A method for evaluating and predicting the thermal strength of coking coal using its characteristic ash content

Abstract

The present invention discloses a method for evaluating and predicting the thermal strength of coking coal by using the characteristic ash component content. The core idea is to use the ash composition of coking coal as the core tool for analyzing its thermal performance, further analyze the coking coal production area and basic coal seams, determine the key components that affect the ash composition, and thereby accurately determine and evaluate its thermal performance.

Description

Technical Field

[0001] The invention relates to the field of detection technology, and in particular to a method for evaluating and predicting the thermal strength of coking coal by utilizing the characteristic ash component content thereof. Background Art

[0002] The quality performance of coking coal is the basis and guarantee of the quality of metallurgical coke. In particular, in recent years, the thermal performance of coke (reactivity CRI and post-reaction strength CSR) has a great impact on the modern blast furnace smelting process and has become a key factor restricting the stable, balanced, high-quality and efficient production of molten iron by blast furnaces. The ironmaking and coking industries have reached an unprecedented level of awareness of its importance and dependence on parameter indicators. The thermal performance indicators of coking coal have become important indicators for the quality and performance control of coking coal and important parameters for trade settlement, and have received extensive attention and attention from the coking industry, coal production industry and futures trading.

[0003] Coking coal is essentially a fossil of ancient plants that have undergone extremely complex physical, chemical, biological, and geological changes. Different geological environments, different ancient plant clusters, different coal-forming ages, different geological movements, and other conditions will cause huge differences in coal quality, especially for coking coal. The reaction is concentrated in the significant differences in the thermal strength of the coke produced during the coking process. Many industrial analysis indicators of coking coal (ash content, volatile matter, sulfur content, and caking index) are very close or even exactly the same, but the thermal strength of the coke produced can be very different. How to use the performance indicators of coking coal itself to predict its thermal strength? Its own performance indicators are relatively easy to detect, and its performance indicators can better characterize the internal properties of coking coal.

[0004] Even the parameters that characterize the coking ability of coking coal, such as the bonding index, gelatinous layer index, and Oya expansion, cannot accurately and objectively characterize the thermal performance of coking coal, and cannot explain why the main technical parameters of many coking coals are not much different, but their thermal performance varies greatly; nor can they clearly explain the phenomenon that the thermal performance of different coking coal blends lowers each other's thermal performance. Summary of the invention

[0005] In order to solve the above technical problems, the object of the present invention is to provide a method for evaluating and predicting the thermal strength of coking coal by using its characteristic ash component content.

[0006] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0007] The present invention provides a method for evaluating and predicting the thermal strength of coking coal by using the characteristic ash component content thereof, comprising the following steps:

[0008] Step 1: Analyze the basic conventional performance technical indicators of coking coal;

[0009] Step 2: Learn in detail where the coking coal is produced and where the raw coal is mined;

[0010] Step 3, analyzing the geological coal-forming data of coking coal and raw coal;

[0011] Step 4, testing the ash composition performance technical indicators of the coking coal;

[0012] Step 5, using the ash composition to calculate the basicity index (MBI) and mineral catalytic index (MCI);

[0013] Calculation formula 1:

[0014] MBI=100%Ad×(K2O+Na2O+CaO+MgO+Fe2O3) / (100-Vd)(Si O2+Al2O3);

[0015] Where: MBI is the alkalinity index; Ad is the dry basis ash; Vd is the dry basis volatile matter;

[0016] Calculation formula 2:

[0017] MCI=100%Ad×(2.2K2O+1.9Na2O+1.6CaO+0.93MgO+Fe2O3) / (100-Vd)(S

[0018] i O2+0.41Al2O3+2.5T iO2);

[0019] Where: MCI is the alkalinity index; Ad is the dry basis ash; Vd is the dry basis volatile matter;

[0020] Step 6: Analyze the alkalinity index (MBI) and mineral catalytic index (MCI);

[0021] Step 7, calculating the gray component influence intensity correlation index;

[0022] The thermal strength correlation index f is composed of the gel layer index Y, the maximum Kiel fluidity α max The logarithm of l gα max The mineral catalytic index MCI and ash content Ad are coupled and calculated, and the calculation expression is:

[0023] f=Y+l gα max -(5MCI+1 / 2Ad).

[0024] Furthermore, the basic conventional performance technical indicators of the coking coal analyzed include ash content, volatile matter, sulfur content, caking index, gelatinous layer index, and Kiel fluidity.

[0025] Furthermore, the technical indicators for detecting the ash composition performance of coking coal mainly include: K2O, Na2O, CaO, MgO, SiO2, Fe2O3, Al2O3, and TiO2.

[0026] Compared with the prior art, the beneficial technical effects of the present invention are:

[0027] The method of the present invention determines the requirements for the intrinsic quality of coking coal by using the influence of the ash components of coking coal on the performance of metallurgical coke, strengthens the understanding and discrimination of the intrinsic performance of coking coal, and thus more scientifically ensures the quality of metallurgical coke. A method for predicting the thermal strength of coking coal by using the characteristic ash component content of coking coal effectively improves the scientific proportion of different coking single coals and improves the thermal performance of metallurgical coke, which is of great significance to blast furnace production, provides strong support for its stable and high production, and produces huge economic benefits and good social benefits. DETAILED DESCRIPTION

[0028] The present invention aims to provide a method for predicting the thermal strength of coking coal by using the characteristic ash content of coking coal. Even if the parameters of coking coal characterizing its coking ability, such as bonding index, gelatinous layer index, and Oya expansion, cannot accurately and objectively characterize the thermal performance of coking coal, and cannot explain why the main technical parameters of many coking coals are not much different, but their thermal performance varies greatly; and cannot clearly explain the phenomenon that the thermal performance of different coking coal blends lowers each other's thermal performance.

[0029] In order to solve the above technical problems, the core idea of ​​the method of using the characteristic ash content of coking coal to evaluate and predict its thermal strength is to use the ash composition of coking coal as the core tool for analyzing its thermal performance, and further, to deeply analyze the coking coal production area and basic coal seams, determine the key components that affect the ash composition, and thus accurately determine and evaluate its thermal performance. Specifically, it includes the following steps:

[0030] Step 1: Analyze the basic conventional performance technical indicators of coking coal, such as ash content, volatile matter, sulfur content, bonding index, gelatinous layer index, etc.

[0031] Step 2: Learn in detail where the coking coal is produced and where the raw coal is mined;

[0032] Step 3, analyzing the geological coal-forming data of coking coal and raw coal;

[0033] Step 4: Detect the ash composition performance technical indicators of coking coal, mainly including: K2O, Na2O, CaO, MgO, SiO2, Fe2O3, Al2O3, TiO2, etc.;

[0034] Step 5: Calculate the basicity index (MBI) and mineral catalytic index (MCI) using the ash composition.

[0035] Calculation formula 1

[0036] MB I=100%Ad×(K2O+Na2O+CaO+MgO+Fe2O3) / (100-Vd)(SiO2+Al2O3)

[0037] Where: MBI is the alkalinity index; Ad is the dry basis ash; Vd is the dry basis volatile matter.

[0038] Calculation formula 2

[0039] MC I=100%Ad×(2.2K2O+1.9Na2O+1.6CaO+0.93MgO+Fe2O3) / (100-Vd)

[0040] (SiO2+0.41Al2O3+2.5TiO2)

[0041] Where: MCI is the alkalinity index; Ad is the dry basis ash; Vd is the dry basis volatile matter.

[0042] Step 6: Analyze the alkalinity index (MBI) and mineral catalytic index (MCI);

[0043] Step 7, calculating the gray component influence intensity correlation index;

[0044] The thermal strength correlation index f is composed of the gel layer index Y, the maximum Kiel fluidity α max The logarithm of l gα max The mineral catalytic index MCI and ash content Ad are coupled and calculated, and the calculation expression is:

[0045] f=Y+l gα max -(5MCI+1 / 2Ad).

[0046] The present invention determines the requirements for the intrinsic quality of coking coal by using the influence of the ash components of coking coal on the performance of metallurgical coke, strengthens the understanding and discrimination of the intrinsic performance of coking coal, and thus more scientifically ensures the quality of metallurgical coke. A method for predicting the thermal strength of coking coal by using the characteristic ash component content of coking coal effectively improves the scientific proportion of different coking single coals and improves the thermal performance of metallurgical coke, which is of great significance for blast furnace production, provides strong support for its stable and high production, and produces huge economic benefits and good social benefits.

[0047] Embodiment 1:

[0048] This embodiment takes two types of single-type coal for coking as examples to illustrate in detail the implementation and application process of the present invention.

[0049] The general performance indicators of the two coking coals are shown in the following table.

[0050] Table 1 Conventional performance indicators of single type of coal for coking

[0051] Mt(%) Ad(%) Vdaf(%) Stad(%) G Y(mm) Coking coal 1# 10.83 10.48 25.78 1.03 84 19 Coking coal 2# 11.02 10.50 25.32 1.01 85 20

[0052] From the table above, we can see that the conventional performance of coking coal 1# and coking coal 2# are basically the same, but the above indicators do not show the difference between the two. However, the thermal performance and coal blending research show great differences between the two. The CSR (heat reaction strength) of coking coal 1# is 67.8%, while the CSR (heat reaction strength) of coking coal 2# is only 36.6%, which is almost half the difference. It is difficult to make a convincing explanation from the conventional performance indicators.

[0053] In this regard, we conducted an investigation on the production areas of coking coal #1 and coking coal #2 and the mining areas of raw coal, and then analyzed the geological coal-forming data of coking coal and raw coal. Coking coal #1 is a high-quality coking coal from the Liliu mining area of ​​the Hedong coalfield in Shanxi, and coking coal #2 is a coking coal from the Heilongjiang region. The main geological data of the two coking coals are shown in Table 2.

[0054] Table 2 Main geological data of two coking coals

[0055]

[0056]

[0057] Subsequently, the technical indicators of ash composition performance of coking coal were tested, mainly including: K2O, Na2O, CaO, MgO, SiO2, Fe2O3, Al2O3, TiO2, etc. The specific data are shown in Table 3.

[0058] Table 3 Detection data of two coking coal ash components

[0059] <![CDATA[k2o]]> <![CDATA[Na2o]]> <![CDATA[sio2]]> cao Mgo <![CDATA[Al2o3]]> <![CDATA[Fe2o3]]> <![CDATA[Tio2]]> <![CDATA[so3]]> Coking coal 1# 0.386 3.16 51.60 2.25 0.89 18.47 2.52 0.70 0.75 Coking coal 2# 0.628 3.14 53.0 9.55 1.90 9.74 3.05 1.50 1.83

[0060] The basicity index (MBI) and mineral catalytic index (MCI) were calculated using ash composition. The basicity index (MBI) of coking coal No. 1 was 1.99 and the mineral catalytic index (MCI) was 2.73; the basicity index (MBI) of coking coal No. 2 was 4.68 and the mineral catalytic index (MCI) was 7.46;

[0061] Analyzing the basicity index (MBI) and mineral catalytic index (MCI) of coking coal 1# and coking coal 2#, it can be seen that the basicity index (MBI) of 2# is 2.35 times that of coking coal 1#, and the mineral catalytic index (MCI) is 2.73 times that of coking coal 1#. From the analysis of ash composition, the components K2O and CaO that intensify the coke melting reaction of coking coal 1# are much lower than those of coking coal 2#, so the CSR (heat reaction strength) of the smelted coke after reaction of coking coal 1# is much higher than that of coking coal 2#, which makes a scientific and convincing explanation from the internal components of coking coal.

[0062] The embodiments described above are only descriptions of the preferred modes of the present invention, and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A method for evaluating and predicting the thermal strength of coking coal by using its characteristic ash content, characterized in that: The steps include: Step 1: Analyze the basic conventional performance technical indicators of coking coal; Step 2: Learn in detail where the coking coal is produced and where the raw coal is mined; Step 3, analyzing the geological coal-forming data of coking coal and raw coal; Step 4, testing the ash composition performance technical indicators of the coking coal; Step 5, using the ash composition to calculate the basicity index (MBI) and mineral catalytic index (MCI); Calculation formula 1: MBI=100%Ad×(K2O+Na2O+CaO+MgO+Fe2O3) / (100-Vd)(SiO2+Al2O3); Where: MBI is the alkalinity index; Ad is the dry basis ash; Vd is the dry basis volatile matter; Calculation formula 2: MCI=100%Ad×(2.2K2O+1.9Na2O+1.6CaO+0.93MgO+Fe2O3) / (100-Vd)(SiO2+0.41Al2O3+2.5TiO2); Where: MCI is the alkalinity index; Ad is the dry basis ash; Vd is the dry basis volatile matter; Step 6: Analyze the alkalinity index (MBI) and mineral catalytic index (MCI); Step 7, calculating the correlation index f of the ash component affecting the thermal intensity; The thermal strength correlation index f is composed of the gel layer index Y, the maximum Kiel fluidity α max The logarithm of lgα max The mineral catalytic index MCI and ash content Ad are coupled and calculated, and the calculation expression is f = Y + lgα max -(5MCI+1 / 2Ad).

2. The method for evaluating and predicting the thermal strength of coking coal by using the characteristic ash content of coking coal according to claim 1, characterized in that: The basic conventional performance technical indicators of the coking coal analyzed include ash content, volatile matter, sulfur content, caking index, gelatinous layer index, and Kiel fluidity.

3. The method for evaluating and predicting the thermal strength of coking coal by using the characteristic ash content of coking coal according to claim 1, characterized in that: The technical indicators for detecting the ash composition performance of coking coal mainly include: K2O, Na2O, CaO, MgO, SiO2, Fe2O3, Al2O3, and TiO2.

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