CN115859024A - Method for predicting coal-based fluidity characteristic parameters - Google Patents
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Abstract
The invention relates to a method for predicting coal-based fluidity characteristic parameters, which firstly measures the volatile component V of raw material coal daf And a maximum thickness Y of the colloidal layer according to V daf Different selection formulas of the numerical range calculate the maximum Gieseler fluidity logarithm value lgMF of the raw material coal 0 And according to V daf The initial softening temperature T of the raw material coal is calculated by numerical value s Maximum flow temperature T max And a curing temperature T r According to V daf Difference of sum Y Using different correction factor pairs lgMF 0 And (5) correcting to obtain a corrected maximum Gieseler fluidity logarithm value lgMF, and predicting the characteristic parameters of the Gieseler fluidity of the raw material coal. Compared with the results of the characteristic parameters of the Gieseler fluidity determined by the GB/T25213-2010 method, the prediction method has high accuracy of the predicted value and strong applicability to coal types, and can provide an important role in guiding actual coking and coal blending.
Description
Technical Field
The invention belongs to the technical field of coal quality analysis and coal blending coking in coal and coking industries, and particularly relates to a prediction method for characteristic parameters of Gieseler fluidity of single coal or blended coal for coking.
Background
The coal used as the coking raw material accounts for more than 85 percent of the total coking cost, and the high-quality main coking coal and the fat coal have good caking and coking characteristics and relatively high proportion in the coking and blending coal. However, as the mining strength of high-quality main coking coal and fat coal is increased, the reserves are reduced day by day, and the price is relatively high, so that the contradiction between supply and demand is more prominent at present. Therefore, the method for coking by blending the coking coals with different coal quality characteristics is an important way for reasonably utilizing various coking coal resources and reducing the coking coal blending cost.
Coking enterprises generally adopt indexes of volatile components, ash content, sulfur content, bond index and colloidal layer index to guide coal blending, the method has universal applicability, and the evaluation of coking coal and the coking coal blending are guided to a great extent in China. However, with the expansion of the range of coking coals and the complexity of coal quality characteristics of different coal sources, the defects of the conventional coal quality indexes, particularly the process indexes reflecting the caking and coking characteristics of coking coals, are increasingly highlighted.
The caking index is obtained by mixing coking coal and special anthracite according to a certain proportion and then testing the strength of the obtained coke by using a rotary drum, and has the advantages of quick and simple measuring process, good reproducibility, stable measured value and additivity in a certain range, but the deficiency is that the caking index has poor capacity of distinguishing strong caking coal.
The colloidal layer index is used for simulating industrial coking conditions, a certain amount of coal is put into a coal cup, single-side heating is carried out in a special electric furnace, so that a series of isothermal layer surfaces are formed on coal samples in the coal cup, and the maximum thickness Y value and the final shrinkage X value of the colloidal layer are measured through a probe. The colloidal layer index has a certain additivity, and in most cases only represents the number of colloidal bodies, but not necessarily the quality thereof.
Therefore, the currently commonly used bond index and colloidal layer index have certain limitations.
In contrast, the Kirschner fluidity is that a stirring paddle is inserted into a crucible filled with a coal sample, a constant rotating moment is applied to the stirring paddle in the temperature rising process, the rotating speed of the stirring paddle at the corresponding temperature is recorded, and then characteristic parameters such as the maximum fluidity, the softening temperature, the maximum fluidity temperature, the curing temperature and the like of the coal sample are measured and obtained.
The Gieseler fluidity is generally applied in the United states, japan and other countries, but the use of the Gieseler fluidity by most domestic coal and coking enterprises is still very little. The reason for this is that, although the Kirschner fluidity can reflect the number and properties of the colloidal particles at the same time, the measurement process is highly required to be standardized, and the shape, size, wear, preparation of coal sample, and charging method of the stirrer have a large influence on the measurement value, so that the requirement on the detection level of the inspector is high. In addition, in the actual production process, certain timeliness is needed for the actual measurement of single coal and coal blending, and the detection cost of the base fluidity is high, so that the current base fluidity measurement cannot meet the requirements of actual coking work of most enterprises, and the application of the base fluidity measurement is limited.
Therefore, aiming at the problems of the measurement process of the Gieseler fluidity index and the use of the Gieseler fluidity index in coal and coking enterprises, a simple, rapid and accurate Gieseler fluidity prediction method is developed by establishing a Gieseler fluidity characteristic parameter prediction model based on commonly used coal quality conventional indexes through correlation analysis of correlation between the Gieseler fluidity characteristic parameters and different coal quality characteristic parameters, so as to provide guidance for deeply knowing the properties of coking coal, formulating a scientific coking coal blending scheme and reducing the coking coal blending cost.
Disclosure of Invention
The invention aims to establish a method for predicting coal-based fluidity characteristic parameters so as to accurately and quickly predict the coal-based fluidity characteristic parameters on the basis of conventional indexes of coal quality.
On the basis of the commonly used conventional indexes of the coal quality, the invention selects the volatile component V with strong correlation by correlating and analyzing the correlation between the Gieseler fluidity parameter and different coal quality characteristic parameters daf And the maximum thickness Y of the gelatinous layer, and establishing a prediction model and a prediction method of coal-based fluidity characteristic parameters for guiding coking and coal blending.
On the basis of the research, the invention establishes the following specific method for predicting the characteristic parameters of the coal-based fluidity:
first, the volatile matter V of the raw coal was measured daf And a colloidal layer maximum thickness Y;
secondly, according to the measured volatile component V daf Selecting one of the following two formulas to calculate the logarithm of maximum Gieseler fluidity lgMF of the raw material coal 0 ：
16％＜V daf ≤30％，lgMF 0 ＝0.098V daf +0.152Y-2.374
30％＜V daf ≤40％，lgMF 0 ＝-0.060V daf +0.165Y+2.110
Finally, the initial softening temperature T of the raw material coal is calculated according to the following formula s Maximum fluidity temperature T max And a curing temperature T r ：
T max ＝517.90-2.20V daf
Thereby, the characteristic parameter of the Gieseler fluidity of the raw material coal is predicted.
In the above formula:
V daf -dry ash-free basis volatiles of raw coal,%;
y is the maximum thickness of a colloidal layer of the raw material coal, mm;
lgMF 0 -the log of maximum Gieseler fluidity of the feed coal;
T s -the initial softening temperature of the raw coal, deg.c;
T max -maximum flow temperature, deg.c, of the feed coal;
T r the curing temperature of the raw coal, DEG C.
In the method for predicting coal-based fluidity characteristic parameters established by the invention, the raw material coal can be single coking coal or blended coking coal.
Furthermore, the invention can also aim at the calculated logarithm of maximum Gieseler fluidity lgMF of the raw material coal 0 And correcting to obtain a corrected maximum Gieseler fluidity logarithm value lgMF so as to enable the prediction result of the characteristic parameter of the Gieseler fluidity to be more accurate.
Specifically, when the volatile matter fraction value of the raw material coal is in a range of 16% < V daf Less than 20 percent, or less than or equal to 25 percent V daf When the content is less than or equal to 30 percent, according to the formula lgMF = lgMF 0 -0.20 make a correction;
the range of the volatility value of the raw material coal meets the condition that V is more than or equal to 20 percent daf If less than 25%, according to the formula lgMF = lgMF 0 +0.20 for correction;
the range of the volatility value of the raw material coal meets the condition that the volatility value is more than 30 percent and less than V daf When the content is less than or equal to 35 percent, according to the formula lgMF = lgMF 0 -0.25 make a correction;
the range of the volatility value of the raw material coal meets 35 percent < V daf When the content is less than or equal to 40 percent, according to the formula lgMF = lgMF 0 +0.35 correction.
Furthermore, the invention can also combine the maximum thickness Y value of the colloidal layer with the calculated logarithm value lgMF of the maximum Gieseler fluidity of the raw material coal 0 A correction is made to obtain a corrected log value lgMF of maximum coriolis fluidity.
When the volatile fraction value range of the raw material coal meets V which is more than or equal to 28 percent daf ≤30％，And when the maximum thickness Y value of the colloidal layer is more than or equal to 25mm, the formula lgMF = lgMF 0 -0.30 make a correction;
when the volatility fraction value range of the raw material coal meets the condition that 30 percent is less than V daf Not more than 33 percent, and when the maximum thickness Y value of the colloidal layer is not less than 25mm, according to the formula lgMF = lgMF 0 -0.40 correction.
The invention selects more than 30 coking coals with different metamorphism degrees, coal quality characteristics and Kirschner fluidity, and performs multiple regression analysis on the Kirschner fluidity characteristic parameters of the coking coals and coal quality parameters such as industrial analysis, caking index, colloid layer index and the like respectively, and finally selects the volatile component V which is commonly used but has higher correlation degree with the Kirschner fluidity characteristic parameters daf And the maximum thickness Y of the colloidal layer are two key coking coal quality indexes, and a coal-based fluidity characteristic parameter prediction model formula and a prediction method are constructed.
The method for predicting the characteristic parameters of the coal-based fluidity, which is established by the invention, has the advantages of convenient operation and use of the model, strong universality and guiding effects on deeply knowing the properties of coking coal, more reasonably using different types of coking coal resources, making a scientific coking and coal blending scheme and reducing the coking and coal blending cost.
The method for predicting the characteristic parameters of the coal-based fluidity has scientific and accurate prediction result. By selecting a plurality of coking single coals and/or blended coals with different metamorphism degrees and Kirschner fluidity, calculating the Kirschner fluidity characteristic parameter by using the prediction method provided by the invention, and comparing the characteristic parameter with the Kirschner fluidity characteristic parameter result determined by the national standard GB/T25213-2010 method, the predicted value of the Kirschner fluidity characteristic parameter obtained by the method provided by the invention is proved to have high accuracy, particularly, after being corrected, the predicted value has smaller error with the actual value of the Kirschner fluidity characteristic parameter and higher correlation, and can provide important function for guiding actual coking and blending coals.
Detailed Description
The following examples are given to further illustrate the embodiments of the present invention. The following examples are only for more clearly illustrating the technical solutions of the present invention so as to enable those skilled in the art to better understand and utilize the present invention, and do not limit the scope of the present invention.
Unless otherwise specified, the production process, the experimental method or the detection method related to the embodiments of the present invention are all conventional methods in the prior art, and the names and/or the abbreviations thereof all belong to conventional names in the field, which are very clear and definite in the related fields of application.
The various instruments, equipments, raw materials or reagents used in the examples of the present invention are not particularly limited in their sources, and are all conventional products commercially available from normal commercial sources, and can be prepared by conventional methods well known to those skilled in the art.
Example 1
The method comprises the steps of selecting 10 kinds of raw material coal with different volatile components and colloidal layer indexes from different producing areas of Shanxi and Shaanxi, wherein the raw material coal comprises single coal and blended coal, and the specific coal type composition and source of each raw material coal are listed in the following table.
According to the national standard GB/T25213-2010 "constant moment Gieseler plasticity instrument method for coal plasticity determination" of the people's republic of China, the 10 kinds of raw material coal are subjected to measurement of Gieseler fluidity characteristic parameters.
5g of coal sample is placed into a special crucible and compacted, then is placed into a coal caldron, the coal caldron is lowered to the bottom of the crucible and enters a molten solder bath with the temperature of 300 ℃, and the temperature is raised at the speed of 3 ℃/min. When the base flow meter drum speed reached 1.0dd/min, the temperature and number of degrees of rotation were read at 1min intervals until the drum was no longer rotating. According to the test result, the maximum logarithm of Gieseler fluidity lgMF and the initial softening temperature T of each raw material coal are finally obtained s Maximum flow temperature T max And a curing temperature T r The actual value of (c).
The actual values of the coal-based fluidity characteristics of each raw material finally measured are shown in table 1.
Example 2
According to the national standards GB/T30732-2014 for coal industry analytical methods and instruments and GB/T479-2016 for measuring the index of bituminous coal colloidal layer, the volatile components V of the 10 kinds of raw material coal in example 1 were measured respectively daf And a maximum thickness Y of the colloidal layer, the specific measurement results are shown in Table 1.
Volatile component V according to the above measurement daf And the maximum thickness Y of the colloidal layer, and predicting the characteristic parameters of the Gieseler fluidity of various raw material coals according to a coal Gieseler fluidity characteristic parameter prediction model formula and a prediction method constructed by the invention.
Wherein, the volatile components V of the raw material coal C1, C2, C3, C6, C8, C9 and C10 daf The numerical value satisfies 16% < V daf Less than or equal to 30 percent, so according to the formula lgMF 0 ＝0.098V daf +0.152Y-2.374, respectively calculating their respective maximum Gieseler fluidity lgMF 0 A value;
volatile matter V of C4, C5 and C7 of raw coal daf The numerical value satisfies 30% < V daf Less than or equal to 40 percent, so according to the formula lgMF 0 ＝-0.060V daf +0.165Y +2.110, respectively calculating their respective maximum Gieseler fluidity lgMF 0 The value is obtained.
Next, the lgMF obtained by the above calculation is used 0 The values are corrected to obtain a corrected maximum Gieseler fluidity lgMF value:
c1 volatile content satisfying 16% < V daf < 20%, therefore according to the formula lgMF = lgMF 0 -0.20 correction;
the volatile component of C2 is more than or equal to 20 percent and less than or equal to V daf < 25%, so according to the formula lgMF = lgMF 0 +0.20 for correction;
the volatile component of C3 satisfies V of more than or equal to 25% daf Less than or equal to 30%, so according to the formula lgMF = lgMF 0 -0.20 make a correction;
the volatile component of C4 satisfies 30% < V daf Less than or equal to 35 percent, so according to the formula lgMF = lgMF 0 -0.25 make a correction;
the volatile component of C5 satisfies 35% < V daf Less than or equal to 40%, so according to the formula lgMF = lgMF 0 +0.35 for correction;
the volatile component of C6 satisfies V of more than or equal to 25 percent daf Less than or equal to 30 percent, but the maximum thickness Y value of the colloidal layer is more than or equal to 25mm, and the volatile component also meets the requirement of V being less than or equal to 28 percent daf Less than or equal to 30 percent, therefore, the formula lgMF = lgMF is not adopted 0 0.20, but according to the formula lgMF = lgMF 0 -0.30 make a correction;
the volatile matter of C7 satisfies 30% < V daf Less than or equal to 35 percent, but the maximum thickness Y value of the colloidal layer is more than or equal to 25mm, and the volatile component also meets the requirement that V is more than 30 percent daf Less than or equal to 33 percent, therefore, the formula lgMF = lgMF is not adopted 0 0.25, but according to the formula lgMF = lgMF 0 -0.40 make a correction;
the volatile components of C8 and C9 meet the requirement that V is more than or equal to 25 percent daf Less than or equal to 30%, so according to the formula lgMF = lgMF 0 -0.20 make a correction;
c10 volatile component satisfies 20% V or more daf < 25%, so according to the formula lgMF = lgMF 0 +0.20 for correction.
Then, the initial softening temperatures T of the 10 kinds of raw coal were calculated according to the following formulas s Maximum flow temperature T max And a curing temperature T r ：
T max ＝517.90-2.20V daf
The maximum logarithm value of Gieseler fluidity lgMF and the initial softening temperature T of each raw material coal are obtained through the processes s Maximum flow temperature T max And a curing temperature T r Predicted values, listed in table 1.
Meanwhile, table 1 also compares the actual value and the predicted value of the characteristic parameter of the kirschner flow rate of various raw material coals and provides a difference value obtained by subtracting the predicted value from the actual value.
TABLE 1 predicted and measured values of characteristic parameters of coal-based fluidity of raw materials
From the difference between the predicted value and the actual value of the characteristic parameter of the Gieseler fluidity of various raw material coals in the table 1, the method for predicting the characteristic parameter of the Gieseler fluidity of the coal, which is provided by the invention, can ensure higher accuracy and strong universality in the actual application process. According to the method, guidance effects can be provided for more reasonably using different types of coking coal resources, making a scientific coking and coal blending scheme and reducing the coking and coal blending cost.
The above embodiments of the present invention are not intended to be exhaustive or to limit the invention to the precise form disclosed. Various changes, modifications, substitutions and alterations to these embodiments will be apparent to those skilled in the art without departing from the principles and spirit of this invention.
Claims (8)
1. A method of predicting coal-based fluidity characteristic parameters, the method comprising:
measuring the volatile component V of the raw material coal daf And a colloidal layer maximum thickness Y;
according to the determined volatile component V daf Selecting one of the following two formulas to calculate the logarithm of maximum Gieseler fluidity lgMF of the raw material coal 0 ：
16％＜V daf ≤30％，lgMF 0 ＝0.098V daf +0.152Y-2.374；
30％＜V daf ≤40％，lgMF 0 ＝-0.060V daf +0.165Y+2.110；
And respectively calculating the initial softening temperature T of the raw material coal according to the following formula s Maximum fluidity temperature T max And a curing temperature T r ：
T max ＝517.90-2.20V daf ；
Thereby predicting the characteristic parameters of the Gieseler fluidity of the raw material coal;
in the above formula:
V daf -dry ash-free basis volatiles of raw coal,%;
y is the maximum thickness of a colloidal layer of the raw material coal, mm;
lgMF 0 -the log of maximum Gieseler fluidity of the feed coal;
T s -the initial softening temperature of the raw coal, deg.c;
T max -maximum fluidity temperature of the raw coal, ° c;
T r the curing temperature of the raw coal, DEG C.
2. The method for predicting coal-based fluidity characteristic parameter of claim 1, wherein the raw material coal is coking single coal or coking blended coal.
3. The method of predicting coal-based fluidity characteristic parameter as claimed in claim 1 or 2, further comprising when the range of the volatility fraction value of the raw material coal satisfies 16% < V daf V less than 20% or less than 25% daf When the content is less than or equal to 30 percent, according to the formula lgMF = lgMF 0 Maximum logarithm of Gieseler fluidity lgMF of 0.20 for the coal feedstock 0 And correcting to obtain the corrected logarithm value lgMF of the maximum Gieseler fluidity.
4. The method of claim 1 or 2, further comprising predicting the coal-based fluidity characteristic parameter when the volatility fraction value of the raw material coal satisfies 20% ≦ V daf If < 25%, according to the formula lgMF = lgMF 0 +0.20 maximum Gieseler fluidity logarithm of the feed coal lgMF 0 And correcting to obtain the corrected logarithm value lgMF of the maximum Gieseler fluidity.
5. The method for predicting coal-based fluidity characteristic parameter as claimed in claim 1 or 2, further comprising when the range of the volatility fraction value of the raw material coal satisfies 30% < V daf When the content is less than or equal to 35 percent, according to a formula lgMF = lgMF 0 Maximum logarithm of Gieseler fluidity lgMF of 0.25 for the coal feedstock 0 And correcting to obtain the corrected logarithm value lgMF of the maximum Gieseler fluidity.
6. The method of predicting coal-based fluidity characteristic parameter as claimed in claim 1 or 2, further comprising when the range of the volatility fraction value of the raw material coal satisfies 35% < V daf When the content is less than or equal to 40 percent, according to the formula lgMF = lgMF 0 +0.35 maximum logarithm of Gieseler fluidity for raw coal lgMF 0 And correcting to obtain the corrected logarithm value lgMF of the maximum Gieseler fluidity.
7. The method of predicting coal-based fluidity characteristic parameter of claim 1 or 2, further comprising when the volatility value range of the raw material coal satisfies 28% ≦ V daf Not more than 30 percent and the maximum thickness Y value of the colloidal layer is not less than 25mm according to the formula lgMF = lgMF 0 Maximum logarithm of Gieseler fluidity lgMF of 0.30 for the coal feedstock 0 And correcting to obtain the corrected logarithm value lgMF of the maximum Gieseler fluidity.
8. Method for predicting coal-based fluidity characteristic parameter according to claim 1 or 2The method is characterized by also comprising the step of when the volatility value range of the raw material coal meets the condition that V is more than 30 percent daf Not more than 33 percent, and when the maximum thickness Y value of the colloidal layer is not less than 25mm, according to the formula lgMF = lgMF 0 Maximum logarithm of Gieseler fluidity lgMF of 0.40 for the coal feedstock 0 And correcting to obtain the corrected logarithm value lgMF of the maximum Gieseler fluidity.
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