A method for testing the bulk density of coal yard in coal-fired power plants based on big data

Abstract

The present invention discloses a method for testing the bulk density of a coal yard in a coal-fired power plant based on big data. All coal types are classified according to moisture content, ash content, volatile matter content, and compaction times, and then the bulk density D of the coal types with known weights is measured separately in a loose and free state for each category. 0,ar and the bulk density D under the maximum pressure max,ar ; Calculate the average bulk density of the coal pile under the actual stacking state conditions, in the loose and free state, and after compaction n times, and calculate a series of regression coefficients k; Fit a first-order regression model according to the moisture content, ash content, volatile matter content, and compaction times; Calculate the k value according to the moisture content, ash content, volatile matter content, compaction times of the coal pile to be measured and the first-order regression model, and then predict the bulk density through the formula. The present invention obtains the k value linear regression equation through big data fitting, so the predicted bulk density has a very high accuracy, and it is a feasible method for testing the bulk density during the inventory of the coal yard in a coal-fired power plant.

Description

Technical Field

[0001] The present invention belongs to the technical field of coal yard inventory, and particularly relates to a method for testing the bulk density of a coal yard in a coal-fired power plant based on big data. Background Art

[0002] Each modern coal-fired power plant consumes more than one million tons of coal per year. The fuel cost of the power plant accounts for 80% of the entire production cost. Regular inventory of fuel is the only way to ensure reasonable supply. The inventory of the coal storage in the coal yard is related to the calculation of coal consumption and economic indicators of the power plant, and is also the key and difficult point of fuel management work. Therefore, establishing a scientific coal inventory system plays a very important role in reasonable coal storage, ensuring the quality of coal entering the furnace, reducing losses, and is also an effective way to reduce the power generation cost and improve the economic benefits of the power plant.

[0003] At present, the methods for testing the bulk density of the coal yard mainly refer to the Guidelines for Coal Yard Inventory in Coal-fired Power Plants DL / T 1878-2018. The bulk density is mainly obtained by the following three methods. 1. Measured method: The operation process of this method is relatively complex, requires a large amount of data, and has a large error. 2. Empirical value method: Estimate the bulk density of the coal yard based on historical data and empirical values. This method is simple and easy to implement, but the accuracy is affected by the accuracy of the empirical values. 3. Simulation method: This method is relatively simple to operate, but the differences between the simulation conditions and the actual conditions need to be considered. Generally, the regression coefficient k value of the power plant is taken as 0.5 at present. The error of the bulk density is large. Therefore, by adopting a reasonable k value, the true value of the bulk density of the coal yard can be obtained more accurately, so as to achieve accurate inventory of the coal yard. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for testing the bulk density of a coal yard in a coal-fired power plant based on big data. By fitting big data to obtain a linear regression equation of the regression coefficient k value, the accuracy of the measured bulk density by this method is very high, and it is a feasible method for testing the bulk density of the coal yard inventory in a coal-fired power plant.

[0005] The technical solution of the present invention is as follows: A method for testing the bulk density of a coal yard in a coal-fired power plant based on big data, the steps are as follows:

[0006] Step S1: Classify all coal types according to moisture, ash content, volatile matter, and compaction times, and then measure the bulk density D of the coal types with known mass in a loose and free state and the bulk density D under the maximum pressure for each category respectively. 0,ar and the bulk density D under the maximum pressure max,ar ;

[0007] Step S2: Use a coal yard inventory instrument to measure the volume of a coal pile with known mass, obtain the volume V of the coal pile, and calculate the bulk density of the coal pile under the actual stacking state conditions. ;

[0008] Step S3: According to Calculate the regression coefficient k;

[0009] Step S4: Use a coal pan meter to measure the volume of the coal pile in a loose free state and after compaction for n times, where n=1, 2, 3, ..., N, and N is the total number of compaction times; calculate a series of regression coefficients according to step S3;

[0010] Step S5: fitting a regression model according to moisture, ash, volatile matter, and compaction times;

[0011] Step S6: Calculate the k value using a linear regression model based on the moisture, ash, volatile matter, and compaction times of the coal pile to be tested, and then use the formula Predicted bulk density .

[0012] Further preferably, the bulk density D in the loose free state is 0,ar Calculate as follows:

[0013] ;

[0014] Where: It represents the mass of the container when it is filled with coal sample in a loose and free state; m0 is the mass of the empty container; V0 is the volume of the container.

[0015] Further preferably, the bulk density D at maximum pressure max,ar Calculate as follows:

[0016] ;

[0017] Where: It represents the mass of the container when it is filled with coal samples under maximum pressure; m0 is the mass of the empty container; V0 is the volume of the container.

[0018] Further preferably, the stacking density of the coal pile under the actual stacking state is calculated as follows:

[0019] ;

[0020] Where: G re is the mass of the coal pile; V is the volume of the coal pile.

[0021] Further preferably, the linear regression model is: ; In the formula: C is the fitting constant; a, b, c, d are fitting coefficients; M is moisture; A is ash content; is the volatile matter; n is the number of compactions.

[0022] Further preferably, the process conditions and parameters of each compaction operation are the same.

[0023] The present invention measures the volume of coal of known quality according to moisture content, ash content, volatile matter, and compaction times respectively, and the bulk density D in the loose and free state 0,ar and the bulk density D under the maximum pressure max,ar is measured. According to the relationship among mass, volume, and bulk density, a series of regression coefficient k values can be calculated, and then a first-order regression model of the k value can be obtained. The predicted bulk density can be used without actually measuring the bulk density, and the accuracy of the predicted bulk density by this method is very high, which is a feasible method for testing the bulk density of coal yard inventory in coal-fired power plants. Specific implementation mode

[0024] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the present invention.

[0025] A method for testing the bulk density of a coal yard in a coal-fired power plant based on big data, the steps are as follows:

[0026] Step S1: According to the coal intake structure of the thermal power plant, classify all coal types according to moisture content, ash content, volatile matter, and compaction times, and then measure the bulk density D of the coal types of known quality in the loose and free state respectively 0,ar and the bulk density D under the maximum pressure max,ar ;

[0027] The bulk density D in the loose and free state 0,ar is measured: Slowly load the coal sample into a container of known volume (0.125 m 3 ), and the falling height of the coal sample should be as small as possible, and the maximum should not exceed 0.6 m. The coal sample is filled until the entire coal surface is about 100 mm higher than the top surface of the container, and the coal sample higher than the container is scraped off with a hard straight board to make the coal sample surface flush with the top of the container. The bulk density D in the loose and free state 0,ar is calculated according to the following formula:

[0028] ;

[0029] In the formula: represents the mass of the container filled with the coal sample in the loose and free state; m0 is the mass of the empty container; V0 is the volume of the container.

[0030] The bulk density D under the maximum pressure max,arMeasurement: According to the actual on-site situation, place the density box steadily into the coal pit dug by the excavator, then load the coal sample used for density measurement into the density box. After filling it up, add more coal until it is 200 mm - 300 mm higher, pad with thick plastic film, fill the dug coal pit with the excavated coal, and level it. Use the crawler bulldozer or loader used on-site during stacking to walk directly above the density box. Pay attention to making the crawler or wheels of the bulldozer or loader face the density box directly when walking. After the entire crawler has passed or both the front and rear wheels have passed over the density box, one rolling is completed. Carefully dig out or lift the density box with the excavator. If the amount of coal in the density box after compaction is significantly lower than the upper edge of the box body, this operation fails. Use a scraper to scrape off the coal stored above the upper edge of the density box. Weigh the density box filled with coal using an electronic weighing scale, accurate to 0.1 kg. According to the above steps, rolling tests can be carried out 3 times, 5 times, 7 times, 10 times,..., N times respectively. The final number of rolling times is based on the condition that the change in the compacted bulk density before and after does not exceed 5%. The bulk density D under the maximum pressure max,ar Calculate according to the following formula:

[0031] ;

[0032] In the formula: represents the mass of the container filled with the coal sample under the maximum pressure; m0 is the mass of the empty container; V0 is the volume of the container.

[0033] Step S2: Use a coal volume measuring instrument to measure the volume of the coal pile with a known mass to obtain the coal pile volume V; calculate the bulk density of the coal pile under the actual stacking state according to the following formula :

[0034] ;

[0035] In the formula: G re is the mass of the coal pile; V is the volume of the coal pile.

[0036] Step S3: Calculate the regression coefficient k according to ;

[0037] Step S4: Use a coal volume measuring instrument to measure the volume of the coal pile in the loose and free state and after being compacted n times, where n = 1, 2, 3,..., N, and N is the total number of compactions; calculate a series of regression coefficients according to Step S3; it should be noted that the technological conditions and parameters of each compaction operation are the same;

[0038] Step S5: Fit a first-order regression model according to moisture, ash, volatile matter, and the number of compactions: ; In the formula: C is the fitting constant term; a, b, c, d are fitting coefficients; M is moisture; A is ash; is volatile matter; n is the number of compactions;

[0039] Step S6: Calculate the k value using the first-order regression model based on the moisture content, ash content, volatile matter, and compaction times of the coal pile to be measured, and then predict the bulk density through the formula Predict the bulk density .

[0040] The present invention selects 28 coal types entering a thermal power plant and conducts tests on the k value under the stacking processes of free and loose state, compaction 2 times, and compaction 4 times respectively, as shown in Table 1:

[0041]

[0042]

[0043]

[0044] The fitting linear relationship and the Durbin-Watson index of the present invention are R 2 > 0.9, 1.5 < DW < 2.5.

[0045] Table 2: Linear correlation coefficient and Durbin-Watson (DW) value.

[0046]

[0047] In Table 2, the linear correlation coefficient R and the adjusted R 2 are both greater than 0.90, indicating a very strong correlation of the first-order regression model; the Durbin-Watson (DW) value is 1.921, indicating that in the regression analysis, the first-order autocorrelation of the residuals is very low, that is, there is no obvious correlation between the error terms of the first-order regression model, indicating that the accuracy and reliability of the first-order regression model of the present invention are relatively high.

[0048] Perform a unary regression fitting on the measured data in Table 1 above, and the fitting constants and coefficients are shown in Table 3. The formula for the fitting first-order regression model is: .

[0049] Table 3. Fitting constants and coefficients

[0050]

[0051] In Table 3, B represents the original influence degree of the independent variable on the dependent variable, and Beta represents the influence strength after eliminating the dimension.

[0052] To verify the accuracy of the model formula, detect the bulk density of other types of coal in the power plant, and predict the bulk density through the formula by using the k value calculated according to the prediction model formula of the present invention , and the results are shown in Table 4.

[0053] Table 4. Comparison table of actual detection values and predicted values

[0054]

[0055] As can be seen from Table 4, the differences between the actual detection results and the predicted results of the model formula are all less than the repeatability limit of 0.10 t / m specified in the DL / T 1878-2018 standard. 3 requirements.

[0056] Those skilled in the art can easily understand that the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for testing the bulk density of a coal yard in a coal-fired power plant based on big data, characterized in that, The steps are as follows: Step S1: Classify all coal types according to moisture, ash, volatile matter, and compaction times, and then measure the bulk density D of coal types with known mass in loose free state according to the categories. 0,ar and bulk density at maximum pressure D max,ar ; Step S2: Use a coal volume measuring instrument to measure the volume of a coal pile with a known mass, obtain the volume V of the coal pile, and calculate the bulk density of the coal pile under the condition of the actual stacking state ; Step S3: According to calculate the regression coefficient k; Step S4: Use a coal bunker measuring instrument to measure the volume of the coal pile in a loose and free state after being compacted n times, where n = 1, 2, 3, …, N and N is the total number of compaction times; calculate a series of regression coefficients according to Step S3; Step S5: Fit a first-order regression model based on moisture content, ash content, volatile matter, and compaction times; Step S6: Calculate the k value using a first-order regression model based on the moisture content, ash content, volatile matter content, and compaction times of the coal pile to be measured, and then predict the bulk density through the formula .​ 2. The method for testing the bulk density of a coal yard in a coal-fired power plant based on big data according to claim 1, wherein Bulk density D in loose and free state 0,ar It is calculated according to the following formula: ； In the formula: represents the mass of the container filled with coal samples in a loose and free state; m0 is the mass of the empty container; V0 is the volume of the container.

3. The method for testing the bulk density of a coal yard in a coal-fired power plant based on big data according to claim 1, characterized in that, Bulk density D at maximum pressure max,ar Calculated according to the following formula: ； Wherein: represents the mass of the container filled with coal samples under the maximum pressure; m0 is the mass of the empty container; V0 is the volume of the container.

4. The method for testing the bulk density of the coal yard in a coal-fired power plant based on big data according to claim 1, wherein The bulk density of the coal pile under the actual stacking state is calculated according to the following formula: ； Where: G re is the mass of the coal pile; V is the volume of the coal pile.

5. The method for testing the bulk density of a coal yard in a coal-fired power plant based on big data according to claim 1, wherein, The primary regression model is as follows: where C is the fitting constant term; a, b, c, and d are fitting coefficients; M is the moisture content; A is the ash content; is the volatile matter; and n is the compaction times.

6. The method for testing the bulk density of a coal yard in a coal-fired power plant based on big data according to claim 1, wherein, The process conditions and parameters for each compaction operation are the same.

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