to remove the gas adsorbed on the surface, and then nitrogen is adsorbed at liquid nitrogen temperature (77K). The adsorption amount under different pressures is measured and the specific surface area is calculated using the BET equation. After obtaining the specific surface area data of the fly ash sample, a regression model is established using a multivariate linear regression equation. The historical specific surface area, historical trace element content, and historical activity index are then used to fit the regression model using the least squares method to calculate the regression coefficient of each variable. The regression coefficient of the specific surface area is the weight of the influence of the specific surface area on the activity index. After obtaining the weight of the influence of the specific surface area on the activity index, this information can be used to optimize the activity evaluation model. Specifically, an optimization unit can be added to the model, which adjusts the predicted value of the activity index according to the size of the specific surface area. The optimization unit will combine the specific surface area with the trace element content and further improve the model's prediction accuracy of the fly ash activity index through weight adjustment. In this way, the activity evaluation model can more accurately reflect the influence of specific surface area on the activity performance of fly ash and optimize the prediction results.
[0052] In summary, the embodiments of the present application have at least the following technical effects:
[0053] In the embodiment of the present application, a fly ash sample is first sampled from a batch of fly ash to be inspected to obtain a fly ash sample; then, the fly ash particle size and moisture content are analyzed for interference with the detection of trace element content using an X-ray fluorescence spectrometer, generating a preset particle size threshold and a preset moisture content threshold; then, the fly ash sample is crushed and sieved with the preset particle size threshold as the target to generate a first pre-treated sample; further, with the preset moisture content threshold as the target, the first pre-treated sample is dried and pressed into a thin sheet to generate a second pre-treated sample; then, the second pre-treated sample is subjected to microelement detection using the X-ray fluorescence spectrometer to generate microelement content information; finally, the fly ash activity index is analyzed based on the microelement content information to generate an activity index detection result. These technical effects jointly solve the technical problem of insufficient microelement analysis accuracy caused by uneven particle size and fluctuations in moisture content in existing fly ash activity index detection methods, achieving the technical effect of effectively eliminating the interference of particle size and moisture on microelement detection and improving microelement detection accuracy by optimizing the sample pre-treatment process.
[0054] Example 2, based on the same inventive concept as the fly ash activity index detection method based on trace element analysis in the previous embodiment, Figure 2As shown, the present application provides a fly ash activity index detection system based on trace element analysis, and the system includes: a sample sampling module 11: sampling the fly ash of the batch to be inspected to obtain a fly ash sample; an interference impact analysis module 12: analyzing the interference impact of the fly ash particle size and moisture content on the trace element content detection for the X-ray fluorescence spectrum detection instrument, and generating a preset particle size threshold and a preset moisture content threshold; a crushing and screening module 13: crushing and screening the fly ash sample with the preset particle size threshold as the target to generate a first pretreated sample; a drying and pressing module 14: drying the first pretreated sample and pressing it into a thin sheet with the preset moisture content threshold as the target to generate a second pretreated sample; a trace element detection module 15: performing trace element detection on the second pretreated sample through the X-ray fluorescence spectrum detection instrument to generate trace element content information; an activity analysis module 16: performing fly ash activity index analysis based on the trace element content information to generate an activity index detection result.
[0055] Furthermore, the interference impact analysis module 12 is further configured to perform the following method:
[0056] Collect historical spectral detection data of the X-ray fluorescence spectrometer, perform digital simulation modeling, and generate an X-ray penetration model; determine preset X-ray emission intensity information; load the X-ray penetration model with the X-ray emission intensity information, perform granularity iterative simulation, and determine the preset granularity threshold at which the granularity interference influence threshold satisfies the preset threshold; load the X-ray penetration model with the X-ray emission intensity information, perform water content iterative simulation, and determine the preset water content threshold at which the water content interference influence threshold satisfies the preset threshold.
[0057] Furthermore, the interference impact analysis module 12 is further configured to perform the following method:
[0058] The X-ray penetration model is loaded, and by adjusting the fly ash particle size, the particle size interference influence simulation is iterated to determine a first particle size distribution range in which the particle size interference influence threshold satisfies a preset threshold; multiple groups of particle size distribution difference samples with different particle size distribution uniformities are constructed using the first particle size distribution range; the particle size interference influence simulation of the multiple groups of particle size distribution difference samples is executed using the X-ray penetration model to determine a particle size uniformity parameter that satisfies a preset threshold; and the preset particle size threshold is generated using the first particle size distribution range and the particle size uniformity parameter.
[0059] Furthermore, the interference impact analysis module 12 is further configured to perform the following method:
[0060] Construct a moisture content simulation sample; load the X-ray penetration model, use the preset particle size threshold as a simulation constraint, simulate the moisture content interference effect on the moisture content simulation sample, determine the moisture content range that meets the preset threshold, and generate the preset moisture content threshold.
[0061] Furthermore, the interference impact analysis module 12 is further configured to perform the following method:
[0062] The simulation of particle size interference includes the scattering effect and signal intensity simulation when X-rays propagate in fly ash particles.
[0063] Furthermore, the interference impact analysis module 12 is further configured to perform the following method:
[0064] The simulation of water content interference includes the simulation of water absorption, volatile substance interference and analysis accuracy.
[0065] Furthermore, the crushing and screening module 13 is also used to perform the following method:
[0066] Collect initial particle size information of the fly ash sample; compare the initial particle size information with the preset particle size threshold, make a crushing parameter decision based on the comparison result, perform crushing control on the fly ash sample, and then perform sample screening according to the preset particle size threshold to generate the first pretreated sample.
[0067] Furthermore, the activity analysis module 16 is further configured to perform the following method:
[0068] Collect historical trace element content data and corresponding application environment and activity index samples to train an activity evaluation model; analyze the trace element content information using the activity evaluation model, output the change trend of the activity index with the application environment, and generate the activity index detection result.
[0069] Furthermore, the activity analysis module 16 is further configured to perform the following method:
[0070] The specific surface area of the fly ash sample is measured; based on the relationship between the specific surface area and the trace element content, the influence weight of the specific surface area on the activity index is generated through regression analysis; and the optimization unit of the activity evaluation model is constructed based on the influence weight of the specific surface area on the activity index.
[0071] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.
[0072] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.
[0073] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, this application is intended to include such modifications and variations as fall within the scope of this application and its equivalents.
Claims
1. A method for detecting fly ash activity index based on trace element analysis, characterized in that: include: Sampling the fly ash of the batch to be inspected to obtain a fly ash sample; For X-ray fluorescence spectrometry detection instruments, analyze the interference effects of fly ash particle size and moisture content on trace element content detection, and generate preset particle size thresholds and preset moisture content thresholds; crushing and screening the fly ash sample with the preset particle size threshold as a target to generate a first pretreated sample; Taking the preset moisture content threshold as a target, drying the first pretreated sample and then pressing it into a thin sheet to generate a second pretreated sample; Performing trace element detection on the second pretreated sample by the X-ray fluorescence spectrometry detection instrument to generate trace element content information; The fly ash activity index is analyzed according to the trace element content information to generate an activity index test result.
2. The fly ash activity index detection method based on trace element analysis according to claim 1, characterized in that: For X-ray fluorescence spectrometry detection instruments, the interference effects of fly ash particle size and moisture content on trace element content detection are analyzed, and preset particle size thresholds and preset moisture content thresholds are generated, including: Collecting historical spectrum detection data of the X-ray fluorescence spectrum detection instrument, performing digital simulation modeling, and generating an X-ray penetration model; Determining preset X-ray emission intensity information; Loading the X-ray penetration model with the X-ray emission intensity information, performing granularity iterative simulation, and determining a preset granularity threshold at which a granularity interference impact threshold satisfies a preset threshold; The X-ray penetration model is loaded with the X-ray emission intensity information, and a water content iterative simulation is performed to determine a preset water content threshold at which the water content interference impact threshold satisfies a preset threshold.
3. The fly ash activity index detection method based on trace element analysis according to claim 2, characterized in that: The X-ray penetration model is loaded with the X-ray emission intensity information, and a granularity iterative simulation is performed to determine a preset granularity threshold at which a granularity interference impact threshold satisfies a preset threshold, including: The X-ray penetration model is loaded, and the particle size interference impact simulation is iterated by adjusting the fly ash particle size to determine a first particle size distribution range in which the particle size interference impact threshold satisfies a preset threshold; Constructing multiple groups of particle size distribution difference samples with different particle size distribution uniformities based on the first particle size distribution range; Performing a particle size interference effect simulation on the plurality of groups of particle size distribution difference samples using the X-ray penetration model to determine a particle size uniformity parameter that meets a preset threshold; The preset particle size threshold is generated based on the first particle size distribution range and the particle size uniformity parameter.
4. The fly ash activity index detection method based on trace element analysis according to claim 2, characterized in that: The X-ray penetration model is loaded with the X-ray emission intensity information, and a water content iterative simulation is performed to determine that the water content interference impact threshold satisfies the preset water content threshold, including: Construct a water content simulation sample; The X-ray penetration model is loaded, and the preset particle size threshold is used as a simulation constraint to simulate the water content interference effect on the water content simulation sample, determine the water content range that meets the preset threshold, and generate the preset water content threshold.
5. The fly ash activity index detection method based on trace element analysis according to claim 3, characterized in that: The simulation of particle size interference includes the scattering effect and signal intensity simulation when X-rays propagate in fly ash particles.
6. The fly ash activity index detection method based on trace element analysis according to claim 4, characterized in that: The simulation of water content interference includes the simulation of water absorption, volatile substance interference and analysis accuracy.
7. The fly ash activity index detection method based on trace element analysis according to claim 1, characterized in that: The fly ash activity index is analyzed based on the trace element content information to generate an activity index test result, including: Collect historical trace element content data and corresponding application environment and activity index samples to train activity evaluation models; The trace element content information is analyzed using the activity evaluation model, and the changing trend of the activity index with the application environment is output to generate the activity index detection result.
8. The fly ash activity index detection method based on trace element analysis according to claim 7, characterized in that: After training the activity evaluation model, it also includes: Specific surface area measurements were performed on fly ash samples; Based on the relationship between specific surface area and trace element content, the influence weight of specific surface area on activity index was generated through regression analysis; The optimization unit of the activity evaluation model is constructed based on the influence weight of the specific surface area on the activity index.
9. The fly ash activity index detection method based on trace element analysis according to claim 1, characterized in that: The fly ash sample is crushed and screened based on the preset particle size threshold to generate a first preprocessed sample, including: Collecting initial particle size information of the fly ash sample; The initial particle size information is compared with the preset particle size threshold, and a crushing parameter decision is made according to the comparison result. After the fly ash sample is crushed and controlled, the sample is screened according to the preset particle size threshold to generate the first pretreated sample.
10. The fly ash activity index detection system based on trace element analysis is characterized by: The system is used to perform the fly ash activity index detection method based on trace element analysis according to any one of claims 1 to 9, comprising: Sample sampling module: sampling the fly ash of the batch to be inspected to obtain fly ash samples; Interference impact analysis module: Analyzes the interference effects of fly ash particle size and moisture content on trace element content detection in X-ray fluorescence spectrometry instruments, and generates preset particle size thresholds and preset moisture content thresholds; Crushing and screening module: crushing and screening the fly ash sample with the preset particle size threshold as the target to generate a first pre-processed sample; Drying and pressing module: taking the preset moisture content threshold as the target, drying the first pre-treated sample and pressing it into a thin slice to generate a second pre-treated sample; Trace element detection module: performing trace element detection on the second pretreated sample by the X-ray fluorescence spectrometer to generate trace element content information; Activity analysis module: performs fly ash activity index analysis based on the trace element content information and generates activity index test results.