Method for analyzing raw material-structure-strength correlation of geopolymer material based on deconvolution quantification and dimension reduction

Abstract

The invention discloses a method for analyzing raw material-structure-strength correlation of a geopolymer material based on deconvolution quantification and dimension reduction, and belongs to the technical field of comprehensive utilization of solid wastes. The method comprises the following steps: obtaining component content data and element proportion data in a solid waste raw material under different raw material ratio conditions, mechanical strength data of a geopolymer material, and oxygen bond energy spectrum peak information, silicon spectrum peak data and aluminum spectrum peak data of the geopolymer material; the proportion data of the oxygen bond type, the silicon type and the aluminum type of the geopolymer material are obtained through deconvolution calculation; performing standardized conversion on the obtained data to obtain dimensionless standardized data; and carrying out correlation analysis and non-metric multi-dimensional scaling analysis on the obtained dimensionless standardized data, and analyzing the incidence relation among the raw material components, the microstructure and the mechanical strength of the geopolymer material. According to the method, the incidence relation between different raw material components of the geopolymer material prepared from the complex solid waste and the mechanical strength of the microstructure can be accurately analyzed.

Description

Technical Field

[0001] This invention relates to a method for the correlation of raw materials, structure, and strength of polymer materials based on deconvolution quantization and dimensionality reduction analysis, belonging to the field of comprehensive utilization technology of solid waste. Background Technology

[0002] Due to the advantages of geopolymers in terms of mechanical strength, acid and alkali corrosion resistance, and durability, numerous studies have been reported on the preparation of geopolymers using different raw materials. Among them, geopolymers prepared using solid waste raw materials (such as fly ash, slag, and iron-aluminum sludge) have more significant advantages in terms of cost control, energy conservation and emission reduction, and solid waste disposal, and therefore have received increasing attention in the field of geopolymer preparation.

[0003] The characteristics of solid waste raw materials in terms of cost, yield, and main components (silicon, aluminum, and oxygen) make them potential candidates for preparing geopolymer materials. Furthermore, geopolymer materials prepared from a combination of multiple solid waste raw materials also possess certain advantages in terms of structural strength and early hardening properties, and reduce over-reliance on any single substance, making their advantages even more pronounced in actual production and manufacturing processes.

[0004] However, compared to natural raw materials (such as metakaolin), solid waste raw materials inevitably contain a series of impurities during the generation process. These impurities affect the mechanical strength of geopolymer materials at different levels. However, the influence of impurities on the strength of geopolymer materials is complex and multi-directional. It is difficult to determine how a specific substance will affect the geopolymer forming process during the geological polymerization process, which directly limits the application of solid waste as a raw material for geopolymer preparation.

[0005] Furthermore, due to the complex composition of solid waste, the proportions of each component change synchronously when adjusting the ratio of different solid waste raw materials during the preparation process. Analyzing changes in only a single substance can be misleading, thus significantly interfering with the final result of material ratio control and making it impossible to achieve precise control over the material's structural strength. Therefore, rationally determining the structural characteristics and corresponding mechanical strength formed under complex component changes is fundamental to optimizing raw material ratios and achieving precise structural control.

[0006] X-ray fluorescence spectroscopy (XRF) can analyze the composition of solid waste raw materials, while X-ray photoelectron spectroscopy (XPS) and solid-state nuclear magnetic resonance silicon / aluminum spectroscopy (SMR) can also be used. 29 Si / 27 Al MAS-NMR can resolve the Si-OT (T=Al, Si, Na, H, etc.) structure, silicon seed configuration, and aluminum seed configuration in geopolymer materials. However, different methods resolve different types of structures, making it difficult to determine the influence relationship between different structures. Furthermore, their influence on mechanical strength is mainly determined by subjective judgment, making it difficult to form a unified analytical method.

[0007] Therefore, geopolymer materials prepared from solid waste raw materials have large differences in raw material composition, various molding structures, and large differences in mechanical strength. How to analyze the interrelationships between them is one of the technical problems that urgently need to be solved in this field. Summary of the Invention

[0008] To address the aforementioned technical problems, the present invention aims to provide a method for analyzing the correlation between raw materials, structure, and strength of geopolymer materials based on deconvolution quantization and dimensionality reduction. This invention utilizes detection technology to quantify the microstructure through deconvolution calculations, and then employs data standardization transformation, correlation analysis, and non-metric multidimensional scaling analysis to accurately analyze the correlation between different raw material components and microstructure parameters of geopolymer materials prepared from complex solid waste and their mechanical strength.

[0009] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0010] A method for determining the raw material-structure-strength correlation of polymer materials based on deconvolution quantization and dimensionality reduction analytical methods includes the following steps:

[0011] Step 1: Testing and Data Acquisition

[0012] The solid waste raw materials and the geopolymer materials prepared from the solid waste raw materials were tested to obtain the component content data and element ratio data of the solid waste raw materials under different raw material ratios, the mechanical strength data of the geopolymer materials under different raw material ratios, and the oxygen bond energy spectrum peak information, silicon spectrum peak data and aluminum spectrum peak data of the geopolymer materials under different raw material ratios.

[0013] Step 2: Deconvolution Calculation

[0014] By using the oxygen bond energy spectrum peak information, silicon spectrum peak data and aluminum spectrum peak data obtained in step one of the deconvolution calculation, the proportion data of oxygen bond type, silicon type and aluminum type of geopolymer material under different raw material ratio conditions are obtained.

[0015] Step 3: Standardization Conversion

[0016] The component content data and element ratio data of solid waste raw materials under different raw material ratio conditions, the mechanical strength data of geopolymer materials under different raw material ratio conditions, and the proportion data of oxygen bond type, silicon type and aluminum type of geopolymer materials under different raw material ratio conditions obtained in Step 1 and Step 2 are standardized and converted to obtain dimensionless standardized data.

[0017] Step 4: Dimensionality Reduction Analysis

[0018] The dimensionless standardized data obtained in step three are subjected to correlation analysis and non-metric multidimensional scaling analysis to obtain correlation matrix images and non-metric multidimensional scaling analysis images between different index parameters, thereby analyzing the correlation between raw material components, microstructure and mechanical strength of geopolymer materials.

[0019] According to a specific embodiment of the present invention, preferably, in step one, the solid waste raw material is tested using an X-ray fluorescence spectrometer (XRF).

[0020] More preferably, X-ray fluorescence spectrometry (XRF) is used to test the solid waste raw materials to obtain component content data under different raw material ratios. Based on the measured component contents, the content ratios of key elements are calculated to obtain elemental ratio data in the solid waste raw materials under different raw material ratios. Further preferably, the measured component contents include the contents of two or more components selected from silicon dioxide, aluminum oxide, calcium oxide, sodium oxide, ferric oxide, magnesium oxide, and titanium dioxide. Further preferably, the content ratios of the key elements include two or more selected from Al / Ca ratio, Al / Na ratio, Si / Al ratio, Si / Na ratio, and Si / Ca ratio. These content ratios refer to ratios of mass content.

[0021] According to a specific embodiment of the present invention, preferably, in step one, the testing of the geopolymer material prepared from solid waste raw materials includes mechanical strength testing, as well as testing using X-ray photoelectron spectroscopy (XPS) and solid-state nuclear magnetic resonance spectroscopy (MAS-NMR).

[0022] More preferably, after preparing geopolymer materials using solid waste raw materials with different raw material ratios, the mechanical strength of the geopolymer materials is tested to obtain mechanical strength data of the geopolymer materials under different raw material ratios. Even more preferably, the mechanical strength is compressive strength.

[0023] More preferably, X-ray photoelectron spectroscopy (XPS) is used to test the geopolymer materials to obtain oxygen bond energy spectrum peak information of the geopolymer materials under different raw material ratios; silicon spectra of solid-state nuclear magnetic resonance spectroscopy are used... 29 Si MAS-NMR was used to test the geopolymer materials, obtaining silicon peak data of the geopolymer materials under different raw material ratios; aluminum peak data was obtained using a solid-state nuclear magnetic resonance spectrometer. 27 Al MAS-NMR was used to test the geopolymer material and obtain aluminum peak data of the geopolymer material under different raw material ratios. More preferably, the particles of the crushed geopolymer material after mechanical strength testing were collected, then ground and sieved, and tested using X-ray photoelectron spectroscopy and solid-state nuclear magnetic resonance spectroscopy. Preferably, the sieving was performed through a 200-mesh sieve. The sieved material can be sealed and stored before the above tests are performed.

[0024] Obtaining the aforementioned multi-source data through step one can overcome the shortcomings of using a single method to quantitatively analyze the correlation between raw material components, microstructure, and mechanical strength of geopolymer materials. This is beneficial for a more accurate and comprehensive analysis of the correlation between the mechanical strength of geopolymer materials under the influence of complex raw material components and microstructure.

[0025] According to a specific embodiment of the present invention, preferably, in step two, the oxygen bonds include Si-O-Al bonds, Si-O-Si bonds, Si-O-Na bonds, and Si-OH bonds; the silicon seed includes Q... 0 Configuration, Q 1 Configuration, Q 2 Configuration, Q 4 (4Al) configuration, Q 4 (3Al) configuration, Q 4 (2Al) configuration, Q 4 (1Al) configuration and Q 4 (0Al) configuration; the aluminum species include six-coordinate Al-third type hydrated aluminate configuration, six-coordinate Al-calcium sulfoaluminate configuration, six-coordinate Al-ettlingite configuration, five-coordinate Al configuration, and four-coordinate Al-Q configuration. 4 (4Si) configuration and tetracoordinated Al configuration.

[0026] By quantifying the proportion of microstructures through deconvolution calculation in step two, we can more intuitively analyze the impact of different microstructures on the mechanical strength of geopolymer materials.

[0027] According to a specific embodiment of the present invention, preferably, in step three, the standardization conversion adopts the z-score standardization method.

[0028] The dimensionless standardized data obtained in step three can reduce the impact of differences in numerical range and dimensions.

[0029] According to a specific embodiment of the present invention, preferably, in step four, the correlation analysis adopts the following formula:

[0030]

[0031] In the formula, x i x j The correlation coefficient r represents ij The two corresponding related features, x ki Representing the characteristic quantity x i The kth factor, Representing the characteristic quantity x i The average value, x kj Representing the characteristic quantity x jThe kth factor, Representing the characteristic quantity x j The average value, where n represents the total number of factors in the characteristic quantity.

[0032] Specifically, x i This can include dimensionless standardized data on the component content of solid waste raw materials under different raw material ratios, dimensionless standardized data on the elemental proportions of solid waste raw materials under different raw material ratios, dimensionless standardized data on the proportion of oxygen bond types in geopolymer materials under different raw material ratios, dimensionless standardized data on the proportion of silicon types in geopolymer materials under different raw material ratios, and dimensionless standardized data on the proportion of aluminum types in geopolymer materials under different raw material ratios. Let x i Taking dimensionless standardized data of component content in solid waste raw materials under different raw material ratios as an example, then x ki Dimensionless standardized data representing the component content in solid waste raw materials under certain raw material ratio conditions. x represents the average value of dimensionless standardized data on the component content in solid waste raw materials under different raw material ratios. j This can include dimensionless standardized data on the mechanical strength of geopolymer materials under different raw material ratios. Then x kj This indicates that under a certain raw material ratio (and x) ki The data represents dimensionless standardized data on the mechanical strength of geopolymer materials with the same raw material ratios. This represents the dimensionless standardized average of the mechanical strength data of solid waste raw materials under different raw material ratios. 'n' represents the number of "different raw material ratios". Based on this, the correlation between the components, elemental proportions, oxygen bond types, silicon types, and aluminum types in the solid waste raw materials and the mechanical strength of the geopolymer materials can be derived.

[0033] According to a specific embodiment of the present invention, preferably, in step four, the non-metric multidimensional scaling analysis includes: based on the dimensionless standardized data obtained in step three, calling the vegan library to draw a Shepard plot, and calling the ggplot2 library to draw a non-metric multidimensional scaling analysis image, thereby obtaining a non-metric multidimensional scaling analysis image between different index parameters.

[0034] According to a specific embodiment of the present invention, preferably, step four includes: analyzing index parameters that show a significant positive correlation with mechanical strength based on the obtained correlation matrix image; observing the spatial distance characteristics of different index parameters based on the obtained non-metric multidimensional scaling analysis image, and analyzing index parameters that maintain a relatively close spatial distance with mechanical strength; thereby resolving the correlation between the raw material composition, microstructure, and mechanical strength of the geopolymer material. The index parameters include raw material composition, elemental proportions in the raw materials, and the microstructure of the geopolymer material; the microstructure of the geopolymer material includes oxygen bond types, silicon types, and aluminum types.

[0035] The correlation analysis and non-metric multidimensional scaling analysis in step four help to more objectively and directly demonstrate the influence of different raw material components and microstructures on the mechanical strength of geopolymer materials, making it easier to identify and judge.

[0036] The present invention has at least the following beneficial effects:

[0037] This invention, based on component content data, elemental ratio data, mechanical strength data, oxygen bond energy spectrum peak information, silicon peak data, and aluminum peak data, quantifies the microstructure through deconvolution calculations. Then, using data standardization transformation, correlation analysis, and non-metric multidimensional scaling analysis, it can accurately analyze the correlation between the mechanical strength of different raw material components and microstructure parameters of geopolymer materials prepared from complex solid waste. Geopolymer materials prepared from solid waste raw materials exhibit significant differences in raw material components, diverse microstructure configurations, and large variations in mechanical strength. This invention, based on detection technology, deconvolution calculations, data standardization transformation, and data dimensionality reduction analysis, provides a reliable approach to exploring the correlations between these factors, and can intuitively reflect the influence of complex components and microstructure parameters on mechanical strength. In particular, this invention obtains quantitative proportion data of different microstructures through deconvolution calculations, thus providing fundamental data for quantitative analysis of the formation of microstructures by different raw material components. Furthermore, this invention employs statistical methods to quantify data obtained from precision equipment, with mutually supporting data conclusions. This overcomes the subjectivity inherent in traditional methods that rely on manual identification, providing a more objective and direct demonstration of the influence of different raw material components and microstructures on the strength of geopolymer materials, facilitating identification and judgment. This invention elucidates the impact of raw material components and microstructure on mechanical strength, overcoming the limitation of using a single method to quantitatively analyze the correlation between raw material components, microstructure, and mechanical strength of geopolymer materials. It can more accurately and comprehensively analyze the correlation between mechanical strength under the influence of complex components and microstructures.

[0038] Moreover, this invention can perform quantitative analysis by integrating raw material composition and microstructure test data as well as macroscopic mechanical property test data, which improves data processing speed, reduces the complexity of assessing the impact of multiple data sources and complex components and microstructures on the mechanical strength of geopolymer materials, saves time and costs, and provides a reliable analytical method for studying key influencing factors of mechanical strength of solid waste-derived geopolymer materials.

[0039] Furthermore, this invention can also be used to analyze the key elements in the preparation of geopolymer materials from multi-source solid waste, and to identify key components in complex raw material compositions that have a significant impact on the microstructure and mechanical strength of geopolymer materials. This provides a basis for a deeper understanding of the microstructure composition of complex solid waste-derived geopolymer materials and for improving their mechanical strength, as well as for targeted strengthening of geopolymer materials. It also helps to broaden the selection and sources of raw materials for the preparation of geopolymer materials. Attached Figure Description

[0040] Figure 1 This is a flowchart illustrating the method for determining the raw material-structure-strength correlation of polymer materials based on deconvolution quantization and dimensionality reduction analysis in a specific embodiment of the present invention.

[0041] Figure 2 These are the proportions of solid waste material powders with different ratios in the embodiments of the present invention.

[0042] Figure 3 This is a correlation matrix image of different parameters and mechanical strength in an embodiment of the present invention.

[0043] Figure 4 This is a Shepard image from the non-metric multidimensional scaling analysis process in an embodiment of the present invention.

[0044] Figure 5 This is a non-metric multidimensional scale analysis image of the mechanical strength correlation of 12 geopolymer materials prepared from different raw materials in the embodiments of the present invention.

[0045] Figure 6 This is a non-metric multidimensional scaling analysis image showing the correlation between different parameters and mechanical strength in an embodiment of the present invention. Detailed Implementation

[0046] To provide a clearer understanding of the technical features, objectives, and beneficial effects of the present invention, the present invention will now be described in detail below, but this should not be construed as limiting the scope of the invention.

[0047] It should be noted that, unless otherwise specified, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0048] Unless otherwise specified, all raw materials, reagents, instruments and equipment used in this invention can be purchased from the market or prepared by existing methods.

[0049] It should be understood that the terms “comprising,” “including,” and / or “containing” as used herein specify the presence of the stated features, integers, steps, components, or combinations thereof, but do not exclude the presence or addition of one or more other features, integers, steps, components, or combinations thereof.

[0050] The endpoints and any values ​​of the ranges disclosed in this invention are not limited to the precise ranges or values, and these ranges or values ​​should be understood to include values ​​close to these ranges or values. For numerical ranges, the endpoint values ​​of the various ranges, the endpoint values ​​of the various ranges and individual point values, and individual point values ​​can be combined with each other to obtain one or more new numerical ranges, which should be considered as specifically disclosed in this invention.

[0051] In a specific embodiment of the present invention, such as Figure 1 As shown, the method of the present invention based on deconvolution quantization and dimensionality reduction analytical analysis of raw material-structure-strength correlation of polymer materials includes the following steps:

[0052] Step 1: Testing and Data Acquisition

[0053] Selection and pretreatment of solid waste raw materials: Select solid waste raw materials according to actual needs, and pretreat the solid waste raw materials by drying, grinding, sieving and other processes;

[0054] Testing of solid waste raw materials: X-ray fluorescence spectrometry was used to test the solid waste raw materials to obtain component content data under different raw material ratios. Based on the measured component content, the content ratio of key elements was calculated to obtain element ratio data in solid waste raw materials under different raw material ratios.

[0055] Preparation of geopolymer materials: Geopolymer materials are prepared by using solid waste raw materials with different raw material ratios through alkali activation, molding and curing methods. The specific methods for preparing geopolymer materials can adopt conventional methods in the field, and the present invention does not impose any special restrictions on them.

[0056] Mechanical strength testing of geopolymer materials: The compressive strength of geopolymer materials was tested using an unconfined pressure testing machine to obtain mechanical strength data of geopolymer materials under different raw material ratios;

[0057] XPS analysis of geopolymer materials 29 Si MAS-NMR and 27Al MAS-NMR testing: After mechanical strength testing, the crushed geopolymer material particles were collected, ground, and sieved. The oxygen bond energy spectrum peak information of the geopolymer material under different raw material ratios was obtained by X-ray photoelectron spectroscopy, the silicon spectrum peak data of the geopolymer material under different raw material ratios was obtained by silicon spectrum testing of solid-state nuclear magnetic resonance spectroscopy, and the aluminum spectrum peak data of the geopolymer material under different raw material ratios was obtained by aluminum spectrum testing of solid-state nuclear magnetic resonance spectroscopy.

[0058] Step 2: Deconvolution Calculation

[0059] The oxygen bond energy spectrum peak information, silicon spectrum peak data and aluminum spectrum peak data obtained in step one of the deconvolution calculation are used to realize the deconvolution quantification of the microstructure and obtain the proportion data of oxygen bond type, silicon type and aluminum type of geopolymer material under different raw material ratio conditions.

[0060] Step 3: Standardization Conversion

[0061] The component content data and element ratio data of solid waste raw materials under different raw material ratio conditions, the mechanical strength data of geopolymer materials under different raw material ratio conditions, and the proportion data of oxygen bond type, silicon type and aluminum type of geopolymer materials under different raw material ratio conditions obtained in Step 1 and Step 2 are standardized and converted to obtain dimensionless standardized data.

[0062] Step 4: Dimensionality Reduction Analysis

[0063] The dimensionless standardized data obtained in step three are subjected to correlation analysis and non-metric multidimensional scaling analysis to obtain correlation matrix images and non-metric multidimensional scaling analysis images between different index parameters, which visually analyze the correlation between raw material components, microstructure and mechanical strength of geopolymer materials.

[0064] Example

[0065] Step 1: Testing and Data Acquisition

[0066] For the selection and pretreatment of solid waste raw materials: four materials, namely fly ash, silicon slag, blast furnace slag and steel slag, were selected as raw materials to prepare multi-source solid waste-derived geopolymer materials; after drying these solid waste raw materials at 70℃, they were ground and then passed through a 200-mesh sieve, and the powder was retained for later use.

[0067] Solid waste raw materials were tested: XRF was used to test the solid waste raw materials (powder after the above pretreatment) to obtain component content data under different raw material ratios. Based on the measured component contents, the content ratios of key elements were calculated to obtain elemental ratio data in the solid waste raw materials under different raw material ratios. The measured component contents included silicon dioxide, aluminum oxide, calcium oxide, ferric oxide, magnesium oxide, and titanium dioxide. The content ratios of the key elements included Al / Ca ratio, Al / Na ratio, Si / Al ratio, Si / Na ratio, and Si / Ca ratio. These content ratios refer to mass content ratios.

[0068] Preparation of Geopolymer Materials: Composite Solid Waste Raw Material Preparation: Different solid waste material powders were combined in a reactor according to different ratios. The mixture was stirred for 5 minutes at a speed of 500 r / min to obtain the composite solid waste raw material. Ultrasonic Enhanced Composite Alkali Activator Preparation: An appropriate amount of hydrated sodium silicate solution was weighed, and sodium hydroxide was added to the hydrated sodium silicate solution. The mixture was stirred thoroughly until completely dissolved, and then vibrated using an ultrasonic vibrator until the solution was clear and transparent. Its modulus M (SiO2 / Na2O) was adjusted to 1.50. This alkali activator should be prepared and used immediately; storage time should not be too long. Slurry Preparation: The composite solid waste raw material and the ultrasonic enhanced composite alkali activator were mixed at a mass ratio of 2:1. An appropriate amount of deionized water was added, with a mass ratio of 1:6 between the deionized water and the composite solid waste raw material. The mixture was stirred for 5 minutes at a speed of 2000 r / min to obtain an alkali-activated inorganic cementitious material slurry. Molding and Curing: The slurry is injected into the mold and agitated until no air bubbles emerge. The material and mold are then sealed together with a polyethylene film and placed in a constant temperature curing chamber at 70℃ for 12 hours. Afterward, it is removed and cured at room temperature. In the preparation of the composite solid waste raw material, different proportions of solid waste powder were investigated, including fly ash (0 wt% to 100 wt%), silicon slag (10 wt% to 40 wt%), blast furnace slag (0 wt% to 100 wt%), and steel slag (0 wt% to 100 wt%). Specifically, the proportions of different solid waste powders were... Figure 2 As shown in the figure. FA represents fly ash, SF represents silica slag, BFS represents blast furnace slag, and SR represents steel slag.

[0069] Mechanical strength testing was conducted on the geopolymer materials: the compressive strength of the geopolymer materials was tested using an unconfined pressure testing machine to obtain mechanical strength data for geopolymer materials under different raw material ratios. The compressive strength was tested after curing at room temperature for 7, 14, and 28 days. Twelve types of materials with significant differences in raw material composition, component content, and compressive strength were selected to determine the applicability of the method in this invention to different raw materials, different component ratios, and different compressive strength distribution ranges. The definition of "significant difference" can be determined by those skilled in the art based on common knowledge. The mechanical strength range of the 12 selected geopolymer materials after 28 days of curing was 8.40–115.77 MPa.

[0070] XPS analysis of geopolymer materials 29 Si MAS-NMR and 27 Al MAS-NMR testing: Particles of the crushed geopolymer material after compressive strength testing were collected, ground, and then sieved through a 200-mesh sieve. The powder was then sealed and stored. XPS was used to analyze the geopolymer powder to obtain oxygen bond energy spectrum peak information of the geopolymer material under different raw material ratios. 29 Si MAS-NMR was used to test geopolymer powders and obtain silicon peak data of geopolymers under different raw material ratios; 27 Al MAS-NMR was used to test geopolymer powders and obtain aluminum peak data of geopolymers under different raw material ratios.

[0071] Step 2: Deconvolution Calculation

[0072] The oxygen bond energy spectrum peak information, silicon spectrum peak data and aluminum spectrum peak data obtained in step one of the deconvolution calculation are used to realize the deconvolution quantification of the microstructure based on the peak position and intensity, so as to obtain the proportion data of oxygen bond type, silicon type and aluminum type of geopolymer material under different raw material ratio conditions.

[0073] Deconvolution calculations can be performed using conventional software, and this invention does not impose any special limitations. The fitting parameters R for the oxygen bond energy spectrum peak information, silicon spectrum peak data, and aluminum spectrum peak data obtained through software deconvolution calculations are as follows: 2 It should be 0.995 or higher.

[0074] Based on XPS analysis of O bond structure types, the O bonds in geopolymer materials are classified into Si-O-Al bonds, Si-O-Si bonds, Si-O-Na bonds, and Si-OH bonds.

[0075] based on 29 Si MAS-NMR silicon spectral data, classifying the silicon seed configuration into Q 0 Configuration, Q 1 Configuration, Q2 Configuration, Q 4 (4Al) configuration, Q 4 (3Al) configuration, Q 4 (2Al) configuration, Q 4 (1Al) configuration, Q 4 (0Al) configuration.

[0076] based on 27 Al MAS-NMR aluminum spectrum data classified the aluminum species into six-coordinate Al-type III hydrated aluminate configuration, six-coordinate Al-calcium sulfoaluminate configuration, six-coordinate Al-ettlingite configuration, five-coordinate Al configuration, and four-coordinate Al-Q configuration. 4 (4Si) configuration, tetracoordinated Al configuration.

[0077] Step 3: Standardization Conversion

[0078] The component content data and element ratio data of solid waste raw materials under different raw material ratio conditions, the mechanical strength data of geopolymer materials under different raw material ratio conditions, and the proportion data of oxygen bond type, silicon type and aluminum type of geopolymer materials under different raw material ratio conditions obtained in Step 1 and Step 2 are standardized and converted to obtain dimensionless standardized data.

[0079] Data standardization was performed using the z-score standardization method. This method standardizes the data based on the mean and standard deviation of the original data. For the sequences x1, x2, x3, ..., x... n With modifications, the z-score standardization formula is calculated as follows:

[0080]

[0081] The resulting new sequence is y1, y2, y3, ... y n The mean is 0, the variance is 1, and it is a dimensionless number.

[0082] The standardized data of the converted component content and element ratio are shown in Table 1.

[0083] Table 1

[0084]

[0085] The standardized data of the proportions of oxygen bond types and silicon type after conversion are shown in Table 2.

[0086] Table 2

[0087]

[0088] The standardized data of the proportion of different aluminum types after conversion are shown in Table 3.

[0089] Table 3

[0090]

[0091] The standardized data of the mechanical strength of the converted geopolymer material are shown in Table 4.

[0092] Table 4

[0093]

[0094] Step 4: Dimensionality Reduction Analysis

[0095] The dimensionless standardized data obtained in step three are subjected to correlation analysis and non-metric multidimensional scaling analysis to obtain correlation matrix images and non-metric multidimensional scaling analysis images between different index parameters, which visually analyze the correlation between raw material components, microstructure and mechanical strength of geopolymer materials.

[0096] Correlation analysis:

[0097] Different parameters are selected for assignment, and the parameters with correlation are defined as x. i x j , characteristic quantity x i x j The correlation coefficient r between them ij The calculation formula is shown below:

[0098]

[0099] In the formula, x i x j The correlation coefficient r represents ij The two corresponding related features, x ki Representing the characteristic quantity x i The kth factor, Representing the characteristic quantity x i The average value, x kj Representing the characteristic quantity x j The kth factor, Representing the characteristic quantity x j The average value, where n represents the total number of factors in the characteristic quantity.

[0100] Specifically, x iThese are dimensionless standardized data for the component content of solid waste raw materials under different raw material ratios, the elemental proportions of solid waste raw materials under different raw material ratios, the proportion of oxygen bond types in geopolymer materials under different raw material ratios, the proportion of silicon types in geopolymer materials under different raw material ratios, and the proportion of aluminum types in geopolymer materials under different raw material ratios. Let x be the value of x. i Taking dimensionless standardized data of component content in solid waste raw materials under different raw material ratios as an example, then x ki Dimensionless standardized data representing the component content in solid waste raw materials under certain raw material ratio conditions. x represents the average value of dimensionless standardized data on the component content in solid waste raw materials under different raw material ratios. j For dimensionless standardized data of the mechanical strength of geopolymer materials under different raw material ratios, then x kj This indicates that under a certain raw material ratio (and x) ki The data represents dimensionless standardized data on the mechanical strength of geopolymer materials with the same raw material ratios. This represents the average value of dimensionless standardized data on the mechanical strength of solid waste materials under different raw material ratios. n represents the number of "different raw material ratios," which is 12 in this embodiment.

[0101] Based on the above formula, the correlation matrix image can be obtained, such as Figure 3 As shown.

[0102] Nonmetric multidimensional scaling analysis:

[0103] Nonmetric multidimensional scaling (NMDS) analysis is a dimensionality reduction analysis method that simplifies research objects in a multidimensional space to a low-dimensional space for location, analysis, and classification. NMDS analysis can also preserve the original relationships between objects and reflect the sequential relationships between research objects.

[0104] The R language software was used to perform nonmetric multidimensional scaling analysis on dimensionless standardized data, and the Shepard plot was drawn using the vegan library, as shown below. Figure 4 As shown. Furthermore, the ggplot2 library is used to plot non-metric multidimensional scaling analysis images, such as... Figure 5 and Figure 6 As shown.

[0105] By analyzing the relevant matrix image ( Figure 3Analysis reveals that the raw material components showing a significant positive correlation with mechanical strength mainly include CaO and MgO; the microstructures showing a significant positive correlation with mechanical strength mainly include Si-O-Al bonds, Si-O-Si bonds, and Q... 1 Configuration, Q 2 Configuration, Q 4 (4Al) configuration, Q 4 (3Al) configuration, six-coordinate Al-ettlingite configuration, five-coordinate Al configuration, four-coordinate Al-Q configuration 4 (4Si) configuration. Other microstructures that show a positive correlation with mechanical strength include: Si-O-Na bonds, Q... 0 Configuration, tetracoordinated Al configuration.

[0106] By analyzing images using non-metric multidimensional scaling ( Figure 6 Analysis reveals that, in different dimensional projections, the raw material components maintaining a relatively close spatial distance from the mechanical strength UCS mainly include: MgO and CaO; the microstructures maintaining a relatively close spatial distance mainly include: Si-O-Al bonds, Q... 4 (4Al) configuration, Q 4 (3Al) configuration, six-coordinated Al-ettringite configuration, four-coordinated Al-Q 4 (4Si) configuration.

[0107] By comprehensively analyzing the correlation matrix image and the non-metric multidimensional scaling analysis image, the number of major influencing parameters obtained after dimensionality reduction analysis is significantly less than the number of highly correlated parameters in the correlation matrix, effectively reducing the complexity of key influencing parameters. At the same time, the extracted key parameters all maintain a high correlation with mechanical strength, indicating that key parameters were extracted from multidimensional data.

[0108] Therefore, embodiments of the present invention utilize X-ray fluorescence spectroscopy, X-ray photoelectron spectroscopy, and solid-state nuclear magnetic resonance silicon and aluminum spectroscopy techniques to quantify the microstructure through deconvolution calculations. Then, by using data standardization transformation, correlation analysis, and non-metric multidimensional scaling analysis, the correlation between the mechanical strength of different raw material components and microstructure parameters of geopolymer materials prepared from complex solid waste can be analyzed relatively accurately.

[0109] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of the substantive technical content of the present invention. The substantive technical content of the present invention is broadly defined within the scope of the claims. Any technical entity or method implemented by others that is completely identical to or an equivalent modification of the claims is considered to be covered within the scope of the claims.

Claims

1. A method for quantitatively and dimensionally analyzing the correlation between raw materials-microstructure-strength of geopolymer materials based on deconvolution, comprising the following steps: Step 1:Testing and data acquisition Testing solid waste raw materials and geopolymer materials prepared from the solid waste raw materials to obtain component content data and element ratio data of the solid waste raw materials under different raw material ratios, mechanical strength data of the geopolymer materials under different raw material ratios, and oxygen bond spectral peak information, silicon spectral peak data and aluminum spectral peak data of the geopolymer materials under different raw material ratios; Step 2:Deconvolution calculation Using the oxygen bond spectral peak information, silicon spectral peak data and aluminum spectral peak data obtained in step 1 to obtain the proportion data of oxygen bond types, silicon types and aluminum types of the geopolymer materials under different raw material ratios; Step 3:Standardization conversion Standardizing and converting the component content data and element ratio data of the solid waste raw materials under different raw material ratios, the mechanical strength data of the geopolymer materials under different raw material ratios, and the proportion data of the oxygen bond types, silicon types and aluminum types of the geopolymer materials under different raw material ratios obtained in steps 1 and 2 to obtain dimensionless standardized data; Step 4:Dimensional analysis Performing correlation analysis and non-metric multidimensional scaling analysis on the dimensionless standardized data obtained in step 3 to obtain correlation matrix images and non-metric multidimensional scaling analysis images between different index parameters, and analyzing the correlation between the raw material components, microstructure and mechanical strength of the geopolymer materials.

2. The method of quantifying and dimensionally reducing analytically the correlation of feedstock-structure-strength of convolutively based materials of claim 1, wherein, In step 1, the solid waste raw materials are tested by using an X-ray fluorescence spectrometer.

3. The method of quantifying and dimensionally reducing analytically the correlation of feedstock-structure-strength of a convolutively based polymeric material according to claim 2, wherein, In step 1, the solid waste raw materials are tested by using an X-ray fluorescence spectrometer to obtain component content data of the solid waste raw materials under different raw material ratios, and the content ratio of key elements is calculated according to the measured component content to obtain element ratio data of the solid waste raw materials under different raw material ratios; Preferably, the measured component content includes the content of two or more of the following components: silicon dioxide, aluminum oxide, calcium oxide, sodium oxide, iron oxide, magnesium oxide and titanium dioxide; Preferably, the content ratio of the key elements includes two or more of the following ratios: Al / Ca ratio, Al / Na ratio, Si / Al ratio, Si / Na ratio and Si / Ca ratio.

4. The method of quantifying and dimensionally reducing the structure-strength correlation of a polymeric material analytically based on deconvolution according to claim 1, wherein, In step 1, testing the geopolymer materials prepared from the solid waste raw materials includes mechanical strength testing, and testing by using an X-ray photoelectron spectrometer and a solid-state nuclear magnetic resonance spectrometer.

5. The method of quantifying and dimensionally resolving the feedstock-structure-strength correlations of a convolutively-based polymeric material according to claim 4, wherein, In step 1, after preparing geopolymer materials from solid waste raw materials with different raw material ratios, the mechanical strength of the geopolymer materials is tested to obtain mechanical strength data of the geopolymer materials under different raw material ratios; Preferably, the mechanical strength is compressive strength.

6. The method of quantifying and dimensionally resolving the feedstock-structure-strength correlations of polymeric materials based on deconvolution according to claim 4, wherein, In step one, the geopolymer material is tested by an X-ray photoelectron spectrometer to obtain oxygen bond spectral peak information of the geopolymer material under different raw material ratios; the geopolymer material is tested by a solid-state nuclear magnetic resonance spectrometer to obtain silicon spectral peak data of the geopolymer material under different raw material ratios; and the geopolymer material is tested by the solid-state nuclear magnetic resonance spectrometer to obtain aluminum spectral peak data of the geopolymer material under different raw material ratios. Preferably, the broken geopolymer material particles after the mechanical strength test are collected, and then ground and sieved, and then tested by the X-ray photoelectron spectrometer and the solid-state nuclear magnetic resonance spectrometer.

7. The method of quantifying and dimensionally reducing the structure-strength correlation of a-priori polymeric materials based on deconvolution according to claim 1, wherein, In step two, the oxygen bond species include Si-O-Al bonds, Si-O-Si bonds, Si-O-Na bonds, and Si-O-H bonds; the silicon bond species include Q 0 configurations, Q 1 configurations, Q 2 configurations, Q 4 (4Al) configurations, Q 4 (3Al) configurations, Q 4 (2Al) configurations, Q 4 (1 Al) configurations, and Q 4 (0 Al) configurations; the aluminum bond species include six-coordinated Al - third class hydroaluminate configurations, six-coordinated Al - ettringite configurations, six-coordinated Al - thaumasite configurations, five-coordinated Al configurations, four-coordinated Al - Q 4 (4Si) configurations, and four-coordinated Al configurations.

8. The method of quantifying and dimensionally reducing the structure-strength correlations of a-priori polymeric materials based on deconvolution according to claim 1, wherein, In step three, the standardized conversion adopts a z-score standardization method.

9. The method of quantifying and dimensionally reducing the structure-strength correlations of a-priori polymeric materials based on deconvolution according to claim 1, wherein, In step four, the correlation analysis adopts the following formula: In the formula, x i x j The correlation coefficient r represents ij The two corresponding related features, x ki Representing the characteristic quantity x i The kth factor, Representing the characteristic quantity x i The average value, x kj Representing the characteristic quantity x j The kth factor, Representing the characteristic quantity x j The average value, where n represents the total number of factors in the characteristic quantity.

10. The method of quantifying and dimensionally reducing the structure-strength correlations of a-priori polymeric materials based on deconvolution according to claim 1, wherein, In step four, the non-metric multidimensional scaling analysis includes: based on the non-dimensional standardized data obtained in step three, calling a vegan library to draw a Shepard plot, and calling a ggplot2 library to draw a non-metric multidimensional scaling analysis image, to obtain a non-metric multidimensional scaling analysis image between different index parameters.

11. The method of quantifying and dimensionally reducing analytically polymeric materials' feedstock-structure-strength correlations based on deconvolution according to claim 1, wherein, Step four includes: based on the obtained correlation matrix image, analyzing the index parameters that have a significant positive correlation with the mechanical strength; based on the obtained non-metric multidimensional scaling analysis image, observing the spatial distance characteristics of different index parameters, and analyzing the index parameters that maintain a relatively close spatial distance with the mechanical strength; thereby analyzing the correlation between the raw material components, the microstructure and the mechanical strength of the geopolymer material.

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