Technical economic evaluation method for evaluating coke quality and cost performance, electronic equipment and readable storage medium

Abstract

The invention provides a technical and economic evaluation method for evaluating coke quality and cost performance, electronic equipment and a readable storage medium. The technical and economic evaluation method comprises the following steps: acquiring a plurality of index data representing coke quality; determining the influence weight of the multiple pieces of index data on the fuel ratio through grey correlation analysis; calculating a coke quality score according to the influence weight and the multiple pieces of index data; and determining the cost performance index of the coke according to the coke quality score and the coke price.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of blast furnace ironmaking production, and particularly relates to a technical and economic evaluation method for evaluating coke quality and cost performance, an electronic device and a readable storage medium. BACKGROUND

[0002] Coke is a high-strength, porous material made by heating coal to 1100°C in an inert atmosphere. It can be regarded as a non-homogeneous composite material composed of organic carbon, inorganic minerals and pores. Coke is the main fuel for blast furnace smelting, and plays the roles of heat source, reducing agent, carburizing agent and column skeleton in the blast furnace. In order to ensure the quality of steel and production safety, the supporting role of coke as a column skeleton becomes the main reason that cannot be replaced. Because coke is the only raw material that remains solid in the softening zone, it is the channel for the flow of reducing gas and liquid slag iron, and is an important guarantee for the smooth progress of gas-solid reactions in the blast furnace. With the development of large coal injection technology, the decline of iron ore quality, the gradual depletion of high-quality coking coal resources, and the increasingly severe environmental situation, the contradiction between the higher requirements for coke quality and the gradual deterioration of coke quality has become increasingly prominent. It is crucial to build an evaluation system for evaluating the quality and cost performance of coke. The influence of coke quality on blast furnace smelting and the entire steel production process is as follows:

[0003] (1) Influence on blast furnace smelting effect: Coke, as an important reducing agent and fuel in the blast furnace smelting process, its quality directly affects the smelting effect and product quality of the blast furnace. The reduction performance, permeability and operating conditions of coke will be affected by the quality of coke.

[0004] (2) Promotion of environmental protection policy: Under the promotion of the concept of green and low-carbon development, steel enterprises need to strengthen the management of energy emissions. The evaluation of coke quality not only relates to the smelting efficiency, but also involves environmental emission problems, so improving the quality of coke helps to reduce environmental pollution.

[0005] (3) Improve resource utilization: The evaluation of coke quality can promote the rational use of coking coal, optimize the coal blending structure and coking process, improve the quality of coke, and improve the utilization rate of resources.

[0006] (4) Market competitiveness: With the implementation of the policy of eliminating backward production capacity and improving industry threshold by the state, the concentration of the coke industry is gradually increasing, and enterprises with advanced technology and safety and environmental protection will gain greater market competitiveness. The evaluation of coke quality helps enterprises to optimize product structure, improve coke quality and meet market demand.

[0007] (5) Economic benefits: the quality of coke directly affects the cost of ironmaking. High-quality coke can improve the production efficiency of blast furnaces, reduce coke ratio, and reduce energy consumption, thereby improving the economic benefits of enterprises. Other researchers have also proposed similar methods for evaluating coke quality. Method one is 1) selecting a benchmark coke, the ash content of the benchmark coke is Ad*, the sulfur content is St,d*, the crushing strength is M40*, the abrasion resistance is M10*, the coke reactivity is CRI*, the coke strength after reaction is CSR*, and the benchmark coke is scored as P1 in combination with the actual production level of the ironmaking plant; 2) selecting a coke to be determined, the ash content of the coke to be determined is Ad measured, the sulfur content is St,d measured, the crushing strength is M40 measured, the abrasion resistance is M10 measured, the coke reactivity is CRI measured, and the coke strength after reaction is CSR measured; 3) establishing a comprehensive evaluation index P measured to evaluate the quality of the coke to be determined, the comprehensive evaluation index P measured satisfies the following mathematical relationship: P measured = P1 + (Ad measured-Ad*) x 104 x KAd + (St,d measured-St,d*) x 104 x KSt,d + (M40 measured-M40*) x 104 x KM40 + (M10 measured-M10*) x 104 x KM10 + (CRI measured-CRI*) x 104 x KCRI + (CSR measured-CSR*) x 104 x KCSR. Method two is 1) selecting coke proximate analysis indicators, cold strength indicators, and hot strength indicators as influence factors representing coke quality, wherein the proximate analysis indicators include ash content and sulfur content, the cold strength indicators B2 include M40 and M10, and the hot strength indicators include CRI and CSR; 2) setting the weight values of the above indicators, wherein the weight-(ash content) = 1 / 3, the weight-(sulfur content) = 2 / 3, the weight-(proximate analysis indicators) = 0.106; the weight-(M40) = 1 / 4, the weight-(M10) = 3 / 4, the weight-(cold strength indicators) = 0.260; the weight-(CRI) = 1 / 3, the weight-(CSR) = 2 / 3, and the weight-(hot strength indicators) = 0.634; 3) calculating the coke quality index CQI: CQI = (ash content score x weight-(ash content) + sulfur content score x weight-(sulfur content)) x weight-(proximate analysis indicators

[0008] )+(M40 score x weight-(M40) + M10 score x weight-(M10)) x weight-(cold strength indicators) + (CRI score x weight-(CRI) + CSR score x weight-(CSR)) x weight-(hot strength indicators), to calculate the coke quality index.

[0009] The third method is to divide the coke sample into two groups, detect the reactivity and strength after reaction of the first group of coke samples, based on the reactivity and strength after reaction of the first group of coke samples meeting the requirements, detect the ash content distribution and optical texture content distribution of the second group of coke samples, based on the ash content distribution of the second group of coke samples, calculate the ash catalytic index of the second group of coke samples, and based on the ash catalytic index and the optical texture content distribution, determine whether the quality of the coke sample is qualified.

[0010] The fourth method is a coke quality evaluation method based on the blast furnace permeability index. First, the weight loss data of the coke sample is determined by using a TG-DSC synchronous analyzer to determine the volume reaction rate constant k-v. Second, the coke sample is weighed, and the degradation strength index of the coke before and after the dissolution reaction is measured and calculated. Third, according to the relationship between the degradation strength index and the porosity, the porosity of the coke before and after the dissolution reaction is determined. Finally, the permeability index K of the coke before and after the dissolution reaction is determined, and the evaluation index and method of coke selection and use are determined based on the permeability index K.

[0011] The common shortcomings of the four methods are that the selected indicators affecting the quality of the coke are less, the evaluation results are not accurate, and the quality of the coke cannot be fully represented. In addition, in the mathematical expressions (comprehensive evaluation index P, coke quality index CQI) used in methods one and two, there are some parameters, such as Kad, Kad in the comprehensive evaluation index P, and weight in the coke quality index CQI. The values of these parameters are artificially set values obtained from production practice experience, only a rough range, and no accurate value, and the values of different blast furnaces are different, and the values of the same blast furnace in different smelting periods are also different. Therefore, the results calculated by using the formula are controversial. SUMMARY

[0012] The present application provides a technical and economic evaluation method for evaluating the quality and cost performance of coke, an electronic device and a readable storage medium, wherein the technical and economic evaluation method for evaluating the quality and cost performance of coke mainly comprises:

[0013] obtaining a plurality of index data representing the quality of coke; determining the influence weight of the plurality of index data on the fuel ratio by gray correlation analysis; calculating the coke quality score according to the influence weight and the plurality of index data; and determining the cost performance index of the coke according to the coke quality score and the coke price.

[0014] Further, the multiple index data characterizing the coke quality are obtained, including: obtaining data of total moisture, crushing strength, abrasion resistance, reactivity, post-reaction strength, sulfur content, ash content, volatile content, high-temperature strength, isomelting loss rate, and post-isomelting loss strength of the coke; and performing dimensionless processing with direction on the multiple index data according to a preset interval range to obtain processed index data, wherein an index positively correlated with the fuel ratio is processed by a first formula, and an index negatively correlated with the fuel ratio is processed by a second formula.

[0015] Further, the influence weight of the multiple index data on the fuel ratio is determined by gray correlation analysis, including: obtaining fuel ratio data as a reference sequence and the multiple index data as a comparison sequence; performing dimensionless processing on the reference sequence and the comparison sequence to obtain dimensionless sequence data; calculating an absolute difference value between the dimensionless sequence data to determine a maximum difference value and a minimum difference value; calculating a gray correlation coefficient according to the absolute difference value, the maximum difference value, and the minimum difference value; calculating a gray correlation degree according to the gray correlation coefficient to determine the influence weight of the multiple index data on the fuel ratio.

[0016] Further, the coke quality score is calculated according to the influence weight and the multiple index data, including: obtaining the index data processed by the dimensionless processing; multiplying the index data processed by the dimensionless processing by the corresponding influence weight to obtain a weighted score of each index; summing the weighted scores of the indexes to obtain the coke quality score; and ranking the coals according to the coke quality score.

[0017] Further, the cost performance index of the coke is determined according to the coke quality score and a coke price, including: obtaining an actual price of the coke; calculating a coke cost performance index by dividing the coke quality score by the actual price; selecting a reference coke to obtain a price and a quality score of the reference coke; calculating a predicted price of other coals in combination with the quality scores of the other coals according to a quotient of the price and the quality score of the reference coke; and determining a price difference by a difference between the predicted price and an actual price.

[0018] Further, the dimensionless processing with direction on the multiple index data according to the preset interval range includes: determining an interval range of the multiple index data; processing, for an index positively correlated with the fuel ratio, by a formula X'=(X-Xmin) / (Xmax-Xmin), where X is an original index data, Xmin is a minimum value of the index, Xmax is a maximum value of the index, and X' is processed data; and processing, for an index negatively correlated with the fuel ratio, by a formula X'=(Xmax-X) / (Xmax-Xmin) to obtain processed index data.

[0019] Further, the non-dimensionalization of the reference sequence and the comparison sequence includes: using an initialization method, processing the reference sequence and the comparison sequence through the formula X'(t)=X(t) / X(1), wherein X(t) is the data of the sequence at time t, X(1) is the first data of the sequence, and X'(t) is the processed data; if a standardization method is used, processing through the formula X'(t)=(X(t)-Xmean) / Xstd, wherein Xmean is the mean of the sequence, and Xstd is the standard deviation of the sequence, to obtain the non-dimensional sequence data.

[0020] Further, the ranking of the coke according to the coke quality score includes: obtaining quality scores of a plurality of coke samples; sorting the plurality of coke samples from high to low according to the quality scores to obtain a coke ranking; determining a priority selection order of the coke according to the coke ranking and a corresponding cost performance index; and generating a recommended scheme of coke procurement through the priority selection order.

[0021] Further, the present application also provides an electronic device, and the electronic device comprises:

[0022] a processor;

[0023] a memory, wherein the memory has computer readable instructions stored thereon, and the computer readable instructions are loaded and executed by the processor to implement the technical and economic evaluation method for evaluating coke quality and cost performance.

[0024] Further, the present application also provides a computer readable storage medium, wherein the computer readable storage medium has program codes stored therein, and the program codes can be called and executed by a processor to implement the technical and economic evaluation method for evaluating coke quality and cost performance.

[0025] The technical scheme provided by the embodiments of the present application can have the following beneficial effects:

[0026] The application discloses a coke quality evaluation and cost performance analysis method, which is characterized in that a plurality of index data representing coke quality are acquired, the influence weight of each index on fuel ratio is determined by grey correlation analysis, and then coke quality score is calculated and combined with price to determine the cost performance index. The method first performs dimensionless processing on the original index data, and then performs grey correlation analysis with fuel ratio as the reference sequence to calculate the influence weight of each index. On this basis, the dimensionless index data are multiplied by the corresponding weight and summed to obtain the coke quality score. Finally, the cost performance index is calculated in combination with the actual price, and the price difference is determined by comparison with the benchmark coke, thereby providing a scientific basis for coke procurement. The application realizes comprehensive evaluation of coke quality and objective analysis of cost performance, which helps to optimize coke procurement decision and improve blast furnace smelting efficiency and economic benefits. BRIEF DESCRIPTION OF DRAWINGS

[0027] Figure 1 A work flow chart of a technical and economic evaluation system for evaluating coke quality and cost performance according to the application;

[0028] Figure 2 A step chart of a technical and economic evaluation method for evaluating coke quality and cost performance according to the application. DETAILED DESCRIPTION

[0029] In order to enable personnel in the technical field to better understand the technical solutions in the specification, the technical solutions in the specification embodiments will be clearly and completely described below in combination with the drawings in the specification embodiments. Obviously, the described embodiments are only some of the embodiments of the specification, not all. Based on the embodiments in the specification, all other embodiments obtained by those of ordinary skill in the art without creative labor should be within the protection scope of the specification.

[0030] As shown in Figure 2 the technical and economic evaluation method for evaluating coke quality and cost performance, the electronic device and the readable storage medium, the technical and economic evaluation method for evaluating coke quality and cost performance can specifically include:

[0031] S101, acquiring a plurality of index data representing coke quality; determining the influence weight of the plurality of index data on fuel ratio by grey correlation analysis.

[0032] S102, calculating coke quality score according to the influence weight and the plurality of index data; determining the cost performance index of coke according to the coke quality score and coke price.

[0033] S103, the acquisition of coke quality characterization of a plurality of index data, comprising: coke of full water, crushing strength, wear resistance, reactivity, strength after reaction, sulfur content, ash content, volatile matter, high temperature strength, isomelting loss rate, isomelting loss strength data; according to the preset interval range, the plurality of index data is processed with directionless dimensionless, and the processed index data is obtained, wherein the index positively correlated with fuel ratio is processed by the first formula, and the index negatively correlated with fuel ratio is processed by the second formula.

[0034] S104, the influence weight of the plurality of index data on fuel ratio is determined by gray correlation analysis, comprising: obtaining fuel ratio data as a reference sequence, and the plurality of index data as a comparison sequence; the reference sequence and comparison sequence are processed dimensionless, and the dimensionless sequence data is obtained; the absolute difference between the dimensionless sequence data is calculated, and the maximum difference and the minimum difference are determined; according to the absolute difference, the maximum difference and the minimum difference, the gray correlation coefficient is calculated; according to the gray correlation coefficient, the gray correlation degree is calculated, and the influence weight of the plurality of index data on fuel ratio is determined.

[0035] S105, the coke quality score is calculated according to the influence weight and the plurality of index data, comprising: obtaining the index data processed dimensionless; the dimensionless processed index data is multiplied by the corresponding influence weight, and the weighted score of each index is obtained; the weighted score of each index is summed up, and the coke quality score is obtained; according to the coke quality score, the coke is ranked.

[0036] S106, the coke performance price ratio index is determined according to the coke quality score and coke price, comprising: obtaining the actual price of coke; the coke performance price ratio index is calculated by dividing the coke quality score by the actual price; the price and quality score of the reference coke are obtained; according to the quotient of the price and quality score of the reference coke, the quality score of other coke is combined to calculate the predicted price of other coke; the price difference is determined by the difference between the predicted price and the actual price.

[0037] S107, the plurality of index data is processed with directionless dimensionless according to the preset interval range, comprising: determining the interval range of the plurality of index data; for the index positively correlated with fuel ratio, the formula X'=(X-Xmin) / (Xmax-Xmin) is used for processing, wherein X is the original index data, Xmin is the minimum value of the index, Xmax is the maximum value of the index, and X' is the processed data; for the index negatively correlated with fuel ratio, the formula X'=(Xmax-X) / (Xmax-Xmin) is used for processing, and the processed index data is obtained.

[0038] S108. The dimensionless processing of the reference sequence and the comparison sequence includes: using an initialization method, processing the reference sequence and the comparison sequence using the formula X'(t)=X(t) / X(1), where X(t) is the data of the sequence at time t, X(1) is the first data of the sequence, and X'(t) is the processed data; if a standardization method is used, processing is performed using the formula X'(t)=(X(t)-Xmean) / Xstd, where Xmean is the mean of the sequence and Xstd is the standard deviation of the sequence, to obtain dimensionless sequence data.

[0039] S109. Ranking coke based on the coke quality score includes: obtaining quality scores of multiple coke samples; sorting the multiple coke samples from high to low according to the quality scores to obtain a coke ranking; determining the priority selection order of coke based on the coke ranking and the corresponding cost-effectiveness index; and generating a recommended coke procurement plan based on the priority selection order.

[0040] The present invention also provides an electronic device, the electronic device comprising:

[0041] processor;

[0042] A memory storing computer-readable instructions, which, when loaded and executed by the processor, implement any of the technical and economic evaluation methods for evaluating the quality and cost-effectiveness of coke.

[0043] The present invention also provides a computer-readable storage medium storing program code, which can be invoked by a processor to execute any of the technical and economic evaluation methods for evaluating the quality and cost-effectiveness of coke.

[0044] Example 1:

[0045] like Figure 2 As shown, the technical and economic evaluation system for evaluating coke quality and cost-effectiveness described in this invention operates as follows:

[0046] (1) Selection of quality indicators

[0047] To more accurately grasp coke quality, thereby guiding blast furnace production and providing a basis for a steel company's coke procurement, this invention establishes a coke quality evaluation model. Twelve key indicators characterizing coke quality are selected: total moisture content (Mt), crush resistance (M...). 40 ), abrasion resistance (M) 10 ), Reactivity (CRI), Post-reaction strength (CSR), Sulfur (S), Ash (A), Volatile matter (V), High-temperature strength (M5) 高温 M10 高温 ) and the constant rate of reaction (CRR 25 ) and the constant strength after reaction (CSR 25 ).

[0048] The basis for selecting these indicators will be introduced as follows:

[0049] 1) Total moisture: The increase of moisture in coke will absorb the physical heat in the blast furnace. For each 1% increase of moisture, the coke consumption in the blast furnace will increase by 1.1%-1.3%.

[0050] 2) Ash content: Ash is the impurities and inert materials in coke. High ash content will have adverse effects on coke and blast furnace. When coke is in the high temperature environment of the coke oven, ash is produced by combustion. The increase of ash will destroy the internal structure of coke and increase the cracks in coke, which will not only reduce the strength of coke, but also increase the surface area of coke. Due to the increase of cracks, CO2 is more likely to diffuse from the cracks to the inside of the coke, which will aggravate the deterioration of thermal performance. In addition, the main components of ash are SiO2, Al2O3 and other acidic oxides. Since their melting point is high, they can only be used with CaO and other fluxes to coexist to form low-melting-point compounds to be discharged from the blast furnace in the form of slag. For each 1% increase of ash, the coke ratio in the blast furnace will increase by 1%-2%. Ash is related to the adsorption of alkali metals.

[0051] 3) Volatile matter: Volatile matter is an important indicator to measure the maturity of coke. Generally, the volatile matter of metallurgical coke should be less than 1.8%. If the volatile matter content is high, it means that the coke is not mature. At the same time, volatile matter is also one of the indicators for pollution control in the coking plant. If the volatile matter of coke increases, the amount of smoke released during coke pushing will increase significantly. Therefore, it is generally required to ensure that the volatile matter of coke does not exceed 2%.

[0052] 4) Sulfur content: Sulfur is a harmful component in coke. Only 5%-20% of the sulfur brought into the blast furnace by the charge escapes with the blast furnace gas, and the rest participates in the sulfur cycle in the furnace and can only be discharged by the slag. High sulfur content in coke will increase the sulfur content of pig iron, reduce the quality of pig iron, increase the slag basicity and make the blast furnace operation indicators decline. For each 0.1% increase of sulfur in coke, the coke ratio will increase by 1%-2%.

[0053] 5) High temperature strength: The high temperature strength of coke represents the ability of coke to resist breaking and wear in the high temperature area. After coke enters the lower part of the softening zone (≥ 1500℃), SiO2 and Al2O3 contained in the ash will have a large amount of direct reduction reaction with carbon, which will further deteriorate the coke and reduce its strength. The presence of alkali metals will exacerbate the deterioration reaction and have a great impact on the smooth operation of the blast furnace. Therefore, the high temperature strength of coke is proposed as an indicator to evaluate the quality of coke.

[0054] 6)Mechanical strength: The mechanical strength (crushing strength and abrasion strength) of coke is an important index to measure whether coke can play a role in supporting the skeleton and ensure the normal operation of blast furnace. The drum test can measure the crushing strength and abrasion resistance of coke, although it cannot completely reflect the actual situation of coke at high temperature in the blast furnace, but it can reflect the situation of coke before entering the blast furnace and the mechanical damage in the blast furnace, so it is widely used. Related research results show that, for every 1% increase in the strength index of coke, the coke ratio of blast furnace production can be reduced by 0.7% to 1.5%, the pig iron output can be increased by 0.5% to 1.5%, and the impact is very significant. Therefore, the higher the strength of coke, the better, and the requirements for metallurgical coke are: the crushing strength M 40 should be large, and the abrasion resistance M 10 should be small.

[0055] 7) Thermal strength: The thermal strength (reactivity and post-reaction strength) of coke is the ability of coke to resist chemical erosion and protect the skeleton of the charge in the blast furnace, and is the main index for comprehensive measurement and evaluation of the thermal stability of coke, which is more important than the mechanical strength. The reactivity of coke refers to the chemical reaction ability of coke to the gases it contacts during use, among which the reaction of coke with CO2 is the most important reaction in the blast furnace, so this index generally refers to the ability of coke to react with CO2 at high temperature (1100°C). The post-reaction strength of coke refers to the "remaining" strength measured by a special I-type small drum after the coke is reacted with CO2 at the above temperature for a certain time. After the reaction of coke with CO2 during use, the pores will inevitably change greatly, and thus the strength will decrease significantly. Considering the important role of coke as a column skeleton in the blast furnace, the main basis for determining the quality of coke is the post-reaction strength CSR. In order to ensure the stable and smooth operation of the blast furnace, the blast furnace requires coke with moderate reactivity and high post-reaction strength.

[0056] 8) Iso-melt loss rate and iso-melt post-strength: In the production process, some high CRI (38.6%) and low CSR (38.8%) coke can also be used in the blast furnace (such as the blast furnace smelting of Bayi Steel). Many scholars have found that the dissolution loss of coke in the blast furnace is mainly determined by the oxygen provided by iron oxides (direct reduction). The dissolution reaction behavior of coke is the result of competition between chemical reaction and pore diffusion, and the optimal value range of CRI and CSR of coke is actually determined by the characteristics and operating conditions of the blast furnace. Most people believe that the traditional CRI and CSR test conditions of the blast furnace cannot accurately simulate the degradation behavior of coke in the blast furnace, and the strength of coke after a fixed dissolution rate can better reflect the ability of coke to resist dissolution erosion. Statistics show that the dissolution loss of coke in the blast furnace is about 20% to 30%. Therefore, it is proposed to use the dissolution rate CRR 25 and the post-dissolution strength CSR 25To evaluate the hot performance of coke, it can be used as a supplement to CRI and CSR.

[0057] (2) Quality evaluation model establishment

[0058] Because the quality of coke will affect the coke ratio of blast furnace, the basic idea of this model is to calculate the correlation degree between 12 coke indexes affecting coke quality and coke ratio, explore the weight of different coke indexes on coke ratio, then multiply the weight with coke index and add them up, finally get a score of coke based on different indexes, so as to establish the evaluation model of coke. The establishment process of the evaluation model will be described in detail below.

[0059] 1) Data screening and cleaning

[0060] There are many factors affecting the coke ratio of blast furnace, in addition to the quality of coke, the wind temperature, wind pressure, oxygen enrichment, coal injection, furnace grade, sinter stock intensity and other factors will also affect the coke ratio of blast furnace. In order to exclude the interference of other factors on the coke ratio of blast furnace, the data obtained should be cleaned. First of all, the coke ratio data obtained when the blast furnace production is abnormal due to the operation of workers should be excluded, only the coke ratio data obtained when the blast furnace is in stable and normal production should be kept. Secondly, during the normal production of blast furnace, due to the change of coal injection and the amount of coke breeze, the same type and amount of coke breeze are put into the furnace, but the coke ratio is different. Such data will seriously reduce the accuracy of calculating the correlation between coke index and coke ratio. In order to exclude the interference of this situation on the calculation of the model, the data of coke ratio, coke breeze and coal ratio should be added, that is, to explore the weight of different coke indexes on fuel ratio. Finally, in order to exclude the influence of raw materials on the fluctuation of coke ratio, the two parameters of furnace grade and sinter stock intensity about the quality of raw materials are added in the process of calculating the weight.

[0061] 2) GRA analysis

[0062] The correlation degree between 14 coke indexes and fuel ratio is calculated, and then the influence weight of each index on fuel ratio is determined. The method used is GRA analysis.

[0063] Grey Relational Analysis (GRA) is a method for studying the development of a system, especially in cases with incomplete information or uncertainty. It compares the curve shapes of the reference sequence and the comparison sequence to determine the degree of correlation between them. The basic idea of GRA is to determine the geometric shape similarity between the reference data sequence and several comparison data sequences to determine whether they are closely related, thereby reflecting the degree of correlation between the curves. This method can be used to analyze the degree of influence of various factors on the results, and is also suitable for solving comprehensive evaluation problems over time. The advantage of GRA is that it does not require a large amount of sample data, the calculation process is relatively simple, and it can handle incomplete information problems.

[0064] The steps of GRA analysis generally include: determining the analysis object and the reference sequence, data non-dimensionalization processing, calculating the sequence difference, calculating the maximum and minimum difference, calculating the grey correlation coefficient, calculating the grey correlation degree, and calculating the weight.

[0065] The specific steps are as follows:

[0066] ① Determine the analysis object and the reference sequence

[0067] The analysis object is usually a system of multiple influencing factors. The reference sequence is the target variable or ideal sequence in the system. In this example, the reference sequence is the fuel ratio. For example, assume there are n influencing factors, corresponding to X1, X2,..., Xn, and the fuel ratio is Y. n

[0068] ② Data non-dimensionalization processing

[0069] The data units and orders of magnitude of different factors may be different, so non-dimensionalization processing is needed to make them on the same comparison scale. There are two common non-dimensionalization methods:

[0070] The first is initial valueization, which is to perform ratio processing on all data points with the first value of the sequence, formula (1).

[0071]

[0072] The second is standardization, which normalizes the data to be within a certain interval (such as [0, 1] or [-1, 1]). This processing method ensures that data of different dimensions can be compared on the same standard, formula (2).

[0073]

[0074] ③ Calculate the sequence difference

[0075] Calculate the absolute difference between each factor sequence and the reference sequence (formula 3): ​

[0076] Δ i (t) = |Y'(t) - X'(t)| i (t) (3)

[0077] where Δ i (t) is the difference between the ith factor and the reference sequence at time t.

[0078] IV. Calculate the maximum and minimum difference

[0079] Find the maximum and minimum values among all the differences (Equation 4):

[0080]

[0081] V. Calculate the gray relational coefficient

[0082] According to the difference values, calculate the gray relational coefficient at each time (Equation 5):

[0083]

[0084] where p is the resolution coefficient, generally taking values between 0 and 1, and the commonly used value is 0.5. The role of the resolution coefficient is to adjust the influence of the maximum difference on the correlation coefficient.

[0085] VI. Calculate the gray relational grade

[0086] Take the average of the gray relational coefficients of each factor to obtain the gray relational grade (GRG) (Equation 6)

[0087]

[0088] Here γ i represents the overall correlation degree of the ith factor with the reference sequence.

[0089] According to the gray relational grade of each factor, sort them. The greater the correlation degree, the greater the influence of the factor on the reference sequence (fuel ratio).

[0090] VII. Calculate the weight

[0091] Based on the gray relational grade of each factor with the reference sequence, calculate the influence weight of each factor with the reference sequence by dividing its gray relational grade by the sum of all correlation degrees.

[0092] 3) Set the interval range

[0093] According to the coke data provided by a certain company, the interval range of each index of coke is obtained after summarizing.

[0094] 4) Dimensionless processing with direction

[0095] According to the interval range of each index of coke, 11 quality indexes of coke are processed, wherein, 7 indexes (A d 、V daf 、M 10 、CRI, S t.d 、M5 高温 、CRR 25 ) positively correlated with coke ratio use formula (7), 4 indexes (CSR 25 、M 10 高温 、CSR, M 40 ) negatively correlated with coke ratio use formula (8).

[0096]

[0097] 5) Calculate the comprehensive evaluation score and ranking of coke

[0098] Finally, the index score of the processed coke is multiplied by the weight and then added, and finally the quality score of the coke is obtained, and the score result and the coke ranking are obtained.

[0099] 6) Calculate the cost performance of coke

[0100] The cost performance of coke directly affects the decision of enterprise purchase, and by formulating the coke cost performance calculation method and coke price estimation and price difference calculation, guidance can be provided for a company to purchase coke.

[0101] ①Coke cost performance index: the coke score is divided by the actual price of coke to obtain the coke cost performance index, that is, the coke score value purchased per unit price, and the cost performance of various coals can be compared intuitively.

[0102] ②Coke price prediction: a kind of coke is selected as a reference, and the product of the quotient of the price of the coke and the score and the scores of other coals is obtained to obtain the predicted price of each coke.

[0103] ③Price difference: the difference between the estimated price of coke and the actual purchase price is obtained to obtain the price difference, which can also be used to predict the cost performance of coke, and the coke price difference and the coke cost performance index are consistent.

[0104] As Figure 2 shown, the specific process of the present application is as follows:

[0105] (1) Selection of quality index

[0106] In order to more accurately grasp the quality of coke and then guide the blast furnace production, and provide a basis for a steel company to purchase coke, the present application establishes an evaluation model of coke quality. 12 main indexes representing coke quality are selected: total water (Mt) of coke, crushing strength (M40 ), abrasion resistance (M 10 ), reactivity (CRI), post-reaction strength (CSR), sulfur content (S), ash content (A), volatile matter (V), high-temperature strength (M5 高温 , M 10 高温 ), equal melting loss rate (CRR 25 ), equal melting loss post-strength (CSR 25 ).

[0107] The basis for selecting these indicators will be introduced respectively as follows:

[0108] 1) Total moisture: The increase of moisture in coke will absorb the physical heat in the blast furnace. For each 1% increase of moisture, the coke consumption in the blast furnace will increase by 1.1%-1.3%.

[0109] 2) Ash content: Ash content is the impurities and inert substances in coke. High ash content will have adverse effects on coke and blast furnace. When coke is in the high-temperature environment of the coke oven, combustion produces ash. The increase of ash will destroy the internal structure of coke and increase the cracks in coke, which not only reduces the strength of coke, but also increases the surface area of coke. Due to the increase of cracks, CO2 is more easily diffused from the cracks to the inside of the coke, which exacerbates the deterioration of thermal performance. In addition, the main components of ash are SiO2, Al2O3 and other acidic oxides. Since their melting point is high, CaO and other fluxes can be used to co-produce low-melting-point compounds with them to be discharged from the blast furnace in the form of slag. For each 1% increase of ash, the coke ratio in the blast furnace increases by 1%-2%. Ash content is related to the adsorption of alkali metals.

[0110] 3) Volatile matter: Volatile matter is an important indicator to measure the maturity of coke. Generally, the volatile matter of metallurgical coke is required to be lower than 1.8%. If the volatile matter content is high, it indicates that the coke is not mature. At the same time, volatile matter is also one of the indicators for pollution control in the coking plant. If the volatile matter of coke increases, the amount of smoke released during coke pushing will increase significantly. Therefore, it is generally required to ensure that the volatile matter of coke does not exceed 2%.

[0111] 4) Sulfur content: Sulfur content is a harmful component in coke. Only 5%-20% of the sulfur brought into the blast furnace by the charge escapes with the blast furnace gas, and the rest participates in the sulfur cycle in the furnace and can only be discharged by the slag. High sulfur content in coke will increase the sulfur content of pig iron, reduce the quality of pig iron, increase the slag basicity and make the blast furnace operation indicators decline. For each 0.1% increase of sulfur in coke, the coke ratio increases by 1%-2%.

[0112] 5) High temperature strength: The high temperature strength of coke represents the ability of coke to resist breaking and attrition in the high temperature zone. After coke enters the high temperature zone below the softening zone (≥ 1500°C), the SiO2 and Al2O3 contained in the ash of the coke will have a large direct reduction reaction with carbon, which will further deteriorate the coke and reduce the strength of the coke, and the presence of alkali metal will exacerbate the deterioration reaction and have a great impact on the smooth operation of the blast furnace. Therefore, the high temperature strength of coke is proposed as an index to evaluate the quality of coke.

[0113] 6) Mechanical strength: The mechanical strength (crushing strength and attrition resistance) of coke is an important index for measuring whether coke can play a supporting role and ensure the normal operation of the blast furnace. The drum test can measure both the crushing strength and attrition resistance of coke, although it cannot completely reflect the actual situation of coke in the high temperature state in the blast furnace, but it can reflect the situation of coke during transportation before entering the blast furnace and the mechanical damage in the blast furnace, and therefore it is widely used. Related research results show that for every 1% increase in the strength index of coke, the coke ratio of blast furnace production can be reduced by 0.7% to 1.5%, the pig iron production can be increased by 0.5% to 1.5%, and the impact is very significant. Therefore, the higher the strength of coke, the better, and the requirements for metallurgical coke are: the crushing strength M 40 should be large, and the attrition resistance M 10 should be small.

[0114] 7) Thermal strength: The thermal strength (reactivity and post-reaction strength) of coke is the ability of coke to resist chemical attack and protect the skeleton of the charge in the blast furnace, and is a main index for comprehensively measuring and evaluating the thermal stability of coke, which is more important than the mechanical strength. The reactivity of coke refers to the chemical reaction ability of coke to the gases it contacts during use, and the reaction of coke with CO2 is the most important reaction in the blast furnace, so this index generally refers to the ability of coke to react with CO2 at high temperature (1100°C). The post-reaction strength of coke refers to the "remaining" strength measured by a special I-type small drum after the coke is reacted with CO2 at the above temperature for a certain time. After the reaction of coke with CO2 during use, the pores will inevitably change greatly, and therefore the strength will decrease significantly. Considering the important role of coke as a column skeleton in the blast furnace, the main basis for determining the quality of coke is the post-reaction strength CSR. In order to ensure the stable and smooth operation of the blast furnace, the blast furnace requires coke to have moderate reactivity and high post-reaction strength.

[0115] 8) Equal melting loss rate and equal melting loss strength: Some high CRI (38.6%) and low CSR (38.8%) coke can also be used in blast furnace (such as the blast furnace smelting of Bayi Steel) during production. Many scholars found that the amount of coke dissolved in the blast furnace is mainly determined by the amount of oxygen provided by iron oxide (direct reduction). The coke dissolution reaction behavior is the result of the competition between chemical reaction and pore diffusion, and the optimal range of CRI and CSR of coke is actually determined by the characteristics and operating conditions of the blast furnace. Mr. Nomura Seiji, Mr. Naito Seisho, Mr. Wang Qi, etc. believe that the traditional CRI and CSR test conditions of the blast furnace cannot accurately simulate the degradation behavior of the coke in the blast furnace, and the strength of the coke after a fixed amount of dissolution can better reflect the ability of the coke to resist dissolution erosion. Statistics show that the amount of coke dissolved in the blast furnace is about 20% to 30%. Therefore, it is proposed to use the coke dissolution rate CRR 25 and the strength of the coke after dissolution CSR 25 to evaluate the thermal performance of the coke, which can be used as a supplement to CRI and CSR.

[0116] (2) Establishment of quality evaluation model

[0117] Since the quality of coke will affect the coke ratio of the blast furnace, the basic idea of this model is to calculate the correlation between the 12 coke indexes that affect the quality of coke and the coke ratio, explore the weight of different coke indexes on the coke ratio, then multiply the weight with the coke index and add it up, finally get the score of the coke based on different indexes, and establish the evaluation model of the coke. The establishment process of the evaluation model will be described in detail below.

[0118] 1) Data screening and cleaning

[0119] There are many factors that affect the coke ratio of the blast furnace, in addition to the quality of coke, wind temperature, wind pressure, oxygen enrichment, coal injection, feed grade, sinter stock strength and other factors will also affect the coke ratio of the blast furnace. In order to exclude the interference of other factors on the coke ratio of the blast furnace, the data obtained need to be cleaned. First of all, the coke ratio data obtained during abnormal production due to worker operation should be excluded, only the coke ratio data obtained during stable and normal production of the blast furnace should be kept. Secondly, during normal production of the blast furnace, due to the change of coal injection and the amount of coke breeze, the same type and amount of coke breeze are fed into the furnace, but the coke ratio is different. Such data will seriously reduce the accuracy of calculating the correlation between coke indexes and coke ratio. In order to exclude the interference of such conditions on the model calculation, the data of coke ratio, coke breeze and coal ratio should be added, that is, to explore the weight of different coke indexes on the fuel ratio. Finally, in order to exclude the influence of raw materials on the fluctuation of coke ratio, two parameters about the quality of raw materials, namely the feed grade and the sinter stock strength, are added in the process of calculating the weight.

[0120] The fuel ratio and the quality index of the coke and raw materials used on the day that can be used to calculate the weight after screening and cleaning are shown in Table 1.

[0121] Table 1 Fuel ratio and coke, raw material quality index data table

[0122]

[0123]

[0124]

[0125] 2) GRA analysis

[0126] The correlation between the 14 coke indexes and the fuel ratio was calculated to determine the influence weight of each index on the fuel ratio, and the method used was GRA analysis.

[0127] Grey Relational Analysis (GRA) is a method for studying the development law of a system, especially suitable for cases with incomplete information or uncertainty. It compares the curve shapes of the reference sequence and the comparison sequence to determine their correlation. The basic idea of GRA is to determine the geometric shape similarity between the reference data sequence and several comparison data sequences to determine whether they are closely related, thereby reflecting the correlation between the curves. This method can be used to analyze the influence of various factors on the results, and is also suitable for solving comprehensive evaluation problems over time. The advantage of GRA is that it does not require a large amount of sample data, the calculation process is relatively simple, and it can handle incomplete information problems.

[0128] The steps of GRA analysis generally include: determining the analysis object and the reference sequence, data non-dimensionalization processing, calculating the sequence difference, calculating the maximum and minimum difference, calculating the grey correlation coefficient, calculating the grey correlation degree, and calculating the weight. The specific steps are as follows:

[0129] Determine the analysis object and the reference sequence

[0130] The analysis object is usually a system of multiple influencing factors. The reference sequence is the target variable or ideal sequence in the system. In this example, the reference sequence is the fuel ratio. For example, assume there are n influencing factors, corresponding to X1, X2,..., Xn. n , the fuel ratio is Y.

[0131] Data non-dimensionalization processing

[0132] The data units and orders of magnitude of different factors may be different, so non-dimensionalization processing is needed to make them on the same comparison scale. There are two common non-dimensionalization methods:

[0133] The first one is initial value, which means all data points are divided by the first value of the sequence. The formula is (1).

[0134]

[0135] The second one is normalization, which means data is normalized to a certain interval (e.g. [0, 1] or [-1, 1]). This method ensures that data with different dimensions can be compared under the same standard. The formula is (2).

[0136]

[0137] Calculate sequence difference

[0138] Calculate the absolute difference between each factor sequence and the reference sequence (formula 3):

[0139] Δ i (t) = |Y'(t) - X'(t)| i (t) | (3)

[0140] Where Δ i (t) is the difference between the ith factor at time t and the reference sequence.

[0141] Calculate the maximum and minimum difference

[0142] Find the maximum and minimum values among all differences (formula 4):

[0143]

[0144] Calculate the gray correlation coefficient

[0145] According to the difference value, calculate the gray correlation coefficient at each time (formula 5):

[0146]

[0147] Where ρ is the resolution coefficient, generally taking values between 0 and 1, and the commonly used value is 0.5. The role of the resolution coefficient is to adjust the influence of the maximum difference on the correlation coefficient.

[0148] Calculate the gray correlation degree

[0149] Take the average of the gray correlation coefficient of each factor to get the gray correlation degree (Gray Relational Grade, GRG) (formula 6)

[0150]

[0151] Here γ i represents the overall correlation degree of the ith factor and the reference sequence.

[0152] According to the grey correlation degree of each factor, the greater the correlation degree, the greater the influence of the factor on the reference sequence (fuel ratio).

[0153] Calculate the weight

[0154] Based on the grey correlation degree of each factor and the reference sequence, the influence weight of each factor and the reference sequence is calculated by dividing the grey correlation degree by the sum of all correlation degrees.

[0155] According to the grey correlation analysis, the correlation degree and weight proportion of the 14 indicators and the fuel ratio are shown in the following table 2:

[0156] Table 2 Correlation degree and weight proportion of coke indicators and fuel ratio

[0157]

[0158] From the table, it is found that the indicators that have greater influence on fuel ratio are the grade of entering the furnace and the strength after reaction, and the indicator that has greater influence on fuel ratio is high temperature strength.

[0159] In view of the large fluctuation range of total water and the small influence on coke ratio of blast furnace, after removing total water, grade of entering the furnace and strength of sintering ore, normalization processing is carried out, and finally the influence weight of 11 indicators affecting coke quality on fuel ratio is obtained, and the results are shown in table 3.

[0160] Table 3 Influence weight of coke indicators on fuel ratio (%)

[0161]

[0162]

[0163] 3) Set interval range

[0164] According to the coke data given by a company, the interval range of each indicator of coke is obtained after summarizing, and the results are shown in table 4.

[0165] Table 4 Interval range of each indicator of coke

[0166]

[0167] 4) Dimensionless processing with direction

[0168] According to the interval range of each indicator of coke, 11 quality indicators of 15 kinds of coke are processed, in which 7 indicators (A d , V daf , M 10 , CRI, S t.d , M5 高温 , CRR 25) using the formula (7), 4 indicators (CSR 25 , M 10 高温 , CSR, M 40 ) using the formula (8).

[0169]

[0170] The results of the treatment of 15 kinds of coke based on formula (7), (8) are shown in Table 5 below.

[0171] Table 5 Treatment results

[0172]

[0173]

[0174] 5) Calculate the comprehensive evaluation score and ranking of coke Finally, the index score of the coke after treatment is multiplied by the weight and then added, and finally the quality score of the coke is obtained, and the score results and coke ranking are shown in Table 6 below. The higher the score of the coke, the better the quality of the coke, and under the condition that other conditions remain unchanged, the smaller the coke ratio required for blast furnace smelting.

[0175] Table 6 Coke score and ranking

[0176]

[0177]

[0178] 6) Calculate the cost performance of coke

[0179] The cost performance of coke directly affects the decision of the enterprise to purchase, and by formulating the coke cost performance calculation method and coke price prediction and price difference calculation, guidance can be provided for a company to purchase coke.

[0180] ①Coke cost performance index: divide the coke score by the actual price of the coke to calculate the coke cost performance index, that is, the coke score value purchased per unit price, which can intuitively compare the cost performance of various coals.

[0181] ②Coke price prediction: select a kind of coke as a benchmark, and through the quotient of the price of the coke and the score obtained multiplied by the scores of other coals, the predicted prices of each coke are obtained.

[0182] ③Price difference: the difference between the predicted price of the coke obtained by calculation and the actual purchase price is obtained, which can also predict the cost performance of the coke, and the coke price difference and the coke cost performance index are consistent.

[0183] Because the score of YC-W is the lowest, the coke is selected as the reference, and then the price prediction and the price difference of other cokes are obtained, as shown in Table 7.

[0184] Table 7 evaluation results of different cokes

[0185]

[0186]

[0187] The technical and economic evaluation system can evaluate the quality and cost performance of the coke, and can quickly and simply judge the quality and economy of various cokes, so that the steel plant can purchase and use the coke with better economy, and the operation personnel can reasonably match the cokes according to the quality of the cokes, which has an important role in avoiding the poor phenomena of the poor blast furnace permeability, the fluctuation of the furnace condition, the increase of the coke ratio and the like caused by the coke quality, thereby improving the smelting effect and the product quality of the blast furnace, reducing the production cost, and improving the benefit and the product competitiveness of the steel plant. The above embodiment is only one of the preferred embodiments of the present application, and should not be used to limit the protection scope of the present application, but any modification or polishing without substantial meaning made on the basis of the main design idea and spirit of the present application, and the technical problems solved by the modification or polishing are still consistent with the present application, which should be included in the protection scope of the present application.

Claims

1. A techno-economic evaluation method for assessing the quality and cost-effectiveness of coke, characterized in that, include: Obtain data on multiple indicators characterizing coke quality; The influence weights of the multiple indicator data on the fuel ratio were determined by grey relational analysis. Calculate the coke quality score based on the influence weights and the multiple indicator data; The cost-effectiveness index of coke is determined based on the coke quality score and coke price.

2. The techno-economic evaluation method for evaluating coke quality and cost-effectiveness according to claim 1, characterized in that, The acquisition of multiple index data characterizing coke quality includes: Obtain data on total water content, crush strength, abrasion resistance, reactivity, post-reaction strength, sulfur content, ash content, volatile matter, high-temperature strength, constant melting rate, and constant melting strength of coke. Based on a preset range, the multiple index data are subjected to directional dimensionless processing to obtain the processed index data. Among them, the indexes positively correlated with the fuel ratio are processed using the first formula, and the indexes negatively correlated with the fuel ratio are processed using the second formula.

3. The techno-economic evaluation method for evaluating coke quality and cost-effectiveness according to claim 1, characterized in that, The determination of the influence weights of the multiple indicator data on the fuel ratio through grey relational analysis includes: The fuel ratio data is used as a reference sequence, and the multiple index data are used as a comparison sequence. The reference sequence and comparison sequence are subjected to dimensionless processing to obtain dimensionless sequence data; Calculate the absolute difference between the dimensionless sequence data, and determine the maximum and minimum differences; Calculate the grey relational coefficient based on the absolute difference, maximum difference, and minimum difference; Based on the gray relational coefficient, the gray relational degree is calculated to determine the influence weight of the multiple indicator data on the fuel ratio.

4. The techno-economic evaluation method for evaluating coke quality and cost-effectiveness according to claim 1, characterized in that, The calculation of the coke quality score based on the influence weights and the multiple indicator data includes: Obtain dimensionless indicator data; The dimensionless index data is multiplied by the corresponding influence weight to obtain the weighted score of each index. The weighted scores of each indicator are summed to obtain the coke quality score; The cokes are ranked according to their quality scores.

5. The techno-economic evaluation method for evaluating coke quality and cost-effectiveness according to claim 1, characterized in that, The determination of the cost-effectiveness index of coke based on the coke quality score and coke price includes: Obtain the actual price of coke; The coke cost-effectiveness index is calculated by dividing the coke quality score by the actual price. Select a benchmark coke and obtain its price and quality score; Based on the quotient of the price and quality score of the benchmark coke, and combined with the quality scores of other cokes, the predicted prices of other cokes are calculated. The price difference is determined by the difference between the predicted price and the actual price.

6. The techno-economic evaluation method for evaluating coke quality and cost-effectiveness according to claim 2, characterized in that, The step of performing directional dimensionless processing on the multiple indicator data according to a preset interval range includes: Determine the range of the multiple indicator data; For indicators that are positively correlated with fuel ratio, the formula X'=(X-Xmin) / (Xmax-Xmin) is used for processing, where X is the original indicator data, Xmin is the minimum value of the indicator, Xmax is the maximum value of the indicator, and X' is the processed data. For indicators that are negatively correlated with fuel ratio, the formula X'=(Xmax-X) / (Xmax-Xmin) is used to process them to obtain the processed indicator data.

7. The techno-economic evaluation method for evaluating coke quality and cost-effectiveness according to claim 3, characterized in that, The dimensionless processing of the reference sequence and comparison sequence includes: An initialization method is adopted, and the reference sequence and comparison sequence are processed by the formula X'(t)=X(t) / X(1), where X(t) is the data of the sequence at time t, X(1) is the first data of the sequence, and X'(t) is the processed data; If a standardization method is used, the data is processed using the formula X'(t)=(X(t)-Xmean) / Xstd, where Xmean is the sequence mean and Xstd is the sequence standard deviation, resulting in dimensionless sequence data.

8. The techno-economic evaluation method for evaluating coke quality and cost-effectiveness according to claim 4, characterized in that, The step of ranking cokes based on their quality scores includes: obtaining the quality scores of multiple coke samples. The coke samples are sorted from high to low according to their quality scores to obtain a coke ranking. Based on the coke ranking and the corresponding cost-effectiveness index, the priority order for selecting coke is determined. Based on the aforementioned priority selection order, a recommended scheme for coke procurement is generated.

9. An electronic device, characterized in that, The electronic device includes: processor; A memory storing computer-readable instructions, which, when loaded and executed by the processor, implement the techno-economic evaluation method for evaluating the quality and cost-effectiveness of coke as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores program code that can be called by a processor to execute the techno-economic evaluation method for evaluating the quality and cost-effectiveness of coke as described in any one of claims 1 to 7.

Images