A method for optimizing proportioning of multi-source solid waste cementitious material containing alkali residue based on mixture design

Abstract

The application discloses a kind of based on mix design's containing alkali residue multi-source solid waste cementing material proportioning optimization method, for obtaining the best five kinds of solid waste component proportion according to preset cementing material performance target, wherein alkali residue is used as alkaline excitation component, determines test point using D optimal mix design method, establishes three-order standard polynomial model, sets the constraint condition of each solid waste component, carries out net paste 3 days compressive strength and 28 days compressive strength test to each test point, obtains the first fitting model based on compressive strength response and the second fitting model based on response according to test data, according to preset performance target, according to the first fitting model and the second fitting model, the optimal five kinds of solid waste component proportion are obtained by solving using multi-objective optimization algorithm;The method solves the problems of low efficiency and high cost of traditional trial-and-error method, realizes the scientization and standardization of multi-source solid waste cementing material proportioning design.

Description

Technical Field

[0001] This invention relates to the field of concrete engineering technology, and in particular to a method for optimizing the mix proportion of alkali-containing slag multi-source solid waste cementitious materials based on mixing design. Background Technology

[0002] Solid waste cementitious materials are green and low-carbon cementitious materials made entirely from industrial solid waste. They can replace cement in roadbeds, building subbases, mine filling, trench backfilling, and other fields. However, due to the significant differences in the physicochemical properties of industrial solid waste raw materials from different regions and sources, there is currently no method for designing the proportions of solid waste cementitious materials. There is no scientific method to guide the selection of raw material types and dosages, and a large number of trials and errors are often used, resulting in low efficiency and high cost in the design of solid waste cementitious material proportions, and often failing to obtain the optimal proportions.

[0003] Taking the design of a five-element solid waste cementitious material as an example, based on the alkali-sulfate composite activation theory, a five-element synergistic cementitious system is constructed, consisting of steel slag / alkali slag (alkaline activator), desulfurized gypsum (sulfate activator), and slag / fly ash (active material). Mechanical activation of multi-source solid wastes such as slag, steel slag, and alkali slag can stimulate their cementitious activity or increase the degree of their cementitious reaction. By rationally proportioning several solid waste powders, a self-cementing, all-solid waste clinker-free cementitious material can be prepared, which can significantly reduce costs by replacing cement and achieving the synergistic utilization of industrial solid waste from mining cities, thereby improving the resource utilization efficiency of multi-source industrial solid waste. However, currently, there is no mathematical model that targets compressive strength and component proportions to reveal the influence mechanism of the main effects and interactions of each component on performance. The proportioning of the five-element synergistic cementitious system is mainly achieved through trial and error and repeated experiments until a proportion that meets performance requirements is obtained. Obviously, the trial-and-error method has low experimental efficiency.

[0004] Therefore, an efficient method for designing the mix proportions of multi-source solid waste cementitious materials is needed. Summary of the Invention

[0005] This invention develops a five-element composite cementitious system of solid waste (SR-SS-DG-GGBS-FA) by using slag powder and raw fly ash as the main cementing components, and adding a certain amount of steel slag powder, alkali slag powder, and desulfurized gypsum powder as activating components. It employs a mixing design method to compound and synergistically cement the solid wastes. The synergistic cementing effect among the solid waste raw materials was investigated using the 3-day and 28-day compressive strength of the solid waste cementitious material paste as the main indicators. The solid waste cementitious material was obtained by optimizing the fitting model.

[0006] To achieve the above objectives, the present invention provides a method for optimizing the proportion of cementitious materials for alkaline slag multi-source solid waste based on mixing design. This method is used to optimize the proportion of five solid waste components according to preset cementitious material performance targets, including: compressive strength target requirements.

[0007] The method includes the following steps:

[0008] Step S1: The experimental points are determined using the D-optimal mixing design method, and a third-order standard polynomial model is established. The response factor of the model is: the proportion of alkali residue added. Steel slag mixing ratio Desulfurized gypsum admixture ratio Slag blending ratio and the proportion of fly ash added ;

[0009] Step S2: Set constraint conditions for each solid waste component to form a constrained test domain, wherein... , , , , ;

[0010] Step S3: Conduct 3-day and 28-day compressive strength tests on the paste at each test point. Based on the test data, obtain a first fitting model and a second fitting model based on the compressive strength response. Perform a significance test on the established fitting models, and require the correlation coefficient between the first fitting model and the second fitting model to be... All are not less than 0.98, and The value is less than 0.05;

[0011] Step S4: Based on the preset performance target, and with the first fitting model and the second fitting model as constraints, the optimal allocation ratio of the five solid waste groups is obtained by solving a multi-objective optimization algorithm.

[0012] Furthermore, the five solid waste raw materials were pretreated using the following methods:

[0013] Slag is dried at 100℃ and then ground for 80 minutes; steel slag is dried at 100℃, crushed, ground for 70 minutes, and then passed through a 75μm sieve; alkali slag is dried at 100℃ and then ground for 8 minutes; desulfurized gypsum is dried at 45℃ and then ground for 20 minutes; fly ash is used directly as raw material.

[0014] Furthermore, the D-optimal mixing design method was used to determine the test points, and 35 test points were selected, including 12 vertices, 5 edge points, 15 surface points, and 3 internal points;

[0015] The constrained test domain of the third-order canonical polynomial model for: ;

[0016] ; ; ;

[0017] in, For the first The lower boundary of each constraint condition. For the first The upper boundary of the constraint condition. The total number of components in the polynomial model. ; For the first The constraint, i.e., the first constraint. The proportional constraints of each solid waste component.

[0018] Furthermore, the aforementioned third-order canonical polynomial model is as follows:

[0019] ; , and These are the first-order, second-order, and third-order coefficients, respectively. This provides the performance metrics for the model.

[0020] Furthermore, the specific expression of the first fitting model based on the compressive strength response is as follows:

[0021] ;in, This represents the predicted compressive strength after 3 days, in MPa. and These represent the proportions of alkaline slag, steel slag, desulfurized gypsum, slag, and fly ash incorporated, respectively.

[0022] Furthermore, the specific expression optimized by the second fitting model based on the compressive strength response is as follows:

[0023] ;in, This represents the predicted compressive strength after 28 days, in MPa.

[0024] Furthermore, a multi-objective optimization algorithm was used to obtain the optimal allocation ratio of the five solid waste groups that satisfies all constraints. The optimization objective is to maximize the utilization rate of solid waste while meeting the requirements of 3-day compressive strength and 28-day compressive strength.

[0025] Compared with the prior art, the beneficial effects of the present invention are:

[0026] This invention replaces the traditional trial-and-error method with mathematical models and nonlinear programming, which greatly shortens the mix design cycle and maximizes the utilization rate of solid waste while meeting the compressive strength requirements, thus realizing the scientific and standardized design of multi-source solid waste cementitious materials. Attached Figure Description

[0027] Figure 1 Schematic diagram of the solid waste mixing constraint test domain: (a) simplex, (b) irregular polygon;

[0028] Figure 2 This is a comparison chart of the 3-day compressive strength test value and the predicted value of the neat pulp according to the present invention;

[0029] Figure 3 This is a response tracking graph of the 3-day strength of the paste according to the present invention;

[0030] Figure 4 This is a comparison chart of the test values ​​and predicted values ​​of the 28-day compressive strength of the cement paste according to the present invention;

[0031] Figure 5 This is a response tracking graph of the 28-day strength of the paste according to the present invention. Detailed Implementation

[0032] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention are described clearly and completely below. Obviously, the described embodiments are only a part of the embodiments of this invention, not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0033] Example

[0034] This embodiment discloses a method for optimizing the proportion of cementitious materials for multi-source solid waste containing alkali slag based on mixing design. It is used to optimize the proportion of five solid waste components according to preset cementitious material performance targets, including: 3-day compressive strength target requirements and 28-day compressive strength target requirements.

[0035] The method includes the following steps:

[0036] Step S1: The experimental points are determined using the D-optimal mixing design method, and a third-order standard polynomial model is established. The response factor of the model is: the proportion of alkali residue added. Steel slag mixing ratio Desulfurized gypsum admixture ratio Slag blending ratio and the proportion of fly ash added .

[0037] The preparation of solid waste cementitious materials ultimately boils down to finding the optimal types and proportions of raw materials, and its experimental design falls within the scope of mixture design. For example, in the preparation and performance evaluation of solid waste cementitious materials, in addition to considering the 3-day compressive strength of the product, other response indicators such as 28-day compressive strength, cost, and consumption rate of difficult-to-utilize solid waste should also be considered.

[0038] The five types of solid waste raw materials were pretreated according to the following methods:

[0039] Slag is dried at 100℃ and then ground for 80 minutes; steel slag is dried at 100℃, crushed, ground for 70 minutes, and then passed through a 75μm sieve; alkali slag is dried at 100℃ and then ground for 8 minutes; desulfurized gypsum is dried at 45℃ and then ground for 20 minutes; fly ash is used directly as raw material.

[0040] In mixture design, the research object is the mixture components expressed as admixture proportions. Therefore, the mixture components should be non-negative, and the sum of all mixture components should be 1. Let the mixture components be denoted as... , representing the first Constraints on the proportion of each component in the mixture. The response is the proportion of each component. The domain of the function, where the values ​​of the mixture components take up are called the mixing region or the region of benefit. The mixing test domain determined by each mixing component is ( In practical applications of solid waste cementitious material mixing, the values ​​of the mixed components are subject to certain additional constraints. The mixed components can only take values ​​within the open interval (0, 1). This experimental domain is a mixing region with upper and lower bound constraints: the constrained experimental domain of the third-order normalized polynomial model. for: ;

[0041] ; ; ;

[0042] in, For the first The lower boundary of each constraint condition. For the first The upper boundary of the constraint condition. The total number of components in the polynomial model. ; For the first The constraint, i.e., the first constraint. The proportional constraints of each solid waste component.

[0043] In response surface methodology, a complete polynomial regression model is used to fit the experiment. However, the mixture experiment is subject to constraints, preventing the complete model from including intercept terms, quadratic terms, cubic terms, etc., and only including linear terms and interaction terms. The linear terms represent the effects of each component of the mixture, and the interaction terms represent the effects between components. The third-order normalized polynomial model is as follows:

[0044] ; , and These are the first-order, second-order, and third-order coefficients, respectively. This provides the performance metrics for the model.

[0045] like Figure 1 The diagram shows a schematic of the experimental domain for the solid waste mixture: (a) a simplex, (b) an irregular polygon; with three constraints. For example, Figure 1 In (a), When the values ​​are greater than 0.1, 0.2, and 0.3 respectively, the constrained test domain is a triangle, i.e., a simplex. The simplex lattice design provides uniformity, regularity, and saturation in the distribution of test points within the test domain. However, when the mixture composition and model order are large (≥3), the number of test points increases significantly, increasing the difficulty of the experiment and reducing its efficiency. For example... Figure 1 In (b), When the values ​​are greater than 0.6, 0.7, and 0.8 respectively, the constrained test domain is an irregular polygon. When the number of constraints increases further, the constrained test domain becomes more complex. In this case, for irregular test domains, D-optimal design is more commonly used to minimize the variance of the coefficients of the fitted regression model, that is, to minimize the fluctuation of the coefficients of the fitted model.

[0046] Step S2: Set constraint conditions for each solid waste component to form a constrained test domain, wherein... , , , , .

[0047] Based on the alkali-sulfate composite activation theory and combined with the characteristics of industrial solid waste emissions, and based on the physicochemical properties of solid waste raw materials, alkali slag (SR), steel slag (SS), desulfurized gypsum (DG), slag (BS), and fly ash (FA) were selected as raw materials, i.e., mixing test factors; with reference to the blending ratio range of solid waste raw materials, i.e. factor constraint conditions, a constrained test domain was formed.

[0048] To reduce experimental costs and improve efficiency, experiments with fewer than 40 mix proportions were designed, meaning fewer than 40 experimental points were included. To accurately fit the local optima within the experimental domain, a third-order normalized polynomial model was chosen for the expected model. The variables in the solid waste cementitious material preparation experiment were the proportion of each solid waste component's mass to the total mass of all solid waste. The sum of the proportions of each solid waste component was equal to 1. The levels of the variables were not independent and were unrelated to the total amount of solid waste, classifying it as a mixing experiment.

[0049] To facilitate experimental design, this study used Design-Expert13 software to design the mixing ratio of each group of solid waste cementitious material. Based on the experimental factor parameters, expected model, and optimal design method, the best subset of the candidate test points was selected as the experimental scheme through optimization algorithm. The mixed experimental design scheme is shown in Table 1.

[0050] As shown in Table 1, the experimental design contains 35 test points, which meets the expected requirements. The experimental design includes 25 model terms, 5 misfit terms, and 5 duplicate terms. The duplicate terms can be used to estimate the error. Among the 35 test points, there are 12 vertices, 5 edge points, 15 surface points, and 3 interior points.

[0051] Table 1 Experimental Design Scheme for Mixing Solid Waste Cementitious Materials (mass percentage, %)

[0052]

[0053] Step S3: Conduct 3-day and 28-day compressive strength tests on the paste at each test point. Based on the test data, obtain a first fitting model and a second fitting model based on the compressive strength response. Perform a significance test on the established fitting models, and require the correlation coefficient between the first fitting model and the second fitting model to be... All are not less than 0.98, and The value is less than 0.05.

[0054] Based on the above mixing ratio scheme for solid waste cementitious materials, compressive strength and 28-day compressive strength tests were conducted with a water-to-solid waste cementitious material ratio of 0.42. Multivariate fitting and analysis of variance were performed on the measured compressive strength values. The analysis of variance included analysis of the lack-of-fit term and the coefficient of determination to determine the most suitable model to represent the experimental data. When the lack-of-fit term was not significant and the coefficient of determination was high, the model was validated. The expected experimental model was a special cubic model. After fitting and analyzing the 3-day compressive strength test results, a quadratic model was adopted. Through the fitted mathematical model, the 3-day strength of cementitious materials with any mixing ratio can be quantitatively estimated without conducting numerous experiments.

[0055] Based on the test results of 3-day compressive strength, a regression model was established between 3-day compressive strength and the proportion of the five mixed components. The optimized expression of the first fitting model based on the compressive strength response is as follows:

[0056] ,in, This represents the predicted compressive strength after 3 days, in MPa. and These represent the proportions of alkaline slag, steel slag, desulfurized gypsum, slag, and fly ash incorporated, respectively.

[0057] As shown in Table 2, the model significance test F=89.44, P<0.0001, indicating that the first fitted model is highly significant; in the lack of fit test, F=2.04, P=0.2217, which is greater than 0.05 and therefore not significant, indicating that the predicted values ​​simulated by the experimental model are significantly correlated with the actual experimental data. It can accurately reflect the relationship between the 3-day compressive strength and the proportions of alkaline slag powder (X1), steel slag powder (X2), desulfurized gypsum powder (X3), slag powder (X4), and fly ash (X5), and can make good predictions for various situations in the experiment.

[0058] Table 2. Analysis of variance and fitting statistics of the first fitting model.

[0059]

[0060] coefficient of determination =0.9829; Corrected coefficient of determination =0.9748; Prediction coefficient of determination =0.9578; Standard deviation =1.31; Coefficient of variation (CV) =9.61%; Signal-to-noise ratio =37.9542; Significance test of linear terms: F =314.76, P < 0.0001, which is highly significant, indicating that the effects of the linear terms X1, X2, X3, X4, and X5 are all highly significant. The interaction components—the proportions of alkali slag powder and desulfurized gypsum powder (X1X3), alkali slag powder and slag powder (X1X4), desulfurized gypsum and slag (X3X4), and desulfurized gypsum and fly ash (X3X5)—have highly significant effects on 3d compressive strength. p <0.01), the proportions of alkaline slag powder and fly ash (X1X5), steel slag powder and fly ash (X2X5), and slag powder and fly ash (X4X5) have a significant impact on 3d compressive strength. p<0.05). The remaining interaction terms are not significant and have little impact on the strength. To simplify the model, insignificant interaction terms can be removed.

[0061] After the analysis of variance is completed, the model is tested for goodness of fit, R0 2 R0 refers to the goodness of fit, also known as the coefficient of determination. It is obtained by dividing the sum of squared deviations of the model by the total sum of squared deviations. It represents the proportion of the sum of squared deviations caused by changes in the level of significant factors in the model to the total sum of squared deviations, indicating the degree to which the regression equation fits the experimental values. 2 R is a value between 0 and 1. The larger this ratio is, and the closer it is to 1, the better. This indicates a better correlation between the experimental and predicted values, a more accurate model, and a more significant regression effect. 2 The value is 0.9829, which indicates that the experimental model fits the actual experiment well. In the actual experiment, about 98.29% of the results can be explained by the fitted model, which has high reliability and excellent model fit.

[0062] Figure 2 The graph shows a comparison between the experimental and predicted values ​​of the 3-day compressive strength of cement paste. Within the UCS3 (3-day compressive strength) range of 0.2 to 29.4 MPa, the predicted and actual points of the first fitted model are generally located near a straight line, indicating that the actual and predicted values ​​are not significantly different. This suggests that the regression model has a good fit and high accuracy. The regression equation can effectively describe the relationship between various factors and the 3-day strength; therefore, this model can be used to analyze and predict the 3-day strength.

[0063] Analysis of variance showed that the proportions of the five raw materials significantly affected the 3-day compressive strength of the cementitious paste. To reveal the influence and extent of each component on the compressive strength of the cementitious material, a response tracking graph of the 3-day strength of the cementitious paste was generated, as shown below. Figure 3 As shown.

[0064] Response tracking plots, also known as component effect plots, can reflect the impact of changes in component content on the properties of controlled low-strength material (CLSM, defined by the American Concrete Institute (ACI 116R) as a material with a 28-day compressive strength not exceeding 8.3 MPa. Typical components of CLSM include cement; coarse and fine aggregates such as sand or certain industrial solid wastes; water; and other industrial wastes, such as fly ash). It is particularly suitable for displaying the effects of four or more components, as a single response surface plot can display the responses of up to three components. The horizontal axis represents the deviation (in proportion) of each component from the reference mix point, and the vertical axis represents the response value (compressive strength) after the deviation changes. The reference mix point is the centroid of the test domain. When analyzing the effect of a certain component on the strength of the neat paste, the proportions of the other four components remain constant at the reference test point. The curve represents the change in neat paste strength when the component increases or decreases. A steeper curve indicates a more significant impact of the component on the neat paste strength. A flatter curve indicates a smaller impact of the component on the neat paste strength.

[0065] The response tracking graphs show that the tracking curves for slag powder and steel slag powder have steeper slopes and wider fluctuation ranges, indicating a significant impact on strength. Slag content shows a clear positive correlation with compressive strength, primarily because the hydration activity of slag powder is fully activated by alkali slag and desulfurized gypsum, leading to a gradual increase in compressive strength. Analysis of the physicochemical properties of slag powder reveals its high hydration activity. Furthermore, because the slag powder content ranges from 20% to 60%, the curve for slag powder exhibits a wider fluctuation range and steeper slope, indicating that slag powder has the most positive and effective impact on strength. The curve for alkali slag shows a relatively gentle increasing trend, indicating that alkali slag, as an alkaline activating component, promotes the hydration of slag powder. Simultaneously, the extremely fine particle size of alkali slag allows it to act as a nucleation crystallizer, promoting the formation of hydration products. The tracking curves for steel slag powder and fly ash show consistent fluctuation trends, with compressive strength gradually decreasing as the content increases. This indicates that the hydration activity of steel slag powder and fly ash is low; increasing the content does not promote the hydration reaction but instead has an adverse effect on strength. Steel slag has low activity and a long hydration induction period, which significantly negatively impacts early strength (3 days). Furthermore, steel slag exhibits inhibition of the hydration reaction, primarily due to the presence of the C12A7 phase (representing a calcium aluminate compound in cement clinker minerals; the complete chemical formula of C12A7 is...). The addition of desulfurized gypsum (i.e., dodecacalcium heptaaluminate) delayed the dissolution of gypsum and the formation of ettringite, inhibited the formation of CH and CSH, and reduced early strength. As the amount of desulfurized gypsum increased, the strength of the cement paste gradually decreased, indicating that a small amount of desulfurized gypsum could stimulate the cementitious properties of slag powder. The sulfate ions produced when it dissolved in water reacted with aluminate ions and calcium ions to form ettringite. However, when the amount was large, the presence of steel slag resulted in a faster hydration rate of calcium aluminate in the slag, providing sufficient calcium ions and inhibiting the dissolution of excess desulfurized gypsum. This meant that excessive gypsum could not produce more ettringite and therefore did not have a positive impact on strength; instead, it caused a decrease in strength.

[0066] Using the same method, we can obtain a second fitting model based on the 28-day strength response. As shown in Table 3, the model significance test F=89.44, P<0.0001, indicating that the first fitting model is extremely significant. In the lack of fit test, F=2.04, P=0.2217, which is greater than 0.05 and therefore not significant. This indicates that the predicted values ​​simulated by the experimental model are significantly correlated with the actual experimental data. It can accurately reflect the relationship between the 3-day compressive strength and the proportions of alkaline slag powder (X1), steel slag powder (X2), desulfurized gypsum powder (X3), slag powder (X4), and fly ash (X5), and can make good predictions for various situations in the experiment.

[0067] Table 3. Analysis of variance and fitting statistics of the first fitting model.

[0068]

[0069] coefficient of determination =0.9572; Corrected coefficient of determination =0.9394; Predictive coefficient of determination =0.9080; Standard deviation =2.48; Coefficient of variation (CV) =7.92%; Signal-to-noise ratio =23.8904; Model significance test F =53.69, p <0.0001 indicates that the regression model is highly significant. Lack of fit test. F =2.91, p =0.1198, which is greater than 0.05, indicating that the experimental model's simulation matches the actual situation. The model term "Model" contains 5 first-order terms and 6 interaction terms. p The values ​​are all less than 0.01, indicating highly significant terms. (Optimization model coefficient of determination) =0.9572, corrected coefficient of determination =0.9394, prediction coefficient of determination =0.9080, all three are close to 1, and and The difference was only 0.0314, much less than 0.2, indicating that the regression effect was significant. The coefficient of variation (CV) was 7.92%, which met the fitting requirements.

[0070] The specific expression for the second fitting model based on the compressive strength response is as follows:

[0071] ;in, This represents the predicted compressive strength after 28 days, in MPa.

[0072] According to the analysis of variance, the interaction terms, ranked from most significant to least significant, are: X2X3>X2X4>X1X2>X3X4>X1X4>X2X5. Based on the mathematical model of the coded values ​​(the second fitting model), the coefficients of the interaction terms, ranked from most significant to least significant, are: X2X4>X2X3>X1X2>X3X4>X2X5>X1X4. This shows that the strength of significance and the magnitude of the interaction term coefficients are not entirely consistent or correlated, but generally, the stronger the significance of the interaction term, the larger its coefficient. Due to the adverse effect of steel slag on the development of compressive strength, all terms interacting with steel slag have negative coefficients.

[0073] Figure 4 This graph compares the measured values ​​of the 28-day strength test of cement paste with the predicted values ​​from the model. The points in the graph are located near the straight line, indicating that the regression model has a good fit and high accuracy.

[0074] Step S4: Based on the preset performance target, the optimal allocation ratio of the five solid waste groups is obtained by using a multi-objective optimization algorithm based on the first fitting model and the second fitting model.

[0075] The optimal allocation ratio of the five solid waste components was obtained by using a multi-objective optimization algorithm to satisfy all constraints. The optimization objective was to maximize the utilization rate of solid waste while meeting the requirements of 3-day compressive strength and 28-day compressive strength.

[0076] like Figure 5 As shown in the figure, the response tracking diagram of the 28-day strength of the paste of the present invention shows that the tracking curve of steel slag powder first decreases and then increases with the increase of admixture dosage, while the slope of the tracking curve of fly ash decreases.

[0077] The 28-day strength tracking curve shows an overall strength increase of approximately 5 MPa compared to the 3-day curve, indicating that the increase in compressive strength increases with the curing age, and the rate of strength increase is relatively rapid in the first 3 days. The tracking curve for slag powder has a steeper slope, indicating its most significant impact on strength. Similar to the 3-day strength influence pattern, the 28-day tracking curve is nearly a straight line, suggesting that with a higher admixture dosage, the increased curing age fully activates the cementitious activity.

[0078] The tracking curve of steel slag powder showed a trend of first decreasing and then increasing with the increase of admixture dosage. This is because the C12A7 phase in steel slag has a fast hydration rate, which delays the dissolution of gypsum and the formation of hydration products, thus having an inhibitory effect. However, when the admixture dosage of steel slag is large and the curing time is long, the hydration reaction of tricalcium silicate in steel slag compensates for the adverse effects on early strength and promotes the growth of strength.

[0079] The slope of the tracking curve for fly ash decreased significantly compared to the 3-day curve, and its effect on the 28-day strength was not significant. This is because fly ash has low activity and a slow hydration rate. When the curing time is extended to 28 days, its hydration gradually begins to react, but it has not yet played a significant role in strength growth. The tracking curves for alkali residue and desulfurized gypsum nearly overlapped. With the increase of the content of alkali residue and desulfurized gypsum, the 28-day strength of the paste showed a slight increasing trend. When the content of desulfurized gypsum was large, there was no decreasing trend in the 3-day tracking curve, indicating that when the curing time is extended, excessive desulfurized gypsum will have a positive effect on strength, but the effect is not significant.

[0080] The above method can also be used to match the optimal ratio of three, four or more types of combined solid waste.

[0081] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for optimizing the proportion of cementitious materials for multi-source solid waste containing alkali slag based on mixing design, used to optimize the proportion of five solid waste components according to preset cementitious material performance targets, wherein the preset cementitious material performance targets include: The target requirements for 3-day compressive strength and 28-day compressive strength are defined; the method is characterized by comprising the following steps: Step S1: The D-optimal mixing design method is used to determine the test points, and a third-order standard polynomial model is established. The response factor of the model is: the proportion of alkali residue added. Steel slag mixing ratio Desulfurized gypsum admixture ratio Slag blending ratio and the proportion of fly ash added ; The D-optimal mixing design method was used to determine the test points, and 35 test points were selected, including 12 vertices, 5 edge points, 15 surface points, and 3 internal points. The constrained test domain of the third-order canonical polynomial model for: ; ； ； ； in, For the first The lower boundary of each constraint condition. For the first The upper boundary of the constraint condition. The total number of components in the polynomial model. ; For the first The constraint condition, i.e., the first constraint condition. The proportional constraints of each solid waste component; The third-order canonical polynomial model is as follows: ; , and These are the first-order, second-order, and third-order coefficients, respectively. Output metrics for the model's performance; Step S2: Set constraint conditions for each solid waste component to form a constrained test domain, wherein... , , , , ; Step S3: Conduct 3-day and 28-day compressive strength tests on the paste at each test point. Based on the test data, obtain a first fitting model and a second fitting model based on the compressive strength response. Perform a significance test on the established fitting models, and require the correlation coefficient between the first fitting model and the second fitting model to be... All are not less than 0.98, and The value is less than 0.05; The optimized expression of the first fitting model based on compressive strength response is as follows: ;in, This represents the predicted compressive strength after 3 days, in MPa. and These represent the proportions of alkaline slag, steel slag, desulfurized gypsum, slag, and fly ash incorporated, respectively. The specific expression for the second fitting model based on compressive strength response is as follows: ;in, This represents the predicted compressive strength after 28 days, in MPa. Step S4: Based on the preset performance target, and with the first fitting model and the second fitting model as constraints, the optimal allocation ratio of the five solid waste groups is obtained by solving the problem through a multi-objective optimization algorithm.

2. The method for optimizing the proportion of alkali-containing slag multi-source solid waste cementitious materials based on mixing design according to claim 1, characterized in that, The five types of solid waste raw materials were pretreated according to the following methods: Slag is dried at 100℃ and then ground for 80 minutes; steel slag is dried at 100℃, crushed, ground for 70 minutes, and then passed through a 75μm sieve; alkali slag is dried at 100℃ and then ground for 8 minutes; desulfurized gypsum is dried at 45℃ and then ground for 20 minutes; fly ash is used directly as raw material.

3. The method for optimizing the proportion of alkali-containing slag multi-source solid waste cementitious materials based on mixing design according to claim 1, characterized in that, The optimal allocation ratio of the five solid waste components was obtained by using a multi-objective optimization algorithm to satisfy all constraints. The optimization objective was to maximize the utilization rate of solid waste while meeting the requirements of 3-day compressive strength and 28-day compressive strength.

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