Coke average particle size adjusting method, device, equipment and medium
Through linear regression analysis and coupling function generation methods, coke with target particle size is selected, which solves the problem of cost increase in coke quality improvement in blast furnace smelting, and achieves the effect of reducing costs and improving resource utilization.
Patent Information
- Application Number
- CN202510025346.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-01-06
- Publication Date
- 2025-05-06
AI Technical Summary
During blast furnace smelting, the increase in coke quality leads to an increase in costs, making it difficult for enterprises to find a balance between reducing blast furnace fuel ratio and coke cost, which in turn affects the minimization of molten fuel costs.
By obtaining historical coke quality data, performing linear regression analysis, generating the first and second functions, and coupling to generate the third function to select the coke of the target particle size, realizing the quantitative relationship between the coke cost and the actual coke ratio and the average particle size of the coke.
By analyzing the impact of the average particle size of coke on the cost of ton of iron, selecting the target particle size, reducing the cost during blast furnace smelting, and improving resource utilization.
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Figure CN119940054A_ABST
Abstract
Description
Technical Field
[0001] The invention relates to the technical field of blast furnace smelting, and in particular to a method, device, equipment and medium for adjusting the average particle size of coke. Background Art
[0002] The quality of coke has a great impact on the technical indicators of blast furnaces and the cost of molten iron. In order to achieve advanced fuel ratio indicators, good coke quality is often required as support, but the improvement of coke quality will also cause a significant increase in coke costs.
[0003] When organizing production, enterprises often unilaterally focus on lower blast furnace fuel ratio or lower coke cost. In this case, it is difficult to achieve the lowest molten iron fuel cost. Molten iron fuel cost includes coke cost per ton of iron and coal injection cost per ton of iron. Since the price of coal injection is much lower than that of coking coal, the coke cost per ton of iron is also much higher than the coal injection cost per ton of iron. Therefore, when controlling the fuel cost per ton of iron, the main focus should be on controlling the coke cost per ton of iron.
[0004] Based on the above reasons, it is more reasonable to set the lowest coke cost per ton of iron when formulating coke quality requirements, and to comprehensively consider the impact of coke indicators on blast furnace coke ratio and the impact of coke indicators on coke cost. Summary of the invention
[0005] In view of this, the present invention provides a method, device, equipment and medium for adjusting the average particle size of coke to improve the resource utilization rate in the furnace smelting process.
[0006] In a first aspect, the present invention provides a method for adjusting the average particle size of coke, the method comprising:
[0007] Obtain historical coke quality data; historical coke quality data includes coke cost and average particle size of coke;
[0008] Performing linear regression on the coke cost and the average particle size of the coke to obtain a first function; the first function is used to indicate the linear relationship between the coke cost and the average particle size of the coke;
[0009] Performing linear regression on the average particle size of coke and the actual coke ratio of coke used in the blast furnace to obtain a second function; the second function is used to indicate the linear relationship between the actual coke ratio and the average particle size of coke;
[0010] The first function and the second function are coupled to generate a third function to select coke of target particle size.
[0011] In a possible implementation, the historical coke quality data also includes coke crushing strength and coke post-reaction strength.
[0012] In a possible implementation, obtaining historical coke quality data includes:
[0013] Obtaining raw coke quality data;
[0014] According to the range requirements of the average particle size of coke, the range requirements of the crushing strength of coke and the range requirements of the strength of coke after reaction, the original coke quality data is screened to obtain the historical coke quality data.
[0015] In a possible implementation, the first function is y=a*MS+b, wherein a and b are constants, MS is the average particle size of coke, and y is the cost of coke.
[0016] In a possible implementation, the second function is z=c*MS+d, wherein c and d are constants, MS is the average particle size of coke, and z is actual coke ratio data.
[0017] In a possible implementation, the third function is t=ac*MS 2 +(bc+ad)*MS+bd; where a, b, c, d are constants, MS is the average particle size of coke, and t is the cost of coke per ton of iron.
[0018] In a possible implementation, the target particle size of coke is selected to minimize the coke cost per ton of iron, including:
[0019] When -(bc+ad) / (2ac)<(MS min +MS max ) / 2, the quality data of coke used in blast furnace meets M40 min ≤M40≤M40 max 、CSR min ≤CSR≤CSR max 、MS=MS max ;
[0020] When -(bc+ad) / (2ac)≥(MS min +MS max ) / 2, the quality data of coke used in blast furnace meets M40 min ≤M40≤M40 max 、CSR min ≤CSR≤CSR max 、MS=MS min ;
[0021] Among them, M40 is the coke crushing strength; M40 min The lower limit of the range requirement for coke crushing strength; M40 max The upper limit of the range of coke crushing strength; CSR is the strength after coke reaction; CSR min The lower limit of the range of strength requirements for coke after reaction; CSR maxis the upper limit of the range of strength requirements for coke after reaction; MS is the average particle size of coke; MS max The upper limit of the range requirement for the average particle size of coke; MS min An upper limit is required for the range of the average particle size of the coke.
[0022] In a second aspect, a device for adjusting the average particle size of coke is provided, the device comprising:
[0023] A data acquisition module is used to acquire historical coke quality data; the historical coke quality data includes coke cost and average particle size of coke;
[0024] A first regression module is used to perform a linear regression on the coke cost and the average particle size of the coke to obtain a first function; the first function is used to indicate a linear relationship between the coke cost and the average particle size of the coke;
[0025] A second regression module is used to perform linear regression on the average particle size of coke and the actual coke ratio of coke used in the blast furnace to obtain a second function; the second function is used to indicate the linear relationship between the actual coke ratio and the average particle size of coke;
[0026] The coupling module is used to couple the first function and the second function to generate a third function to select coke of target particle size.
[0027] In a possible implementation, the historical coke quality data also includes coke crushing strength and coke post-reaction strength.
[0028] In a possible implementation, the data acquisition module is used to:
[0029] Obtaining raw coke quality data;
[0030] According to the range requirements of the average particle size of coke, the range requirements of the crushing strength of coke and the range requirements of the strength of coke after reaction, the original coke quality data is screened to obtain the historical coke quality data.
[0031] In a possible implementation, the first function is y=a*MS+b, wherein a and b are constants, MS is the average particle size of coke, and y is the cost of coke.
[0032] In a possible implementation, the second function is z=c*MS+d, wherein c and d are constants, MS is the average particle size of coke, and z is actual coke ratio data.
[0033] In a possible implementation, the third function is t=ac*MS 2 +(bc+ad)*MS+bd; where a, b, c, d are constants, MS is the average particle size of coke, and t is the cost of coke per ton of iron.
[0034] In a possible implementation, the coupling module is used to:
[0035] When -(bc+ad) / (2ac)<(MS min +MS max ) / 2, the quality data of coke used in blast furnace meets M40 min ≤M40≤M40 max 、CSR min ≤CSR≤CSR max 、MS=MS max ;
[0036] When -(bc+ad) / (2ac)≥(MS min +MS max ) / 2, the quality data of coke used in blast furnace meets M40 min ≤M40≤M40 max 、CSR min ≤CSR≤CSR max 、MS=MS min ;
[0037] Among them, M40 is the coke crushing strength; M40 min The lower limit of the range requirement for coke crushing strength; M40 max The upper limit of the range of coke crushing strength; CSR is the strength after coke reaction; CSR min The lower limit of the range of strength requirements for coke after reaction; CSR max is the upper limit of the range of strength requirements for coke after reaction; MS is the average particle size of coke; MS max The upper limit of the range requirement for the average particle size of coke; MS min An upper limit is required for the range of the average particle size of the coke.
[0038] In a third aspect, a computer device is provided, comprising: a memory and a processor, the memory and the processor are communicatively connected to each other, computer instructions are stored in the memory, and the processor executes the above-mentioned method for adjusting the average particle size of coke by executing the computer instructions.
[0039] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the above-mentioned method for adjusting the average particle size of coke.
[0040] In a fifth aspect, a computer program product is provided, comprising computer instructions for causing a computer to execute the above-mentioned method for adjusting the average particle size of coke.
[0041] The technical solution provided by this application may have the following beneficial effects:
[0042] Before blast furnace smelting, the computer equipment can first obtain historical coke quality data, including coke cost and average particle size of coke, and then perform linear regression on the coke cost and the average particle size of coke to obtain a first function; the computer then performs linear regression on the average particle size of coke and the actual coke ratio of coke used in the blast furnace to obtain a second function, and then the computer equipment couples the first function with the second function to generate a third function, which can quantify the relationship between the coke cost, the actual coke ratio and the average particle size of coke. After obtaining the third function relationship, the influence of the average particle size of coke on the cost of coke per ton of iron can be analyzed based on the characteristics of the function curve, thereby selecting the target particle size to minimize the cost in the blast furnace smelting process and improve the resource utilization rate in the blast furnace smelting process. BRIEF DESCRIPTION OF THE DRAWINGS
[0043] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.
[0044] Figure 1 A structural schematic diagram of a coke average particle size adjustment system according to an embodiment of the present application is shown.
[0045] Figure 2 The present invention is a method flow chart of a method for adjusting the average particle size of coke according to an exemplary embodiment.
[0046] Figure 3 The present invention is a method flow chart of a method for adjusting the average particle size of coke according to an exemplary embodiment.
[0047] Figure 4 It is a structural schematic diagram of a coke average particle size adjustment device provided in an embodiment of the present application.
[0048] Figure 5 It is a structural schematic diagram of a computer device provided by an optional embodiment of the present invention. DETAILED DESCRIPTION
[0049] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.
[0050] In the description of the embodiments of the present application, the term "corresponding" may indicate a direct or indirect correspondence between two items, or an association relationship between the two items, or a relationship between indication and being indicated, configuration and being configured, and the like.
[0051] The quality of coke has a great impact on the technical indicators of blast furnaces and the cost of molten iron. In order to achieve advanced fuel ratio indicators, good coke quality is often required as support, but the improvement of coke quality will also cause a significant increase in coke costs.
[0052] When organizing production, enterprises often unilaterally focus on lower blast furnace fuel ratio or lower coke cost. In this case, it is difficult to achieve the lowest molten iron fuel cost. Molten iron fuel cost includes coke cost per ton of iron and coal injection cost per ton of iron. Since the price of coal injection is much lower than that of coking coal, the coke cost per ton of iron is also much higher than the coal injection cost per ton of iron. Therefore, when controlling the fuel cost per ton of iron, the main focus should be on controlling the coke cost per ton of iron.
[0053] Please refer to Figure 1 , which shows a structural schematic diagram of a coke average particle size adjustment system involved in an embodiment of the present application. The system includes a computer device in a blast furnace smelting scene.
[0054] In the present application, the computer device stores historical data in the blast furnace smelting scenario. Optionally, the historical data can be manually recorded and stored in the computer device. Or the historical data can be directly acquired by the computer device through sensors, automatic procurement systems, etc. For example, there are corresponding sensors in the blast furnace, which can automatically acquire the particle size of coke in the historical smelting process; and the real-time price of coke can be collected in the automatic procurement system, so that the cost data of coke can be recorded in the computer device.
[0055] When the computer device stores historical data of the blast furnace smelting scenario, the relationship between the cost or resource consumption in the blast furnace smelting process and the various parameters in the smelting process can be established based on the historical data, so as to adaptively adjust parameters such as the average particle size of coke to improve the resource utilization efficiency of blast furnace smelting and reduce the cost of blast furnace smelting.
[0056] Figure 2 is a method flow chart of a method for adjusting the average particle size of coke according to an exemplary embodiment. The method is executed by a computer device. Figure 2 As shown, the method for adjusting the average particle size of coke may include the following steps:
[0057] Step 201, obtaining historical coke quality data; the historical coke quality data includes coke cost and average particle size of coke.
[0058] The acquisition of historical data is the basis of the whole process, including coke cost and average particle size of coke, that is, the average value of particle size distribution. In the embodiment of the present application, historical data may be internal production records of the enterprise or market procurement information, which is used to reflect the cost and performance differences of coke of different particle sizes.
[0059] Step 202: Perform a linear regression on the coke cost and the average particle size of the coke to obtain a first function; the first function is used to indicate the linear relationship between the coke cost and the average particle size of the coke.
[0060] Linear regression is used to analyze the relationship between coke cost (assumed to be variable C in this embodiment) and average coke particle size (assumed to be variable D in this embodiment), and a function is constructed: C=aD+b, where a and b are regression coefficients of linear regression.
[0061] The first function is to quantify the effect of coke particle size on coke cost. Generally speaking, a larger average particle size may require more complex processing, resulting in higher costs, while a smaller particle size may reduce costs due to a higher crushing rate.
[0062] By establishing the first function model, we can intuitively understand the impact of particle size changes on coke costs and guide cost estimation when purchasing or producing coke.
[0063] Step 203, linearly regressing the average particle size of the coke and the actual coke ratio of the coke used in the blast furnace to obtain a second function; the second function is used to indicate the linear relationship between the actual coke ratio and the average particle size of the coke.
[0064] The relationship between the average particle size of coke (assumed to be variable D in this embodiment) and the actual coke ratio of the blast furnace (assumed to be variable R in this embodiment) is analyzed by linear regression to construct a function:
[0065] R=cD+d
[0066] Among them, c and d are regression coefficients.
[0067] The second function is used to reflect the effect of coke particle size on the coke ratio of the blast furnace. Generally, a larger particle size can improve the permeability of the blast furnace, thereby reducing the coke ratio, but an excessively large particle size may lead to a decrease in reaction efficiency and increase the coke ratio instead.
[0068] Step 204: The first function and the second function are coupled to generate a third function to select coke of a target particle size.
[0069] The first function and the second function are coupled to generate an objective function (third function) to balance the coke cost and blast furnace operating efficiency. For example, the objective function may be: total cost = C (D) + kR (D);
[0070] Where k is the weight coefficient, which is used to balance the impact of coke cost and coke ratio on total cost.
[0071] By analyzing the objective function, we can find the granularity range with the lowest total cost, which is the target granularity that should be selected.
[0072] In summary, before blast furnace smelting, the computer equipment can first obtain the historical coke quality data, including the coke cost and the average particle size of the coke, and then perform linear regression on the coke cost and the average particle size of the coke to obtain a first function; the computer then performs linear regression on the average particle size of the coke and the actual coke ratio of the coke used in the blast furnace to obtain a second function. At this time, the computer equipment then couples the first function with the second function to generate a third function. The third function can quantify the relationship between the coke cost, the actual coke ratio and the average particle size of the coke. After obtaining the third function relationship, the influence of the average particle size of the coke on the cost of coke per ton of iron can be analyzed based on the characteristics of the function curve, thereby selecting the target particle size to minimize the cost in the blast furnace smelting process and improve the resource utilization rate in the blast furnace smelting process.
[0073] Figure 3 is a method flow chart of a method for adjusting the average particle size of coke according to an exemplary embodiment. The method is executed by a computer device. Figure 3 As shown, the method for adjusting the average particle size of coke may include the following steps:
[0074] Step 301, obtaining original coke quality data;
[0075] Step 302, based on the range requirements of the average particle size of coke, the range requirements of the crushing strength of coke and the range requirements of the strength after reaction of coke, the original coke quality data is screened to obtain the historical coke quality data.
[0076] Step 303: Perform a linear regression on the coke cost and the average particle size of the coke to obtain a first function; the first function is used to indicate the linear relationship between the coke cost and the average particle size of the coke.
[0077] The first function is y=a*MS+b, wherein a and b are constants, MS is the average particle size of coke, and y is the cost of coke.
[0078] Step 304 , linearly regress the average particle size of the coke and the actual coke ratio of the coke used in the blast furnace to obtain a second function; the second function is used to indicate the linear relationship between the actual coke ratio and the average particle size of the coke.
[0079] The second function is z=c*MS+d, wherein c and d are constants, MS is the average particle size of coke, and z is the actual coke ratio data.
[0080] Step 305: The first function and the second function are coupled to generate a third function to select coke of a target particle size.
[0081] The third function is t=ac*MS 2 +(bc+ad)*MS+bd; where a, b, c, d are constants, MS is the average particle size of coke, and t is the cost of coke per ton of iron.
[0082] Optionally, when -(bc+ad) / (2ac)<(MS min +MS max ) / 2, the quality data of coke used in blast furnace meets M40 min ≤M40≤M40 max 、CSR min ≤CSR≤CSR max 、MS=MS max ;
[0083] When -(bc+ad) / (2ac)≥(MS min +MS max ) / 2, the quality data of coke used in blast furnace meets M40 min ≤M40≤M40 max 、CSR min ≤CSR≤CSR max 、MS=MS min ;
[0084] Among them, M40 is the coke crushing strength; M40 min The lower limit of the range requirement for coke crushing strength; M40 max The upper limit of the range of coke crushing strength; CSR is the strength after coke reaction; CSR min The lower limit of the range of strength requirements for coke after reaction; CSR max is the upper limit of the range of strength requirements for coke after reaction; MS is the average particle size of coke; MS max The upper limit of the range requirement for the average particle size of coke; MS min An upper limit is required for the range of the average particle size of the coke.
[0085] (1) Based on the blast furnace production data of a steel company, the upper and lower limit requirements of the blast furnace for coke M40, CSR, and average particle size (MS) are obtained: M40 min =86%, M40 max =88%, CSR min =65%, CSR max =67%,MS min =46mm, MS max =52mm;
[0086] (2) Collect the coke quality during a period of stable coking coal prices1, screen the historical data of coke quality according to the upper and lower limits of coke M40, CSR, and MS, and classify them by MS, and add the coke cost item to the data. The data are shown in Table 1;
[0087] Table 1 Coke quality and cost data
[0088] MS / mm M40 / ％ CSR / % Coke cost / yuan / t 46 87.5 67 2588 47 86 65.5 2601 48 86.5 65 2608 49 87.5 67 2622 50 87.5 66.5 2631 51 87 67 2638 52 88 66 2652
[0089] (3) With MS as the independent variable and coke cost as the dependent variable (y), a linear regression analysis was performed on the selected coke quality historical data, and the relationship was obtained: y = 10.3*MS + 2114.3;
[0090] (4) The actual coke ratio data (z) corresponding to the above-mentioned screened coke used in the blast furnace were collected, as shown in Table 2. The data were linearly regressed with MS as the independent variable and coke ratio as the dependent variable to obtain the relationship: z = -2.2*MS + 467.1;
[0091] Table 2 Coke ratio data
[0092] MS / mm M40 / ％ CSR / % Coke ratio / kg / t 46 87.5 67 366.5 47 86 65.5 363.5 48 86.5 65 361.5 49 87.5 67 359 50 87.5 66.5 357 51 87 67 356 52 88 66 352.5
[0093] (5) Coke cost per ton of iron = coke cost * blast furnace coke ratio = y * z = -22.66 * MS 2 +159.7*MS+987589.5, the curve opens downward, and the axis of symmetry is: MS=3.5mm;
[0094] (6)(MS min +MS max ) / 2=49mm, curve symmetry axis MS=3.5mm<(MS min +MS max ) / 2, during the period of stable coking coal prices, the cost of coke per ton of iron is lowest when the blast furnace uses coke with 86%≤M40≤88%, 65%≤CSR≤67%, MS=52mm.
[0095] Example 2
[0096] (1) Based on the blast furnace production data of a steel company, the upper and lower limit requirements of the blast furnace for coke M40, CSR, and average particle size (MS) are obtained: M40 min =86%, M40 max =88%, CSR min =65%, CSR max =67%,MS min =46mm, MS max =52mm;
[0097] (2) Collect the coke quality during a period of stable coking coal prices2, screen the historical data of coke quality according to the upper and lower limits of coke M40, CSR, and MS, and classify them by MS, and add the coke cost item to the data. The data are shown in Table 3;
[0098] Table 3 Coke quality and cost data
[0099]
[0100]
[0101] (3) A linear regression analysis was performed on the selected coke quality historical data with MS as the independent variable and coke cost as the dependent variable (y), and the relationship was obtained: y = 16*MS + 1850.6;
[0102] (4) The actual coke ratio data (z) corresponding to the above-mentioned screened coke used in the blast furnace were collected, as shown in Table 4. The data were linearly regressed with MS as the independent variable and coke ratio as the dependent variable to obtain the relationship: z = -1.9*MS + 450;
[0103] Table 4 Coke ratio data
[0104] MS / mm M40 / ％ CSR / % Coke ratio / kg / t 46 86 66.5 363 47 87 65 361 48 86.5 66 359.5 49 88 65.5 357 50 87.5 67 355.5 51 86 66 353 52 87.5 67 352
[0105] (5) Coke cost per ton of iron = coke cost * blast furnace coke ratio = y * z = -30.4 * MS 2 +3683.86*MS+832770, the curve opens downward, and the axis of symmetry is: MS=60.6mm;
[0106] (6)(MS min +MS max ) / 2=49mm, curve symmetry axis MS=60.6mm>(MS min +MS max ) / 2, during the period of stable coking coal prices, the cost of coke per ton of iron is lowest when the blast furnace uses coke with 86%≤M40≤88%, 65%≤CSR≤67%, and MS=46mm.
[0107] In summary, before blast furnace smelting, the computer equipment can first obtain the historical coke quality data, including the coke cost and the average particle size of the coke, and then perform linear regression on the coke cost and the average particle size of the coke to obtain a first function; the computer then performs linear regression on the average particle size of the coke and the actual coke ratio of the coke used in the blast furnace to obtain a second function. At this time, the computer equipment then couples the first function with the second function to generate a third function. The third function can quantify the relationship between the coke cost, the actual coke ratio and the average particle size of the coke. After obtaining the third function relationship, the influence of the average particle size of the coke on the cost of coke per ton of iron can be analyzed based on the characteristics of the function curve, thereby selecting the target particle size to minimize the cost in the blast furnace smelting process and improve the resource utilization rate in the blast furnace smelting process.
[0108] In the embodiments of the present application, a coke average particle size adjustment device is also provided, which is used to implement the above embodiments and preferred implementations, and will not be repeated here. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function.
[0109] The above-described apparatus is preferably implemented in software, but implementation in hardware, or a combination of software and hardware is also possible and contemplated.
[0110] The present application provides a device for adjusting the average particle size of coke. Figure 4 : is a structural schematic diagram of a coke average particle size adjustment device provided in an embodiment of the present application, the device comprising:
[0111] The data acquisition module 401 is used to acquire historical coke quality data; the historical coke quality data includes coke cost and average particle size of coke;
[0112] A first regression module 402 is used to perform a linear regression on the coke cost and the average particle size of the coke to obtain a first function; the first function is used to indicate the linear relationship between the coke cost and the average particle size of the coke;
[0113] The second regression module 403 is used to perform a linear regression on the average particle size of the coke and the actual coke ratio of the coke used in the blast furnace to obtain a second function; the second function is used to indicate the linear relationship between the actual coke ratio and the average particle size of the coke;
[0114] The coupling module 404 is used to couple the first function and the second function to generate a third function to select coke of a target particle size.
[0115] In a possible implementation, the historical coke quality data also includes coke crushing strength and coke post-reaction strength.
[0116] In a possible implementation, the data acquisition module is used to:
[0117] Obtaining raw coke quality data;
[0118] According to the range requirements of the average particle size of coke, the range requirements of the crushing strength of coke and the range requirements of the strength of coke after reaction, the original coke quality data is screened to obtain the historical coke quality data.
[0119] In a possible implementation, the first function is y=a*MS+b, wherein a and b are constants, MS is the average particle size of coke, and y is the cost of coke.
[0120] In a possible implementation, the second function is z=c*MS+d, wherein c and d are constants, MS is the average particle size of coke, and z is actual coke ratio data.
[0121] In a possible implementation, the third function is t=ac*MS 2 +(bc+ad)*MS+bd; where a, b, c, d are constants, MS is the average particle size of coke, and t is the cost of coke per ton of iron.
[0122] In a possible implementation, the coupling module is used to:
[0123] When -(bc+ad) / (2ac)<(MS min +MS max ) / 2, the quality data of coke used in blast furnace meets M40 min ≤M40≤M40 max 、CSR min ≤CSR≤CSR max 、MS=MS max ;
[0124] When -(bc+ad) / (2ac)≥(MS min +MS max ) / 2, the quality data of coke used in blast furnace meets M40 min ≤M40≤M40 max 、CSR min ≤CSR≤CSR max 、MS=MS min ;
[0125] Among them, M40 is the coke crushing strength; M40 min The lower limit of the range requirement for coke crushing strength; M40 max The upper limit of the range of coke crushing strength; CSR is the strength after coke reaction; CSR min The lower limit of the range of strength requirements for coke after reaction; CSR maxis the upper limit of the range of strength requirements for coke after reaction; MS is the average particle size of coke; MS max The upper limit of the range requirement for the average particle size of coke; MS min An upper limit is required for the range of the average particle size of the coke.
[0126] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.
[0127] In summary, before blast furnace smelting, the computer equipment can first obtain the historical coke quality data, including the coke cost and the average particle size of the coke, and then perform linear regression on the coke cost and the average particle size of the coke to obtain a first function; the computer then performs linear regression on the average particle size of the coke and the actual coke ratio of the coke used in the blast furnace to obtain a second function. At this time, the computer equipment then couples the first function with the second function to generate a third function. The third function can quantify the relationship between the coke cost, the actual coke ratio and the average particle size of the coke. After obtaining the third function relationship, the influence of the average particle size of the coke on the cost of coke per ton of iron can be analyzed based on the characteristics of the function curve, thereby selecting the target particle size to minimize the cost in the blast furnace smelting process and improve the resource utilization rate in the blast furnace smelting process.
[0128] The coke average particle size adjustment device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.
[0129] An embodiment of the present invention further provides a computer device, which can be implemented as the computer device in the above-mentioned roller press.
[0130] See also Figure 5 , Figure 5 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Figure 5As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process instructions executed in the computer device, including instructions stored in or on the memory to display graphic information in a graphical user interface on an external input / output device (such as a display device coupled to an interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 5 A processor 10 is taken as an example.
[0131] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.
[0132] The memory 20 stores instructions executable by at least one processor 10, so that at least one processor 10 executes the method shown in the above embodiment.
[0133] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.
[0134] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.
[0135] The computer device further comprises a communication interface 30 for the computer device to communicate with other devices or a communication network.
[0136] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.
[0137] A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of the computer program instruction in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc., and accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.
[0138] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.
Claims
1. A method for adjusting the average particle size of coke, characterized in that: The method comprises: Acquiring historical coke quality data; the historical coke quality data includes coke cost and average particle size of coke; Performing a linear regression on the coke cost and the average particle size of the coke to obtain a first function; the first function is used to indicate a linear relationship between the coke cost and the average particle size of the coke; Performing linear regression on the average particle size of coke and the actual coke ratio of coke used in a blast furnace to obtain a second function; the second function is used to indicate the linear relationship between the actual coke ratio and the average particle size of the coke; The first function and the second function are coupled to generate a third function to select coke of target particle size.
2. The method according to claim 1, characterized in that The historical coke quality data also includes coke crushing strength and coke post-reaction strength.
3. The method according to claim 2, characterized in that The obtaining of historical coke quality data includes: Obtaining raw coke quality data; According to the range requirements of the average particle size of coke, the range requirements of the crushing strength of coke and the range requirements of the strength of coke after reaction, the original coke quality data is screened to obtain the historical coke quality data.
4. The method according to any one of claims 1 to 3, characterized in that: The first function is y=a*MS+b, wherein a and b are constants, MS is the average particle size of coke, and y is the cost of coke.
5. The method according to claim 4, characterized in that The second function is z=c*MS+d, wherein c and d are constants, MS is the average particle size of coke, and z is the actual coke ratio data.
6. The method according to claim 5, characterized in that The third function is t=ac*MS 2 +(bc+ad)*MS+bd; where a, b, c, d are constants, MS is the average particle size of coke, and t is the cost of coke per ton of iron.
7. The method according to claim 6, characterized in that The coke of the target particle size is selected, comprising: When -(bc+ad) / (2ac)<(MS min +MS max ) / 2, the quality data of coke used in blast furnace meets M40 min ≤M40≤M40 max 、CSR min ≤CSR≤CSR max 、MS=MS max ; When -(bc+ad) / (2ac)≥(MS min +MS max ) / 2, the quality data of coke used in blast furnace meets M40 min ≤M40≤M40 max 、CSR min ≤CSR≤CSR max 、MS=MS min ; Among them, M40 is the coke crushing strength; M40 min The lower limit of the range requirement for coke crushing strength; M40 max The upper limit of the range of coke crushing strength; CSR is the strength after coke reaction; CSR min The lower limit of the range of strength requirements for coke after reaction; CSR max is the upper limit of the range of strength requirements for coke after reaction; MS is the average particle size of coke; MS max The upper limit of the range requirement for the average particle size of coke; MS min An upper limit is required for the range of the average particle size of the coke.
8. A device for adjusting the average particle size of coke, characterized in that: The device comprises: A data acquisition module, used to acquire historical coke quality data; the historical coke quality data includes coke cost and average particle size of coke; A first regression module, used for performing a linear regression on the coke cost and the average particle size of the coke to obtain a first function; the first function is used for indicating a linear relationship between the coke cost and the average particle size of the coke; A second regression module is used to perform a linear regression on the average particle size of the coke and the actual coke ratio of the coke used in the blast furnace to obtain a second function; the second function is used to indicate the linear relationship between the actual coke ratio and the average particle size of the coke; A coupling module is used to couple the first function and the second function to generate a third function to select coke of a target particle size.
9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method for adjusting the average particle size of coke according to any one of claims 1 to 7 by executing the computer instructions.
10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method for adjusting the average particle size of coke according to any one of claims 1 to 7.