A method for predicting a final coal separation ratio optimization model based on heat generation

Abstract

The application belongs to the technical field of fine coal separation, and specifically discloses a method for optimizing the selection proportion of fine coal based on heat value prediction, which comprises the following steps: step S1: determining the initial value of the comprehensive heat value of the fine coal of commercial coal based on the comprehensive heat value requirement of the fine coal of commercial coal; step S2: obtaining the inverse function f1(x) of the delta curve by using the arctangent function fitting; step S3: obtaining the inverse function f2(x) of the beta curve by using the arctangent function fitting; step S4: determining the objective function of the selection proportion optimization model of the fine coal of commercial coal based on the yield of each component of the fine coal; and step S5: determining the selection proportion X and the separation density p based on the objective function of step S4 and in combination with the ash content of each component of the fine coal and the initial value of the comprehensive heat value. The application can accurately predict the selection proportion of the fine coal by constructing a selection proportion prediction model, effectively avoid resource waste, improve the quality of mixed coal, and ensure the maximization of economic benefits.

Description

Technical Field

[0001] This invention relates to the field of fine coal sorting technology, and in particular to a method for optimizing the proportion of fine coal entering the sorting process based on calorific value prediction. Background Technology

[0002] The main products of thermal coal preparation plants are usually blends of lump clean coal and mixed coal. Fine coal smaller than 13mm, due to its fine particle size and high ash content, cannot meet the calorific value requirements for sale if directly used as mixed coal. If all fine coal is sorted, the resulting clean coal will have a higher calorific value than the market demand for mixed coal, leading to resource waste and economic losses for coal enterprises. Therefore, in the thermal coal sorting process, a portion of the fine coal is typically sorted to produce fine clean coal with a higher calorific value than the sales requirements, while a portion of the low-calorific-value fine raw coal is not sorted and directly blended with the fine clean coal to ultimately form a mixed coal product that meets the sales calorific value. During production, a portion of the 13-6mm fine raw coal undergoes heavy media processing. Hydrocyclone separation involves the direct blending of some raw coal with other materials. However, frequent fluctuations in raw coal quality lead to variations in washability. Current processes use fixed separation parameters, and the adjustment of the blending ratio relies solely on manual experience and delayed calorific value feedback. The coal blending process also depends on manual operation, resulting in poor blend quality stability and frequent direct economic losses due to coal quality disputes. The proportion of fine coal entering the separation system for sorting and the proportion of raw coal going directly into the blend without sorting is a crucial parameter. Therefore, it is necessary to design a method that can accurately predict the proportion of fine coal entering the separation system to effectively avoid resource waste, improve blend quality, and ensure maximum economic benefits. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method for optimizing the proportion of fine coal fed into the blending process based on calorific value prediction. By constructing a model for predicting the proportion of fine coal fed into the blending process, the method can accurately predict the proportion of fine coal fed into the blending process, effectively avoid resource waste, improve the quality of blended coal, and ensure maximum economic benefits.

[0004] To achieve the above objectives, the technical solution adopted by the present invention is: a method for optimizing the proportion of fine coal entering the slurry based on calorific value prediction, comprising the following steps: Step S1: Determine the initial value of the comprehensive calorific value of fine coal entering the slurry based on the comprehensive calorific value requirement of commercial coal fine coal; Step S2: Obtain the inverse function f1(x) of the δ curve by fitting the arctangent function, that is, obtain the functional relationship between the yield of each component of the fine coal and the sorting density, which facilitates obtaining the yield of each component of the fine coal in the heavy medium cyclone separator; Step S3: Obtain the inverse function f2(x) of the β curve by fitting the arctangent function, that is, obtain the functional relationship between the ash content of each component of the fine coal and the yield, which facilitates obtaining the ash content of each component of the fine coal in the heavy medium cyclone separator; Step S4: Determine the objective function of the optimization model for the proportion of fine coal entering the slurry based on the yield of each component of the fine coal described in Step S2; Step S5: Determine the proportion X and the sorting density ρ based on the objective function described in Step S4, combined with the ash content of each component of the fine coal described in Step S3 and the initial value of the comprehensive calorific value described in Step S1.

[0005] Preferably, the total calorific value is expressed in terms of ash content as follows:

[0006] (1)

[0007] In the formula: k is the slope; b is the intercept; It is ash.

[0008] Preferably, the components of the fine coal include fine clean coal, natural grade 13-6mm non-selected portion, and fine middlings; the range of the separation density ρ is as follows: The sorting density ρ includes a first sorting density ρ1 and a second sorting density ρ2.

[0009] Preferably, the ash content It can be expressed by the following formula:

[0010] (14)

[0011] In the formula: n is the number of components of commercial coal fines, n=3; Let be the yield of the i-th component; The ash content of the i-th component; This represents the yield of commercial coal fines.

[0012] Preferably, the yield of the commercial coal fines is... It can be expressed by the following formula:

[0013] (5)

[0014] In the formula: n is the number of components of commercial coal fines, n=3; Let be the yield of the i-th component.

[0015] Preferably, the functional relationship between the yield and the sorting density is expressed by the following formula:

[0016] (8)

[0017] In the formula: ρ is the sorting density.

[0018] Preferably, the functional relationship between ash content and yield is expressed by the following formula:

[0019] (16)

[0020] In the formula: γ is the yield.

[0021] Preferably, the objective function of the optimization model for the proportion of commercial coal fines entering the beneficiation process is expressed by the following formula:

[0022] (twenty four)

[0023] In the formula: ρ is the yield of commercial coal fines; ρ1 is the first sorting density; X is the proportion of coal selected.

[0024] Preferably, the initial value of the comprehensive calorific value of the commercial coal fines is 5500 kcal / kg.

[0025] Compared with the prior art, the present invention has the following advantages:

[0026] 1. This invention constructs a coal selection ratio prediction model to accurately predict the coal selection ratio of fine coal, effectively avoiding resource waste, improving the quality of blended coal, and ensuring maximum economic benefits.

[0027] 2. This invention improves the sorting accuracy of fine coal, reduces energy consumption, and achieves green production by predicting the proportion of coal to be sorted based on calorific value.

[0028] 3. The model constructed in this invention adjusts the feed ratio based on the fluctuation of raw coal ash content, realizes real-time monitoring of the feed ratio, and provides the feed ratio to the central control center to avoid economic losses caused by the lag in the testing process.

[0029] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. Attached Figure Description

[0030] Figure 1 This is a schematic diagram of the process of the present invention. Detailed Implementation

[0031] This invention discloses a method for optimizing the proportion of fine coal entering the beneficiation process based on calorific value prediction, comprising the following steps:

[0032] Step S1: Determine the initial value of the comprehensive calorific value of commercial coal fines based on the comprehensive calorific value requirements of commercial coal fines;

[0033] The total calorific value is expressed in terms of ash content as follows:

[0034] (1)

[0035] In the formula: k is the slope; b is the intercept; It is ash.

[0036] Collect at least 100 sets of ash content and calorific value data, and set the ash content data as... The calorie data is set to The slope k is calculated using the least squares method:

[0037] (2)

[0038] In the formula: The arithmetic mean of all gray data; This is the arithmetic mean of all calorific value data.

[0039] Based on slope k and ash arithmetic mean Arithmetic mean of calorific value Calculate the intercept b:

[0040] (3)

[0041] The initial value of the comprehensive calorific value of commercial coal fines is set at 5500 kcal / kg.

[0042] Right now:

[0043] (4)

[0044] Taking the Wenjiapo coal preparation plant as an example, the slope was calculated. ,intercept The total heat output If the comprehensive calorific value of commercial coal fines is required to be no less than 5500 kcal / kg, then:

[0045] .

[0046] Step S2: Use the arctangent function to fit the inverse function f1(x) of the δ curve, that is, obtain the functional relationship between the yield of each component of the fine coal and the sorting density, which is convenient for obtaining the yield of each component of the fine coal in the heavy medium cyclone.

[0047] The components of the fine coal include fine clean coal, natural grade 13-6mm non-selected portion, and fine middlings;

[0048] The range of the sorting density ρ is as follows: The sorting density ρ includes a first sorting density ρ1 and a second sorting density ρ2.

[0049] The δ curve is the density curve in the raw coal washability curve. It is an inherent property of raw coal and can be obtained by referring to the raw coal washability curve. The relationship between the corresponding separation density and yield can be obtained through the inverse function f1(x) of the δ curve.

[0050] Assuming the yield of 13-6mm natural grade raw coal in the mine is "unit 1", and the yield of commercial coal fines is... The yield of the i-th component of commercial coal fines is ( ), yield of commercial coal fines It can be expressed by the following formula:

[0051] (5)

[0052] In the formula: n is the number of components of commercial coal fines, n=3; Let be the yield of the i-th component.

[0053] Assume the yield of the final clean coal (three-product heavy medium cyclone clean coal) is... The calculation formula is as follows:

[0054] (6)

[0055] (7)

[0056] Where: X is the selection ratio; ρ1 is the first separation density of the heavy medium cyclone separator; The final clean coal yield of the heavy medium cyclone separator; This represents the functional relationship between the clean coal yield at the end of the heavy medium cyclone separator and the sorting density.

[0057] Taking the Wenjiapo coal preparation plant as an example, the relationship between clean coal yield and separation density is obtained through nonlinear fitting. The functional relationship between yield and separation density is expressed by the following formula:

[0058] (8)

[0059] In the formula: ρ is the sorting density.

[0060] but:

[0061] (9)

[0062] Assume the yield of washed mixed coal 2 (natural grade 13-6mm excluding beneficiation portion) is The calculation formula is as follows:

[0063] (10)

[0064] Assume the yield of washed mixed coal 3 (coal in the three-product heavy medium cyclone) is... The calculation formula is as follows:

[0065] (11)

[0066] (12)

[0067] In the formula: ρ is the middlings yield of the heavy medium cyclone; ρ2 is the second separation density of the heavy medium cyclone.

[0068] In summary, the formula for calculating the yield of commercial coal fines is as follows:

[0069] (13)

[0070] Step S3: Use the arctangent function to fit the inverse function f2(x) of the β curve, that is, obtain the functional relationship between the ash content and yield of each component of the fine coal, which is convenient for obtaining the ash content of each component of the fine coal in the heavy medium cyclone.

[0071] The β curve is the cumulative floating matter curve in the raw coal washability curve. It is an inherent property of raw coal and is obtained by referring to the raw coal washability curve. The relationship between ash content and yield in the fine coal separation process can be obtained through the inverse function f2(x) of the β curve.

[0072] Assume the ash content of commercial coal fines is... The yield of commercial coal fines is The gray part of the i-th component is The yield of the i-th component is The ash content of commercial coal fines It can be expressed by the following formula:

[0073] (14)

[0074] In the formula: n is the number of components of commercial coal fines, n=3.

[0075] The ash content of each component of commercial coal fines is calculated:

[0076] Ash content of fine coal (three-product heavy medium hydrocyclone fine coal) It can be expressed by the following formula:

[0077] (15)

[0078] In the formula: The final clean coal yield of the first stage of the heavy medium cyclone separator; This represents the functional relationship between the ash content of the finished coal and the yield in a heavy medium cyclone separator.

[0079] Taking the Wenjiapo coal preparation plant as an example, the relationship between ash content and yield is obtained through nonlinear fitting. The nonlinear fitting first determines the approximate relationship based on the shape of the data graph, and then calculates the formula and coefficients using the least squares method. The functional relationship between ash content and yield is expressed by the following formula:

[0080] (16)

[0081] In the formula: For yield.

[0082] but:

[0083] (17)

[0084] Ash content of washed mixed coal 2 (natural grade 13-6mm non-selected portion) It can be expressed by the following formula:

[0085] (18)

[0086] In the formula: The ash content of the raw coal in the mine, which is 13-6mm in natural grade, is determined by the ash content analyzer on the underside belt of the grading screen.

[0087] Ash content of washed mixed coal 3 (coal in a three-product heavy medium cyclone separator) It can be expressed by the following formula:

[0088] (19)

[0089] (20)

[0090] In the formula: ρ is the middlings yield of the heavy medium cyclone; ρ2 is the second separation density of the heavy medium cyclone.

[0091] The diameter of the first section of the heavy medium cyclone separator at the Wenjiapo coal preparation plant is 1400mm, and the diameter of the second section is 1000mm. Calculations show the following relationship between the second separation density ρ2 and the first separation density ρ1:

[0092] (twenty one)

[0093] The combined formulas (14) and (20) are used to calculate the ash content of commercial coal fines:

[0094] (twenty two)

[0095] Combining equations (1) and (22), the formula for the relationship between the calorific value and ash content of commercial coal fines can be transformed into:

[0096] (twenty three)

[0097] Step S4: Based on the yield of each component of the fine coal described in Step S2, determine the objective function of the optimization model for the proportion of fine coal entering the beneficiation process in commercial coal.

[0098] The objective function of the optimization model for the proportion of fine coal entering the beneficiation process is expressed by the following formula:

[0099] (twenty four)

[0100] In the formula: ρ is the yield of commercial coal fines; ρ1 is the first sorting density; X is the proportion of coal selected.

[0101] Step S5: Based on the objective function described in Step S4, and combined with the ash content of each component of the fine coal described in Step S3 and the initial value of the comprehensive calorific value described in Step S1, determine the inclusion ratio X and the separation density ρ.

[0102] The range of values ​​for the selection ratio X is: Based on equations (4) and (24), and combined with the minimize function, the optimal sorting ratio X and the first sorting density ρ1 are obtained. Based on the first sorting density ρ1, the second sorting density ρ2 is obtained, and the maximum commercial coal fines yield is further obtained. Maximum calorific value of commercial coal fines .

[0103] Example 2

[0104] Assuming the ash content of raw coal The yield was 20.05%, based on the yield of fine coal obtained from the experiment. The yield was 72.24%, representing a medium coal production rate. The objective function of the optimization model for the proportion of fine coal entering the beneficiation process is 10.58%. Using the minimize function in conjunction with the ash content of raw coal , and Solving for the optimal selection ratio X = 0.8102, we obtain the optimal first sorting density. Maximize the yield of fine coal The corresponding total calorific value of fine coal .

[0105] The above description is merely a preferred embodiment of the present invention and does not constitute any limitation on the present invention. Any simple modifications, alterations, or equivalent structural transformations made to the above embodiments based on the technical essence of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for optimizing the proportion of fine coal fed into the beneficiation process based on calorific value prediction, characterized in that, Includes the following steps: Step S1: Determine the initial value of the comprehensive calorific value of commercial coal fines based on the comprehensive calorific value requirements of commercial coal fines; Step S2: Use the arctangent function to fit the inverse function f1(x) of the δ curve, that is, obtain the functional relationship between the yield of each component of the fine coal and the sorting density, which is convenient for obtaining the yield of each component of the fine coal in the heavy medium cyclone. Step S3: Use the arctangent function to fit the inverse function f2(x) of the β curve, that is, obtain the functional relationship between the ash content and yield of each component of the fine coal, which is convenient for obtaining the ash content of each component of the fine coal in the heavy medium cyclone. Step S4: Based on the yield of each component of the fine coal described in Step S2, determine the objective function of the optimization model for the proportion of fine coal entering the beneficiation process in commercial coal. Step S5: Based on the objective function described in Step S4, and combined with the ash content of each component of the fine coal described in Step S3 and the initial value of the comprehensive calorific value described in Step S1, determine the inclusion ratio X and the separation density ρ. The total calorific value is expressed in terms of ash content as follows: （1） In the formula: k is the slope; b is the intercept; It is ash content; The ash It can be expressed by the following formula: （14） In the formula: n is the number of components in commercial coal fines, n=3; Let be the yield of the i-th component; The ash content of the i-th component; The yield of commercial coal fines; The functional relationship between yield and sorting density is expressed by the following formula: （8） In the formula: ρ is the sorting density; The functional relationship between ash content and yield is expressed by the following formula: （16） In the formula: γ is the yield; The objective function of the optimization model for the proportion of fine coal entering the beneficiation process is expressed by the following formula: （24） In the formula: ρ is the yield of commercial coal fines; ρ1 is the first sorting density; X is the proportion of coal selected.

2. The method for optimizing the proportion of fine coal fed into the beneficiation process based on calorific value prediction according to claim 1, characterized in that, The components of the fine coal include fine clean coal, natural grade 13-6mm non-selected portion, and fine middlings; The range of the sorting density ρ is as follows: The sorting density ρ includes a first sorting density ρ1 and a second sorting density ρ2.

3. The method for optimizing the proportion of fine coal fed into the beneficiation process based on calorific value prediction according to claim 1, characterized in that, The yield of the commercial coal fines It can be expressed by the following formula: （5） In the formula: n is the number of components in commercial coal fines, n=3; Let be the yield of the i-th component.

4. The method for optimizing the proportion of fine coal fed into the beneficiation process based on calorific value prediction according to claim 1, characterized in that, The initial value of the comprehensive calorific value of the commercial coal fines is 5500 kcal / kg.

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