CN114956749B - Method for determining mix proportion of mine filling body - Google Patents
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- C—CHEMISTRY; METALLURGY
- C04—CEMENTS; CONCRETE; ARTIFICIAL STONE; CERAMICS; REFRACTORIES
- C04B—LIME, MAGNESIA; SLAG; CEMENTS; COMPOSITIONS THEREOF, e.g. MORTARS, CONCRETE OR LIKE BUILDING MATERIALS; ARTIFICIAL STONE; CERAMICS; REFRACTORIES; TREATMENT OF NATURAL STONE
- C04B28/00—Compositions of mortars, concrete or artificial stone, containing inorganic binders or the reaction product of an inorganic and an organic binder, e.g. polycarboxylate cements
- C04B28/02—Compositions of mortars, concrete or artificial stone, containing inorganic binders or the reaction product of an inorganic and an organic binder, e.g. polycarboxylate cements containing hydraulic cements other than calcium sulfates
- C04B28/08—Slag cements
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- C—CHEMISTRY; METALLURGY
- C04—CEMENTS; CONCRETE; ARTIFICIAL STONE; CERAMICS; REFRACTORIES
- C04B—LIME, MAGNESIA; SLAG; CEMENTS; COMPOSITIONS THEREOF, e.g. MORTARS, CONCRETE OR LIKE BUILDING MATERIALS; ARTIFICIAL STONE; CERAMICS; REFRACTORIES; TREATMENT OF NATURAL STONE
- C04B38/00—Porous mortars, concrete, artificial stone or ceramic ware; Preparation thereof
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- C—CHEMISTRY; METALLURGY
- C04—CEMENTS; CONCRETE; ARTIFICIAL STONE; CERAMICS; REFRACTORIES
- C04B—LIME, MAGNESIA; SLAG; CEMENTS; COMPOSITIONS THEREOF, e.g. MORTARS, CONCRETE OR LIKE BUILDING MATERIALS; ARTIFICIAL STONE; CERAMICS; REFRACTORIES; TREATMENT OF NATURAL STONE
- C04B2111/00—Mortars, concrete or artificial stone or mixtures to prepare them, characterised by specific function, property or use
- C04B2111/00034—Physico-chemical characteristics of the mixtures
- C04B2111/00198—Characterisation or quantities of the compositions or their ingredients expressed as mathematical formulae or equations
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- C—CHEMISTRY; METALLURGY
- C04—CEMENTS; CONCRETE; ARTIFICIAL STONE; CERAMICS; REFRACTORIES
- C04B—LIME, MAGNESIA; SLAG; CEMENTS; COMPOSITIONS THEREOF, e.g. MORTARS, CONCRETE OR LIKE BUILDING MATERIALS; ARTIFICIAL STONE; CERAMICS; REFRACTORIES; TREATMENT OF NATURAL STONE
- C04B2201/00—Mortars, concrete or artificial stone characterised by specific physical values
- C04B2201/20—Mortars, concrete or artificial stone characterised by specific physical values for the density
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02W—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO WASTEWATER TREATMENT OR WASTE MANAGEMENT
- Y02W30/00—Technologies for solid waste management
- Y02W30/50—Reuse, recycling or recovery technologies
- Y02W30/91—Use of waste materials as fillers for mortars or concrete
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- Organic Chemistry (AREA)
- Chemical Kinetics & Catalysis (AREA)
- Inorganic Chemistry (AREA)
- Curing Cements, Concrete, And Artificial Stone (AREA)
- Manufacture And Refinement Of Metals (AREA)
Abstract
The invention discloses a method for determining the mix proportion of a mine filling body, which comprises the following steps: selecting the raw material mixing amount influencing the performance of the filling body as a main variable, and selecting the performance parameter of the filling body as a response amount; limiting the variation range of the main variable, and collecting response quantity test data under different main variable mix ratios in the variation range of the main variable; establishing a response surface model by using the response quantity test data; calculating the main variable mix proportion under different response quantity conditions based on the response surface model; calculating weight factors of different response quantities; and combining the main variable mix proportion and the weight factor to obtain the final optimized mix proportion of the filling body. According to the method, the raw material mixing ratio under different response quantities is calculated by establishing the response surface model between the principal variable and the response quantity, and the final optimized mixing ratio of the filling body is obtained by combining the weight factors of different response quantities obtained by calculation.
Description
Technical Field
The invention belongs to the technical field of mine filling, and particularly relates to a method for determining the mix proportion of a mine filling body.
Background
Large scale mining operations produce large quantities of iron ore tailings and stopes. On one hand, the accumulation of a large amount of tailing sand can cause air and water pollution, and surface subsidence and even geological disasters can be caused by open-pit mining, so that serious problems of ecological environment and safe production are caused; on the other hand, a large amount of goafs are formed in mines, peripheral areas and karst areas, which becomes an important problem restricting the safe development of mines and the urbanization development of the upper parts of the goafs, and has great safe production risks, so that a reasonable solution is urgently needed.
The filling method is one of the most direct and effective modes for treating the goaf, the filling material is the core for ensuring the filling engineering quality, the common filling material at present mostly takes cement and substitutes thereof as cementing materials, and broken stones, sand, tailings and the like are aggregates, so that the problems of high filling cost, serious environmental pollution, high added value and low utilization rate of the tailings and the like exist.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, and provides a method for determining the mix proportion of a mine filling body.
The invention provides the following technical scheme:
a method for determining the mix proportion of a mine filling body comprises the following steps:
selecting the raw material mixing amount influencing the performance of the filling body as a main variable, and selecting the performance parameter of the filling body as a response amount;
limiting the variation range of the main variable, and collecting response quantity test data under different main variable mix ratios in the variation range of the main variable;
establishing a response surface model by using the response quantity test data;
calculating a main variable mix ratio under different response quantity conditions based on the response surface model;
calculating the variance of different response quantities changing along with the main variable, and obtaining the weight factors of different response quantities according to the ratio of the variance;
and combining the main variable mixing ratio and the weighting factor to obtain the final optimized mixing ratio of the filling body.
Further, the raw materials of the filling body comprise tailing sand and a cementing material, and the cementing material comprises slag, desulfurized gypsum, cement, lime and silica powder.
Further, the packing body has a gel-sand ratio defined as 1:6; the water content of the tailings sand is limited to be not more than 50%, and the nonuniform coefficient C of the tailings sand is c Greater than 5, coefficient of curvature C u 1 to 3; in the cementing material, the mass fraction of the slag is limited to 70%, and the mass fraction of the desulfurized gypsum is limited to 15%.
Further, the selected main variable is the mixing amount of cement, lime and silicon powder; the range of variation of the defined primary variables is: in the cementing material, the mass fraction of the cement is 5-15%, the mass fraction of the lime is not more than 5%, and the mass fraction of the silica powder is not more than 5%.
Further, the selected response quantities are the fluidity, shrinkage ratio and compressive strength of the filling body;
the fluidity is measured according to the standard ' cement mortar fluidity measurement method ' (GB/T2419-2005 ');
the calculation formula of the contraction ratio is as follows:
S＝(R A -R B )/R B ×100％
wherein S is the shrinkage ratio, R A Volume after shrinkage response test, R B Pre-test volume for contractility response;
the compressive strength is 28 days compressive strength.
Further, within the variation range of the main variables, 16 groups of different main variable mixing ratios are designed, the mixed materials are prepared according to the main variable mixing ratios, and then response quantity test data under different main variable mixing ratios are tested and collected.
Further, the step of establishing a response surface model using the response quantity test data includes:
selecting a quadratic polynomial equation to carry out mathematical modeling;
substituting the response quantity test data into a quadratic polynomial equation to obtain a model regression equation coefficient;
and evaluating the effectiveness of the model by using variance analysis to obtain a response surface model.
Further, when a quadratic polynomial equation is selected for mathematical modeling, the model form is as follows:
wherein Y is a vector of experimental values of the response quantity, beta i And beta ij Is the coefficient of the regression equation, x i And x j Doping with i-th and j-th materials, respectivelyQuantity, q is the number of principal variable experimental values;
the following equation matrix is established:
Y’＝Xβ+ε
in the formula, Y' is an n multiplied by 1 vector of the response quantity experimental value, n is the number of the response quantity, and beta is the n multiplied by 1 vector of the parameter to be estimated; x is an n multiplied by p matrix of a main variable, p is more than or equal to q, p is the number of terms in the model, and epsilon is an error vector of n multiplied by 1, and the method has the following characteristics:
E(ε)＝0，cov(εε’)＝σ 2 I n
where E is the mathematical expectation of the vector, E (ε) is the mathematical expectation of the vector ε, cov is the covariance, ε ' is the transposed vector of ε, cov (ε ') is the covariance of vector ε and vector ε ', σ 2 Is the error variance, I n Is an identity matrix.
Further, the variance of different responses with the change of the main variable is calculated by the following formula:
D i 2 ＝[(X i1 -M i ) 2 +(X i2 -M i ) 2 +…+(X in -M i ) 2 ]/N i
in the formula D i 2 Is the variance of the ith response quantity, M i Is the average value of the test results of the ith response quantity, X i1 Is the 1 st data, X, in the test result of the ith response i2 …X in By analogy, N is the number of the response quantities, N i The number of test results being the ith response;
the weighting factor T of the ith response quantity i Comprises the following steps:
T i ＝D i 2 /(D 1 2 +D 2 2 +…+D n 2 )
in the formula, D 1 2 Is the variance of the 1 st response, D 2 2 …D n 2 And so on.
Further, the final optimized mixing proportion of the filling body is as follows:
Q j ＝Q j1 ×T 1 +Q j1 ×T 2 +…+Q jk ×T k
in the formula, Q j For the final optimum mix ratio of the jth raw material, Q j1 For the optimal mixing ratio of the jth raw material to the 1 st response quantity, Q j2 …Q jk By analogy, T 1 Weight factor, T, for the 1 st response 2 …T k And by analogy, k is the number of raw materials.
Compared with the prior art, the invention has the beneficial effects that:
(1) According to the method, a response surface model between a principal variable and a response quantity is established, the raw material mixing ratio under different response quantity conditions is calculated through the response surface model, meanwhile, the final optimized mixing ratio of the filling body is obtained by combining the weight factors of different response quantities obtained through calculation, the reliability is high, and the performance optimization of the filling body is realized;
(2) The invention provides a method for efficiently and conveniently determining the mix proportion of the mine filling body, which has simple design steps and is beneficial to solving the problem of accumulation of a large amount of iron ore tailings produced in large-scale mining operation.
Drawings
FIG. 1 is a schematic view of a fluidity response surface model in embodiment 2 of the present invention;
FIG. 2 is a schematic diagram of a model of a response surface against pressure intensity in example 2 of the present invention;
FIG. 3 is a schematic diagram of a shrinkage ratio response surface model in example 2 of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
Example 1
The embodiment provides a method for determining the mix proportion of a mine filling body, which comprises the following steps:
(1) Selecting the raw material mixing amount influencing the performance of the filling body as a main variable, and selecting the performance parameter of the filling body as a response amount.
(2) A quadratic polynomial equation is selected for mathematical modeling, and the model form is as follows:
wherein Y is a vector of experimental values of the response quantity, beta i And beta ij Is the coefficient of the regression equation, x i And x j The doping amounts of the ith material and the j material are respectively, and q is the number of main variable experimental values;
the following equation matrix is established:
Y’＝Xβ+ε
in the formula, Y' is an n multiplied by 1 vector of the response quantity experimental value, n is the number of the response quantity, and beta is an n multiplied by 1 vector of the parameter to be estimated; x is an n multiplied by p matrix of a main variable, p is more than or equal to q, p is the number of terms in the model, and epsilon is an error vector of n multiplied by 1, and the method has the following characteristics:
E(ε)＝0，cov(εε’)＝σ 2 I n
where E is the mathematical expectation of the vector, E (ε) is the mathematical expectation of the vector ε, cov is the covariance, ε ' is the transposed vector of ε, cov (ε ') is the covariance of vector ε and vector ε ', σ 2 Is the error variance, I n Is an identity matrix.
(3) And limiting the variation range of the main variable, designing a plurality of groups of different main variable mixing ratios within the variation range of the main variable, preparing a mixed material according to the main variable mixing ratios, and then testing and collecting response quantity test data under different mixing ratios.
(4) Substituting the response quantity test data in the step (3) into the quadratic polynomial equation in the step (2) to obtain a model regression equation coefficient beta i And beta ij 。
(5) And evaluating the effectiveness of the model by using variance analysis to obtain a response surface model.
(6) And calculating the main variable mix proportion under different response quantity conditions based on the response surface model.
(7) And calculating the variance of different response quantities changing along with the main variable, and obtaining the weight factors of different response quantities according to the ratio of the variance.
In particular toOf the ith response quantity, D i 2 Comprises the following steps:
D i 2 ＝[(X i1 -M i ) 2 +(X i2 -M i ) 2 +…+(X in -M i ) 2 ]/N i
in the formula, M i Is the average value of the test results of the ith response quantity, X i1 Is the 1 st data, X, in the test result of the ith response quantity i2 …X in By analogy, N is the number of the response quantities, N i The number of test results being the ith response;
the weighting factor T of the ith response quantity i Comprises the following steps:
T i ＝D i 2 /(D 1 2 +D 2 2 +…+D n 2 )
in the formula, D 1 2 Is the variance of the 1 st response, D 2 2 …D n 2 And so on.
(8) And (5) combining the main variable mixing ratio in the step (6) and the weight factor in the step (7) to obtain the final optimal mixing ratio of the filling body as follows:
Q j ＝Q j1 ×T 1 +Q j1 ×T 2 +…+Q jk ×T k
in the formula, Q j For the final optimum mix ratio of the jth raw material, Q j1 For the optimal mixing ratio of the jth raw material to the 1 st response quantity, Q j2 …Q jk By analogy, T 1 Weight factor, T, for the 1 st response 2 …T k And by analogy, k is the number of raw materials.
Example 2
In this example, a mine filling material mix ratio was designed by using the method in example 1.
The filling body comprises tailing sand and cementing materials, and the glue-sand ratio (mass ratio of the cementing materials to the tailing sand) of the filling body is defined as 1:6. the water content of the tailings sand is limited to be not more than 50%, and the nonuniform coefficient C of the tailings sand is c Greater than 5, coefficient of curvature C u Is 1 to 3. The cementing material comprises slag, desulfurized gypsum, cement, lime and silica powder. In the cementing material, the mass fraction of the slag is limited to 70%, and the mass fraction of the desulfurized gypsum is limited to 15%.
Slag is a byproduct in the blast furnace ironmaking process, and the slag discharged in nature can seriously pollute the environment, occupy farming land and hinder vegetation growth. And the dust of the slag pollutes the air and natural water areas. Under alkaline environment, slag can be excited to form a gel with certain strength. The embodiment adopts the industrial waste slag, changes waste into valuable, limits the slag content of 70 percent in the mixing proportion, can effectively reduce the cement mixing amount, obviously reduces the carbon emission in the tailing sand filling process, and is economic and environment-friendly.
The main variable is the mixing amount of cement, lime and silica powder.
Selecting the response quantity as fluidity, contraction ratio and compressive strength; the fluidity is measured according to the standard method for measuring fluidity of cement mortar (GB/T2419-2005); the compressive strength is 28 days compressive strength; the calculation formula of the contraction ratio is as follows:
S＝(R A -R B )/R B ×100％
wherein S is the shrinkage ratio, R A Volume after shrinkage response test, R B The pre-volume is measured as the contraction ratio response volume.
In the embodiment, the tailing mortar is sampled from the beach area of the Anhui Maanshan iron ore tailing pond, and the water content of the tailing mortar is 50%. After the tailing slurry was dried, the basic physical properties thereof were measured, and it can be seen from table 1 that the tailing sand satisfies the above-mentioned defined conditions.
TABLE 1 physical properties of tailings sands
In Table 1, the specific gravity is the ratio of the tailings sand to the density of pure water at 4 ℃, D (10) is the diameter of the mesh corresponding to a throughput of 10%, and D (30), D (50), D (60), D (90) and so on. Based on the above-mentioned limiting conditions, the mixing amount ranges of cement, lime and silica fume are defined as shown in Table 2 by considering the amounts of cement, lime and silica fume as main variables.
TABLE 2 Primary variable ranges
| Variables of | Minimum value | Maximum value |
| Cement (C) | 5％ | 15％ |
| Lime (L) | 0％ | 5％ |
| Silica powder (SF) | 0％ | 5％ |
The mix ratios of 16 different principal variables were designed and the mixture was stirred using a JJ-5 cement mortar mixer, initial mixing was carried out at low speed for 60 seconds to reach steady state, then at rest for 90 seconds and then at high speed for 90 seconds. The response test data of the mixes at different mix proportions were tested and collected, with the results shown in table 3.
TABLE 3 Primary variable mix and response test results
Response modeling was performed according to the results in table 3, and response surface models for different responses were as follows:
(1) Fluidity =85.87 × C +64.19 × L +123.50 × SF +56.77 × C × L-10.87 × C × SF +23.61 × L × SF
The fluidity response surface model is shown in fig. 1. From FIG. 1, it can be seen that the cement content with the maximum fluidity is 8%, the lime content is 2%, and the silica fume content is 5%.
(2) Compressive strength =0.53 xc-0.13 xl-0.48 xsf +2.82 xc × SF +3.52 × L × SF
The compressive strength response surface model is shown in fig. 2. As can be seen from FIG. 2, the cement content having the highest compressive strength was 11%, the lime content was 1%, and the silica powder content was 3%.
(3) Shrinkage ratio =3.4 × C +3.97 × L-1.23 × SF-4.39 × C × L +1.43 × C × SF
The shrinkage ratio response surface model is shown in fig. 3. As can be seen from fig. 3, the cement content with the minimum shrinkage ratio was 7%, the lime content was 3%, and the silica powder content was 5%.
The variances of fluidity, compressive strength and shrinkage ratio were calculated to be 0.081, 0.096 and 0.089, respectively, according to the test results, and accordingly, the weight factors of the different responses were 30%, 36% and 34%, respectively.
Then the final optimal mix ratio is:
the cement mixing amount is 8% × 30% +11% × 36% +7% × 34% =8.7%;
the lime mixing amount is 2% multiplied by 30% +3% multiplied by 36% +1% multiplied by 34% =2%;
the silica fume doping amount is 5% × 30% +3% × 36% +5% × 34% =4.3%.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, it is possible to make various improvements and modifications without departing from the technical principle of the present invention, and those improvements and modifications should be considered as the protection scope of the present invention.
Claims (7)
1. A method for determining the mix proportion of a mine filling body is characterized by comprising the following steps:
selecting the mixing amount of raw materials influencing the performance of the filling body as a main variable, selecting the performance parameters of the filling body as response amounts, wherein the selected main variable is the mixing amount of cement, lime and silica powder, and the selected response amounts are the fluidity, the shrinkage ratio and the compressive strength of the filling body;
limiting the variation range of the main variable, and collecting response quantity test data under different main variable mix ratios in the variation range of the main variable;
and establishing a response surface model by using the response quantity test data, and performing mathematical modeling by selecting a quadratic polynomial equation, wherein the model form is as follows:
wherein Y is a vector of experimental values of the response quantity, beta i And beta ij Is the coefficient of the regression equation, x i And x j The doping amounts of the ith material and the j material are respectively, and q is the number of main variable experimental values;
the following equation matrix is established:
Y’＝Xβ+ε
in the formula, Y' is an n multiplied by 1 vector of the response quantity experimental value, n is the number of the response quantity, and beta is the n multiplied by 1 vector of the parameter to be estimated; x is an n multiplied by p matrix of a main variable, p is more than or equal to q, p is the number of terms in the model, and epsilon is an error vector of n multiplied by 1, and the method has the following characteristics:
E(ε)＝0，cov(εε’)＝σ 2 I n
where E is the mathematical expectation of the vector, E (ε) is the mathematical expectation of the vector ε, cov is the covariance, ε 'is the transposed vector of ε, cov (ε') is the vector ε and the vector ε' covariance, σ 2 Is the error variance, I n Is an identity matrix;
calculating a main variable mix ratio under different response quantity conditions based on the response surface model;
calculating the variance of different response quantities changing along with the main variable, and obtaining the weight factors of different response quantities according to the ratio of the variance, wherein the calculation formula of the variance of different response quantities changing along with the main variable is as follows:
D i 2 ＝[(X i1 -M i ) 2 +(X i2 -M i ) 2 +…+(X in -M i ) 2 ]/N i
in the formula, D i 2 Is the variance of the ith response quantity, M i Is the average value of the test results of the ith response quantity, X i1 Is the 1 st data, X, in the test result of the ith response i2 …X in By analogy, N is the number of the response quantities, N i The number of test results being the ith response;
the weighting factor T of the ith response quantity i Comprises the following steps:
T i ＝D i 2 /(D 1 2 +D 2 2 +…+D n 2 )
in the formula, D 1 2 Is the variance of the 1 st response, D 2 2 …D n 2 And so on;
and combining the main variable mix proportion and the weight factor to obtain the final optimized mix proportion of the filling body as follows:
Q j ＝Q j1 ×T 1 +Q j2 ×T 2 +…+Q jk ×T k
in the formula, Q j For the final optimum mix ratio of the jth raw material, Q j1 For the optimal mixing ratio of the jth raw material to the 1 st response quantity, Q j2 …Q jk By analogy, T 1 Weight factor, T, for the 1 st response 2 …T k And by analogy, k is the number of raw materials.
2. The method for determining the mix proportion of the mine filling material according to claim 1, wherein the raw materials of the filling material comprise tailings sand and a cementing material, and the cementing material comprises slag, desulfurized gypsum, cement, lime and silica fume.
3. The method of determining the mix ratio of the mine filling according to claim 2, wherein the gel-sand ratio of the filling is defined as 1:6; the water content of the tailing sand is limited to be not more than 50%, and the nonuniform coefficient C of the tailing sand is c Greater than 5, coefficient of curvature C u 1 to 3; in the cementing material, the mass fraction of the slag is limited to 70%, and the mass fraction of the desulfurized gypsum is limited to 15%.
4. A method of determining a mix proportion of a mine filling material according to claim 2, wherein the range of the defined principal variables is: in the cementing material, the mass fraction of the cement is 5-15%, the mass fraction of the lime is not more than 5%, and the mass fraction of the silica powder is not more than 5%.
5. The method for determining the mix proportion of the mine filling material according to claim 1, wherein the shrinkage ratio is calculated by the formula:
S＝(R A -R B )/R B ×100％
wherein S is the shrinkage ratio, R A Volume after shrinkage response test, R B Pre-test volume for contractility response;
the compressive strength is 28 days compressive strength.
6. The method for determining the mix proportion of the mine filling material according to claim 1, wherein 16 different sets of the main variable mix proportions are designed within the variation range of the main variable, the mixed materials are prepared according to the main variable mix proportions, and then response quantity test data under different main variable mix proportions are tested and collected.
7. The method for determining the mix ratio of the mine filling material according to claim 1, wherein the step of establishing a response surface model using the response amount test data further comprises:
substituting the response quantity test data into a quadratic polynomial equation to obtain a model regression equation coefficient;
and evaluating the effectiveness of the model by adopting variance analysis to obtain a response surface model.
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|---|---|
| CN114956749A (en) | 2022-08-30 |
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