Coke post-reaction strength prediction method integrating optical structure and pore structure characteristics

Abstract

The invention relates to a coke post-reaction strength prediction method integrating optical tissue and pore structure characteristics. The method comprises the following steps: 1) analyzing an optical tissue structure; 2) analyzing pore structure parameters; and 3) carrying out comprehensive influence analysis and establishing a CRS prediction formula. According to the method, the CRS is predicted by adopting microscopic optical organization structure and pore structure characteristic indexes, and compared with a conventional prediction method adopting macroscopic indexes, the workload required by microscopic index determination is greatly reduced, so that the field coke oven test operation and response time is shortened, the efficiency is improved, and the cost is reduced; besides, the microstructure can essentially reveal the change rule of coke performance, the coal blending structure can be adjusted in a targeted manner according to the prediction result, the applicability is wider, and the prediction result is more accurate.

Description

Technical Field

[0001] The invention relates to the technical field of coal coke chemical industry, and in particular to a method for accurately evaluating the quality and performance of coke by comprehensively analyzing characteristic parameters of optical organizational structure and pore structure. Background Art

[0002] Coke is an important solid fuel obtained by high-temperature dry distillation of coal or other carbonaceous materials in the coking industry. It is widely used in the fields of steel, metallurgy, etc. In the process of steel smelting, coke not only serves as a reducing agent and energy source, but also provides heat and serves as an important raw material for the reaction skeleton inside the blast furnace. Therefore, the quality of coke directly affects the efficiency of the ironmaking process, product quality, and environmental pollution. Traditional coke quality assessment methods rely on indicators such as ash content, volatile matter, and elemental composition, but these indicators often cannot fully and accurately reflect the performance of coke in actual use, especially its complex behavior under high temperature and high pressure conditions.

[0003] The microstructural characteristics of coke, as an important manifestation of its internal structure, can fundamentally reflect the formation process, physical and chemical properties of coke and its performance during use. Microstructural characteristics include the optical structure and pore structure distribution of coke, which have a significant impact on the hot strength of coke and its combustion process and reaction performance in the furnace. Traditional coke quality assessment methods mainly rely on macroscopic chemical analysis or mechanical property testing, and cannot reveal the impact of coke microstructure on its overall performance.

[0004] In recent years, with the rapid development of microscopic detection technology in the field of coal coke, especially the application of high-resolution microscopes and image analysis technology, the research on coke quality assessment based on microstructural characteristics has gradually become a new direction for coke quality control. Through careful observation and analysis of the microstructure of coke, its quality characteristics can be more accurately evaluated, and an important basis can be provided for the optimization of the coke production process. In the prior art, the quality of coke is mainly predicted by the distribution characteristics of the optical structure of coke. However, the structure of coke, especially the microstructure, is very complex and extremely inhomogeneous. There are limitations in predicting the quality of coke only by a single optical structure distribution characteristic index.

[0005] Therefore, a coke quality assessment method based on microstructure characteristics is urgently needed. By introducing modern optical microscopy technology and image processing technology, the optical structure and pore structure characteristics of coke are comprehensively considered to achieve rapid and accurate analysis of the coke microstructure, providing a more scientific and accurate quality assessment method for the production and application of coke. This can not only improve the control accuracy of the coke production process, but also improve the performance stability and economy of coke in actual use. Summary of the invention

[0006] The present invention provides a method for predicting the post-reaction strength of coke based on comprehensive optical organization and pore structure characteristics. The method adopts microscopic optical organization structure and pore structure characteristic indicators to predict CRS. Compared with the conventional prediction method adopting macroscopic indicators, the workload required for the determination of microscopic indicators is greatly reduced, thereby reducing the on-site coke oven test operation and response time, improving efficiency and reducing costs. In addition, the microstructure can essentially reveal the changing law of coke performance, and the coal blending structure can be adjusted in a targeted manner according to the prediction results. The method has a wider applicability and more accurate prediction results.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] A method for predicting the post-reaction strength of coke based on comprehensive optical structure and pore structure characteristics comprises the following steps:

[0009] 1) Optical tissue structure analysis;

[0010] Multiple coke samples produced from different sources and under different process conditions were selected, and coke optical slices were made by cold mounting. The optical structure characteristics of the coke optical slices were observed using a polarizing optical microscope. The types of each structure were determined by rotating the stage. After shooting, the percentage of each optical component was obtained, and the proportion of fine-grained mosaic structure, medium-grained mosaic structure and coarse-grained mosaic structure was calculated. In addition, the post-reaction strength CRS of the coke samples was tested, and the correlation between the hot strength of the coke and CRS was analyzed.

[0011] 2) Analysis of pore structure parameters;

[0012] The grayscale image of the coke light sheet was taken using a polarizing optical microscope to obtain the initial image; the image was then analyzed using porosity software to obtain the pore structure characteristic parameters of the coke sample, including porosity and pore size distribution; the correlation between the average pore diameter, average porosity and CSR of the coke sample was analyzed;

[0013] 3) Comprehensive impact analysis and establishment of CRS prediction formula;

[0014] Based on the CRITIC method, the influence weights of coke mosaic tissue content, average pore diameter and average porosity on CSR are calculated, and the weight index is calculated by combining variance and conflict degree. The weight of each index is standardized, and a multivariate linear regression model equation is established to obtain the prediction formula of CRS as follows:

[0015] CSR=28.274+0.186X1+0.613X2+0.201X3

[0016] Where, X1 is the proportion of coke mosaic structure, X2 is the average pore diameter, and X3 is the average porosity.

[0017] In the step 1), the parameters of the polarized optical microscope are set as follows: field of view 50×10 times, scanning interval 0.30-0.40 mm, scanning delay 500 ms, focusing frequency 1 s, and at least 200 effective points are photographed.

[0018] Compared with the prior art, the present invention has the following beneficial effects:

[0019] 1) Coke is widely used in metallurgical industries such as steel and aluminum. The post-reaction strength (CSR) of coke is an important indicator for evaluating the quality of coke, which directly affects the smelting efficiency and product quality. By predicting the CSR of coke, metallurgical enterprises can evaluate the quality of coke in advance, thereby optimizing the production process, reducing raw material waste and improving production efficiency.

[0020] 2) Quality control during the coke production process is the key to improving coke performance; by establishing a CRS prediction model, production companies can monitor coke quality in real time during the coke production process, adjust production parameters in a timely manner, and ensure the stability and high quality of the final product.

[0021] 3) Accurate prediction of CSR helps optimize the use of coke, avoids excessive consumption of substandard coke, helps reduce energy consumption and reduces waste gas emissions, and therefore plays a positive role in environmental protection.

[0022] 4) By adopting data-based prediction methods and combining them with intelligent production equipment, the coke production process can be automated and optimized, improving production efficiency and reducing labor costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a relationship diagram between the strength of coke after reaction and the proportion of mosaic structure in the present invention.

[0024] Figure 2 It is the microscopic image of the pore structure of coke before and after the treatment in the present invention (Leer coal).

[0025] Figure 3 It is a diagram of the cumulative content of coke pores in the present invention.

[0026] Figure 4 It is the pore distribution diagram of coke in the present invention.

[0027] Figure 5 It is a relationship diagram between the characteristic parameters of the coke pore structure and the strength after reaction in the present invention. DETAILED DESCRIPTION

[0028] The present invention discloses a method for predicting the post-reaction strength of coke based on the characteristics of optical organization and pore structure, and evaluates the quality of coke by analyzing the microstructure and pore structure parameters of coke. In order to comprehensively consider the influence of the optical organization and pore structure of coke on its post-reaction strength CRS, it is first necessary to determine the correlation between the two and the post-reaction strength CRS respectively, and then comprehensively evaluate the influence of the two on CRS. The specific process is as follows:

[0029] (1) Optical tissue structure analysis;

[0030] First, coke samples from different sources and produced under different process conditions were selected to ensure the representativeness and coverage of the samples. After the coke samples were obtained from the site, coke optical slices were made by cold mounting, and then their optical structure characteristics were observed using a polarizing optical microscope, and the types of each structure were determined by rotating the stage.

[0031] The parameters of the polarized optical microscope are preferably set as follows: field of view 50×10, scanning spacing 0.30-0.40 mm, scanning delay 500 ms, and focusing frequency 1 s. At least 200 valid points are photographed (according to the provisions of "YB / T077-2017 Determination Method of Optical Structure of Coke", cement, pores, cracks and cell cavity structures in optical tissue are regarded as invalid points).

[0032] After the shooting is completed, the percentage of each optical component is obtained, and the proportion of fine-grained mosaic tissue, medium-grained mosaic tissue and coarse-grained mosaic tissue is calculated. The above-mentioned mosaic structure refers to the microscopic components of coke, which are semi-coke fragments, isotropic substances and coked to varying degrees, distributed together in a mosaic manner to form a relatively dense microstructure. The current research results show that the higher the proportion of anisotropic tissues, especially mosaic tissues, the higher the coke strength and the better the quality.

[0033] The present invention selects 20 typical coke samples, detects the proportion of mosaic tissue in their optical structure (including fine-grained mosaic tissue, medium-grained mosaic tissue and coarse-grained mosaic tissue), and detects their post-reaction strength CSR. The results are shown in Table 1:

[0034] Table 1 Mosaic structure proportion and CSR value of 20 typical coke samples

[0035] Serial number Mosaic tissue ratio / % CSR / % 1 7.5 50.5 2 9.5 42.5 3 18.9 57.5 4 20 54.5 5 21.2 61.4 6 40.4 65.4 7 40.5 52.9 8 41.6 63.3 9 44.2 56.5 10 44.5 68.2 11 60.2 63.8 12 61.3 62.5 13 62 61.6 14 65.3 63.8 15 65.7 66.4 16 66.7 60.8 17 69.5 66.4 18 70.3 68 19 77.2 62.3 20 78.1 66.7

[0036] The data in Table 1 were analyzed for correlation, and the results are as follows: Figure 1 As shown, the correlation coefficient between the hot strength of coke and CSR is 0.67, and the variance is 0.525.

[0037] (2) Analysis of pore structure parameters;

[0038] The pore structure of coke is one of its important microscopic characteristics. The parameters of coke such as porosity, pore size distribution, pore shape characteristics have a significant impact on its specific surface area, reactivity and thermal conductivity.

[0039] The above-mentioned microscope test system is used to take grayscale images of the coke light film to obtain an initial image; the image is then analyzed by porosity software to obtain the characteristic parameters of the pore structure of the coke sample, mainly including porosity and pore size distribution.

[0040] Taking the coke corresponding to Lier single coal as an example, the microscopic images of the coke pore structure before and after treatment are as follows: Figure 2 As shown. Figure 2 After analyzing the pore size and pore distribution, we get Figure 3 Results shown.

[0041] The current research results show that the pores are small and evenly distributed, and the pore walls are relatively smooth, and the corresponding coke quality is good. The present invention selects 20 groups of typical coking coals to perform microscopic image detection on the corresponding cokes, and obtains the average pore diameter and average porosity distribution, as well as the corresponding post-reaction strength CSR, as shown in Table 2.

[0042] Table 2 Average pore diameter, average porosity and post-reaction strength of 20 groups of coke

[0043] Serial number Average pore diameter / μm Average porosity / % CSR 1 20.89 58.21 66.7 2 20.09 60.13 62.3 3 20.73 56.36 68.0 4 23.49 58.99 66.4 5 27.34 65.37 60.8 6 27.49 58.66 66.4 7 29.42 58.35 63.8 8 30.79 58.74 61.6 9 31.42 73.78 62.5 10 33.02 59.77 63.8 11 34.26 59.06 68.2 12 35.36 66.42 56.5 13 36.45 69.48 63.3 14 36.66 65.6 52.9 15 38.95 59.33 65.4 16 40.25 63.62 61.4 17 40.70 68.70 54.5 18 41.41 72.61 57.5 19 41.64 65.88 50.5 20 43.55 67.81 42.5

[0044] It can be seen from Table 2 that the hot strength of coke is inversely proportional to its pore size and porosity. In order to quantify the influence of coke pore structure, the correlation between the CSR value of coke and the pore structure parameters in Table 2 was analyzed. The results are shown in Figure 4 The correlation coefficient between the CSR of coke and the average pore diameter of coke is -0.96, and the variance is 0.907; the correlation coefficient between the CRS of coke and the average porosity is -0.72, and the variance is 0.488.

[0045] (3) Comprehensive impact analysis and establishment of CRS prediction formula;

[0046] In order to comprehensively consider the influence of the mosaic tissue content, average pore diameter and average porosity of coke on CRS, the influence weights of these three factors on CSR are calculated based on the CRITIC method. The calculation process is as follows:

[0047] Mosaic tissue proportion: C1 = (1-|r2|) + (1-|r3|) = 0.321;

[0048] Average pore diameter: C2 = (1-|r1|) + (1-|r3|) = 0.541;

[0049] Average porosity: C3 = (1-|r1|) + (1-|r2|) = 0.310;

[0050] C j (C1, C2, C3) represents the conflict degree of each index, rj represents the correlation coefficient, among which r1 represents the correlation coefficient between the hot strength of coke and the content of mosaic tissue, r2 represents the correlation coefficient between the hot strength of coke and the average pore diameter, and r3 represents the correlation coefficient between the hot strength of coke and the average porosity.

[0051] The weight index is calculated by combining variance and conflict degree, see formula (1):

[0052]

[0053] In the formula, Represents variance.

[0054] Substituting into the calculation, we can obtain: mosaic tissue weight E1 = 0.168, average pore diameter weight E2 = 0.553, average porosity weight E3 = 0.181.

[0055] The weight of each indicator is standardized, see formula (2):

[0056]

[0057] The calculated standard weight of mosaic tissue is W1=0.186, the standard weight of average pore diameter is W2=0.613, and the standard weight of average porosity is W3=0.201.

[0058] The multivariate linear regression model equation is established as shown in formula (3):

[0059] CSR=k+W1X1+W2X2+W3X3 (3)

[0060] Wherein, k is the regression term constant, and k = 28.274; X1 is the proportion of mosaic tissue, X2 is the average pore diameter, and X3 is the average porosity.

[0061] Finally, the prediction formula of CSR is obtained, see formula (4):

[0062] CSR=28.274+0.186X1+0.613X2+0.201X3 (4).

[0063] In order to more intuitively embody the present invention, the embodiments of the present invention are further described in conjunction with examples. The following examples are only preferred specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any technical solution that can be obviously obtained by a person skilled in the art within the technical scope disclosed in the present invention, including simple changes or equivalent replacements, is within the protection scope of the present invention.

[0064] [Example 1]

[0065] The process of predicting the coke post-reaction strength CSR by combining the optical structure and pore structure characteristics in this embodiment is as follows:

[0066] (1) Sampling

[0067] Sample source: Coke sample used in A 7m top-loading coke oven of coking plant.

[0068] Sample specifications: particle size range is 30~40mm.

[0069] Sampling quantity: 10kg, mix evenly and then divide the samples into 3 sub-samples.

[0070] (2) Detection

[0071] Coarse-grained mosaic tissue distribution: Characterization using optical microscopy and image analysis software.

[0072] Average pore diameter and average porosity: The volume method and image analysis method were combined to determine the test results using Porosity software: coarse-grained mosaic tissue distribution: 62%; average pore diameter: 48 μm; average porosity: 36%.

[0073] The actual CSR test result is 62.2%.

[0074] (3) Substitute into the CSR prediction formula for verification

[0075] Substituting the data obtained in step (2) into formula (4), the calculated result is CSR=62.418%. Compared with the actual CSR test result, the error is 0.218 and the relative error is 0.3%.

[0076] [Example 2]

[0077] The process of predicting the coke post-reaction strength CSR by combining the optical structure and pore structure characteristics in this embodiment is as follows:

[0078] (1) Sampling

[0079] Sample source: Coke sample from the B 6m top-loading coke oven of the coking plant.

[0080] Sample specifications: particle size range is 25~35mm.

[0081] Sampling quantity: 8kg, mix evenly and then divide the samples into 2 sub-samples.

[0082] (2) Detection

[0083] Coarse-grained mosaic tissue distribution: Characterization using optical microscopy and image analysis software.

[0084] Average pore diameter and average porosity: The volume method and image analysis method were combined to determine the test results using Porosity software: coarse-grained mosaic tissue distribution: 58%; average pore diameter: 52 μm; average porosity: 32%.

[0085] The actual CSR test result is 75.2%.

[0086] (3) Substitute into the CSR prediction formula for verification

[0087] Substituting the data obtained in step (2) into formula (4), the calculated result is CSR=77.254%. Compared with the actual CSR test result, the error is 2.054 and the relative error is 2.73%.

[0088] [Example 3]

[0089] The process of predicting the coke post-reaction strength CSR by combining the optical structure and pore structure characteristics in this embodiment is as follows:

[0090] (1) Sampling

[0091] Sample source: Coke sample from coking plant C 6.25m ramming coke oven.

[0092] Sample specifications: particle size range is 30~45mm.

[0093] Sampling quantity: 12kg, mix evenly and then divide the sample into 2 sub-samples.

[0094] (2) Detection

[0095] Coarse-grained mosaic tissue distribution: Characterization using optical microscopy and image analysis software.

[0096] Average pore diameter and average porosity: The volume method and image analysis method were combined to obtain the test results using Porosity software. The distribution of coarse-grained mosaic tissue was 54.1%; the average pore diameter was 46 μm; and the average porosity was 48%.

[0097] The actual CSR test result is 74.0%.

[0098] (3) Substitute into the CSR prediction formula for verification

[0099] Substituting the data obtained in step (2) into formula (4), the calculated result is CSR=76.183%. Compared with the actual CSR test result, the error is 2.183 and the relative error is 2.95%.

[0100] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for predicting the strength of coke after reaction based on the characteristics of optical organization and pore structure, characterized in that: The steps include: 1) Optical tissue structure analysis; Multiple coke samples produced from different sources and under different process conditions were selected, and coke optical slices were made by cold mounting. The optical structure characteristics of the coke optical slices were observed using a polarizing optical microscope. The types of each structure were determined by rotating the stage. After shooting, the percentage of each optical component was obtained, and the proportion of fine-grained mosaic structure, medium-grained mosaic structure and coarse-grained mosaic structure was calculated. In addition, the post-reaction strength CRS of the coke samples was tested, and the correlation between the hot strength of the coke and CRS was analyzed. 2) Analysis of pore structure parameters; The grayscale image of the coke light sheet was taken using a polarizing optical microscope to obtain the initial image; the image was then analyzed using porosity software to obtain the pore structure characteristic parameters of the coke sample, including porosity and pore size distribution; the correlation between the average pore diameter, average porosity and CSR of the coke sample was analyzed; 3) Comprehensive impact analysis and establishment of CRS prediction formula; Based on the CRITIC method, the influence weights of coke mosaic tissue content, average pore diameter and average porosity on CSR are calculated, and the weight index is calculated by combining variance and conflict degree. The weight of each index is standardized, and a multivariate linear regression model equation is established to obtain the prediction formula of CRS as follows: CSR=28.274+0.186X1+0.613X2+0.201X3 Where X1 is the proportion of coke mosaic structure, X2 is the average pore diameter, and X3 is the average porosity.

2. The method for predicting the post-reaction strength of coke based on the comprehensive optical structure and pore structure characteristics according to claim 1, characterized in that: In the step 1), the parameters of the polarized optical microscope are set as follows: field of view 50×10 times, scanning interval 0.30-0.40 mm, scanning delay 500 ms, focusing frequency 1 s, and at least 200 effective points are photographed.

Images