A method for predicting thermal properties of a multi-source mixed coke

Abstract

The application discloses a kind of multi-source mixed coke thermal property prediction method, belong to coke thermal property analysis field.It is as follows: one, the proportion of each optical organization of different source coke is determined by optical organization method, then the total proportion of isotropic structure and the optical organization in the total proportion of coke and broken piece organization is calculated;Two, the porosity of coke is determined;Three, the content of various ash components in coke is determined, then the ash basicity index of coke is calculated;Four, according to the set model, the comprehensive thermal property index of coke is calculated according to the determination results of steps one to three;Five, the thermal property index of mixed coke is calculated according to the thermal property of different sources and the proportion in mixed coke.The application can reduce the error of single property parameter prediction of mixed coke thermal property by comprehensive prediction of various property parameters of coke, so as to accurately adjust the mixing ratio of mixed coke and coke source according to the prediction result.

Description

Technical Field

[0001] This invention belongs to the field of coke thermal property analysis, and more specifically, relates to a method for predicting the thermal properties of multi-source mixed coke. Background Technology

[0002] With the development of large-scale blast furnaces and low-carbon smelting technology, people have gradually paid more attention to the thermal properties of blast furnace coke, and research on the thermal properties of coke has become increasingly in-depth. These studies mainly focus on the relationship between blast furnace smelting and coke quality, the degradation behavior and mechanism of coke in the blast furnace, and evaluation methods for the thermal properties of coke. These include influencing factors of thermal properties, the composition of the reaction atmosphere, the catalytic effect of mineral components, and the optical structure and pore structure parameters of coke. Mathematical models for predicting the thermal properties of coke and detection and characterization methods have been established for this purpose.

[0003] However, effective methods are still lacking for judging the thermal properties of mixed coke from different sources. When coke from different sources is mixed, the thermal properties of the mixed coke differ due to their individual thermal properties and mixing ratios. Therefore, to obtain coke with thermal properties suitable for blast furnace smelting, it is necessary to analyze and test mixed cokes with different mixing ratios. When the results do not meet the requirements, the ratios must be continuously adjusted and verified until the ideal result is achieved, which is time-consuming and labor-intensive. Furthermore, if the source of the coke changes, the experiment must be repeated until a new mixing ratio is obtained, which is time-consuming, labor-intensive, and somewhat unpredictable.

[0004] Therefore, inventing a new and faster method for determining the thermal properties of multi-source mixed coke to replace blind analytical testing methods has theoretical guiding significance for rapidly determining the mixing ratio of mixed coke and selecting coke sources.

[0005] A Chinese patent application with patent number CN202110577744.X and publication date of September 10, 2021, discloses a method for measuring the high-temperature reactivity index of coke, comprising the following steps: screening coke raw materials and obtaining coke with a target particle size after pretreatment; taking an appropriate amount of the coke and measuring the apparent porosity A of the coke through a water immersion test; drying the coke after measuring the apparent porosity A; placing the dried coke in a protective gas, then heating it to a target temperature and introducing a mixed gas simulating the internal working conditions of a blast furnace to simulate the reaction inside the blast furnace; obtaining a coke sample after holding it at the set temperature for a set time, and then measuring the apparent porosity B of the coke sample through a water immersion test; calculating the ratio of apparent porosity B to apparent porosity A, and obtaining the high-temperature reactivity index of coke.

[0006] Chinese patent application number CN202210890277.0, published on November 22, 2022, discloses a method for predicting the coal blending structure of tamped coke, comprising: obtaining the proportions of various optical structures of tamped coke through optical microstructure methods; predicting the high-temperature thermal properties of tamped coke by using regression equations; and predicting the coal blending structure of tamped coke by the magnitude of the coal blending prediction index, wherein the coal blending prediction index is the difference between the predictions of high-temperature thermal properties of tamped coke and top-charged coke.

[0007] Both of the above schemes involve methods for predicting the thermal properties of coke. However, one scheme only predicts the thermal properties of coke based on its porosity, while the other only predicts them based on the proportion of various optical structures in the coke. When these methods are used to predict the thermal properties of multi-source mixed coke, the different sources of coke result in significant differences in their composition and structure. This makes it difficult to accurately predict the thermal properties of multi-source mixed coke, leading to large errors. Consequently, this has a significant impact on the mixing ratio of mixed coke and the selection of coke sources. Summary of the Invention

[0008] 1. The problem to be solved

[0009] To address the problem that existing methods for predicting the thermal properties of coke are difficult to apply to multi-source mixed coke, making it difficult to accurately select the mixing ratio and coke source, this invention provides a method for predicting the thermal properties of multi-source mixed coke. By comprehensively predicting various property parameters of coke, the method can reduce the error of predicting the thermal properties of mixed coke using a single property parameter, thereby accurately adjusting the mixing ratio and coke source based on the prediction results.

[0010] 2. Technical Solution

[0011] To solve the above problems, the present invention adopts the following technical solution.

[0012] A method for predicting the thermal properties of multi-source mixed coke includes the following steps:

[0013] I. The proportion of various optical structures in coke from different sources was determined by optical microstructure method, and then the total proportion of isotropic structure, filamentous char and fragmented char in the optical structure was calculated.

[0014] II. Determination of the porosity of coke;

[0015] 3. Determine the content of various ash components in the coke, and then calculate the ash alkalinity index of the coke.

[0016] IV. Calculate the thermal property index of coke using the following formula:

[0017] Cr i =a×P i +b×(I+FF) i +c×AI i -d;

[0018] Among them, Cr i Let be the thermal property index of the i-th type of coke, and P be the porosity of the i-th type of coke, (I+FF) i Let AI be the total proportion of isotropic structure, filamentous structure, and fragmented structure in the optical microstructure of the i-th type of coke. i Let be the ash alkalinity index of the i-th type of coke, where a, b, c, and d are constant values;

[0019] V. Calculate the thermal property index of the mixed coke according to the following formula:

[0020]

[0021] Where Cr is the thermal property index of the mixed coke, n is the total number of sources of coke, and m is the total number of sources of coke. i Let represent the proportion of the i-th type of coke in the mixed coke.

[0022] As a further improvement to the technical solution, in step three, the ash alkalinity index of the coke is calculated according to the following formula:

[0023]

[0024] Wherein, AI is the ash alkalinity index of coke, and CaO, MgO, Fe2O3, Na2O, K2O, SiO2 and Al2O3 represent the content of each ash component in coke.

[0025] As a further improvement to the technical solution, in step four, a is 0.05-0.15, b is 0.5-1.2, c is 1-2, and d is 0.1-0.15.

[0026] As a further improvement to the technical solution, in step one, the optical structure ratio of coke is determined according to the YB / T077-2017 standard.

[0027] As a further improvement to the technical solution, in step three, the ash content of coke is determined in accordance with the GB / T34534-2017 standard.

[0028] As a further improvement to the technical solution, step two uses image analysis to determine the porosity of coke.

[0029] As a further improvement to the technical solution, the specific steps of step two are as follows: select multiple blocks of coke, cut the blocks of coke into light sheets, then place the light sheets under a polarizing microscope with a camera device for scanning, and then use image analysis software to identify and calculate the porosity of each block of coke, and take the average value of the porosity of multiple blocks of coke as the final value.

[0030] As a further improvement to the technical solution, in step two, the number of selected coke blocks is 10-30.

[0031] As a further improvement to the technical solution, in step two, the block coke is cut open at a distance of 50mm from the coke head.

[0032] As a further improvement to the technical solution, after the block coke is cut open, it is ultrasonically cleaned, and then filled with resin, ground, and polished to make a smooth sheet.

[0033] 3. Beneficial effects

[0034] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0035] This invention discloses a method for predicting the thermal properties of multi-source mixed coke. It measures the porosity, ash alkalinity index, isotropic structure, and the total proportion of fibrous and fragmented structures of coke from each source. Then, it processes these three data points using a derived model to predict the thermal properties of the coke. This method comprehensively predicts various property parameters of coke through a uniquely designed model, reducing the error in predicting the thermal properties of mixed coke using a single property parameter. Based on the prediction results, the mixing ratio and source of the mixed coke can be precisely adjusted. The method provides accurate predictions and solves the problem of time-consuming and labor-intensive multiple analyses required by traditional methods. Detailed Implementation

[0036] Exemplary embodiments of the present invention are described in detail below. While these exemplary embodiments have been described in sufficient detail to enable those skilled in the art to practice the invention, it should be understood that other embodiments may be implemented and various changes may be made to the invention without departing from its spirit and scope. The more detailed description of embodiments of the invention below is not intended to limit the scope of the claimed invention, but is merely illustrative and does not limit the description of the features and characteristics of the invention, in order to suggest the best mode for carrying out the invention and to enable those skilled in the art to practice it. Therefore, the scope of the invention is defined only by the appended claims.

[0037] Example

[0038] A method for predicting the thermal properties of multi-source mixed coke is presented for use in blast furnace production. It is particularly suitable for predicting the thermal properties of mixed coke from different sources. The specific steps and technical effects are described in detail below.

[0039] The method for predicting the thermal properties of multi-source mixed coke includes the following steps:

[0040] I. The proportion of various optical structures in coke from different sources was determined by optical microstructure method. Specifically, the ash content of coke was determined according to the method described in GB / T34534-2017 standard, and then the total proportion of isotropic structure, fibrous char and fragmented char in the optical microstructure was calculated.

[0041] This is because different optical structures of coke exhibit varying reactivity with carbon dioxide at high temperatures, with the order of reactivity being: isotropic structure and fibrous char and fragments > fine-grained mosaic > medium-grained mosaic > coarse-grained mosaic > flowable type. Therefore, the higher the content of isotropic structure and fibrous char and fragmented structures, the greater the reactivity. Thus, the sum of isotropic structure, fibrous char, and fragmented structures in coke (I+FF) is selected as an indicator for predicting the thermal properties of coke.

[0042] II. The porosity of coke is determined using an image-based method. Specifically, 10-30 relatively intact coke blocks from the coke head to the coke tail are selected. Each coke block is cut 50 mm from the coke head, and then the cut coke blocks are ultrasonically cleaned. After cleaning, the coke blocks are filled with resin, ground, and polished to form smooth sheets. The preparation process of the coke smooth sheets is conventional prior art in this field and will not be described in detail here. Next, the coke smooth sheets are scanned under a polarizing microscope equipped with a camera, and the captured images are uploaded to a computer. The image analysis software in the computer identifies and calculates the porosity of each coke block. Finally, the average porosity of each coke block is calculated as the final measured coke porosity. In this embodiment, the image analysis software's recognition technology for coke images is conventional prior art in this field and will not be described in detail.

[0043] The porosity determines the contact area between coke and carbon dioxide gas. Since the reaction between coke and carbon dioxide is a gas-solid contact reaction, a larger contact area means more reactive sites and greater reactivity. Therefore, porosity is chosen as an indicator to predict reactivity. Furthermore, coke pores include open and closed pores; as the reaction proceeds, closed pores become open pores. Therefore, image analysis is used to characterize the porosity.

[0044] III. Determine the content of various ash components in coke according to the method described in GB / T34534-2017, and then calculate the ash alkalinity index of coke using the following formula:

[0045]

[0046] Wherein, AI is the ash alkalinity index of coke, and CaO, MgO, Fe2O3, Na2O, K2O, SiO2 and Al2O3 represent the content of each ash component in coke.

[0047] The basicity index of coke ash has a positive catalytic effect on the reactivity of coke with carbon dioxide. From the perspective of ash composition, certain components in coke catalyze the gasification reaction; the higher the basicity, the greater the reactivity of the coke. Simultaneously, some components in coke ash volatilize or sublimate at high temperatures, leading to coke cracking, increased porosity, increased specific surface area, increased pore volume, and open pore channels, thus reducing the resistance to CO2 diffusion and facilitating the contact between coke and CO2. Therefore, ash basicity is chosen as the third indicator for predicting the reactivity of coke.

[0048] IV. Calculate the thermal property index of coke using the following formula:

[0049] Cr i =a×P i +b×(I+FF) i +c×AI i -d;

[0050] Among them, Cr i Let be the thermal property index of the i-th type of coke, and P be the porosity of the i-th type of coke, (I+FF) i Let AI be the total proportion of isotropic structure, filamentous structure, and fragmented structure in the optical microstructure of the i-th type of coke. i Let be the ash alkalinity index of the i-th type of coke, where a, b, c, and d are constant values. In this embodiment, a is 0.05-0.15, b is 0.5-1.2, c is 1-2, and d is 0.1-0.15.

[0051] V. Calculate the thermal property index of the mixed coke according to the following formula:

[0052]

[0053] Where Cr is the thermal property index of the mixed coke, n is the total number of sources of coke, and m is the total number of sources of coke. i Let represent the proportion of the i-th type of coke in the mixed coke.

[0054] This embodiment presents a method for predicting the thermal properties of multi-source mixed coke. It measures the porosity, ash alkalinity index, isotropic structure, and the total proportion of fibrous and fragmented structures of coke from each source. Then, it processes these three types of data using a derived model to predict the thermal properties of the coke. This method comprehensively predicts various property parameters of coke using a uniquely designed model, reducing the error in predicting the thermal properties of mixed coke using a single property parameter. Based on the prediction results, it accurately adjusts the mixing ratio and source of the mixed coke, providing precise predictions and solving the problem of time-consuming and labor-intensive multiple analyses required by traditional methods.

Claims

1. A method for predicting the thermal properties of multi-source mixed coke, characterized in that: Includes the following steps: I. The proportion of various optical structures in coke from different sources was determined by optical microstructure method, and then the total proportion of isotropic structure, filamentous char and fragmented char in the optical structure was calculated. II. Determination of the porosity of coke; 3. Determine the content of various ash components in the coke, and then calculate the ash alkalinity index of the coke. The ash alkalinity index of coke is calculated using the following formula: ； in, The ash alkalinity index of coke. , , , , , and These represent the content of each ash component in the coke; IV. Calculate the thermal property index of coke using the following formula: Cr i =a×P i + b×(I+FF) i + c×AI i -d; Among them, Cr i Let P be the thermal property index of the i-th type of coke. i Let (I+FF) be the porosity of the i-th type of coke. i AI represents the total proportion of isotropic structure, filamentous structure, and fragmented structure in the optical microstructure of the i-th type of coke. i Let be the ash alkalinity index of the i-th type of coke, where a, b, c, and d are constant values; a is 0.05-0.15, b is 0.5-1.2, c is 1-2, and d is 0.1-0.

15. V. Calculate the thermal property index of the mixed coke according to the following formula: ； in, The thermal property index of the mixed coke is given by n, which represents the total number of sources of the coke. Let represent the proportion of the i-th type of coke in the mixed coke.

2. The method for predicting the thermal properties of multi-source mixed coke according to claim 1, characterized in that: In step one, the optical structure ratio of coke is determined according to the YB / T077-2017 standard.

3. The method for predicting the thermal properties of multi-source mixed coke according to claim 1, characterized in that: In step three, the ash content of coke is determined in accordance with the GB / T34534-2017 standard.

4. The method for predicting the thermal properties of multi-source mixed coke according to claim 1, characterized in that: Step two involves using image analysis to determine the porosity of the coke.

5. The method for predicting the thermal properties of multi-source mixed coke according to claim 4, characterized in that: The specific steps of step two are as follows: select multiple coke blocks, cut the coke blocks into light sheets, then place the light sheets under a polarizing microscope with a camera device for scanning, and then use image analysis software to identify and calculate the porosity of each coke block, and take the average value of the porosity of multiple coke blocks as the final value.

6. The method for predicting the thermal properties of multi-source mixed coke according to claim 5, characterized in that: In step two, the number of selected coke blocks is 10-30.

7. The method for predicting the thermal properties of multi-source mixed coke according to claim 5, characterized in that: In step two, the block coke is cut open at a distance of 50mm from the coke head.

8. The method for predicting the thermal properties of multi-source mixed coke according to claim 5, characterized in that: After the coke block is cut open, it is ultrasonically cleaned, and then filled with resin, ground, and polished to make a smooth sheet.

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