Performance prediction method and device for directional activation of silicon-aluminum solid waste and storage medium

Abstract

The invention relates to a performance prediction method and device for directional activation of silicon-aluminum solid waste and a storage medium, and belongs to the technical field of solid waste based polymer preparation, the method comprises the following steps: obtaining a first linear function through fitting based on the total content of silicon, the total content of aluminum, the content of active silicon and the content of active aluminum in the silicon-aluminum solid waste with different components; on the basis of the ratio of the predicted content of the active silicon to the predicted content of the active aluminum and the compressive strength of the geopolymer, a second linear function is obtained through fitting; and optimizing the second linear function based on the predicted content of the active silicon, the predicted content of the active aluminum and the compressive strength of the geopolymer to obtain a performance prediction model. According to the performance prediction method for directional activation of the silicon-aluminum solid waste, factors such as chemical components of the solid waste and the directional activation treatment effect are comprehensively considered, the quantitative relation between the content of the active components and the performance of the geopolymer is established, and accurate prediction of the performance of the geopolymer can be achieved.

Description

Technical Field

[0001] This invention relates to the field of polymer technology for solid waste sites, and in particular to a method, apparatus, and storage medium for predicting the performance of directional activation of silica-alumina solid waste. Background Technology

[0002] my country is currently in a crucial stage of rapid economic development and comprehensive social progress, resulting in a sustained high demand for various building materials. The building materials industry, especially the cement industry, plays a vital role in promoting infrastructure construction and urbanization as a fundamental sector of the national economy. However, the cement industry is also one of the world's largest sources of CO2 emissions.

[0003] Meanwhile, my country generates a large amount of industrial solid waste annually, much of which contains a high proportion of silicon and aluminum components, such as slag, fly ash, desulfurization gypsum, and incinerator fly ash. These silicon-aluminum solid wastes not only occupy vast amounts of land but can also cause environmental pollution and safety hazards due to improper disposal and long-term storage. For example, open-air storage of fly ash can lead to heavy metal leakage, polluting soil and groundwater; large-scale accumulation of slag can trigger geological disasters. Therefore, effectively utilizing these bulk industrial solid wastes and transforming them into economically valuable building materials is a crucial way to achieve industrial upgrading and energy conservation and emission reduction.

[0004] Geopolymers, as a novel green cementitious material, have received widespread attention in recent years. Primarily composed of SiO2 and Al2O3, they are prepared through alkali activation, forming a cementitious material with a three-dimensional network structure. Geopolymers possess advantages such as high strength, good durability, and strong resistance to chemical erosion, and produce almost no carbon dioxide emissions during production. Therefore, they are frequently used in the building materials industry to replace traditional high-CO2-emission cement. With the continuous development of related technologies, the raw material system for geopolymers has become increasingly diversified, expanding from initial natural mineral materials to industrial wastes such as slag, fly ash, desulfurized gypsum, and waste incineration fly ash. This resource utilization method not only reduces the pressure of industrial solid waste accumulation but also lowers dependence on natural resources, resulting in significant environmental and economic benefits.

[0005] However, in practical applications, the raw material system for geopolymers faces a complex problem that urgently needs to be addressed: the relationship between composition, activity, and performance is intricate and difficult to predict. The composition of silica-alumina solid wastes from different sources varies significantly, resulting in different performances in geopolymers. For example, the activity of fly ash mainly depends on its glass content and particle size distribution, while the activity of slag is closely related to its silica-alumina ratio. Currently, this field still relies primarily on intuitive experimental methods to explore performance trends, lacking systematic theoretical support. This method is not only time-consuming and labor-intensive but also difficult to achieve accurate prediction and optimization. Furthermore, traditional research methods often require extensive experimental verification, making it difficult to quickly respond to the actual needs of industrial production. In practical applications, due to the lack of effective predictive models, enterprises often struggle to quickly adjust production processes based on the specific composition of solid waste, leading to resource waste and low production efficiency.

[0006] Therefore, how to comprehensively integrate the two key factors of raw material composition and particle size distribution, and establish a theoretical model applicable to solid waste raw materials, is a complex and critical problem that urgently needs to be solved in geopolymer preparation technology. Developing a model that can accurately predict the content and performance of active components after directional activation of aluminosilicate solid waste is of paramount practical significance for improving solid waste utilization, optimizing geopolymer performance, and promoting the sustainable development of the building materials industry. It will not only help achieve efficient resource utilization of industrial solid waste, but also provide strong support for my country's carbon emission reduction goals, while simultaneously driving the building materials industry towards a green, low-carbon, and efficient development direction. Summary of the Invention

[0007] In view of this, it is necessary to provide a method, apparatus and storage medium for predicting the performance of directional activation of silicon-aluminum solid waste, so as to achieve the purpose of accurately predicting the performance of directionally activated silicon-aluminum solid waste.

[0008] To achieve the above objectives, in a first aspect, the present invention provides a method for predicting the performance of targeted activation of silicon-aluminum solid waste, comprising: Based on the total silicon content, total aluminum content, active silicon content, and active aluminum content in different components of silicoaluminous solid waste, a first linear function is obtained through fitting; the first linear function is used to predict the predicted content of active silicon and the predicted content of active aluminum. Based on the ratio of the predicted content of active silicon to the predicted content of active aluminum, and the compressive strength of the geopolymer, a second linear function is obtained by fitting; the geopolymer is prepared using silicon-aluminum solid waste of different components under the same process. Based on the predicted content of the active silicon, the predicted content of the active aluminum, and the compressive strength of the geopolymer, the second linear function is optimized to obtain a performance prediction model; the performance prediction model is used to predict the compressive strength of silicon-aluminum solid waste after directional activation.

[0009] In one possible implementation, the optimization of the second linear function based on the predicted content of the active silicon, the predicted content of the active aluminum, and the compressive strength of the geopolymer to obtain a performance prediction model includes: The sum of the predicted content of the active silicon and the predicted content of the active aluminum, the first average value of the predicted content of the active silicon, the second average value of the predicted content of the active aluminum, and the third average value of the compressive strength of the geopolymer are obtained. Based on the first average value, the second average value, and the third average value, a correction coefficient is determined; The correction term is determined based on the correction coefficient, the sum, the first average value, the second average value, and the third average value; The performance prediction model is determined based on the second linear function and the correction term.

[0010] In one possible implementation, before obtaining the first linear function by fitting the total silicon content, total aluminum content, active silicon content, and active aluminum content in the silica-alumina solid waste based on different components, the method further includes: Based on the raw material characteristics of silicon-aluminate solid waste, the total silicon content and total aluminum content were obtained; Based on the alkaline solubility characteristics of silicon and aluminum in silicon-aluminum solid waste, the content of active silicon and active aluminum participating in the hydration reaction was obtained.

[0011] In one possible implementation, the expression for the first linear function is as follows:

[0012] in, This indicates the predicted content of active silicon or active aluminum. Indicates the total content of silicon or aluminum. Indicates the activity and stability of solid waste. This indicates the baseline activity of solid waste.

[0013] In one possible implementation, the expression for the second linear function is as follows:

[0014] in, Indicates the compressive strength of the geopolymer. Indicates the ratio, , These represent the fitting parameters.

[0015] In one possible implementation, the performance prediction model is expressed as follows:

[0016]

[0017] in, This represents the predicted compressive strength value. Represents the sum value. This represents the first average value. This represents the second average. This represents the third average. This represents the correction factor.

[0018] In one possible implementation, the silica-alumina solid waste includes any one of the following: Fly ash, slag, stone waste, metallurgical slag or tailings slag.

[0019] Secondly, the present invention also provides a performance prediction device for the targeted activation of silicon-aluminum solid waste, comprising: The first fitting unit is used to obtain a first linear function by fitting the total silicon content, total aluminum content, active silicon content, and active aluminum content in silicoaluminous solid waste of different components; the first linear function is used to predict the predicted content of active silicon and the predicted content of active aluminum. The second fitting unit is used to obtain a second linear function by fitting the ratio of the predicted content of the active silicon to the predicted content of the active aluminum, and the compressive strength of the geopolymer; the geopolymer is prepared using the different components of silicon-aluminum solid waste under the same process. An optimization unit is used to optimize the second linear function based on the predicted content of the active silicon, the predicted content of the active aluminum, and the compressive strength of the geopolymer to obtain a performance prediction model; the performance prediction model is used to predict the compressive strength of the silicon-aluminum solid waste after directional activation.

[0020] Thirdly, the present invention also provides an electronic device, including a memory and a processor, wherein, The memory is used to store programs; The processor, coupled to the memory, is used to execute the program stored in the memory to implement the performance prediction method for the targeted activation of silicon-aluminum solid waste as described in any of the above implementations.

[0021] Fourthly, the present invention also provides a computer-readable storage medium for storing a computer-readable program or instructions, which, when executed by a processor, can implement the steps in the performance prediction method for the directional activation of silicon-aluminate solid waste described in any of the above implementations.

[0022] The beneficial effects of this invention are as follows: The performance prediction method, device, and storage medium for the targeted activation of silicoaluminous solid waste provided by this invention obtain a first linear function by fitting the total silicon content, total aluminum content, active silicon content, and active aluminum content in silicoaluminous solid waste of different components. This first linear function can then be used to predict the predicted content of active silicon and active aluminum. Furthermore, a second linear function is obtained by fitting the ratio of these two components to the compressive strength of the geopolymer. The second linear function is then corrected based on the predicted content of active silicon, the predicted content of active aluminum, and the compressive strength of the geopolymer to obtain a performance prediction model. By comprehensively considering factors such as the chemical composition of the solid waste and the effect of targeted activation treatment, a quantitative relationship between the content of active components and the performance of the geopolymer is established. This enables accurate prediction of the geopolymer performance, providing strong support for the efficient resource utilization of industrial solid waste, the performance optimization of geopolymers, and the sustainable development of the building materials industry. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 A schematic flowchart of an embodiment of the performance prediction method for targeted activation of silicon-aluminate solid waste provided by the present invention; Figure 2 One of the schematic diagrams for linear fitting of the silica-alumina properties of fly ash with different components provided by the present invention; Figure 3 The second schematic diagram of linear fitting of the silica-alumina properties of fly ash with different components provided by the present invention; Figure 4 A schematic diagram illustrating the linear fitting between the silica-alumina ratio and geopolymer properties of fly ash provided by this invention; Figure 5 One of the schematic diagrams of linear fitting of the silica-alumina properties of red mud with different components provided by the present invention; Figure 6 The second schematic diagram of linear fitting of the silica-alumina properties of red mud with different components provided by the present invention; Figure 7 A schematic diagram illustrating the linear fitting of the silica-alumina ratio of red mud and geopolymer properties provided by this invention. Figure 8 A schematic diagram of an embodiment of the performance prediction device for targeted activation of silicon-aluminate solid waste provided by the present invention; Figure 9 A schematic diagram of an embodiment of the electronic device provided by the present invention. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0026] In the description of the embodiments of the present invention, unless otherwise stated, "multiple" means two or more. "And / or" describes the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone.

[0027] The terms "first," "second," etc., used in the embodiments of this invention are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a technical feature defined with "first" or "second" may explicitly or implicitly include at least one of that feature.

[0028] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0029] Before demonstrating the embodiments, the following terms will be explained.

[0030] Targeted activation refers to the process of selectively stimulating the reactivity of silicon and aluminum components in solid waste through specific processes (such as alkali dissolution, heat treatment, mechanical grinding, etc.) so that they can participate more efficiently in subsequent hydration reactions (such as geopolymer synthesis).

[0031] This invention provides a method, apparatus, and storage medium for predicting the performance of targeted activation of silicon-aluminum solid waste, which will be described below.

[0032] Figure 1 A schematic flowchart of an embodiment of the performance prediction method for targeted activation of silicon-aluminate solid waste provided by the present invention is shown below. Figure 1 As shown, the performance prediction method for targeted activation of silica-alumina solid waste includes: S101. Based on the total silicon content, total aluminum content, active silicon content, and active aluminum content in different components of silicoaluminous solid waste, a first linear function is obtained by fitting; the first linear function is used to predict the predicted content of active silicon and the predicted content of active aluminum. S102. Based on the ratio of the predicted content of the active silicon to the predicted content of the active aluminum, and the compressive strength of the geopolymer, a second linear function is obtained by fitting; the geopolymer is prepared using the different components of silicon-aluminum solid waste under the same process. S103. Based on the predicted content of the active silicon, the predicted content of the active aluminum, and the compressive strength of the geopolymer, the second linear function is optimized to obtain a performance prediction model; the performance prediction model is used to predict the compressive strength of the silicon-aluminum solid waste after directional activation.

[0033] In S101, the silicon-aluminate solid waste includes various solid wastes that use silicon and aluminum as reactants. In some embodiments of the present invention, the silicon-aluminate solid waste includes any one of the following: fly ash, slag, stone waste, metallurgical slag or tailings slag.

[0034] The total silicon content (T) in the silicon-aluminate solid waste was obtained based on the raw material characteristics of the silicon-aluminate solid waste. Si Total aluminum content T Al The content A of active Si effectively participating in the hydration reaction was obtained based on the alkaline solubility characteristics of silicon and aluminum. Si and the content of active Al A Al .

[0035] By analyzing the same type of silica-alumina solid waste with different components, respectively using T Si T Al Using A as the horizontal axis Si A Al A scatter plot is drawn with the vertical axis, and then linear fitting is performed sequentially to obtain the theoretical first linear function for predicting the content of active ingredients. The first linear function can predict the content of active ingredients from the total content.

[0036] In S102, geopolymers are prepared using the same type of silica-alumina solid waste with different components under the same process, and the compressive strength Y of the geopolymers is obtained.

[0037] The predicted content of active silicon can be predicted based on the first linear function. and the predicted content of active aluminum The ratio of the two A scatter plot was drawn with the horizontal axis representing the compressive strength Y as the vertical axis. Then, a linear fit was performed to obtain a second linear function. The second linear function was initially fitted using the influence of the silicon-aluminum ratio.

[0038] In S103, the second linear function is corrected by the average predicted content of active silicon, the average predicted content of active aluminum, and the average compressive strength of geopolymer, to obtain the final performance prediction model.

[0039] This performance prediction model can be used to accurately predict the compressive strength of the geopolymer after directional activation by directly measuring the total silicon and aluminum content of solid waste.

[0040] In summary, the performance prediction method for targeted activation of silicoaluminous solid waste provided in this embodiment of the invention obtains a first linear function by fitting the total silicon content, total aluminum content, active silicon content, and active aluminum content in silicoaluminous solid waste of different components. This first linear function can then be used to predict the predicted content of active silicon and active aluminum. Furthermore, a second linear function is obtained by fitting the ratio of these two components to the compressive strength of the geopolymer. The second linear function is then corrected based on the predicted content of active silicon, the predicted content of active aluminum, and the compressive strength of the geopolymer to obtain a performance prediction model. By comprehensively considering factors such as the chemical composition of the solid waste and the effect of targeted activation treatment, a quantitative relationship between the content of active components and the performance of the geopolymer is established. This enables accurate prediction of the geopolymer's performance, providing strong support for the efficient resource utilization of industrial solid waste, the performance optimization of geopolymers, and the sustainable development of the building materials industry.

[0041] In some embodiments of the present invention, before obtaining the first linear function by fitting the total silicon content, total aluminum content, active silicon content, and active aluminum content in the silicoaluminous solid waste based on different components, the method further includes: Based on the raw material characteristics of silicon-aluminate solid waste, the total silicon content and total aluminum content were obtained; Based on the alkaline solubility characteristics of silicon and aluminum in silicon-aluminum solid waste, the content of active silicon and active aluminum participating in the hydration reaction was obtained.

[0042] The total Si content T in the raw material was obtained based on the characteristics of solid waste. Si and total Al content T Al Then, based on the alkaline solubility characteristics of silicon and aluminum, the content A of active Si effectively participating in the hydration reaction was obtained. Si and the content of active Al A Al .

[0043] For example, the alkaline leaching silicon-aluminum content test is obtained by stirring and leaching 2g of solid waste raw material in a constant temperature water bath at 25~50℃ for 24h in a sodium hydroxide solution of not less than 5mol / L. The content data used for the final calculation is based on 100g of solid waste raw material, preferably using a 6-8mol / L constant temperature water bath at 40℃ for stirring and leaching for 24h.

[0044] In some embodiments of the present invention, the expression of the first linear function is as follows:

[0045] in, This indicates the predicted content of active silicon or active aluminum. Indicates the total content of silicon or aluminum. Indicates the activity and stability of solid waste. This indicates the baseline activity of solid waste.

[0046] For the same solid waste with different components, T Si T Al Using A as the horizontal axis Si A Al A scatter plot was drawn with the vertical axis, and then linear fitting was performed sequentially to obtain the theoretically predicted content of the active ingredient. The first linear function of is shown below: (1) Where k represents the activity stability of the solid waste, and b represents the baseline activity of the solid waste.

[0047] In some embodiments of the present invention, the expression of the second linear function is as follows:

[0048] in, Indicates the compressive strength of the geopolymer. Indicates the ratio, , These represent the fitting parameters.

[0049] The compressive strength Y of geopolymers was obtained by preparing the same solid waste with different components using the same process. The experimental data for compressive strength Y of each solid waste were obtained by applying the same production process to the same solid waste with different components.

[0050] With silicon-aluminum ratio A scatter plot was drawn with the x-axis representing the compressive strength Y as the y-axis. Then, a linear fit was performed to obtain the primary linear function, i.e., the second linear function, as shown below: (2) The silicon-to-aluminum ratio is the ratio of the predicted content of active silicon to the predicted content of active aluminum. This primary linear function is initially fitted by the influence of the silicon-to-aluminum ratio.

[0051] In some embodiments of the present invention, the optimization of the second linear function based on the predicted content of the active silicon, the predicted content of the active aluminum, and the compressive strength of the geopolymer to obtain a performance prediction model includes: The sum of the predicted content of the active silicon and the predicted content of the active aluminum, the first average value of the predicted content of the active silicon, the second average value of the predicted content of the active aluminum, and the third average value of the compressive strength of the geopolymer are obtained. Based on the first average value, the second average value, and the third average value, a correction coefficient is determined; The correction term is determined based on the correction coefficient, the sum, the first average value, the second average value, and the third average value; The performance prediction model is determined based on the second linear function and the correction term.

[0052] In some embodiments of the present invention, the expression of the performance prediction model is as follows:

[0053]

[0054] in, This represents the predicted compressive strength value. Represents the sum value. This represents the first average value. This represents the second average. This represents the third average. This represents the correction factor.

[0055] Further optimization of the primary linear function is performed, and T is calculated for all points. Si T Al The average value is used to obtain the first average value of the predicted active silicon content. The second average of the predicted content of active aluminum The third average compressive strength of geopolymers And calculate the sum of the predicted content of active silicon and the predicted content of active aluminum. .

[0056] Thus, the correction term is obtained. The correction term further refines the second linear function by influencing the silicon and aluminum content.

[0057] Adding this correction term to the second linear function yields the refined performance prediction model, as shown below: (3) in, The correction factor is expressed as follows: (4) The performance prediction method for the targeted activation of silicon-aluminate solid waste provided in this invention establishes a quantitative relationship between the content of active components and the performance of geopolymers by comprehensively considering factors such as the chemical composition of solid waste and the effect of targeted activation treatment. This enables accurate prediction and optimization of geopolymer performance, providing strong support for the efficient resource utilization of industrial solid waste, the performance optimization of geopolymers, and the sustainable development of the building materials industry.

[0058] The following detailed description of the performance prediction method for the targeted activation of siliceous aluminate solid waste provided by the present invention, using fly ash and red mud as examples, with specific embodiments.

[0059] Example 1: Based on the prediction model of active component content and performance of fly ash targeted activation, the data in Table 1 are obtained according to the alkali solubility characteristics of fly ash.

[0060] Table 1: Silica and aluminum characteristics of fly ash with different compositions

[0061] Linear fitting was performed based on the data in Table 1, as follows: Figure 2 and Figure 3 As shown, Figure 2 This is one of the schematic diagrams showing the linear fitting of the silica-alumina properties of fly ash with different components provided by the present invention. Figure 3 This is the second schematic diagram of linear fitting of the silica-alumina characteristics of fly ash with different components provided by the present invention, which obtains the theoretically predicted content of active components ( A linear function of ). (5) (6) Further tests were conducted on the compressive strength of geopolymers prepared from the same solid waste under the same process and the silicon-aluminum ratio calculated by formulas (5) and (6), as shown in Table 2.

[0062] Table 2: Relationship between compressive strength and theoretical active Si and active Al

[0063] Perform a linear fit on the above data, such as... Figure 4 As shown, Figure 4 A schematic diagram of the linear fitting between the silica-alumina ratio of fly ash and the properties of geopolymers provided by this invention yields the primary linear function: (7) Further optimization of the primary linear function is performed, and μ is calculated. Si =0.93887, μ Al =0.9951, μ Y =26.89, add correction term Where the correction coefficient q is Therefore, the refined performance prediction model is obtained: (8) Based on this prediction model, the predicted compressive strength can be obtained by substituting the easily measurable total silicon and aluminum of different fly ash components, with an error not exceeding 5 MPa.

[0064] Example 2: Based on the prediction model of active component content and performance of red mud targeted activation, the data in Table 3 are obtained according to the alkali solubility characteristics of red mud silicon and aluminum.

[0065] Table 3: Silicate and aluminum characteristics of red mud with different components

[0066] Linear fitting was performed based on the data in Table 3, such as... Figure 5 and Figure 6 As shown, Figure 5 This is one of the schematic diagrams showing the linear fitting of the silica-alumina properties of red mud with different components provided by the present invention. Figure 6 This is the second schematic diagram of linear fitting of the silica-alumina characteristics of red mud with different components provided by the present invention, which yields the theoretically predicted content of active components ( A linear function of ). (9) (10) Further tests were conducted on the compressive strength of geopolymers prepared from the same solid waste under the same process and the silicon-aluminum ratio calculated by formulas (9) and (10), as shown in Table 4.

[0067] Table 4: Correlation data between compressive strength and theoretical active Si and active Al

[0068] Perform a linear fit on the above data, such as... Figure 7 As shown, Figure 7 This is a schematic diagram of the linear fitting between the silica-alumina ratio of red mud and the properties of geopolymers provided by this invention, yielding the primary linear function: (11) Further optimization of the primary linear function is performed, and μ is calculated. Si =0.0926075, μ Al =0.9871104, μ Y =14.22, add correction term Where the correction coefficient q is Therefore, the refined performance prediction model is obtained:

[0069] Based on this prediction model, the total silicon and aluminum content of red mud with different components can be easily measured to obtain the predicted compressive strength, and the error does not exceed 5 MPa.

[0070] This invention addresses key issues in existing geopolymer preparation technologies, namely, the difficulty in predicting the component-activity-performance relationship, the lack of universal theoretical models, the low efficiency of industrial solid waste resource utilization, and the difficulty in optimizing geopolymer performance. Currently, the raw material systems for geopolymers are complex and diverse, with significant differences in composition, activity, and performance among silicoaluminous solid wastes from different sources. This makes it difficult for companies to quickly adjust production processes based on the specific composition of the solid waste, impacting resource utilization and production efficiency. Furthermore, due to the lack of effective prediction and optimization methods, companies often need to conduct extensive experimental verification when preparing geopolymers from silicoaluminous solid wastes, which is not only time-consuming and labor-intensive but may also lead to resource waste, limiting the widespread application of industrial solid waste in the building materials field. In addition, geopolymer performance optimization mainly relies on empirical formulation adjustments, lacking scientific theoretical guidance, making it difficult to achieve ideal results and limiting its application in high-performance building materials.

[0071] This invention proposes a predictive model for the content and performance of active components based on the targeted activation of silica-alumina solid waste. By comprehensively considering factors such as the chemical composition of solid waste and the effect of targeted activation treatment, a quantitative relationship between the content of active components and the performance of geopolymers is established. This model enables accurate prediction and optimization of geopolymer performance, providing strong support for the efficient resource utilization of industrial solid waste, the performance optimization of geopolymers, and the sustainable development of the building materials industry.

[0072] To better implement the performance prediction method for the targeted activation of silicoaluminous solid waste in the embodiments of the present invention, based on the performance prediction method for the targeted activation of silicoaluminous solid waste, the corresponding method is as follows: Figure 8 As shown, this embodiment of the invention also provides a performance prediction device for the targeted activation of silicon-aluminum solid waste. The performance prediction device 800 for the targeted activation of silicon-aluminum solid waste includes: The first fitting unit 801 is used to obtain a first linear function by fitting the total silicon content, total aluminum content, active silicon content, and active aluminum content in silicoaluminous solid waste of different components; the first linear function is used to predict the predicted content of active silicon and the predicted content of active aluminum. The second fitting unit 802 is used to obtain a second linear function by fitting based on the ratio of the predicted content of the active silicon to the predicted content of the active aluminum, and the compressive strength of the geopolymer; the geopolymer is prepared using the different components of silicon-aluminum solid waste under the same process. The optimization unit 803 is used to optimize the second linear function based on the predicted content of the active silicon, the predicted content of the active aluminum, and the compressive strength of the geopolymer to obtain a performance prediction model; the performance prediction model is used to predict the compressive strength of the silicon-aluminum solid waste after directional activation.

[0073] The performance prediction device 800 for the targeted activation of silicon-aluminum solid waste provided in the above embodiments can realize the technical solutions described in the above embodiments of the performance prediction method for the targeted activation of silicon-aluminum solid waste. The specific implementation principles of each module or unit can be found in the corresponding content in the above embodiments of the performance prediction method for the targeted activation of silicon-aluminum solid waste, which will not be repeated here.

[0074] like Figure 9 As shown, the present invention also provides an electronic device 900. The electronic device 900 includes a processor 901, a memory 902, and a display 903. Figure 9 Only some components of the electronic device 900 are shown, but it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0075] In some embodiments, processor 901 may be a central processing unit (CPU), microprocessor, or other data processing chip, used to run program code stored in memory 902 or process data, such as the performance prediction method for targeted activation of silicon-aluminum solid waste in this invention.

[0076] In some embodiments, processor 901 may be a single server or a group of servers. The server group may be centralized or distributed. In some embodiments, processor 901 may be local or remote. In some embodiments, processor 901 may be implemented on a cloud platform. In some embodiments, the cloud platform may include private cloud, public cloud, hybrid cloud, community cloud, distributed cloud, internal cloud, multi-cloud, etc., or any combination thereof.

[0077] In some embodiments, memory 902 may be an internal storage unit of electronic device 900, such as a hard disk or memory of electronic device 900. In other embodiments, memory 902 may also be an external storage device of electronic device 900, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. equipped on electronic device 900.

[0078] Furthermore, the memory 902 may include both internal storage units of the electronic device 900 and external storage devices. The memory 902 is used to store application software and various types of data installed on the electronic device 900.

[0079] In some embodiments, display 903 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an organic light-emitting diode (OLED) touchscreen, etc. Display 903 is used to display information from electronic device 900 and to display a visual user interface. Components 901-903 of electronic device 900 communicate with each other via a system bus.

[0080] In one embodiment, when processor 901 executes the performance prediction program for the targeted activation of silicon-aluminum solid waste in memory 902, the following steps can be implemented: Based on the total silicon content, total aluminum content, active silicon content, and active aluminum content in different components of silicoaluminous solid waste, a first linear function is obtained through fitting; the first linear function is used to predict the predicted content of active silicon and the predicted content of active aluminum. Based on the ratio of the predicted content of active silicon to the predicted content of active aluminum, and the compressive strength of the geopolymer, a second linear function is obtained by fitting; the geopolymer is prepared using silicon-aluminum solid waste of different components under the same process. Based on the predicted content of the active silicon, the predicted content of the active aluminum, and the compressive strength of the geopolymer, the second linear function is optimized to obtain a performance prediction model; the performance prediction model is used to predict the compressive strength of silicon-aluminum solid waste after directional activation.

[0081] It should be understood that when the processor 901 executes the performance prediction program for the directional activation of silicon-aluminum solid waste in the memory 902, in addition to the functions mentioned above, it can also perform other functions, as detailed in the description of the corresponding method embodiments above.

[0082] Furthermore, this embodiment of the invention does not specifically limit the type of electronic device 900 mentioned. Electronic device 900 can be a mobile phone, tablet computer, personal digital assistant (PDA), wearable device, laptop computer, or other portable electronic device. Exemplary embodiments of portable electronic devices include, but are not limited to, portable electronic devices running iOS, Android, Microsoft, or other operating systems. The aforementioned portable electronic device can also be other portable electronic devices, such as a laptop computer with a touch-sensitive surface (e.g., a touch panel). It should also be understood that in some other embodiments of the invention, electronic device 900 may not be a portable electronic device, but rather a desktop computer with a touch-sensitive surface (e.g., a touch panel).

[0083] Accordingly, embodiments of the present invention also provide a computer-readable storage medium for storing computer-readable programs or instructions. When the programs or instructions are executed by a processor, they can implement the steps or functions in the performance prediction method for directional activation of silicon-aluminum solid waste provided in the above-described method embodiments.

[0084] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware (such as a processor, controller, etc.), and the computer program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0085] The performance prediction method, apparatus, and storage medium for the targeted activation of silicon-aluminum solid waste provided by the present invention have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for performance prediction of directional activation of a silico-aluminous solid waste, characterized by, The method comprises the following steps: obtaining a first linear function by fitting the total content of silicon, the total content of aluminum, the content of active silicon and the content of active aluminum in the different components of the silico-aluminous solid waste; the first linear function is used to predict the predicted content of active silicon and the predicted content of active aluminum; obtaining a second linear function by fitting the ratio of the predicted content of active silicon and the predicted content of active aluminum, and the compressive strength of the geopolymer; the geopolymer is prepared by using the different components of the silico-aluminous solid waste under the same process; optimizing the second linear function based on the predicted content of active silicon, the predicted content of active aluminum and the compressive strength of the geopolymer to obtain a performance prediction model; the performance prediction model is used to predict the compressive strength of the silico-aluminous solid waste after directional activation.

2. The method of performance prediction of directional activation of a silico-aluminous solid waste according to claim 1, characterized in that, The step of optimizing the second linear function based on the predicted content of active silicon, the predicted content of active aluminum and the compressive strength of the geopolymer to obtain a performance prediction model comprises the following steps: obtaining the sum of the predicted content of active silicon and the predicted content of active aluminum, the first average of the predicted content of active silicon, the second average of the predicted content of active aluminum and the third average of the compressive strength of the geopolymer; determining a correction coefficient based on the first average, the second average and the third average; determining a correction term based on the correction coefficient, the sum, the first average, the second average and the third average; determining the performance prediction model based on the second linear function and the correction term.

3. The method of performance prediction of directional activation of a silico-aluminous solid waste according to claim 1, characterized in that, Before the step of obtaining a first linear function by fitting the total content of silicon, the total content of aluminum, the content of active silicon and the content of active aluminum in the different components of the silico-aluminous solid waste, the method further comprises the following steps: obtaining the total content of silicon and the total content of aluminum based on the raw material characteristics of the silico-aluminous solid waste; obtaining the content of active silicon and the content of active aluminum participating in the hydration reaction based on the silicon-aluminum alkali dissolution characteristics of the silico-aluminous solid waste.

4. The method of performance prediction of directional activation of a silico-aluminous solid waste according to claim 1, characterized in that, The expression of the first linear function is as follows: wherein, represents a predicted content of active silicon or active aluminum, represents a total content of silicon or aluminum, represents an active stability of the solid waste, represents a reference activity of the solid waste.

5. The method of performance prediction of directional activation of a silico-aluminous solid waste according to claim 2, characterized in that, The expression of the second linear function is as follows: wherein, represents the compressive strength of the geopolymer, represents the ratio, , respectively represent the fitting parameters.

6. The method of performance prediction of directional activation of a silico-aluminous solid waste according to claim 5, characterized in that, The expression of the performance prediction model is as follows: wherein, represents a compressive strength prediction value, represents a sum value, represents a first average value, represents a second average value, represents a third average value, represents a correction coefficient.

7. The method of performance prediction of directional activation of a silico-aluminous solid waste according to claim 1, characterized in that, The silico-aluminous solid waste comprises any one of the following: fly ash, slag, stone waste, metallurgical slag or tailing slag.

8. A device for predicting the performance of directional activation of a silico-aluminous solid waste, characterized in that it comprises: The method comprises the following steps: a first fitting unit is configured to obtain a first linear function by fitting the total content of silicon, the total content of aluminum, the content of active silicon and the content of active aluminum in the different components of the silico-aluminous solid waste; the first linear function is used to predict the predicted content of active silicon and the predicted content of active aluminum; a second fitting unit is configured to obtain a second linear function by fitting the ratio of the predicted content of active silicon and the predicted content of active aluminum, and the compressive strength of the geopolymer; the geopolymer is prepared by using the different components of the silico-aluminous solid waste under the same process; an optimization unit is configured to optimize the second linear function based on the predicted content of active silicon, the predicted content of active aluminum and the compressive strength of the geopolymer to obtain a performance prediction model; the performance prediction model is used to predict the compressive strength of the silico-aluminous solid waste after directional activation.

9. An electronic device, comprising: comprising a memory and a processor, wherein the memory, configured to store a program; the processor, coupled to the memory, configured to execute the program stored in the memory, so as to implement the steps in the performance prediction method for directional activation of siliceous and aluminous solid waste according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, A computer readable storage medium for storing a program or instructions, which, when executed by a processor, can implement the steps in the performance prediction method for directional activation of siliceous and aluminous solid waste according to any one of claims 1 to 7.

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