Alkali-activated slag cement carbonization performance control method based on product composition design

Abstract

The present invention discloses a method for regulating the carbonation performance of alkali-activated slag cement based on product composition design, comprising the following steps: (1) analyzing the composition of reaction products of alkali-activated slag cement prepared from raw materials with different chemical compositions, including the product aluminum-silicon ratio, hydrotalcite content and hydrated calcium aluminate content; (2) establishing a reaction product composition prediction model based on the regulation of the chemical composition of the raw materials; (3) conducting an accelerated carbonization test to measure the carbonation depth of alkali-activated slag cement with different reaction product compositions at different carbonization ages; (4) establishing a carbonation depth prediction model for alkali-activated slag cement based on the reaction product composition, thereby proposing a method for regulating the carbonation performance of alkali-activated slag cement based on product composition design, and finally achieving the purpose of achieving macroscopic performance regulation through microscopic composition design.

Description

Technical Field

[0001] The invention belongs to the technical field of civil engineering and construction, and in particular relates to a method for regulating and controlling carbonization performance of alkali-activated slag cement based on product composition design. Background Art

[0002] Alkali-activated slag cement is a cementitious material that is prepared by mixing slag with potential hydraulic properties with an alkaline activator and hardening it through a series of physical and chemical reactions. Compared with ordinary Portland cement, it has excellent properties such as rapid hardening, early strength, and resistance to chemical corrosion. In addition, the energy consumption and carbon emissions during the preparation process are significantly reduced, and it has significant advantages in energy conservation and emission reduction. It is a green cementitious material. However, the carbonization resistance of alkali-activated slag cement is poor. Carbonization will lead to a decrease in the pH value of the pore solution of the alkali-activated slag system, product decomposition, and steel corrosion, which will eventually reduce the service life of the alkali-activated slag structure. Therefore, precise control of the carbonization resistance of alkali-activated slag cement is the key to the application and promotion of alkali-activated slag cement.

[0003] Product composition is one of the main reasons that determine the anti-carbonation performance of cement-based materials. The anti-carbonation performance of cement-based structures is currently mainly improved by reducing the water-cement ratio to slow down the diffusion rate of carbon dioxide or by increasing the thickness of the protective layer of cement-based materials on a macro scale. This approach is single and does not consider the multi-scale relationship between the product composition of the material and the carbonation performance. The correlation between the product composition and the anti-carbonation performance has not been established, so it is difficult to propose a method to regulate material performance from the microscopic composition design level. Summary of the invention

[0004] In view of the above problems, the present invention proposes a method for regulating the carbonization performance of alkali-activated slag cement based on product composition design. The method controls the chemical composition of the powdered precursor and keeps the composition and dosage of the activator unchanged to obtain an alkali-activated slag cement hardener with similar pore structure but different product composition, and then analyzes the relationship between the chemical composition of the raw materials and the composition of the reaction products; conducts an accelerated carbonization test to measure the carbonization depth of the hardener at different carbonization times, thereby obtaining the relationship between the composition of the reaction product and the carbonization performance; finally, establishes the relationship between the chemical composition of the raw materials, the composition of the alkali-activated slag cement reaction products and the carbonization performance, and proposes a method for regulating the carbonization performance of alkali-activated slag cement based on product composition design, so as to achieve the purpose of regulating the anti-carbonation performance of alkali-activated slag cement based on microscopic composition.

[0005] In order to achieve the above objectives, the present invention provides a method for regulating the carbonization performance of alkali-activated slag cement based on product composition design, which specifically comprises the following steps:

[0006] 1. Use raw materials with different chemical compositions to prepare alkali-activated slag cement;

[0007] 2. Quantitative analysis of the main product composition of alkali-activated slag cement, including the product aluminum-silicon ratio, hydrotalcite content and hydrated calcium aluminate content;

[0008] 3. Establish a prediction model for the aluminum-silicon ratio, hydrotalcite content and calcium aluminate hydrate content of the alkali-activated slag cement reaction product based on the chemical composition of the raw materials, so as to determine the relationship between the raw material composition and the reaction product composition;

[0009] 4. Carry out accelerated carbonization experiments on alkali-activated slag cement, and determine the carbonization depth of alkali-activated slag cement at different carbonization ages. Refer to GBJ82-85 "Test Methods for Long-term Performance and Durability of Ordinary Concrete" to test the carbonization depth of alkali-activated slag cement with different raw material compositions at different ages;

[0010] 5. Analyze the relationship between the composition of alkali-activated slag cement reaction products and the carbonation depth, and propose a carbonation depth prediction model based on the composition of alkali-activated slag cement reaction products, establish the relationship between the chemical composition of raw materials, the composition of alkali-activated slag cement reaction products and carbonation performance, and finally propose a method for regulating the carbonation performance of alkali-activated slag cement based on product composition design, so as to achieve the purpose of regulating macroscopic performance through microscopic composition design.

[0011] The method of establishing a prediction model for the composition of alkali-activated slag cement reaction products based on the chemical composition of the raw materials, thereby determining the relationship between the composition of the raw materials and the composition of the reaction products, specifically comprises the following steps:

[0012] (1) The silicon dioxide content, aluminum oxide content, magnesium oxide content and calcium oxide content in the raw materials are selected as independent variables, respectively R MgO , R CaO The product aluminum-silicon ratio (A / S), hydrotalcite content (MAH), and calcium aluminate hydrate content (CAH) are taken as dependent variables and are denoted as R A / S , R MAH , R CAH ;

[0013] (2) Combine the independent variables and dependent variables selected in (1) to establish the following R equations with unknown parameters a1 to c3. A / S , R MAH , R CAH Prediction Model:

[0014]

[0015] (3) Based on the principle of least squares method, the chemical composition of different raw materials and product composition are substituted into the prediction model to solve the unknown parameters. The specific steps include:

[0016] ① The established prediction model is uniformly recorded as:

[0017] Y=aZ1+bZ2+c

[0018] ②Based on R A / S , R MAH , R CAH The corresponding normal equations are listed for the data set number m:

[0019]

[0020] ③Substitute the chemical composition parameters of the raw materials and the composition data of the reaction products into the normal equation in ② to solve the unknown parameters a1~c3.

[0021] (4) Substituting a1-c3 calculated in (3) into the established prediction model, the prediction models of the product aluminum-silicon ratio (A / S), hydrotalcite-like content (MAH), and calcium aluminate hydrate content (CAH) were obtained respectively;

[0022] (5) According to the prediction model obtained in (4), the predicted values ​​of the product aluminum-silicon ratio (A / S), hydrotalcite-like content (MAH), and calcium aluminate hydrate content (CAH) can be calculated according to the chemical composition of the raw materials;

[0023] (6) Compare and analyze the predicted values ​​and measured values ​​of the aluminum-silicon ratio (A / S), hydrotalcite-like content (MAH), and calcium aluminate hydrate content (CAH) of the product in step (5), and perform fitting regression. The regression coefficient is preferably greater than or equal to 0.90.

[0024] The carbonization depth prediction model based on the composition of the alkali-activated slag cement reaction product is established, the relationship between the chemical composition of the raw materials, the composition of the alkali-activated slag cement reaction product and the carbonization performance is established, and finally a method for regulating the carbonization performance of the alkali-activated slag cement based on the product composition design is proposed. The specific steps include:

[0025] (1) The aluminum-silicon ratio (A / S), hydrotalcite-like content (MAH), calcium aluminate hydrate content (CAH) and age of the reaction product were selected as independent variables, denoted by R A / S , R MAH , R CAH , t, carbonization depth as the dependent variable, denoted as x c ;

[0026] (2) Combining the independent variables and dependent variables selected in (1), the following carbonization depth prediction model containing unknown parameters a~e is established:

[0027]

[0028] (3) Based on the principle of least squares method, the product composition parameters and carbonization depth values ​​at different ages are substituted into the carbonization depth prediction model to solve the unknown parameters. The specific steps include:

[0029] ① Expand and organize the established prediction model to obtain:

[0030]

[0031] ② Order get:

[0032] x c =aZ1+bZ2+cZ3+dZ4+e

[0033] ③ List the corresponding normal equation based on the number of carbonization depth data sets m:

[0034]

[0035] ④Substitute the product composition parameters and carbonization depth values ​​at different ages into the normal equation ③ to solve the unknown parameters a~e.

[0036] (4) Substituting a to e calculated in (3) into the established prediction model to obtain a carbonization depth prediction model;

[0037] (5) Calculate the predicted value of carbonization depth according to the prediction model obtained in (4);

[0038] (6) Compare and analyze the predicted value of carbonization depth in step (5) with the measured value, and perform fitting regression. The regression coefficient should be greater than or equal to 0.90.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] This method clarifies the relationship between the product composition and anti-carbonation performance of alkali-activated slag cement, and establishes a carbonation depth prediction model based on the product composition of alkali-activated slag cement, thereby achieving the purpose of regulating the anti-carbonation performance of alkali-activated slag cement based on product composition. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 This is a comparison chart of the product A / S measured value and the predicted value.

[0042] Figure 2 This is a comparison chart of the measured value and predicted value of MAH content.

[0043] Figure 3 This is a comparison chart of the measured and predicted values ​​of CAH content.

[0044] Figure 4 This is a comparison chart between the measured value and the predicted value of carbonization depth. DETAILED DESCRIPTION

[0045] In order to enable those skilled in the art to better understand the technical solution of the present invention, the technical solution of the present invention is further described in detail below in conjunction with embodiments.

[0046] Example 1

[0047] In this embodiment, a method for designing and regulating the carbonization performance of alkali-activated slag cement based on product composition includes the following steps:

[0048] Step 1: Prepare alkali-activated slag cement using raw materials of different chemical compositions. The specific preparation steps include:

[0049] (1) According to the raw material ratio shown in Table 1, weigh a certain amount of sodium hydroxide and water, mix, stir, and dissolve to prepare the required alkaline activator solution, and let it stand for use;

[0050] (2) According to the raw material ratio shown in Table 1, powdered precursors with different chemical compositions are prepared for use;

[0051] (3) Referring to the cement mortar strength test method GB / T17671-1999, powdered precursor, alkaline activator and sand were mixed to prepare alkali-activated slag cement with different raw material compositions.

[0052] Table 1 Raw material ratio

[0053]

[0054]

[0055] Step 2: Quantitatively analyze the main product composition in alkali-activated slag cement, specifically including analyzing the product aluminum-silicon ratio, hydrotalcite content and hydrated calcium aluminate content:

[0056] The test results of the composition of alkali-activated slag cement products with different raw material compositions are shown in Table 2.

[0057] Table 2 Composition of alkali activated slag cement products with different raw material compositions

[0058]

[0059] Step 3: Establish a prediction model for the aluminum-silicon ratio, hydrotalcite content and calcium aluminate hydrate content of the alkali-activated slag cement reaction product composition based on the chemical composition of the raw materials, so as to determine the relationship between the raw material composition and the reaction product composition. The specific steps include:

[0060] (1) The silicon dioxide content, aluminum oxide content, magnesium oxide content and calcium oxide content in the raw materials are selected as independent variables, respectively RMgO , R CaO , aluminum-silicon ratio (A / S), hydrotalcite content (MAH), and calcium aluminate hydrate content (CAH) were used as dependent variables and denoted as R A / S , R MAH , R CAH ;

[0061] (2) Combine the independent variables and dependent variables selected in (1) to establish the following R equations with unknown parameters a1 to c3. A / S , R MAH , R CAH Prediction Model:

[0062]

[0063] (3) Based on the principle of least squares method, the chemical composition of different raw materials and product composition are substituted into the prediction model to solve the unknown parameters. The specific steps include:

[0064] ① The established prediction model is uniformly recorded as:

[0065] Y=aZ1+bZ2+c

[0066] ②Based on R A / S , R MAH , R CAH The corresponding normal equations for the 7 sets of data are listed as follows:

[0067]

[0068] ③Substitute the data in Table 3 into the normal equation in ② and solve the unknown parameters a1~c3 to obtain:

[0069]

[0070] (4) Substituting a1 to c3 calculated in (3) into the established prediction model, we obtain:

[0071]

[0072] (5) According to the prediction model obtained in (4), the predicted values ​​of the product aluminum-silicon ratio, hydrotalcite content and calcium aluminate hydrate content are calculated, see Table 3

[0073] Table 3 Chemical composition of raw materials and product aluminum-silicon ratio, hydrotalcite content and calcium aluminate hydrate content

[0074]

[0075] (6) Comparison and analysis of the measured and predicted values ​​of the aluminum-silicon ratio, hydrotalcite content and calcium aluminate hydrate content of the product in (5), see Figures 1 to 3 , the R obtained by linear fitting2 They are 0.998, 0.976, and 0.992 respectively, indicating that the established model has high accuracy.

[0076] The above model establishes the relationship between the chemical composition of the raw materials and the aluminum-silicon ratio of the products, the content of hydrotalcite-like substances and the content of calcium aluminate hydrate. From the established model, it can be seen that the content of magnesium oxide in the raw materials has a significant effect on the amount of hydrotalcite-like substances produced, and the content of aluminum oxide has a significant effect on the amount of calcium aluminate hydrate produced.

[0077] Step 4: Carry out accelerated carbonization experiment of alkali-activated slag cement and measure the carbonization depth of alkali-activated slag cement at different carbonization ages:

[0078] The test results of carbonation depth of alkali-activated slag cement prepared with raw materials of different chemical compositions are shown in Table 4.

[0079] Table 4 Carbonation depth of alkali-activated slag cement prepared using raw materials with different chemical compositions

[0080]

[0081] Step 5: Analyze the relationship between the composition of alkali-activated slag cement reaction products and carbonation depth, and propose a carbonation depth prediction model based on the composition of alkali-activated slag cement reaction products, establish the relationship between the chemical composition of raw materials, the composition of alkali-activated slag cement reaction products and carbonation performance, and finally propose a method for regulating the carbonation performance of alkali-activated slag cement based on product composition design, so as to achieve the purpose of regulating macroscopic performance through microscopic composition design. The specific steps include:

[0082] (1) The aluminum-silicon ratio (A / S), hydrotalcite-like content (MAH), calcium aluminate hydrate content (CAH) and age of the reaction product were selected as independent variables, denoted by R A / S , R MAH , R CAH , t, carbonization depth as the dependent variable, denoted as x c ;

[0083] (2) Combining the independent variables and dependent variables selected in (1), the following carbonization depth prediction model containing unknown parameters a~e is established:

[0084]

[0085] (3) Based on the principle of least squares method, the raw material composition parameters of alkali-activated slag cement and the carbonation depth values ​​at different ages are substituted into the carbonation depth prediction model to solve the unknown parameters. The specific steps include:

[0086] ① Expand and organize the established prediction model to obtain:

[0087]

[0088] ② Order get:

[0089] x c =aZ1+bZ2+cZ3+dZ4+e

[0090] ③ Based on 28 sets of carbonization depth data, the corresponding normal equations are listed:

[0091]

[0092] ④Substitute the data in Table 5 into the normal equation in ③ and solve the unknown parameters a~e to obtain:

[0093] a=-3.41, b=-0.02, c=0.31, d=-0.86, e=-0.23

[0094] (4) Substituting a~e calculated in (3) into the established prediction model, we obtain:

[0095]

[0096] The model establishes the relationship between the aluminum-silicon ratio (A / S), hydrotalcite-like content (MAH), calcium aluminate hydrate content (CAH) of the reaction products, as well as the age and carbonization depth, so that the macroscopic properties (anti-carbonization performance) can be controlled by product composition design.

[0097] (5) According to the prediction model obtained in (4), the predicted value of carbonization depth is calculated, see Table 5

[0098] Table 5 Carbonization depth of different product compositions and different ages

[0099]

[0100]

[0101] (6) Comparison of the predicted and measured carbonization depths in (5), see Figure 4 , the R obtained by linear fitting 2 =0.96, indicating that the predicted value is consistent with the measured value, the model has high accuracy, and the established relationship between the product composition of alkali-activated slag cement and the carbonation depth has high accuracy, which can realize the design and regulation of macroscopic properties (anti-carbonation performance) based on the microscopic product composition, which is of great significance.

[0102] The above only describes the preferred specific implementation methods of the present invention, which does not limit the protection scope of the present invention. Any modification or transformation made by the technical scheme and inventive concept of the present invention by technicians in the relevant field within the technical scope disclosed by the present invention shall be within the protection scope of the present invention.

Claims

1. A method for regulating the carbonization performance of alkali-activated slag cement based on product composition design, characterized in that: The specific steps include: (1) Using raw materials with different chemical compositions to prepare alkali-activated slag cement; (2) Quantitative analysis of the main product composition of alkali-activated slag cement, including the product aluminum-silicon ratio, hydrotalcite content, and calcium aluminate hydrate content; (3) Establish a prediction model for the aluminum-silicon ratio, hydrotalcite content, and calcium aluminate hydrate content of the alkali-activated slag cement reaction product based on the chemical composition of the raw materials, so as to determine the relationship between the raw material composition and the reaction product composition; The raw material chemical composition includes silicon dioxide, aluminum oxide, magnesium oxide and calcium oxide. (4) Carry out accelerated carbonization experiments on alkali-activated slag cement and measure the carbonization depth of alkali-activated slag cement at different carbonization ages; (5) The relationship between the composition of alkali-activated slag cement reaction products and carbonation depth is analyzed, and a carbonation depth prediction model based on the composition of alkali-activated slag cement reaction products is proposed. The relationship among the chemical composition of raw materials, the composition of alkali-activated slag cement reaction products and carbonation performance is established. Finally, a method for regulating the carbonation performance of alkali-activated slag cement based on product composition design is proposed, so as to achieve the purpose of regulating macroscopic performance through microscopic composition design.

2. The method for controlling the carbonization performance of alkali-activated slag cement based on product composition design according to claim 1, characterized in that: The method of establishing a prediction model for the composition of alkali-activated slag cement reaction products based on the chemical composition of the raw materials, thereby determining the relationship between the composition of the raw materials and the composition of the reaction products, specifically comprises the following steps: (1) The silicon dioxide content, aluminum oxide content, magnesium oxide content and calcium oxide content in the raw materials were selected as independent variables. Denoted as R MgO , R CaO The product aluminum-silicon ratio (A / S), hydrotalcite content (MAH), and calcium aluminate hydrate content (CAH) are taken as dependent variables and are denoted as R A / S , R MAH , R CAH ; (2) Combine the independent variables and dependent variables selected in (1) to establish the following R equations with unknown parameters a1 to c3. A / S , R MAH , R CAH Prediction Model: (3) Based on the principle of least squares method, the chemical composition of different raw materials and product composition are substituted into the prediction model to solve the unknown parameters. The specific steps include: ① The established prediction model is uniformly recorded as: Y=aZ1+bZ2+c ②Based on R A / S , R MAH , R CAH The corresponding normal equations are listed for the data set number m: ③ Substitute the chemical composition parameters of the raw materials and the composition data of the reaction products into the normal equation in ② to solve the unknown parameters a1~c3; (4) Substitute the a1~c3 calculated in (3) into the established prediction model to obtain the prediction models of the product aluminum-silicon ratio (A / S), hydrotalcite-like content (MAH), and calcium aluminate hydrate content (CAH); (5) According to the prediction model obtained in (4), the predicted values ​​of the product aluminum-silicon ratio (A / S), hydrotalcite-like content (MAH), and calcium aluminate hydrate content (CAH) can be calculated according to the chemical composition of the raw materials; (6) Compare and analyze the predicted values ​​and measured values ​​of the aluminum-silicon ratio (A / S), hydrotalcite-like content (MAH), and calcium aluminate hydrate content (CAH) of the product in step (5), and perform fitting regression, with the regression coefficient being greater than or equal to 0.

90.

3. The method for controlling the carbonization performance of alkali-activated slag cement based on product composition design according to claim 1, characterized in that: The carbonization depth prediction model based on the composition of the alkali-activated slag cement reaction product is established, the relationship between the chemical composition of the raw materials, the composition of the alkali-activated slag cement reaction product and the carbonization performance is established, and finally a method for regulating the carbonization performance of the alkali-activated slag cement based on the product composition design is proposed. The specific steps include: (1) The aluminum-silicon ratio (A / S), hydrotalcite-like content (MAH), calcium aluminate hydrate content (CAH) and age of the reaction product were selected as independent variables, denoted by R A / S , R MAH , R CAH , t, carbonization depth as the dependent variable, denoted as x c ; (2) Combining the independent variables and dependent variables selected in (1), the following carbonization depth prediction model containing unknown parameters a~e is established: (3) Based on the principle of least squares method, the product composition parameters and carbonization depth values ​​at different ages are substituted into the carbonization depth prediction model to solve the unknown parameters. The specific steps include: ① Expand and organize the established prediction model to obtain: ② Order get: x c =aZ1+bZ2+cZ3+dZ4+e ③ List the corresponding normal equation based on the number of carbonization depth data sets m: ④Substitute the product composition parameters and carbonization depth values ​​at different ages into the normal equation ③ to solve the unknown parameters a~e, (4) Substituting a to e calculated in (3) into the established prediction model to obtain a carbonization depth prediction model; (5) Calculate the predicted value of carbonization depth according to the prediction model obtained in (4); (6) Compare and analyze the predicted value of carbonization depth in step (5) with the measured value, and perform fitting regression, with the regression coefficient being greater than or equal to 0.90.

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