Cement specific surface area calculation method and processor

Abstract

The invention discloses a cement specific surface area calculation method and processor, and the cement specific surface area calculation method comprises the following steps: testing the laser particle size particle distribution of a cement sample, obtaining the cumulative throughput data of each particle size interval, selecting a core particle size interval based on the physical characteristics of the cement particle surface area, and calculating the cement specific surface area according to the selected core particle size interval. And counting particle distribution values of the cement sample in three core particle size intervals, namely X1, X2 and X3, and calculating a specific surface area predicted value according to a prediction formula S = 16.825 X1-2.154 X2-2.972 X3 + 0.062 X12 + 88.427. According to the method for calculating the specific surface area of the cement, testing steps are rapid and convenient, compared with a Blaine method, the specific surface area of the cement can be accurately predicted without testing the specific gravity of materials and testing a Blaine specific surface area instrument with many influence factors, the calculation speed of the specific surface area of the cement is effectively increased, and time and labor cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of cement property prediction, and particularly to a method for calculating the specific surface area of cement and a processor. Background Art

[0002] As one of the basic raw material process industries, the cement manufacturing industry plays an important role in economic construction. Cement grinding is an important process in cement production, and the specific surface area of cement is one of the important indicators for cement quality control. Accurately predicting the specific surface area of cement can provide a basis for optimizing the grinding process scheduling of cement mills, reducing comprehensive energy consumption, and evaluating cement products.

[0003] At a fixed weight, the surface area of cement particles is directly related to the quality and performance of cement. Generally speaking, the larger the specific surface area, the finer the cement particles, and the more sufficient their hydration reaction, resulting in higher strength. The fine powder particles in cement (particles with a particle size below 3 microns) are the main contributors to the specific surface area. A certain proportion of fine powder content in the cement material is required to provide a qualified specific surface area. If the fine powder content is too high and the specific surface area is too high, the performance indicators of the cement will not meet the standards. Therefore, in production, it is necessary to reasonably control the distribution of powder materials with different particle sizes through process or operating parameters, so that the produced cement reaches or approaches the ideal specific surface area.

[0004] Currently, the method for detecting the specific surface area of cement is the Blaine method, that is, using a permeable Blaine specific surface area analyzer for testing. Its testing process is time-consuming and affected by many factors, making the specific surface area testing process more cumbersome and complex. Summary of the Invention

[0005] The purpose of the present invention is to solve the above-mentioned problems in the background art, and to propose a method for calculating the specific surface area of cement and a processor.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A method for calculating the specific surface area of cement includes:

[0008] Using a laser particle size analyzer to test the laser particle size distribution of a cement sample, and obtaining the cumulative passing amount data of each particle size interval;

[0009] Based on the physical characteristics of the surface area of cement particles, a core particle size interval is selected, and the particle distribution values of the cement sample in three said core particle size intervals are statistically analyzed, and the corresponding distribution values are X1, X2, and X3 respectively;

[0010] Based on the three said distribution values, a prediction formula for the specific surface area of cement is fitted by quadratic multiple regression analysis, and the specific surface area prediction value is calculated using the prediction formula;

[0011] The prediction formula is as follows:

[0012] S=16.825X1-2.154X2-2.972X3+0.062X1 2 +88.427;

[0013] Where:

[0014] X1 is the percentage of particles smaller than 3 microns;

[0015] X2 is the percentage of particles between 15 μm and 25 μm;

[0016] X3 is the percentage of particles larger than 80 microns.

[0017] As a further solution of the present invention: the quadratic multiple regression analysis fitting process of the prediction formula includes data set construction, which requires organizing the cumulative throughput data of each particle size interval into a structured table form and listing the independent variables and dependent variables.

[0018] As a further solution of the present invention: the independent variable is the distribution value, and the dependent variable is the cement specific surface area.

[0019] As a further solution of the present invention: an initial model is established based on the independent variables and the dependent variables, and the initial model is as follows:

[0020] S=β0+β1X1+β2X2+β3X3+β4X1 2 +ε;

[0021] Where:

[0022] X1 is the percentage of particles smaller than 3 microns;

[0023] X2 is the percentage of particles between 15 μm and 25 μm;

[0024] X3 is the percentage of particles larger than 80 microns;

[0025] ε is a constant.

[0026] As a further solution of the present invention: the binomial of X1 can reflect the saturation effect of the fine powder, that is, as X1 increases, the growth rate of the specific surface area will slow down.

[0027] As a further solution of the present invention: when implementing regression analysis, it is necessary to use statistical tools to perform multivariate regression analysis, solve the regression coefficients β0, β1, β2, β3, and β4 by the least squares method, input the experimental data into the initial model, calculate the specific numerical value and significance level of each regression coefficient, and form a calculation model.

[0028] As a further solution of the present invention: perform a significance test, goodness-of-fit evaluation, and residual analysis on the calculation model to determine the specific values of the regression coefficients β0, β1, β2, β3, β4 and the specific value of ε.

[0029] As a further solution of the present invention: the specific values of the regression coefficients β0, β1, β2, β3, β4 are 88.427, 16.825, -2.154, -2.972, 0.062 respectively, and the specific value of ε is 88.427.

[0030] A processor adapted to the cement specific surface area calculation method, comprising: a calculation unit for calculating the cement specific surface area.

[0031] Advantages of the present invention:

[0032] (1) In the present invention, the test steps of the cement specific surface area calculation method are fast and convenient. Compared with the Blaine method, it is not necessary to test the specific gravity of the material and the Blaine specific surface area meter with many influencing factors, and the specific surface area of cement can be accurately predicted, which effectively improves the calculation speed of the cement specific surface area and reduces the time and labor costs.

[0033] (2) In the present invention, the error between the predicted specific surface area value predicted by using this cement specific surface area calculation method and the test result of the Blaine specific surface area is low. Compared with the method of online estimation using a laser particle size analyzer, it can more truly reflect the size of the cement specific surface area, thus providing a relatively accurate basis for the optimization of the grinding process scheduling of the cement mill, the reduction of the comprehensive energy consumption, and the evaluation of the cement product.

[0034] (3) In the present invention, during cement production, cement can be sampled online, and then the predicted specific surface area value is calculated using this method. According to the predicted value, the cement production process or operation parameters are adjusted to control the particle size distribution in the particle size ranges of <3μm, 15μm - 25μm, and 80μm, so that the produced cement reaches or approaches the ideal specific surface area size. Description of the drawings

[0035] The present invention will be further described below with reference to the drawings.

[0036] Figure 1 It is a comparison schematic diagram of the online laser particle size detection of the cement specific surface area and the test of the cement specific surface area by the laboratory Blaine specific surface area meter in the prior art;

[0037] Figure 2 It is a comparison schematic diagram of the online laser particle size detection of the cement specific surface area and the test of the cement specific surface area by the laboratory Blaine specific surface area meter in the prior art;

[0038] Figure 3It is a schematic flow diagram of the method for calculating the specific surface area of cement in the present invention. Specific embodiments

[0039] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts shall fall within the protection scope of the present invention.

[0040] The inventor found that, at a fixed weight, the surface area of cement particles is directly related to the quality and properties of cement. Generally speaking, the larger the specific surface area, the finer the cement particles, and the more sufficient their hydration reaction, resulting in higher strength. The fine powder particles in cement (particles with a particle size of less than 3 microns) are the main contributors to the specific surface area, and a certain proportion of fine powder content in the cement material is required to provide a qualified specific surface area. If the fine powder content is too high and the specific surface area is too high, some performance indicators of the cement will be unqualified, and the performance indicators include but are not limited to standard consistency and setting time. Therefore, in cement production, it is necessary to reasonably control the distribution of grinding materials with different particle sizes through processes or operating parameters, so that the produced cement reaches or approaches the ideal specific surface area.

[0041] Currently, the method for detecting the specific surface area of cement is the Blaine method, that is, a permeable Blaine specific surface area instrument is used for testing. There are many influencing factors in this method for testing the specific surface area, such as the specific gravity of the material, the porosity, the test environment temperature, and the compaction degree of the sample cylinder. Among them, the specific gravity test of the material requires the Le Chatelier flask method, that is, kerosene needs to be placed in the container in advance and kept at a constant temperature for more than half an hour. After taking out the reading, the measured material (cement) is loaded, and then put into the constant temperature for reading. The whole process of testing the specific gravity takes 1h - 2h. After obtaining the accurate specific gravity, weigh the material and add it to the sample cylinder of the Blaine instrument and compact it, and test and calculate the specific surface area according to the reading of the Blaine specific surface area instrument. Although the specific surface area measured by the Blaine method is relatively accurate, the whole testing process takes a long time, generally more than 2 hours, and there are many influencing factors in the experiment, such as the specific gravity of the material, the porosity, and the environmental temperature, making the specific surface area testing process cumbersome and complex.

[0042] However, with the development of intelligent factories, some cement enterprises have installed online laser particle size analyzers in the processes of raw material feeding into the mill, mill discharge, and finished cement production to monitor the particle size of cement powder in real time, ensuring a stable particle size distribution range, being easy to operate, and reducing manual operation. Laser particle size analyzers mainly use the principle of laser transmission or scattering to measure the particle size distribution of materials. Operators generally use empirical formulas or calibration coefficients, substituting the particle size and density measured by the laser particle size analyzer into the empirical formula or calibration coefficient to quickly estimate the specific surface area of cement, namely the online estimation method.

[0043] The inventors purchased a batch of cement each from an enterprise in Guangdong and an enterprise in Zhejiang, namely Cement A and Cement B. The actual specific surface area values of Cement A and Cement B were detected using a Blaine specific surface area analyzer, and then the estimated specific surface area values were obtained by combining an online laser particle size analyzer with an empirical formula (or calibration coefficient). The following are the specific data of the two groups of experiments:

[0044] Experiment A:

[0045] The actual specific surface area value of Cement A was detected using a Blaine specific surface area analyzer, and then the estimated specific surface area value of Cement A was obtained by combining an online laser particle size analyzer with an empirical formula (or calibration coefficient). The calculation error data are as follows:

[0046] Table 1 Online specific surface area error data table of Cement A

[0047]

[0048] According to the data in Table 1, the error between the specific surface area estimated by the laser particle size analyzer for Cement A and the test result of the Blaine specific surface area is between 18.9% and 26.2%, with a relatively large error.

[0049] Please refer to Figure 1 As shown, the estimated value of the specific surface area of Cement A obtained by using a laser particle size analyzer through the online estimation method can reflect the trend of the change in the specific surface area value of Cement A, but it is difficult to truly reflect the size of the specific surface area of Cement A.

[0050] Experiment B:

[0051] The actual specific surface area value of Cement B was detected using a Blaine specific surface area analyzer, and then the estimated specific surface area value of Cement B was obtained by combining an online laser particle size analyzer with an empirical formula (or calibration coefficient). The calculation error data are as follows:

[0052] Table 2 Online specific surface area error data table of Cement B

[0053]

[0054]

[0055] According to the data in Table 2, the error between the specific surface area estimated by the laser particle size analyzer for Cement B and the test result of the Blaine specific surface area is between 45% and 47.2%, which is relatively large.

[0056] Please refer to Figure 2 As shown, the estimated value of the specific surface area of Cement B obtained by the online estimation method using a laser particle size analyzer can reflect the trend of the change in the specific surface area value of Cement B, but it is difficult to truly reflect the size of the specific surface area of Cement B.

[0057] From the above two experiments, it can be obtained that the online estimation method adopted in the prior art can reflect the trend of the change in the specific surface area value of cement through a laser particle size analyzer, but it cannot estimate a relatively accurate specific surface area value of cement.

[0058] Please refer to Figure 3 As shown, the present invention provides a method for calculating the specific surface area of cement, including: using a laser particle size analyzer to test the laser particle size distribution of a cement sample and obtaining the cumulative passing amount data in each particle size range.

[0059] Based on the physical characteristics of the cement particle surface area, a core particle size range is selected. Among the particles with a particle size less than 3 microns, the fine powder dominates the contribution to the specific surface area. The particles between 15 microns and 25 microns, denoted as medium particles, will have a negative impact on the porosity. The particles larger than 80 microns, denoted as coarse particles, can significantly reduce the effective surface area. The particle distribution values in the three core particle size ranges of the cement sample are statistically counted, and the corresponding distribution values are X1, X2, and X3 respectively.

[0060] Based on the three distribution values, a prediction formula for the specific surface area of cement is fitted through quadratic multiple regression analysis, and the predicted value of the specific surface area is calculated using the prediction formula.

[0061] The process of fitting the quadratic multiple regression analysis of the prediction formula includes data set construction. The data set construction requires organizing the cumulative passing amount data in each particle size range into a structured table form and listing the independent variables and the dependent variable. The independent variables are the distribution values, namely X1, X2, and X3, and the dependent variable is the specific surface area of cement, denoted as S.

[0062] The inventor has been engaged in this field for many years and speculates based on research experience that the binomial of X1 can reflect the saturation effect of the fine powder, that is, as X1 increases, the growth rate of the specific surface area will slow down. Therefore, an initial model is established based on the independent variable and the dependent variable, and the initial model is as follows:

[0063] S = β0 + β1X1 + β2X2 + β3X3 + β4X1 2 + ε.

[0064] In the formula:

[0065] X1 is the percentage of particle distribution with a particle size less than 3 microns;

[0066] X2 is the percentage of particle distribution between 15 microns and 25 microns;

[0067] X3 is the percentage of particle distribution greater than 80 microns;

[0068] ε is a constant.

[0069] When performing regression analysis, statistical tools (including but not limited to SPSS, statsmodels in Python) are needed to perform multiple regression analysis. The regression coefficients β0, β1, β2, β3, β4 are solved by the least squares method to minimize the sum of squared residuals. The experimental data is input into the initial model, and the specific values of each regression coefficient and the significance level are calculated to form a calculation model. The calculation model is subjected to significance test, goodness-of-fit evaluation and residual analysis to determine the specific values of the regression coefficients β0, β1, β2, β3, β4 and the specific value of ε. The specific values of the regression coefficients β0, β1, β2, β3, β4 are 88.427, 16.825, -2.154, -2.972, 0.062 respectively, and the specific value of ε is 88.427.

[0070] The finally determined prediction formula is as follows:

[0071] S = 16.825X1 - 2.154X2 - 2.972X3 + 0.062X1 2 + 88.427;

[0072] In the formula:

[0073] X1 is the percentage of particle distribution less than 3 microns;

[0074] X2 is the percentage of particle distribution between 15 microns and 25 microns;

[0075] X3 is the percentage of particle distribution greater than 80 microns.

[0076] Among them, the strong positive effect of X1: fine powder directly increases the surface area; the negative effects of X2 and X3: medium particles and coarse particles can reduce the effective surface area; the X1 2 term: can reflect the slowdown in growth rate when the proportion of fine powder is relatively high.

[0077] The specific steps of this method for calculating the specific surface area of cement are as follows:

[0078] S1. Use a laser particle size analyzer to test the laser particle size distribution of the cement sample and obtain the cumulative passing amount data in each particle size range.

[0079] Test the distribution content of cement in the following particle size ranges: < 3μm, 3μm - 10μm, 10μm - 15μm, 15μm - 25μm, 25μm - 30μm, 30μm - 32μm, 32μm - 40μm, 40μm - 45μm, 45μm - 50μm, 50μm - 63μm, 63μm - 80μm, > 80μm.

[0080] S2. Based on the physical properties of the cement particle surface area, select the core particle size ranges, which are < 3μm, 15μm - 25μm, and > 80μm respectively. Statistically analyze the particle distribution values of the cement sample in the three core particle size ranges, and the corresponding distribution values are X1, X2, and X3 respectively.

[0081] S3. Substitute the three distribution values X1, X2, and X3 into the prediction formula to calculate the specific surface area.

[0082] The prediction formula is S = 16.825X1 - 2.154X2 - 2.972X3 + 0.062X1 2 + 88.427. The result calculated according to this prediction formula is the specific surface area value of the cement predicted based on the laser particle size distribution value.

[0083] The inventor purchased two batches of cement from different cities, namely Cement C and Cement D. In order to verify the calculation method of the cement specific surface area, both Cement C and Cement D were tested for their actual specific surface area values using a Blaine specific surface area analyzer, and then the estimated specific surface area values were predicted using a cement specific surface area calculation method in this application. The following are the specific data of the two groups of experiments:

[0084] Experiment C:

[0085] Use a laser particle size analyzer to test the cumulative passing amount of each particle size, as follows:

[0086] Table 3 Data table of cumulative passing amount corresponding to different particle sizes of Cement C

[0087]

[0088]

[0089] Statistically analyze the distribution values of Cement C in the three core particle size ranges < 3μm, 15μm - 25μm, and > 80μm. The corresponding distribution values are X1, X2, and X3 respectively, as follows:

[0090] Table 4 Data table of distribution values corresponding to the core particle size range of Cement C

[0091] Particle size (μm) ＜3 15-25 ＞80 Distribution value (%) 20.23 13.86 2.75

[0092] Calculate the predicted specific surface area value of Cement C based on the prediction formula:

[0093] S=16.825X1-2.154X2-2.972X3+0.062X1 2 +88.427

[0094] =16.825×20.23-2.154×13.86-2.972×2.75+0.062×20.23 2 +88.427

[0095] ≈416.1(m 2 / kg).

[0096] The specific surface area of cement C was tested using a Blaine surface area meter. The specific surface area calculation formula is:

[0097]

[0098] The specific gravity of the material was tested using a Lee pycnometer, ρ = 3.0950 g / cm 3 , take the void ratio as 0.5, weigh the material and put it into the test tube for compaction, the liquid level falling time is t=113.40s, η is the air viscosity, and the specific surface area instrument constant is 23.01.

[0099]

[0100] Therefore, the online specific surface area error of cement C = |(416.1-416.3)| / 416.3×100%≈0.05%.

[0101] Experiment D:

[0102] The cumulative throughput of each particle size was tested using a laser particle size analyzer, as follows:

[0103] Table 5 Cumulative throughput data corresponding to different particle sizes of cement D

[0104]

[0105]

[0106] The distribution values of cement D in three core particle size ranges of <3μm, 15μm-25μm, and >80μm are statistically analyzed. The corresponding distribution values are X1, X2, and X3, as follows:

[0107] Table 6 Distribution data of cement D in core particle size range

[0108] Particle size (μm) ＜3 15-25 ＞80 Distribution value (%) 17.46 13.21 6.88

[0109] The predicted value of the specific surface area of cement D is calculated based on the prediction formula:

[0110] S = 16.825X1 - 2.154X2 - 2.972X3 + 0.062X1 2 + 88.427

[0111] = 16.825×17.46 - 2.154×13.21 - 2.972×6.88 + 0.062×17.46 2 + 88.427

[0112] ≈ 352.2 (m 2 / kg).

[0113] The specific surface area of Cement D was tested using a Blaine specific surface area apparatus. The formula for calculating the specific surface area is:

[0114]

[0115] The specific gravity of the material ρ = 3.0904 g / cm was tested using a Le Chatelier pycnometer 3 , with a void ratio of 0.5. The material was weighed and placed in the test cylinder and compacted. The time for the liquid level to drop was t = 77.30 s, η was the air viscosity, and the instrument constant of the specific surface area apparatus was 23.01.

[0116]

[0117] Therefore, the on-line specific surface area error of Cement D = |(352.2 - 344.2)| / 344.2 × 100% ≈ 2.3%.

[0118] Based on the calculation data of the on-line specific surface area errors of Experiment C and Experiment D, the error between the predicted specific surface area value of the cement predicted by a cement specific surface area calculation method in this application and the test result of the Blaine specific surface area is relatively low, which can more truly reflect the size of the cement specific surface area, thus providing a relatively accurate basis for the optimization of the grinding process scheduling of the cement mill, the reduction of the comprehensive energy consumption, and the evaluation of the cement product.

[0119] Moreover, the test steps of this method are fast and convenient. Compared with the Blaine method, it is not necessary to test the specific gravity of the material and the Blaine specific surface area apparatus with many influencing factors to accurately predict the specific surface area of the cement, which also effectively improves the calculation speed of the cement specific surface area and reduces the time and labor costs.

[0120] In the actual production application process, samples of cement can be taken online during cement production, and then the predicted specific surface area value can be calculated using this method. According to the predicted value, the cement production process or operating parameters can be adjusted to reasonably control the distribution of particle materials with different particle sizes, that is, only control the particle size distribution in the particle size ranges of <3 μm, 15 μm - 25 μm, and 80 μm, so that the produced cement reaches or approaches the ideal specific surface area size.

[0121] In one embodiment, the present invention provides a processor adapted to a method for calculating the specific surface area of cement. The processor includes: a calculation unit configured to calculate the specific surface area of cement.

[0122] The above has described in detail one embodiment of the present invention. However, the above content is only a preferred embodiment of the present invention and should not be considered as limiting the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the application of the present invention should still fall within the scope covered by the claims of the present invention.

Claims

1. A method for calculating the specific surface area of cement, characterized in that, Including: Using a laser particle size analyzer to test the laser particle size distribution of a cement sample and obtain the cumulative passing amount data for each particle size range. Based on the physical properties of the cement particle surface area, a core particle size range is selected, and the particle distribution values in three said core particle size ranges of the cement sample are statistically analyzed. The corresponding distribution values are X1, X2, and X3 respectively. Based on the three said distribution values, a prediction formula for the specific surface area of cement is fitted through quadratic multiple regression analysis, and the predicted value of the specific surface area is calculated using the prediction formula. The prediction formula is as follows: S = 16.825X1 - 2.154X2 - 2.972X3 + 0.062X1 2 + 88.427; In the formula: X1 is the particle distribution percentage of particles smaller than 3 microns. X2 is the particle distribution percentage of particles between 15 microns and 25 microns. X3 is the particle distribution percentage of particles larger than 80 microns.

2. The method for calculating the specific surface area of cement according to claim 1, characterized in that, The quadratic multiple regression analysis fitting process of the prediction formula includes dataset construction. The dataset construction requires organizing the cumulative passing amount data for each particle size range into a structured table form and listing the independent variable and the dependent variable.

3. The method for calculating the specific surface area of cement according to claim 2, wherein, The independent variable is the said distribution value, and the dependent variable is the specific surface area of cement.

4. A method for calculating the specific surface area of cement according to claim 3, characterized in that, Based on the independent variable and the dependent variable, an initial model is established. The initial model is as follows: S = β0 + β1X1 + β2X2 + β3X3 + β4X1 2 + ε; In the formula: X1 is the particle distribution percentage of particles smaller than 3 microns. X2 is the particle distribution percentage of particles between 15 microns and 25 microns. X3 is the particle distribution percentage of particles larger than 80 microns. ε is a constant.

5. A method for calculating the specific surface area of cement according to claim 4, characterized in that, The binomial of X1 can reflect the saturation effect of fine powder, that is, as X1 increases, the growth rate of the specific surface area will slow down.

6. A method for calculating the specific surface area of cement according to claim 4, characterized in that, When implementing the regression analysis, a statistical tool is needed to perform multiple regression analysis. The regression coefficients β0, β1, β2, β3, and β4 are solved by the least squares method. The experimental data is input into the initial model to calculate the specific values of each regression coefficient and the significance level, forming a calculation model.

7. A method for calculating the specific surface area of cement according to claim 6, characterized in that, Perform a significance test, goodness-of-fit evaluation, and residual analysis on the calculation model to determine the specific values of the regression coefficients β0, β1, β2, β3, and β4 and the specific value of ε.

8. A method for calculating the specific surface area of cement according to claim 7, characterized in that, The specific values of the regression coefficients β0, β1, β2, β3, and β4 are 88.427, 16.825, -2.154, -2.972, and 0.062 respectively, and the specific value of ε is 88.

427.

9. A processor adapted to a method for calculating the specific surface area of cement according to any one of claims 1-8, characterized in that, Including: A calculation unit, which is used to calculate the specific surface area of cement.

Images