A method for dynamically evaluating metallurgical coke for charging into a furnace

Abstract

The application provides a method for dynamically evaluating metallurgical coke for furnace, based on the median of a non-normal coke quality data set, two normal data sets are constructed, then the weighted standard deviation of the two normal data sets is calculated, a non-normal process capability index model is created, then the weight of each coke quality index is calculated by using a difference coefficient method, the weight is combined with the established non-normal process capability index model, a non-normal coke quality data multivariate process capability comprehensive evaluation model is obtained, so that the evaluation process of coke quality is realized. The application can accurately evaluate the coke quality of the coking industry for providing metallurgical coke for blast furnace, provide high-quality coke for the ironmaking process, and ensure the stable operation of the blast furnace.

Description

Technical Field

[0001] This invention belongs to the field of iron and steel coking technology, specifically relating to a method for dynamically evaluating metallurgical coke fed into the furnace. Background Technology

[0002] Blast furnace ironmaking is the most resource- and energy-intensive production process in the iron and steel metallurgical process, accounting for about 70% of the total energy consumption of the entire iron and steel process. my country is a major coal-producing country, but its coking coal resources are insufficient, accounting for only 18.9% of its total coal reserves. The proven reserves of coking coal are 307.3 billion tons, while the economically recoverable reserves are only 39.5 billion tons. In 2019, my country's annual coke production was 471 million tons, with a coal-to-coke production ratio of 1.3:1, consuming 612 million tons of coking coal. Of the economically recoverable coking coal reserves, gas coal, coking coal, fat coal, and lean coal account for 46%, 24%, 13%, and 16%, respectively. High-quality coking coal generally refers to coking coal types with good caking properties, such as fat coal and coking coal. To increase coke production and improve quality, enterprises have begun to gradually reduce or even eliminate the use of gas coal with higher volatile matter and poorer caking properties. This means that the existing domestically available high-quality coking coal resources will only last for another 34 years. China's coking coal resources are unevenly distributed, mainly concentrated in North China. The gradual scarcity of high-quality coking coal resources and their uneven distribution have forced domestic steel and coking enterprises to purchase and use imported coking coal, especially those near coastlines and ports. According to statistics from the General Administration of Customs, China imported 74.5 million tons of coking coal in 2019, accounting for 12.17% of total coking coal consumption. The increasing scarcity and uneven distribution of coking coal resources, coupled with enterprises' efforts to improve coke quality by increasing the use of high-quality coking coal, presents a contradiction.

[0003] In modern iron and steel metallurgical processes, large blast furnaces require high-quality, first-grade metallurgical raw materials. The larger the blast furnace volume, the higher the quality requirements for the coke. To ensure stable blast furnace operation, the requirements for coke's cold and hot strength are increasingly stringent, even reaching record highs in ironmaking history. Coke CRI (Cryogenic Reduction Index) is declining, reaching a low of 21%, while CSR (Cryogenic Stability Reduction) is mainly rising, reaching a high of 68%. A blast furnace is a three-dimensional, high-temperature, non-uniform counter-current packed bed where three phases coexist and chemical reactions occur. Instability in any one phase can cause blast furnace malfunctions. Looking at the trends of key technical indicators for coke and blast furnaces over the past five years, the correlation between the main economic and technical indicators of the blast furnace and coke quality is not significant. Therefore, evaluating coke using fixed values ​​is too simplistic. Dynamic evaluation of coke based on blast furnace requirements is more conducive to achieving full-quality utilization of coke and stable blast furnace operation.

[0004] Chinese Patent Application No. CN201910564973.0 (A process for preparing carbonized materials from agricultural and forestry waste for use in blast furnace pulverized coal injection based on hydrothermal reaction) discloses a process for preparing carbonized materials from agricultural and forestry waste for use in blast furnace pulverized coal injection based on hydrothermal reaction. This process utilizes hydrothermal reaction technology to convert agricultural and forestry waste with high volatile matter content, low calorific value, and low utilization value into high-quality hydrothermal carbon with low volatile matter content, low ash content, and high calorific value. The hydrothermal carbon is then mixed with pulverized coal for blast furnace pulverized coal injection, enabling the application of agricultural and forestry waste in blast furnace pulverized coal injection. The hydrothermal carbon prepared in this way meets the performance requirements for blast furnace pulverized coal fuel and can be used as a clean, renewable fuel to partially replace pulverized coal for blast furnace pulverized coal injection. This not only improves the utilization efficiency of waste materials but also reduces carbon dioxide emissions in ironmaking production, resulting in significant economic, social, and ecological benefits.

[0005] Chinese Patent Application No. (CN202310410510.5) discloses a comprehensive evaluation method for the quality stability of coke, comprising: S1. determining the influencing factors related to coke quality; S2. testing the coke, including raw material property testing, chemical composition analysis, physical property testing, thermal stability testing, and combustion characteristic testing, and recording the test results; S3. classifying the coke quality stability using the test results as evaluation indicators; S4. assigning standardized coefficients to the evaluation indicators; S5. multiplying the test results by the corresponding standardized coefficients and weighting them to obtain comprehensive evaluation parameters; S6. classifying the coke into different quality grades according to the comprehensive evaluation parameters, and evaluating the quality stability of the coke according to the quality grades. This invention achieves a comprehensive evaluation of the quality stability of coke and can obtain comprehensive coke quality stability evaluation results, which is of great significance for predicting the quality stability of coke and its performance in blast furnaces.

[0006] Although the aforementioned patents can produce materials that meet the evaluation requirements of metallurgical coke, they cannot more rationally and effectively utilize weakly caking and high-ash, high-sulfur coking coal, given the scarcity of high-quality coking coal resources. Therefore, there is an urgent need to determine a more effective and rational dynamic evaluation method for metallurgical coke. Summary of the Invention

[0007] The purpose of this invention is to provide a method for dynamically evaluating metallurgical coke fed into the furnace, which can accurately evaluate the quality of coke in the coking industry, provide high-quality coke for the ironmaking process, and ensure the stable and smooth operation of the blast furnace.

[0008] To achieve the above objectives, the present invention employs the following technical solution: A method for dynamically evaluating metallurgical coke fed into the furnace, specifically including the following steps: 1) Find the median of each coke quality index in the multivariate coke quality data. Let the median of the j-th coke quality index be Med. j ; 2) Using the median Med j Using the median as the boundary, the j-th coke quality index data is divided into two parts, one of which consists of all data less than the median (Med). j The data consists of one set of data, and another set consists of all data greater than the median Med. j Data composition; 3) Median Med j Add them separately to the two data sets split in the previous step, and then use the median Med. j Using the center of the two datasets as the center, the two datasets are then padded according to the normal distribution to obtain two normal datasets X. j,l With X j,u , where X j,l ={x 1，j,l ,x 2，j,l ,...x 2n+1,j,l}, n1 is less than the median Med j Number of data points, X j,u ={x 1，j,u ,x 2，j,u ,...x 2n+1,j,u}, n2 is greater than the median Med j The number of data points; 4) Calculate the values ​​of the two normally distributed datasets X respectively. j,l With X j,u Weighted standard deviation: , ; 5) Obtain the process capability index based on weighted standard deviation: ; USL in the formula j LSL j These refer to the upper and lower specification limits in the coke quality indicators, respectively, while Tj refers to the ideal target value. 6) Calculate the coefficient of variation for the j-th coke quality index data: ; CV in the formula j，u CV j,l The corresponding difference coefficient, μ j，u ,μ j，l This refers to the corresponding population mean; 7) Based on the coefficient of variation (CV) j Find the weight of the j-th coke quality index data: ; Where y is the number of quality indicators; 8) The calculation formula for the multivariate process capability comprehensive evaluation model of non-normal coke quality data is summarized as follows: ; MC pmk To correct the process capability index.

[0009] The evaluation criteria for coke quality index grades are shown in Table 1; Table 1. Evaluation Level Classification of Modified Process Capability Index Compared with the prior art, the beneficial effects of the present invention are: 1. This invention provides a method for constructing two normal datasets based on the median of a non-normal coke quality dataset, and then calculating the weighted standard deviation of these two normal datasets. Based on this, a non-normal process capability index model is created. The difference coefficient method is then used to calculate the weight of each coke quality indicator. The weights are combined with the established non-normal process capability index model to obtain a multivariate process capability comprehensive evaluation model for non-normal coke quality data, thereby realizing the evaluation process of coke quality.

[0010] 2. Principal component analysis (PCA) relies on a certain number of principal components, so principal component extraction of multivariate data must be completed first. Although the extracted principal components can effectively integrate and calculate process capability, the transformation of known quality indicator variables into meaningless principal component variables obscures the relationship between unit process capability and multivariate process capability, weakening the method's interpretability. The coefficient of variation method, on the other hand, uses the coefficient of variation to measure the fluctuation of the unit mean of quality indicator data, using this as the weight of each quality indicator. This weight is then combined with the unit process capability of each quality indicator to ultimately calculate the multivariate process capability of the quality indicators. Detailed Implementation

[0011] To make the objectives, technical solutions, and advantages of this invention clearer, the specific implementation methods of this invention will be further described below in conjunction with the embodiments. The following embodiments are used to specifically illustrate the content of this invention. These embodiments are only general descriptions of the content of this invention and do not limit the content of this invention.

[0012] The purpose of this invention is to provide a method for dynamically evaluating metallurgical coke fed into blast furnaces. This method can accurately evaluate the quality of metallurgical coke supplied to blast furnaces in the coking industry, providing high-quality coke for the ironmaking process, and ensuring the stable and smooth operation of blast furnaces while more rationally and effectively utilizing weakly caking and high-ash, high-sulfur coking coal.

[0013] Example: We selected coke quality index data from a specific coke oven to conduct a multivariate process capability comprehensive evaluation of non-normally distributed coke quality data. We calculated the median (Med) of each coke quality index data. j As shown in Table 2: Table 2 Median of quality indicators for multi-element coke Next, according to the formula , Calculate by median Med j The standard deviation of the two normally distributed data sets: Where, x j，l Med is less than the median j The data used to complete the normal dataset, where n1 is less than the median Med. j The number of data points, n1=12; x j，u For a median Med j The data used to complete the normal dataset, where n² is greater than the median Med. j The number of data points is n² = 12. The calculation results are shown in Table 3: Table 3 shows the standard deviations of the two normally distributed data sets divided by the median. Based on actual conditions and production requirements, upper limit (USL) specifications for each coke quality indicator are set. j Lower specification limit LSL j and expected value T j As shown in Tables 4 and 5: Table 4 Upper and lower limits of various coke indicators Table 5 Expected values ​​of various coke indicators The standard deviation б of the two normal data sets j.u б j.l and the upper limit (USL) of each coke quality indicator. j Lower specification limit LSL j Expected value T j According to the formula Calculate the process capability index for each coke quality indicator The calculation results are shown in Table 6. Table 6 Process Capability Index of Various Coke Quality Indicators Calculate the mean μ of the two normal data sets divided by the median. j，u ,μ j，l As shown in Table 7: Table 7. Means of the two normal data sets divided by the median. After obtaining the mean μ j，u ,μ j，l Based on the formula Calculate the coefficient of variation (CV) for each coke index. j The calculation results are shown in Table 8: Table 8. Difference coefficients of various coke quality indicators According to the formula Calculate the weights of each coke quality indicator. Where y is the number of coke quality indicators, y=4. The calculation results are shown in Table 9: Table 9. Weights of various coke quality indicators According to the formula The multivariate process capability evaluation results for non-normal coke quality data were calculated as follows: 1.3863.

[0014] Based on the target requirements in actual industrial production processes, the process capability index can be divided into multiple levels according to its numerical value, serving as an evaluation standard for the quality indicator of process capability, as shown in Table 1.

[0015] Based on the evaluation criteria of the above process capability index, 1.3863 falls within the range of 1.33-1.67 in Table 1. Therefore, the coke quality index grade evaluated in this embodiment is good, the production status is relatively stable, and the process capability can be improved to excellent through further optimization.

[0016] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments. In the above embodiments of the present invention, the descriptions of each embodiment have their own emphasis; parts not described in detail in a certain embodiment can be referred to in the relevant descriptions of other embodiments. It should be understood that the disclosed technical content in the several embodiments provided in this application can be implemented in other ways.

Claims

1. A method for dynamically evaluating metallurgical coke fed into a furnace, characterized in that, Specifically, the methods and steps are as follows: 1) Find the median of each coke quality index in the multivariate coke quality data. Let the median of the j-th coke quality index be Med. j ; 2) Using the median Med j Using the median as the boundary, the j-th coke quality index data is divided into two parts, one of which consists of all data less than the median (Med). j The data consists of one set of data, and another set consists of all data greater than the median Med. j Data composition; 3) Median Med j Add them separately to the two data sets split in the previous step, and then use the median Med. j Using the center of the two datasets as the center, the two datasets are then padded according to the normal distribution to obtain two normal datasets X. j,l With X j,u , where X j,l ={x 1，j,l ,x 2，j,l ,...x 2n+1,j,l }, n1 is less than the median Med j Number of data points, X j,u ={x 1，j,u ,x 2，j,u ,...x 2n+1,j,u }, n2 is greater than the median Med j The number of data points; 4) Calculate the values ​​of the two normally distributed datasets X respectively. j,l With X j,u Weighted standard deviation: ， ； 5) Obtain the process capability index based on weighted standard deviation: ； USL in the formula j LSL j These refer to the upper and lower specification limits in the coke quality indicators, respectively, while Tj refers to the ideal target value. 6) Calculate the coefficient of variation for the j-th coke quality index data: ； CV in the formula j，u CV j,l The corresponding difference coefficient, μ j，u ,μ j，l This refers to the corresponding population mean; 7) Based on the coefficient of variation (CV) j Find the weight of the j-th coke quality index data: ； Where y is the number of quality indicators; 8) The calculation formula for the multivariate process capability comprehensive evaluation model of non-normal coke quality data is summarized as follows: ； In the formula, MC pmk To correct the process capability index.

2. The method for dynamically evaluating metallurgical coke fed into the furnace according to claim 1, characterized in that, The evaluation criteria for coke quality index grades are as follows: 1.67 ≤ Correction process capability index MC pmk ≤2, quality indicator level is excellent, 1.33≤corrected process capability index MC pmk <1.67, quality index level is good; 1 ≤ modified process capability index MC pmk <1.33, quality index level is qualified; corrected process capability index MC pmk <1, the quality indicator level is unqualified.

Images