Scientific evaluation method for fat coal and coking coal

Abstract

The invention belongs to the technical field of coking coal blending, and particularly relates to a scientific evaluation method for fat coal and coking coal, which is characterized in that the cost performance of coking coal is evaluated by selecting a random vitrinite reflectance distribution parameter and an adaptability index, and the scientific evaluation method specifically comprises the following steps: 1) selecting a plurality of coals and factory prices Q thereof; 2) establishing an equation; 3) performing regression analysis by using multiple linear regression; (4) P foundation is made to be equal to K (lt); 0.5) Xi (llt; 0.5) + K (0.5 to 0.65) Xi (0.5 to 0.65) + K (0.65 to 0.8) Xi (0.65 to 0.8) + K (0.8 to 0.9) Xi (0.8 to 0.9) + K (0.9 to 1.2) Xi (0.9 to 1.2) + K (1.2 to 1.5) Xi (1.2 to 1.5) + K (1.5 to 1.7) Xi (1.5 to 1.7) + K (1.7 to 1.9) Xi (1.7 to 1.9) + K (1.9 to 2.5) Xi (1.9 to 2.5) + K (gt); 2.5) Xi (gt); 5) setting P final = P foundation (CSR / CSR standard), and 6) determining that the larger the actual difference value of P final-P is, the higher the cost performance of the coal type is, and otherwise, the lower the cost performance is. The method has the beneficial effects that the cost performance evaluation of the fat coal and the coking coal is realized, and the method has important guiding significance for meeting the coking production requirements and controlling the coal blending cost.

Description

Technical Field

[0001] This invention belongs to the field of coking coal blending technology, and particularly relates to a scientific evaluation method for coking coal and coking coal. Background Technology

[0002] With increasingly fierce competition in the coking market, cost reduction and efficiency improvement have become a consensus among coking enterprises. Coking coal costs account for approximately 80% to 90% of coke production costs. Therefore, while ensuring coke quality, reducing coking coal costs is crucial for coking enterprises to achieve cost reduction and efficiency improvement. However, the severe shortage of coking coal resources in my country significantly restricts the reduction of coking coal costs. Therefore, procuring cost-effective coking coal has become a key factor for coking enterprises to reduce costs and improve efficiency.

[0003] Coking coal has a complex quality and diverse evaluation methods. Currently, coking enterprises have the following problems in evaluating coking coal: (1) It is difficult to make an accurate evaluation based on a single or a few performance indicators of coking coal, and it is even more impossible to make a quantitative performance and cost-effectiveness evaluation of different coking coals. (2) The compatibility of coking coal with the enterprise's commonly used coal types is not considered when blending coal, and the compatibility indicators are not included in the evaluation indicators. Summary of the Invention

[0004] The purpose of this invention is to provide a scientific evaluation method for coking coal and bituminous coal, overcoming the shortcomings of existing technologies. By selecting random vitrinite reflectance distribution parameters and adaptability indicators, a scientific evaluation method for coking coal is established, which has important guiding significance for meeting the needs of coking production and controlling coal blending costs.

[0005] To achieve the above objectives, the present invention provides the following technical solution: A scientific evaluation method for coking coal and bituminous coal, which evaluates the cost-effectiveness of coking coal by selecting random vitrinite reflectance distribution parameters and a suitability index, specifically includes the following steps: 1) Select several types of coal commonly used by coking enterprises at present and their delivered prices Q; 2) The equation is established as follows: K （<0.5） X i（<0.5） +K （0.5-0.65） X i（0.5-0.65） +K （0.65-0.8） X i（0.65-0.8） +K （0.8-0.9） X i（0.8-0.9） +K （0.9-1.2） X i（0.9-1.2） +K （1.2-1.5） X i（1.2-1.5） +K （1.5-1.7） X i（1.5-1.7） +K （1.7-1.9） X i（1.7-1.9） +K （1.9-2.5） X i（1.9-2.5） +K（>2.5） X i（>2.5） +K=Q i In the formula: i = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10; Q i Let be the price delivered to the plant for the i-th type of coking coal; K is the intercept of the equation. 3) Use multivariate linear regression analysis to solve for the price K of each component. （<0.5） K （0.5-0.65） K （0.65-0.8） K （0.8-0.9） K （0.9-1.2） K （1.2-1.5） K （1.5-1.7） K （1.7-1.9） K （1.9-2.5） K （>2.5） and the value of the intercept K; 4) Let P 础 =K （<0.5） X i（<0.5） +K （0.5-0.65） X i（0.5-0.65） +K （0.65-0.8） X i（0.65-0.8） +K （0.8-0.9） X i（0.8-0.9） +K （0.9-1.2） X i（0.9-1.2） +K （1.2-1.5） X i（1.2-1.5） +K （1.5-1.7） X i（1.5-1.7） +K （1.7-1.9） X i（1.7-1.9） +K （1.9-2.5） X i（1.9-2.5） +K （>2.5） X i（>2.5） +K; where: P 础 This is the base price for coking coal; 5) Let P 终 =P 础 (CSR / CSR) 标 ); where: CSR 标 The coking coal with the best compatibility with other coking coals in the production of this coking enterprise is the small coke oven test value characterized by the most common production ratio of this coal type; CSR is the small coke oven test value of a certain coking coal completely replacing the best-suited coking coal while keeping the ratio of other coal types unchanged. 6) Substituting the small coke oven test values ​​into the formula in step 5), a scientific evaluation of coking coal can be achieved, P 终 -P 实际 The larger the difference, the higher the cost-effectiveness of the coal type; conversely, the smaller the difference, the lower the cost-effectiveness of the coal type.

[0006] The other coking coals mentioned refer to fat coal and coking coal.

[0007] In step 2), K（<0.5） K represents the price of the random vitrinite reflectance (<0.5) component in coking coal; （0.5-0.65） K represents the price of random vitrinite reflectance components in coking coal. （0.65-0.8） The price of the random vitrinite reflectance (0.65-0.8) component in coking coal; K （0.8-0.9） K represents the price of the random vitrinite reflectance (0.8-0.9) component in coking coal; （0.9-1.2） K represents the price of the random vitrinite reflectance (0.9-1.2) component in coking coal; （1.2-1.5） K represents the price of the random vitrinite reflectance (1.2-1.5) component in coking coal; （1.5-1.7） The price of the random vitrinite reflectance (1.5-1.7) component in coking coal; K （1.7-1.9） K represents the price of the random vitrinite reflectance (1.7-1.9) component in coking coal; （1.9-2.5） K represents the price of the random vitrinite reflectance (1.9-2.5) component in coking coal; （>2.5） The price of the random vitrinite reflectance (>2.5) component in coking coal.

[0008] In step 2), X i（<0.5） X represents the percentage of random vitrinite reflectance (<0.5) components in the i-th type of coking coal; i（0.5-0.65） X represents the percentage of the random vitrinite reflectance (0.5-0.65) component in the i-th type of coking coal; i（0.65-0.8） X represents the percentage of the random vitrinite reflectance component (0.65-0.8) in the i-th type of coking coal; i（0.8-0.9） X represents the percentage of the random vitrinite reflectance component (0.8-0.9) in the i-th type of coking coal; i（0.9-1.2） X represents the percentage of the random vitrinite reflectance component (0.9-1.2) in the i-th type of coking coal; i（1.2-1.5） X represents the percentage of the random vitrinite reflectance component (1.2-1.5) in the i-th type of coking coal; i（1.5-1.7） X represents the percentage of the random vitrinite reflectance component (1.5-1.7) in the i-th type of coking coal; i（1.7-1.9） X represents the percentage of the random vitrinite reflectance component (1.7-1.9) in the i-th type of coking coal; i（1.9-2.5） X represents the percentage of the random vitrinite reflectance component (1.9-2.5) in the i-th type of coking coal; i（>2.5） The percentage of random vitrinite reflectance (>2.5) components in the i-th type of coking coal.

[0009] Compared with the prior art, the beneficial effects of the present invention are: 1) By selecting random vitrinite reflectance distribution parameters and adaptability indicators, a scientific evaluation method for coking coal is established. This method can scientifically and quantitatively evaluate fat coal and coking coal, which has important guiding significance for meeting the needs of coking production and controlling coal blending costs. 2) This invention introduces adaptability parameters into the evaluation equation for the first time, making the evaluation results more reflective of actual production needs and making the evaluation of coking coal quality and cost-effectiveness more reasonable. Detailed Implementation

[0010] The technical solution of the present invention will be clearly and completely described below with reference to specific embodiments. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0011] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the specific embodiments used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the specific embodiments described below are some embodiments of the present invention. For those skilled in the art, other specific embodiments can be obtained based on these specific embodiments without creative effort.

[0012] The components of the embodiments of the invention described and shown in the specific embodiments herein can be arranged and designed in a variety of different configurations. Therefore, the following detailed description of the embodiments of the invention provided in the specific embodiments is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention.

[0013] This invention discloses a scientific evaluation method for coking coal and bituminous coal. This method evaluates the cost-effectiveness of coking coal by selecting random vitrinite reflectance distribution parameters and a suitability index. Specifically, it includes the following steps: 1) Select several types of coal commonly used by coking enterprises at present and their delivered prices Q; 2) The equation is established as follows: K （<0.5） X i（<0.5） +K （0.5-0.65） X i（0.5-0.65） +K （0.65-0.8） X i（0.65-0.8） +K （0.8-0.9） X i（0.8-0.9） +K （0.9-1.2） X i（0.9-1.2） +K （1.2-1.5） X i（1.2-1.5） +K （1.5-1.7） X i（1.5-1.7） +K （1.7-1.9） X i（1.7-1.9） +K （1.9-2.5） X i（1.9-2.5） +K （>2.5） X i（>2.5） +K=Qi Formula (1) In formula (1): i = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10; K （<0.5） The price of the random vitrinite reflectance (<0.5) component in coking coal; K （0.5-0.65） The price of the random vitrinite reflectance (0.5-0.65) component in coking coal; K （0.65-0.8） The price of the random vitrinite reflectance (0.65-0.8) component in coking coal; K （0.8-0.9） The price of the random vitrinite reflectance (0.8-0.9) component in coking coal; K （0.9-1.2） The price of the random vitrinite reflectance (0.9-1.2) component in coking coal; K （1.2-1.5） The price of the random vitrinite reflectance (1.2-1.5) component in coking coal; K （1.5-1.7） The price of the random vitrinite reflectance (1.5-1.7) component in coking coal; K （1.7-1.9） The price of the random vitrinite reflectance (1.7-1.9) component in coking coal; K （1.9-2.5） The price of the random vitrinite reflectance (1.9-2.5) component in coking coal; K （>2.5） The price of the random vitrinite reflectance (>2.5) component in coking coal; X i（<0.5） The percentage of random vitrinite reflectance (<0.5) components in the i-th type of coking coal; X i（0.5-0.65） The percentage of random vitrinite reflectance (0.5-0.65) components in the i-th type of coking coal; X i（0.65-0.8） The percentage of random vitrinite reflectance (0.65-0.8) components in the i-th type of coking coal; X i（0.8-0.9） The percentage of the random vitrinite reflectance (0.8-0.9) component in the i-th type of coking coal; X i（0.9-1.2） The percentage of random vitrinite reflectance components (0.9-1.2) in the i-th type of coking coal; X i（1.2-1.5） The percentage of the random vitrinite reflectance (1.2-1.5) component in the i-th type of coking coal; X i（1.5-1.7）The percentage of the random vitrinite reflectance (1.5-1.7) component in the i-th type of coking coal; X i（1.7-1.9） The percentage of the random vitrinite reflectance (1.7-1.9) component in the i-th type of coking coal; X i（1.9-2.5） The percentage of random vitrinite reflectance (1.9-2.5) components in the i-th type of coking coal; X i（>2.5） The percentage of random vitrinite reflectance (>2.5) components in the i-th type of coking coal; Q i Let be the delivered price of the i-th type of coking coal; K is the intercept of the equation; 3) Using multivariate linear regression analysis, solve for the values ​​of the component prices K (<0.5), K (0.5-0.65), K (0.65-0.8), K (0.8-0.9), K (0.9-1.2), K (1.2-1.5), K (1.5-1.7), K (1.7-1.9), K (1.9-2.5), K (>2.5) and the intercept K; 4) Let P base = K (<0.5) Xi (<0.5) + K (0.5-0.65) Xi (0.5-0.65) + K (0.65-0.8) Xi (0.65-0.8) + K (0.8-0.9) Xi (0.8-0.9) + K (0.9-1.2) Xi (0.9- 1.2)+K(1.2-1.5)Xi(1.2-1.5)+K(1.5-1.7)Xi(1.5-1.7)+K(1.7-1.9)Xi(1.7-1.9)+K(1.9-2.5)Xi(1.9-2.5)+K(>2.5)Xi(>2.5)+K Formula (2) In formula (2): Pbase is the base price of coking coal; 5) Let P 终 =P 础 (CSR / CSR) 标 ) Formula (3) In formula (3): CSR 标 The coking coal that best matches other coking coals (generally fat coal and coking coal) in the production of this coking enterprise is the small coke oven test value that represents the most common production ratio of this coal type; CSR is the small coke oven test value that completely replaces the best-matched coking coal when the ratio of other coal types remains unchanged.

[0014] 3) Substituting the small coke oven test values ​​into formula (3) allows for a scientific evaluation of coking coal. P 终 -P 实际The larger the value, the higher the cost-effectiveness of the coal; conversely, the smaller the value, the lower the cost-effectiveness of the coal.

[0015] To verify the rationality of the coking coal quality and cost-effectiveness evaluation method described in this invention, this embodiment selects 14 types of coking coal commonly used in the coking plant of Fujian Sansteel Minguang Co., Ltd. for evaluation.

[0016] Table 1 shows the random vitrinite reflectance distribution and factory price data for each type of coal.

[0017] Table 1 A 10-variate linear regression analysis was established with the factory gate price as the dependent variable and the corresponding random vitrinite reflectance distribution as the independent variable. The function is as follows: Pbase = -68034.5 + 1056.2 × Xi (< 0.5) + 642.6 × Xi (0.5 - 0.65) + 701.9 × Xi (0.65 - 0.8) + 686.5 × Xi (0.8 - 0.9) + 696.8 × Xi (0.9 - 1.2) + 697.2 × Xi (1.2 - 1.5) + 709.5 × Xi (1.5 - 1.7) + 607.6 × Xi (1.7 - 1.9) + 1548.7 × Xi (1.9 - 2.5) - 85724.3 × Xi (> 2.5) Formula (4) Comparing formula (2) and formula (4), we obtain the following results: The prices of each component are: K (<0.5) = 1056.2, K (0.5-0.65) = 642.6, K (0.65-0.8) = 701.9, K (0.8-0.9) = 686.5, K (0.9-1.2) = 696.8, K (1.2-1.5) = 697.2, K (1.5-1.7) = 709.5, K (1.7-1.9) = 607.6, K (1.9-2.5) = 1548.7, K (>2.5) = -85724.3; K = -68034.5 In this embodiment, the primary coking coal 1 commonly used in coke oven production is selected as the standard coal. The proportions of Prie primary coking coal and Ping primary Shenma coal remain unchanged in other coal types. The small coke oven test values ​​are used to completely replace the coking coal with the best compatibility. The small coke oven coking test data are shown in Table 2.

[0018] Table 2 Table 3 shows the random vitrinite reflectance distribution and delivered price data of Puri primary coking coal and Pinghe primary Shenma coal.

[0019] Table 3 Substituting Pbase of the CSR main coke into formula (3) yields Pfinal = Pbase(CSR / CSR standard) = 1615.64; The final P value of the P value of the P value of the P value is 1615.64 - 1426 = 189.64; Substituting Pbase = 1676.97 into formula (3), we get Pfinal = Pbase(CSR / CSRstandard) = 1803.37; The final P value of the P-actual P value is calculated as follows: P = 1803.37 - 1697 = 106.97 < 189.64. This means that the higher the cost-effectiveness of Coking Coal from Coalpro, the more it should be purchased.

[0020] The present invention provides a scientific evaluation method for coking coal and bituminous coal. Compared with the existing technology, the advantage of this method is that it scientifically quantifies and compares the cost-effectiveness of the same type of coal. The higher the cost-effectiveness, the more priority should be given to purchasing it.

[0021] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for scientifically evaluating fat coal and coking coal, characterized by, By selecting random vitrinite reflectance distribution parameters + adaptability index, the cost performance of coking coal is evaluated, specifically including the following steps: 1) selecting several kinds of coal commonly used by coking enterprises at present and their plant prices Q; 2) Establish the equation as follows: K （<0.5） X i（<0.5） +K （0.5-0.65） X i（0.5-0.65） +K （0.65-0.8） X i（0.65-0.8） +K （0.8-0.9） X i（0.8-0.9） +K （0.9-1.2） X i（0.9-1.2） +K （1.2-1.5） X i（1.2-1.5） +K （1.5-1.7） X i（1.5-1.7） +K （1.7-1.9） X i（1.7-1.9） +K （1.9-2.5） X i（1.9-2.5） +K （>2.5） X i（>2.5） +K=Q i ; where: i = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10; Q i is the delivered price of the i-th coking coal; K is the intercept of the equation; 3) Using multiple linear regression analysis, solve for the values of the component prices K （<0.5） , K （0.5-0.65） , K （0.65-0.8） , K （0.8-0.9） , K （0.9-1.2） , K （1.2-1.5） , K （1.5-1.7） , K （1.7-1.9） , K （1.9-2.5） , K （>2.5） and the intercept K. 4) Let P 础 = K （<0.5） X i（<0.5） + K （0.5-0.65） X i（0.5-0.65） + K （0.65-0.8） X i（0.65-0.8） + K （0.8-0.9） X i（0.8-0.9） + K （0.9-1.2） X i（0.9-1.2） + K （1.2-1.5） X i（1.2-1.5） + K （1.5-1.7） X i（1.5-1.7） + K （1.7-1.9） X i（1.7-1.9） + K （1.9-2.5） X i（1.9-2.5） + K （>2.5） X i（>2.5） + K; where: P 础 is the base price of coking coal; 5) let P 终 = P 础 ( CSR / CSR 标 ) ; where: CSR 标 is the coking coal that the coking plant has the best adaptability to in production, characterized by the value of the small coke oven test using the most common production ratio; and CSR is the value of the small coke oven test of a coking coal that completely replaces the coking coal with the best adaptability, with the ratio of the other coals remaining the same. 6) Put the small coke oven test value into the formula in step 5), and the coking coal can be scientifically evaluated, P 终 -P 实际 The greater the difference, the higher the cost performance of the coal, and vice versa.

2. The method for scientifically evaluating fat coal and coking coal according to claim 1, characterized in that, The other coking coal refers to fat coal and coking coal.

3. The method according to claim 1, characterized in that, K （<0.5） is the price of random vitrinite reflectance (<0.5) components in coking coal; K （0.5-0.65） is the price of random vitrinite reflectance components in coking coal; K （0.65-0.8） is the price of random vitrinite reflectance (0.65-0.8) components in coking coal; K （0.8-0.9） is the price of random vitrinite reflectance (0.8-0.9) components in coking coal; K （0.9-1.2） is the price of random vitrinite reflectance (0.9-1.2) components in coking coal; K （1.2-1.5） is the price of random vitrinite reflectance (1.2-1.5) components in coking coal; K （1.5-1.7） is the price of random vitrinite reflectance (1.5-1.7) components in coking coal; K （1.7-1.9） is the price of random vitrinite reflectance (1.7-1.9) components in coking coal; K （1.9-2.5） is the price of random vitrinite reflectance (1.9-2.5) components in coking coal; K （>2.5） is the price of random vitrinite reflectance (>2.5) components in coking coal.

4. The method according to claim 1, characterized in that, X in step 2) i（<0.5） is the percentage of random vitrinite reflectance (<0.5) component in the i-th coking coal; X i（0.5-0.65） is the percentage of random vitrinite reflectance (0.5-0.65) component in the i-th coking coal; X i（0.65-0.8） is the percentage of random vitrinite reflectance (0.65-0.8) component in the i-th coking coal; X i（0.8-0.9） is the percentage of random vitrinite reflectance (0.8-0.9) component in the i-th coking coal; X i（0.9-1.2） is the percentage of random vitrinite reflectance (0.9-1.2) component in the i-th coking coal; X i（1.2-1.5） is the percentage of random vitrinite reflectance (1.2-1.5) component in the i-th coking coal; X i（1.5-1.7） is the percentage of random vitrinite reflectance (1.5-1.7) component in the i-th coking coal; X i（1.7-1.9） is the percentage of random vitrinite reflectance (1.7-1.9) component in the i-th coking coal; X i（1.9-2.5） is the percentage of random vitrinite reflectance (1.9-2.5) component in the i-th coking coal; X i（>2.5） is the percentage of random vitrinite reflectance (>2.5) component in the i-th coking coal.

Images