Large-mixing-amount phosphate tailing geopolymer material proportion design method based on combination of material mixing design and multiple algorithms

Abstract

The invention provides a large-mixing-amount phosphate tailing geopolymer material proportion design method based on mixing design and multi-algorithm combination. The method comprises the following steps that S1, calcined phosphate tailings, metakaolin, glass and silica fume are subjected to material obtaining; s2, proportioning materials according to the calcined phosphate tailings, metakaolin, glass and silica fume in the step S1 to obtain a phosphate tailing geopolymer material pouring test piece with optimized compressive strength and breaking strength; and S3, manufacturing a phosphate tailing geopolymer material pouring test piece according to the material ratio in the step S2. According to the method, large-volume consumption and resource utilization of the phosphate tailings can be achieved, the high-added-value utilization rate of bulk solid waste represented by the phosphate tailings is increased, and meanwhile generation of the phosphate tailing geopolymer with the compressive strength and the breaking strength is achieved.

Description

Technical Field

[0001] The invention relates to the technical field of phosphate tailings geopolymer proportioning, and in particular to a large-volume phosphate tailings geopolymer material proportioning design method based on mixing design and multi-algorithm combination. Background Art

[0002] In terms of phosphorus tailings materialization, my country's bulk solid waste represented by phosphorus tailings continues to be generated with the continuous development of the mining industry, occupying a large amount of land resources and polluting the local environment. In addition, due to the complex composition and large inventory of tailings, it is difficult to achieve large-scale resource utilization of phosphorus tailings. The technology of using tailings to prepare green geopolymer materials or other cementitious materials is one of the effective methods to achieve high added value utilization of tailings and turn waste into treasure. Among the existing invention patents and research results, there are many technologies for preparing geopolymers or cementitious materials from phosphorus tailings.

[0003] Hu Wenyun [1] and others invented a method for solidifying phosphate tailings, which uses a certain proportion of phosphate tailings, loess, and cement to make a phosphate tailings solidified body with a certain strength. The phosphate tailings play the role of gelling and aggregate in the solidified body, and can be used as a building material. Pan Zhiquan [2] and others invented a method for preparing early-strength cementing materials for roadbeds, which uses waste products such as phosphogypsum and phosphate tailings to prepare high-strength and highly waterproof roadbed materials to solve the problems of insufficient strength and poor waterproofness of roadbed materials in the prior art. Li Jianqiu [3] and others invented a geopolymer-stabilized phosphate tailings pavement base and its preparation method, which stabilizes the phosphate tailings pavement base with calcium carbide slag, phosphorus slag powder, and kaolin geopolymers to achieve the reuse of solid waste and make the pavement base have better mechanical properties and durability. Ye Yicheng [4] and others invented a cementitious material based on hydrated calcium silicate and hydrated magnesium silicate for mine filling and its preparation method. The method uses phosphate tailings powder and dilute hydrochloric acid as the main raw materials to prepare the cementitious material. The filling slurry has a certain fluidity and the strength meets the general mine filling requirements. Jiang Zhongxi [5] and others invented a method for preparing a cementitious material based on phosphate tailings. According to the chemical composition of phosphate tailings, the characteristics of calcium cement and magnesium cement were combined to invent a composite magnesium calcium cement, which utilizes the beneficial components of waste. Jiang Jiadong [6] and others invented a method for preparing cement mineralizer using phosphate tailings, and a method for producing cement clinker using the produced cement mineralizer, which can recycle phosphate tailings and solve its pollution problem. Zhang Jin [7] and others invented a method for preparing high-performance cementitious materials from modified calcium-magnesium phosphate tailings. The high-performance cementitious materials are manufactured by modifying the silicon components remaining after flotation of magnesium-calcium components and phosphorus components in phosphate tailings, which can increase the consumption of phosphate tailings and reduce the pressure on phosphate tailings inventory. Hao Xutao and others [8] invented a casting material for active phosphate tailings. The material is prepared by reacting active phosphate tailings rich in calcium oxide and magnesium oxide with acid phosphate. It is a phosphate chemical bonding material with the characteristics of moderate solidification speed, high strength, and stable strength in the later stage. The above research results have the following defects:

[0004] Many domestic experts have conducted systematic research on the technology and methods of preparing geopolymer Mn{-(SiO2)zAlO2}n·wH2O or cementitious materials from phosphate tailings, which has provided theoretical guidance and technical support to a certain extent, but there are still some shortcomings:

[0005] Reference [1] uses phosphate tailings and other materials to prepare a solidified body with a certain strength, but the curing temperature for forming a good strength is above 40°C, which is difficult to apply in actual engineering. References [2] and [3] use phosphate tailings and other industrial solid wastes to prepare cementitious materials that meet road standards, but the application scenarios of such cementitious materials are limited, and it is difficult to achieve large-scale utilization of phosphate tailings. In addition, the mechanical properties and durability of the roadbed material in reference [3] come from other admixtures such as metakaolin, and the main components of phosphate tailings have not been effectively developed and utilized. References [4] and [5] use different methods to activate phosphate tailings to prepare cementitious materials, but the preparation of raw materials requires a large amount of high-purity chemical reagents, and the cost of engineering application is too high. Reference [6] uses phosphate tailings to prepare cement mineralizers and produce cement clinker, which can reduce the cost of producing cement clinker, but in the process of using auxiliary agents to activate phosphate tailings, a large amount of stable and difficult to decompose CaSO4 will be generated, which will affect the hardening strength of cement. Reference [7] separated the components of phosphate tailings and used the different effective components screened from the phosphate tailings to prepare high-performance cementitious materials. This method can fully utilize the functions of the components in the phosphate tailings, but the preparation process is complicated and the cost is high. Reference [8] used reverse flotation phosphate tailings to react with acid phosphate to prepare a phosphate chemically bonded material with high strength. However, the temperature of calcining the phosphate tailings reached 1000°C, which consumed a lot of energy and may cause phase changes in minerals such as quartz in the phosphate tailings, which is not conducive to the formation of material strength.

[0006] In terms of mix design and optimization of the composition of similar materials:

[0007] Zhang Hongzhi [9] and others invented a design method for all-solid waste foam lightweight fillers. They mainly used the mixture design method to establish a regression model of flow value and compressive strength to determine the material ratio parameters and the corresponding compressive strength, etc., to reduce the cost of mechanical properties testing. Zhao Ming

[10] and others invented a method for preparing filling material ratios from coal-based solid waste. After screening the raw materials, the required reagents were added according to the viscosity to prepare the filling materials from coal-based solid waste materials, which can save land area and reduce environmental damage. Liu Xuewei

[11] and others invented a design method for grouting materials and grouting materials for deep coal-bearing strata. They mainly designed the grouting material ratio based on orthogonal test data and combined multiple algorithms, which is suitable for the reinforcement characteristics of deep coal-bearing strata. The above research results have the following defects:

[0008] Reference [9] uses regression models to predict compressive strength, which can reduce the test time and cost of mechanical properties testing to a certain extent, but it cannot consider the optimization of multiple indicators or properties at the same time. Reference

[10] mainly uses the characteristics of coal gangue and clay, which are similar in composition, to mix with materials such as kaolin to prepare the molding. The ratio problem is limited by the specific composition and water content of coal gangue, and the industrial process is complicated, so the value of large-scale promotion is small. Reference

[11] mainly generates a multivariate linear regression model based on the results of orthogonal experiments. Although it can increase the amount of data, the linear regression model has limited effect on nonlinear problems, resulting in insufficient prediction accuracy. Summary of the invention

[0009] The present invention aims to at least solve the technical problems existing in the prior art, and in particular innovatively proposes a method for designing the proportion of large-volume phosphate tailings geopolymer materials based on a combination of mixing design and multiple algorithms.

[0010] In order to achieve the above-mentioned object of the present invention, the present invention provides a method for designing the proportion of a large amount of phosphate tailings geopolymer material based on a mixture design and a combination of multiple algorithms, comprising the following steps:

[0011] S1, material acquisition of calcined phosphate tailings, metakaolin, glass, and silica fume;

[0012] S2, according to the calcined phosphate tailings, metakaolin, glass, and silica fume in step S1, material proportioning is performed to obtain a phosphate tailings geopolymer material casting specimen with optimized compressive strength and flexural strength;

[0013] S3, preparing a casting specimen of the phosphate tailings geopolymer material according to the material proportions in step S2.

[0014] In a preferred embodiment of the present invention, the method for obtaining the calcined phosphate tailings in step S1 is:

[0015] The phosphate tailings that have absorbed water and agglomerated are ground into powder and spread out to dry naturally, and then placed in a muffle furnace for calcination. When the temperature reaches the rated temperature, the temperature is kept for 2 hours and then cooled naturally to ensure that the dolomite in the phosphate tailings is completely decomposed.

[0016] Or / and calcination rated temperature is 750℃-850℃, heating rate is 5℃ / min;

[0017] The mass percentage of CaMg(CO3)2 in the phosphate tailings is 80%-85%, and after calcination, the dolomite (CaMg(CO3)2) is completely decomposed into free CaO and MgO, with the mass percentage of CaO being 48.7% and the mass percentage of MgO being 34.5%;

[0018] or / and the particle size of the powder mixture is ≤200 mesh;

[0019] Or / and the mass percentage of SiO2 in the metakaolin is 45%-50%, and the mass percentage of Al2O3 is 20-25%;

[0020] Or / and the mass percentage of silica fume SiO2 is 95-98%;

[0021] Or / and the SiO2 content in the water glass is 49-53%, the Na2O content is 25.5-29%, and the modulus is 2.0.

[0022] In a preferred embodiment of the present invention, in step S2, the method for preparing a material ratio of the calcined phosphate tailings, metakaolin, glass and silica fume in step S1 to obtain a phosphate tailings geopolymer material with optimized compressive strength and flexural strength comprises the following steps:

[0023] S21, select the blending ratio range of four raw materials: calcined phosphate tailings, metakaolin, silica fume, and sodium water glass, and code the ratios as A, B, C, and D, and use Design-Expert software to mix the materials;

[0024] S22, mixing the cooled calcined phosphate tailings powder with solid metakaolin, silica fume, and sodium water glass powder according to a proportion, mechanically stirring until the mixture is uniform, adding water and mixing, and stirring to obtain a geopolymer slurry, placing the slurry in a cast iron test mold, casting and molding the slurry, and sealing the slurry with a plastic film after compacting the slurry with a vibrating table;

[0025] S23, conduct compressive strength and flexural strength tests on the specimens whose curing age reaches the preset threshold number of days.

[0026] In a preferred embodiment of the present invention, in step S23, the material ratio of calcined phosphate tailings, metakaolin, glass, and silica fume corresponding to the compressive strength exceeding 20 MPa and the flexural strength exceeding 5 MPa is selected;

[0027] Before or / and demolding, the film should be sealed for 24 hours. After demolding, the geopolymer specimens should be placed in a standard curing box for curing for 28 days. The curing temperature is 20℃±2℃ and the humidity is not less than 95%.

[0028] In a preferred embodiment of the present invention, in step S22, the liquid-to-solid ratio of the mixture is 0.6; the mechanical stirring time is 5 minutes;

[0029] The test mold is attached to the vibration table to prevent it from jumping on the vibration table. The geopolymer slurry is loaded into the test mold at one time and vibrated until there is no obvious large bubble overflowing from the surface.

[0030] In a preferred embodiment of the present invention, step S23 includes:

[0031] Statistical functions:

[0032]

[0033] Among them, y is the response variable;

[0034] p is the number of components;

[0035] b i is the regression coefficient of each component;

[0036] x i is the proportion of the ith component;

[0037] b ij is the regression coefficient of the quadratic term

[0038] x j is the proportion of the jth component;

[0039] b ijk is the regression coefficient of the cubic term

[0040] x k is the proportion of the kth component.

[0041] In a preferred embodiment of the present invention, step S23 also includes:

[0042] Initial position processing:

[0043]

[0044] Among them, x i ′ is the position of the particle after transformation;

[0045] is the initial position of the particle;

[0046] p is the dimension of the particle;

[0047] It represents the initial position of the particle in the jth dimension;

[0048] Speed ​​Handling:

[0049]

[0050] Among them, v i ′ is the transformed particle velocity;

[0051] is the initial velocity;

[0052] p is the dimension of the particle;

[0053] represents the initial velocity of the particle in the jth dimension;

[0054] Particle Speed:

[0055]

[0056]

[0057] in, is the velocity of particle i at the next iteration t+1;

[0058] ω is the inertia weight;

[0059] is the velocity of particle i at the current iteration t;

[0060] c1 is the individual learning factor;

[0061] r1 is the first random number in the interval [0,1);

[0062] c2 is the social learning factor;

[0063] r2 is the second random number in the interval [0,1);

[0064] pbest i is the personal best position found by particle i so far;

[0065] is the position of particle i in the current iteration;

[0066] gbest i is the global best position found by particle i among all particles so far; is the position of particle i at the next iteration t+1;

[0067] Penalty function:

[0068] p(x,r)=f(x)-r×Penalty(x)

[0069]

[0070] Among them, p(x,r) is the augmented objective function after constraint;

[0071] f(x) represents the original objective function;

[0072] r is the penalty factor;

[0073] [(min(0,x i -ub i )] 2 +[min(0,lb i -x i )] 2 is to calculate the violation of each variable relative to its boundary constraints;

[0074] It is to calculate the square of the difference between the sum of all variables and 1;

[0075] x i is the i-th component of the particle position vector;

[0076] ub i represents the upper bound of the i-th component;

[0077] lb i represents the lower bound of the ith component.

[0078] In summary, due to the adoption of the above-mentioned technical scheme, the present invention can consume large quantities of phosphate tailings and utilize them as resources, thereby improving the high added value utilization rate of bulk solid waste represented by phosphate tailings, and at the same time achieving the generation of phosphate tailings geopolymers with compressive strength and flexural strength.

[0079] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0081] Figure 1 The figure is a schematic diagram of the chemical reaction system of the phosphate tailings geopolymer of the present invention.

[0082] Figure 2 Schematic diagram of XRD test results of phosphate tailings.

[0083] Figure 3 Schematic diagram of XRD test results of calcined phosphate tailings.

[0084] Figure 4 This is the flow chart of NSGA-Ⅱ algorithm.

[0085] Figure 5 It is a schematic diagram of the optimization process of the particle swarm algorithm based on Scheffé polynomials and the non-dominated sorting genetic algorithm.

[0086] Figure 6 Schematic diagram of the Pareto front of 28-day compressive strength and flexural strength. DETAILED DESCRIPTION

[0087] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0088] The invention discloses a method for designing the proportion of large-volume phosphate tailing geopolymer materials based on a combination of mixing design and multiple algorithms. The method uses high-temperature calcination to activate and decompose the effective mineral components of the phosphate tailings, adds metakaolin and silica fume, and uses an alkali activator to continuously dissolve silicon and aluminum substances. The aluminosilicate undergoes a depolymerization reaction in an alkaline environment, and the generated product is an aluminosilicate oligomer monomer [SiO4] 4- With [AlO4] 5- , the aluminosilicate oligomers are condensed into an amorphous gel structure to realize the resource utilization of phosphate tailings. In it, the D-optimal mixture is used as the basis for the design experiment, and the statistically significant Scheffé polynomial is established. Then, the machine learning model combining the particle swarm algorithm (PSO) and the non-dominated sorting genetic algorithm (NSGA-Ⅱ) is used to predict the compressive strength and flexural strength of the geopolymer. The algorithm combined with Scheffé-PSO-NSGA-II is used to prepare the phosphate tailings geopolymer material, which specifically includes the following steps:

[0089] Step 1: Grind the agglomerated phosphorus tailings into powder, spread them out and air dry them naturally, then put them into a muffle furnace for calcination, keep them warm for 2 hours after reaching the rated temperature, and then cool them naturally. The rated calcination temperature is 750℃-850℃, the heating rate is 5℃ / min, and the 2h heat preservation is to ensure that the dolomite component in the phosphorus tailings is completely decomposed. The X-ray diffraction patterns of the phosphorus tailings before and after high-temperature calcination are as follows: Figures 1 to 3 The mass percentage of CaMg(CO3)2 in the phosphorus tailings is 80%-85%, and after calcination, the dolomite (CaMg(CO3)2) is completely decomposed into free CaO and MgO, with the mass percentage of CaO being 48.7% and the mass percentage of MgO being 34.5%.

[0090] Step 2: Select the blending ratio range of the four raw materials: calcined phosphate tailings, metakaolin, silica fume, and sodium water glass, and code the ratios as A, B, C, and D. Use the Design-Expert software to design the experiment using the D-optimal mixture design method, and use the designed proportions of each test group to cast the specimens.

[0091] Step 3: Mix the cooled calcined phosphorus tailings powder with solid metakaolin, silica ash, and sodium water glass powder according to the proportion, stir mechanically until the mixture is uniform, add water and mix, stir to obtain geopolymer slurry, put it into a cast iron test mold for casting, vibrate it with a vibration table, and seal it with a plastic film. The particle size of the powder mixture is ≤200 mesh, the mass percentage of metakaolin SiO2 is 45%-50%, the mass percentage of Al2O3 is 20-25%; the mass percentage of silica ash SiO2 is 95-98%; the SiO2 content in water glass is 49-53%, the Na2O content is 25.5-29%, and the modulus is 2.0. The liquid-solid ratio of the mixture is 0.6; the mechanical stirring time is 5min. In addition, the test mold is attached to the vibration table to prevent the test mold from jumping on the vibration table, and the geopolymer slurry is loaded into the test mold at one time, and vibrated until there is no obvious large bubble overflowing from the surface.

[0092] Step 4: The compressive strength and flexural strength of the specimens with a curing age of 28 days were tested, and regression fitting analysis was performed on the test results to generate statistically significant Scheffé polynomials for the compressive and flexural strengths, respectively. The basic form of the polynomials is as follows.

[0093]

[0094] In the formula, y is the response variable, x is i is the proportion of the ith component, b i is the regression coefficient for each component and p is the number of components.

[0095] Step 5: Let the established Scheffé regression equation itself be used as the fitness function. At the same time, in order to solve the constraint problem, the penalty function is a method often used in constrained optimization problems. By penalizing the solution that does not meet the conditions, the penalty function can be applied to the particle swarm algorithm to transform the constraint problem into an unconstrained problem, thereby forming a penalty function particle swarm algorithm, and then realizing the combination of Scheffé polynomial and particle swarm algorithm. The algorithm mainly continuously searches for solutions that make the value of the fitness function (Scheffe polynomial) larger. Finally, the results output by the algorithm should be the compressive strength value and its corresponding proportion combination, the flexural strength value and its corresponding proportion combination.

[0096] Furthermore, in step 5, in order to realize the combination of Scheffé equation fitted by D-optimal mixture test results and particle swarm algorithm, it is necessary to improve the traditional particle swarm algorithm. The improvements are mainly in four aspects: the initial position of particles, initial velocity, particle velocity and direction during iteration, and constraint processing.

[0097] The initial positions of particles are randomly generated in standard PSO, but in order to meet the constraints in the mixture design (that is, the sum of all components must be 1), we need to normalize the initial positions. Assume that the initial position of the particle is It can be normalized by following the steps below,

[0098]

[0099] In the formula, x i ′ is the transformed particle position, p is the particle dimension (i.e., the number of components), represents the initial velocity of the particle in the jth dimension (component). This step ensures that the position of the particle meets the requirements of the mixture, that is, the sum of the components is 1.

[0100] Improved particle swarm velocity and direction formula: In standard PSO, each dimension of velocity can vary independently, but in mixture design, the components of the particles depend on each other, so the velocity also needs to be adjusted accordingly. Assume that the initial velocity is represents the initial velocity of the particle in the jth dimension (component). We normalize it to ensure that the updated velocity satisfies the constraint that the sum is 0. The velocity formula is:

[0101]

[0102] In the formula, v i ′ is the transformed particle velocity. This transformation formula ensures that the updated velocity does not affect the total constraint of the mixture. Because in the mixture problem, after the velocity is updated, the sum of the changes of each component should be 0, ensuring that the particle searches in the space where the sum is 1.

[0103] The particle position update is guided by its own historical best position (individual learning component) and the global best position (social learning component). At the same time, the inertia weight regulates the particle speed to balance the ability of global exploration and local development. As the iteration proceeds, it gradually gathers near the global optimal solution or the approximate optimal solution, showing obvious convergence behavior. The particle speed formula is as follows:

[0104]

[0105]

[0106] In the formula, is the velocity of particle i at the current iteration t; is the velocity of particle i at the next iteration t+1; is the position of particle i at the current iteration; ω is the inertia weight, which determines the degree to which the particle retains its current direction. Here, the initial inertia weight is set to 0.9 and the inertia weight at the end is set to 0.4. i is the personal best position found by particle i so far, gbest i is the global best position found by all particles so far; c1 and c2 are individual learning factors and social learning factors, respectively, which regulate the particle to pbest i and gbest i The trend of movement, the value is 2; r1 and r2 are random numbers in the interval [0,1], providing randomness for each dimension to ensure the diversity of the search

[0107] In order to solve the constraint problem, the penalty function is a method often used in constrained optimization problems. By penalizing the solution that does not meet the conditions, the penalty function can be applied to the particle swarm algorithm to transform the constraint problem into an unconstrained problem, thus forming a penalty function particle swarm algorithm. The basic form of the penalty function can be expressed as:

[0108] p(x,r)=f(x)-r×Penalty(x)

[0109]

[0110] Where p(x,r) is the augmented objective function after taking into account the constraints. f(x) represents the original objective function, and r is the penalty factor. i is the i-th component of the particle position vector; ub i and lb i denote the upper and lower bounds of the ith component, respectively. Calculate the square of the difference between the sum of all variables and 1, reflecting the degree of violation of the equality constraint. i -ub i )] 2 +[min(0,lb i -x i )] 2 Computes the violation of each variable relative to its bounds. If the variable is within its bounds, the result is 0; otherwise, the result is the square of the violation.

[0111] Step 6: The two best solutions generated by the above Scheffé polynomials and PSO algorithm are one for the optimal result of compressive strength and the other for the optimal result of flexural strength. However, in order to find the common optimal solution for compressive strength and flexural strength, and to increase the diversity of solutions, all the historical optimal positions for compressive strength and flexural strength (i.e., the optimal strength value and corresponding ratio combination) in the iteration process are combined to create an archive, and in order to increase the computational efficiency, the K-means clustering method is used to select a certain number of population combinations as the initial population, which is applied to the non-dominated sorting genetic algorithm (NSGA-Ⅱ). The non-dominated sorting genetic algorithm (NSGA-Ⅱ) is improved on the basis of the non-dominated sorting genetic algorithm (NSGA-Ⅱ). It is a genetic algorithm that mixes the parent and offspring and performs non-dominated sorting, and uses crowding degree and crowding operator as the criteria to select the winning individuals in the same order, and then retains the elite solution, and continuously crosses, mutates, and selects until convergence. See the flow chart of the NSGA-Ⅱ algorithm. Figure 4 The optimization process of particle swarm optimization algorithm based on Scheffé polynomial and non-dominated sorting genetic algorithm is shown in Figure 5 .

[0112] Step 7: Select the solution that meets the strength condition from the generated Pareto solution set, cast the test piece according to the mixing ratio corresponding to the solution in step 3, demold the formed phosphate tailings geopolymer and put it into the curing box for curing until the specified age.

[0113] Example:

[0114] Step 1: Grind the agglomerated phosphate tailings into powder, spread them out and air dry them naturally, and then put them into a muffle furnace for calcination to ensure that the dolomite in the phosphate tailings is completely decomposed.

[0115] Step 2: Select the blending ratio range of four raw materials of calcined phosphorus tailings, metakaolin, sodium water glass, and silica fume and encode the ratio as A, B, C, and D, use Design-Expert software to design the experiment using the D-optimal mixture design method, and use the designed test group proportions to cast the test pieces. In this embodiment, phosphorus tailings 40% to 70%, metakaolin 25%-50%, water glass 3%-10%, and silica fume 10%-17%, and the designed test proportion combination is shown in Table 1.

[0116] Table 1D - Optimal mixture design test plan and results

[0117]

[0118]

[0119] Step 3: Mix the cooled calcined phosphate tailings powder with solid metakaolin, silica fume, and sodium water glass powder according to the proportion, stir mechanically until the mixture is uniform, add water at a liquid-solid ratio of 0.6, mix and stir to obtain a geopolymer slurry with a certain fluidity, put it into a cast iron test mold for casting, vibrate it with a vibration table and seal it with a plastic film. Prepare the test specimen according to the size required by the "Test Method for Strength of Cement Mortar (GB / T 17671-2021)" of the Chinese national standard, that is, the test specimen is a prism with a size of 40×40×160mm, and test its compressive strength and flexural strength. The test results are shown in Table 1.

[0120] Step 4: The compressive strength and flexural strength of the specimens with a curing age of 28 days were tested, and regression fitting analysis was performed on the test results to generate statistically significant Scheffé polynomials for compressive and flexural strength, respectively. Based on the Design-expert software, regression fitting analysis was performed on the test results. Tables 2 and 3 show the selected model types, as well as the fitting results and polynomials for compressive strength (Y1-28) and flexural strength (Y2-28). The lower P value and higher lack-of-fit P value of the model indicate that the variables in the model have a significant contribution to the model, and the model has a high degree of fit with the data; the goodness of fit R is guaranteed. 2 The higher the basis, the higher the revised R 2 It can better explain the variation of data, so R 2 and corrected R 2 A higher model can effectively explain the interaction between variables and predict R 2 The level of is used to indicate the strength of the model prediction performance. Therefore, according to the fitting results, the special cubic model is selected for compressive strength and the quadratic model is selected for flexural strength.

[0121] Table 2 Fitting results of compressive strength and flexural strength test data (Fit Summary)

[0122]

[0123]

[0124] Table 3 Polynomials fitted for responses at different ages

[0125]

[0126] Step 5: The above two Scheffé polynomials are used as the fitness function of the improved particle swarm algorithm (PSO). The particle swarm improvement process includes particle initialization, position and velocity update, and the use of penalty functions to deal with boundary (constraint) problems. In this embodiment, the initial population of the particle swarm algorithm is 200, the number of iterations is 500, the penalty function factor is 1, the initial inertia weight ω is 0.9, and the inertia weight is 0.4 at the end; the values ​​of the individual learning factor c1 and the social learning factor c2 are 2.

[0127] Step 6: Combine the historical best results for compressive strength and flexural strength output during the PSO iteration process as the initial population of the non-dominated sorting genetic algorithm (NSGA-Ⅱ). The specific steps are:

[0128] (1) Establishing valid samples: For the two indicators of compressive strength and flexural strength, the large global search capability of the PSO algorithm is used to record the individual best records in the iterative process, and the external archives of the two single targets are merged to increase the diversity of solutions. This diversity is crucial for NSGA-Ⅱ to find a wide range of Pareto frontiers. Since the external archives will generate a large amount of data, a total of 200,000 sets of data, which is not conducive to the calculation and convergence of NSGA-Ⅱ, in order to maintain the representativeness and diversity of the solution, K-means clustering is used to select the optimal solution under the maximum compressive strength and maximum flexural strength from each cluster area to ensure the quality of the solution and reduce the computational burden. The value of K is 200, and finally 400 valid samples are established.

[0129] (2) Population initialization: The above 400 valid samples are used as the initial population. Each individual represents a possible combination of feature variables. During the iteration process, the Scheffé polynomial is used to evaluate the performance index of each individual prediction in the population.

[0130] (3) Non-dominated sorting and crowding distance calculation: The population is non-dominated sorted and the crowding distance is calculated to promote population diversity. In order to maintain the diversity of the population, individuals with larger crowding distances are more likely to be selected into the next generation.

[0131] (4) Selection, mutation and crossover: In the process of selecting individuals as parents based on non-dominated sorting and crowding distance through a binary tournament selection mechanism to generate offspring through single-point crossover and uniform mutation, if both solutions are infeasible, the solution with the smaller constraint violation is selected; the crossover operator is 0.9, and the mutation operator is 0.05. The parents and offspring are merged to form a new population. After the new population continues to sort and calculate the crowding distance, a new generation of population is selected from the merged population. In order to ensure the continuation of excellent solutions and to ensure that the quality and diversity of solutions will not degenerate due to iterations, on the basis of requiring the total constraint that the sum of each proportion is 1, solutions that slightly violate the upper and lower limits are retained until the number of iterations reaches the maximum and the conditions for the end of the algorithm are met. The output Pareto optimal solution set is as follows: Figure 6 As shown, the solution set has good continuity and diversity, indicating that the quality of the solution set is high.

[0132] Step 7: Select the solution that meets the strength condition from the generated Pareto solution set, cast the test piece according to the mixing ratio corresponding to the solution according to step 3, and put the formed phosphorus tailings geopolymer into the curing box for curing after demoulding. After curing to the specified age, the phosphorus tailings geopolymer is obtained. A solution with higher strength is selected from a series of solutions obtained in this embodiment, and its corresponding mix ratio range is shown in Table 4, and its 28-day average predicted compressive strength is 22.65MPa and flexural strength is 5.85MPa. To verify the accuracy of the prediction, the compressive strength and flexural strength tests are carried out after the test piece is cast according to the above mix ratio and cured for 28 days. After 28 days of curing, the average compressive strength is 21.88MPa, and the flexural strength test is 5.53MPa. The actual test value of the average compressive strength in 28 days is 3.52% different from the average value predicted by the algorithm, and the actual test value of the average flexural strength is 5.79% different from the average value predicted by the algorithm. It is proved that the difference between the test and predicted results is small, and the strength of the test specimens and their optimal mix ratio can be effectively and accurately predicted.

[0133] Table 4 Physical test prediction and test values ​​under optimal mix ratio

[0134]

[0135] References

[0136] [1] Hu Wenyun, Huo Xudong. A method for solidification treatment of phosphate tailings and its application [P]. Hubei Province: CN115677277A, 2023-02-03.

[0137] [2] Pan Zhiquan, Wang Yang, Li Mouyu, Fan Chenyang, Rong Xicai. A method for preparing early-strength cementitious material for roadbed[P]. Hubei Province: CN115403347A, 2022-11-29.

[0138] [3] Li Jianqiu, Liu Zudong, Zhang Qinggang, Wen Yuanbo, Wang Jiacai, Zhao Qinglin, Zhou Shangqun. A geopolymer stabilized phosphate tailings pavement base and its preparation method [P]. Guizhou Province: CN113548843A, 2021-10-26.

[0139] [4] Ye Yicheng, Jia Jinpeng, Chen Dunxi, Hu Nanyan, Qiu Zhaokang, Xie Mingke, Xiang Hongyu, Zhu Tao. A cementitious material of hydrated calcium silicate and hydrated magnesium silicate for mine filling and its preparation method [P]. Hubei Province: CN115057638B, 2023-07-25.

[0140] [5] Jiang Zhongxi, Liu Ning, Li Wenfeng, Huang Jiaqi, Li Youqiang, Gan Yuning. A method for preparing cementitious materials based on phosphate tailings[P]. Shanghai: CN114292041A, 2022-04-08.

[0141] [6] Jiang Jiadong, Fan Cheng, Kang Yuanbing, Liu Kaijun, Li Chuan, Yang Zhenfeng. A method for preparing cement mineralizer and producing cement clinker using phosphate tailings [P]. Sichuan Province: CN113480206B, 2022-06-14.

[0142] [7] Zhang Jin, He Aiping, Qin Yuankui, Guo Maosheng, Li Guodong, Hu Xiuquan, You Dahai. A method for preparing high-performance cementitious materials from modified calcium magnesium phosphate tailings[P]. Hubei Province: CN111138098B, 2021-12-14.

[0143] [8] Hao Xutao, Zheng Guangming, Li Fang, Zou Xueyong, Shen Weiyun, Liu Xin, Zhang Zheng, Zhu Paichang. A casting material for active phosphate tailings[P]. Hubei: CN109020467A, 2018-12-18.

[0144] [9] Zhang Hongzhi, He Yanchen, Ge Zhi, Sun Renjuan, Guan Yanhua, Ling Yifeng. Design method of all-solid waste foam lightweight filler[P]. Shandong Province: CN202210277977.2, 2022-07-12.

[0145]

[10] Zhao Ming, Xue Meiping, Zhao Yue, Long Lujun, Fan Yuxu, Lin Yitian. A method for preparing filling materials from coal-based solid waste[P]. Beijing: CN202111108959.3, 2021-12-28.

[0146]

[11] Liu Xuewei, Liu Bin, Liu Quansheng, Kang Yongshui, Zhu Yuanguang, Zhu Lei, Wang Sai. A design method and grouting material for deep coal-bearing strata [P]. Hubei Province: CN202310838209.4, 2023-10-27.

Claims

1. A method for designing the proportion of large-volume phosphate tailings geopolymer materials based on a combination of mixing design and multiple algorithms, characterized in that: The following steps are involved: S1, material acquisition of calcined phosphate tailings, metakaolin, glass, and silica fume; S2, according to the calcined phosphate tailings, metakaolin, glass, and silica fume in step S1, material proportioning is performed to obtain a phosphate tailings geopolymer material casting specimen with optimized compressive strength and flexural strength; S3, preparing a casting specimen of the phosphate tailings geopolymer material according to the material proportions in step S2.

2. The method for designing the proportion of large-volume phosphate tailings geopolymer materials based on the combination of mixing design and multiple algorithms according to claim 1 is characterized in that: The method for obtaining the calcined phosphate tailings in step S1 is: The phosphate tailings that have absorbed water and agglomerated are ground into powder and spread out to dry naturally, and then placed in a muffle furnace for calcination. When the temperature reaches the rated temperature, the temperature is kept for 2 hours and then cooled naturally to ensure that the dolomite in the phosphate tailings is completely decomposed. Or / and calcination rated temperature is 750℃-850℃, heating rate is 5℃ / min; The mass percentage of CaMg(CO3)2 in the phosphate tailings is 80%-85%, and after calcination, the dolomite (CaMg(CO3)2) is completely decomposed into free CaO and MgO, with the mass percentage of CaO being 48.7% and the mass percentage of MgO being 34.5%; or / and the particle size of the powder mixture is ≤200 mesh; Or / and the mass percentage of SiO2 in the metakaolin is 45%-50%, and the mass percentage of Al2O3 is 20-25%; Or / and the mass percentage of silica fume SiO2 is 95-98%; Or / and the SiO2 content in the water glass is 49-53%, the Na2O content is 25.5-29%, and the modulus is 2.

0.

3. The method for designing the proportion of large-volume phosphate tailings geopolymer materials based on the combination of mixing design and multiple algorithms according to claim 1 is characterized in that: In step S2, the method for preparing a material ratio of the calcined phosphate tailings, metakaolin, glass and silica fume in step S1 to obtain a phosphate tailings geopolymer material with optimized compressive strength and flexural strength comprises the following steps: S21, select the mixing ratio range of four raw materials: calcined phosphate tailings, metakaolin, silica fume, and sodium water glass, and code the ratios as A, B, C, and D, and use Design-Expert software to mix the materials; S22, mixing the cooled calcined phosphate tailings powder with solid metakaolin, silica fume, and sodium water glass powder according to a proportion, mechanically stirring until the mixture is uniform, adding water and mixing, and stirring to obtain a geopolymer slurry, placing the slurry in a cast iron test mold, casting and molding the slurry, and sealing the slurry with a plastic film after compacting the slurry with a vibrating table; S23, conduct compressive strength and flexural strength tests on the specimens whose curing age reaches the preset threshold number of days.

4. The method for designing the proportion of large-volume phosphate tailings geopolymer materials based on the combination of mixing design and multiple algorithms according to claim 3 is characterized in that: In step S23, the material ratio of calcined phosphate tailings, metakaolin, glass, and silica fume corresponding to the compressive strength exceeding 20 MPa and the flexural strength exceeding 5 MPa is selected; Before or / and demolding, the film should be sealed for 24 hours. After demolding, the geopolymer specimens should be placed in a standard curing box for 28 days. The curing temperature is 20℃±2℃ and the humidity is not less than 95%.

5. The method for designing the proportion of large-volume phosphate tailings geopolymer materials based on the combination of mixing design and multiple algorithms according to claim 3 is characterized in that: In step S22, the liquid-to-solid ratio of the mixture is 0.6; the mechanical stirring time is 5 minutes; The test mold is attached to the vibration table to prevent it from jumping on the vibration table. The geopolymer slurry is loaded into the test mold at one time and vibrated until there is no obvious large bubble overflowing from the surface.

6. The method for designing the proportion of large-volume phosphate tailings geopolymer materials based on the combination of mixing design and multiple algorithms according to claim 3 is characterized in that: Step S23 includes: Statistical functions: Among them, y is the response variable; p is the number of components; b i is the regression coefficient of each component; x i is the proportion of the ith component; b ij is the regression coefficient of the quadratic term x j is the proportion of the jth component; b ijk is the regression coefficient of the cubic term x k is the proportion of the kth component.

7. The method for designing the proportion of large-volume phosphate tailings geopolymer materials based on the combination of mixing design and multiple algorithms according to claim 3 is characterized in that: Step S23 also includes: Initial position processing: Among them, x i ′ is the position of the particle after transformation; is the initial position of the particle; p is the dimension of the particle; It represents the initial position of the particle in the jth dimension; Speed ​​Handling: Among them, v i ′ is the transformed particle velocity; is the initial velocity; p is the dimension of the particle; represents the initial velocity of the particle in the jth dimension; Particle Speed: in, is the velocity of particle i at the next iteration t+1; ω is the inertia weight; is the velocity of particle i at the current iteration t; c1 is the individual learning factor; r1 is the first random number in the interval [0,1); c2 is the social learning factor; r2 is the second random number in the interval [0,1); pbest i is the personal best position found by particle i so far; is the position of particle i in the current iteration; gbest i is the global best position found by particle i among all particles so far; is the position of particle i at the next iteration t+1; Penalty function: p(x,r)=f(x)-r×Penalty(x) Among them, p(x,r) is the augmented objective function after constraint; f(x) represents the original objective function; r is the penalty factor; [(min(0,x i -ub i )] 2 +[min(0,lb i -x i )] 2 is to calculate the violation of each variable relative to its boundary constraints; It is to calculate the square of the difference between the sum of all variables and 1; x i is the i-th component of the particle position vector; ub i represents the upper bound of the i-th component; lb i represents the lower bound of the ith component.

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