Multi-index orthogonal optimization coal blending method for blast furnace injection pulverized coal

Abstract

The invention relates to the technical field of blast furnace ironmaking, in particular to a multi-index orthogonal optimization coal blending method for blast furnace injection pulverized coal. The method comprises the following steps: firstly, determining a mixed pulverized coal system formed by two or more different types of pulverized coal; then arranging a coal blending test by adopting mathematical optimization orthogonal design, and taking the pulverized coal type as a factor and the blending proportion as a level; detecting a plurality of key assessment indexes of each coal blending scheme obtained by the orthogonal test, wherein the indexes at least comprise four items of fixed carbon, ash content, grindability, effective heat value, combustion rate and cost; standardized score assignment is carried out on each index and a comprehensive score is calculated; and finally, determining the primary and secondary influence sequence and the optimal level combination of each pulverized coal factor through range analysis, thereby obtaining an optimal coal blending scheme. According to the method, the limitation of traditional single-index coal blending is overcome, efficient and low-cost utilization of pulverized coal resources is achieved, and reliable guarantee is provided for efficient and economical operation of the blast furnace.

Description

Technical Field

[0001] This invention relates to the field of blast furnace ironmaking technology, specifically to a multi-index orthogonal optimization coal blending method for blast furnace pulverized coal injection. Background Technology

[0002] Blast furnace ironmaking is the most important production process in the modern steel industry, characterized by its high efficiency and economy. To further reduce costs, pulverized coal injection technology has been widely adopted, replacing some of the expensive metallurgical coke with relatively inexpensive pulverized coal. This technology has played a crucial role in reducing production costs and carbon emissions in my country's metallurgical industry.

[0003] The core of pulverized coal injection (PCO) technology in blast furnaces lies in the formulation of mixed PCO powder. The goal is to minimize coal blending costs and achieve sustainable resource utilization while ensuring good performance of the PCO powder within the furnace. Both theory and practice demonstrate that using a mixture of various PCO powders is the most feasible technical means to optimize PCO injection performance. However, due to the complex relationships between different coal types in terms of composition, performance, and price, achieving the optimal combination of coal types has long been a technical challenge for those skilled in the art.

[0004] Currently, the coal blending methods commonly used or publicly disclosed in the industry have the following limitations: The first category is coal blending methods centered on a single or a few fixed indicators. Examples include component-based blending (such as the method described in patent document CN11253855B) or calorific value blending, which typically aim to ensure that the fixed carbon, ash content, or calorific value of the mixed pulverized coal meets preset requirements. While these methods are simple and easy to implement, they lack precision, fail to comprehensively consider multiple key properties of the pulverized coal, and severely limit the range of coal types that can be selected. Another approach is cost-based blending (such as that mentioned in patent document CN104745753B), which focuses on incorporating a high proportion of inexpensive coal to control costs. However, this can easily lead to a deterioration in key indicators such as the in-furnace combustion performance of the mixed pulverized coal, affecting the stable operation of the blast furnace.

[0005] The second category involves methods that attempt optimization using computer models. For example, patent documents with publication numbers CN117877618A or CN115595390A disclose methods for calculating the cost-effectiveness of pulverized coal or calculating coal blending schemes by establishing models. This type of method represents a future development direction, but existing models generally suffer from problems such as incomplete consideration of factors, insufficient precision in the analysis process, and failure to fully quantify the complex interaction relationships (i.e., correlations) between different coal types, resulting in limited effectiveness and applicability in practical applications.

[0006] The third category is coal blending schemes targeting specific coal types or components. For example, patent document CN110592299A discloses a coal blending method for high-calcium, low-ash-fusion-point bituminous coal, while patent document CN106011340B protects a specific coal blending component. Such schemes are significant in broadening the application scope of specific coal types, but their technical approaches are specialized and not a universal method capable of systematically solving the problem of optimizing the proportions of multiple coal types.

[0007] In summary, existing pulverized coal blending methods for blast furnace injection are either limited by traditional blending concepts, have inherent model flaws, or are only applicable to specific scenarios. They have not yet provided a systematic and scientific solution to the core contradiction of simultaneously expanding the range of coal types that can be used and reducing blending costs while ensuring or even optimizing the overall performance of pulverized coal blending. Therefore, there is an urgent need in this field for a blending method that can simultaneously consider multiple factors and objectives and accurately reveal the interactive relationships between different coal types, in order to truly realize the economic and efficient smelting advantages of blast furnace pulverized coal injection technology. Summary of the Invention

[0008] To overcome the shortcomings of existing technologies, this invention provides a multi-index orthogonal optimization coal blending method for blast furnace pulverized coal injection. This method can simultaneously coordinate multiple factors and objectives and accurately reveal the interactive influence relationships between different coal types, thereby truly realizing the economic and efficient smelting advantages of blast furnace pulverized coal injection technology.

[0009] To achieve the above objectives, the present invention employs the following technical solution: A multi-index orthogonal optimization coal blending method for blast furnace pulverized coal injection includes the following steps: S1. Determine the coal blending factors and levels: Select n types of coal powder to be blended as factors, and set m different blending ratios for each type of coal powder as levels, where n≥2 and m≥2.

[0010] S2. Design an orthogonal experiment: Based on the factors and levels determined in step S1, select the appropriate orthogonal array to arrange a set of coal blending test schemes.

[0011] S3. Determine the assessment indicators and conduct tests: Select at least 4 assessment indicators for evaluating the overall performance of the mixed coal powder, and prepare the mixed coal powder according to the test plan in step S2, and test the values ​​of each assessment indicator for each sample.

[0012] S4. Assigning Scores to Assessment Indicators: Standardize the values ​​of each assessment indicator obtained in step S3, and convert the actual value of each indicator into a single score between 0 and 10.

[0013] S5. Calculate the overall score: Sum all the individual scores obtained in step S4 to obtain the overall score for each test plan.

[0014] S6. Determine the optimal coal blending scheme: Based on the comprehensive score of each test scheme obtained in step S5, perform range analysis to determine the order of importance of each coal powder influencing factor, and determine the optimal coal powder ratio combination accordingly, thus obtaining the optimal coal blending scheme.

[0015] Furthermore, the pulverized coal mentioned in S1 includes two or more of the following: anthracite, bituminous coal, lean coal, semi-lean coal, semi-lean coal, lignite, and peat.

[0016] Furthermore, the pulverized coal mentioned in S1 also includes alternative coal types, which are one or more of the following: semi-coke, upgraded coal, dry quenching coke dust, water-quenched coke powder, blast furnace dust, and biomass fuel.

[0017] Furthermore, the biomass fuel is one or more of charcoal, straw, fruitwood shells, and carbonized rice husks.

[0018] Furthermore, the orthogonal array described in S2 is L 9 (3) 4 L8 (2) 7 ) or L8 (4 1 ×2 4 ).

[0019] Furthermore, the assessment indicators described in S3 include at least four of the following: fixed carbon, ash content, sulfur content, grindability index, calorific value, effective calorific value in the furnace, ash fusion properties, fluidity index, jetting index, reactivity, combustion rate, coal-coke replacement ratio, and coal blending cost of the mixed pulverized coal.

[0020] Furthermore, the formula used for the standardization process described in S4 is: For indicators where a larger value is better, the "optimal performance indicator" is the maximum value and the "worst performance indicator" is the minimum value; for indicators where a smaller value is better, the "optimal performance indicator" is the minimum value and the "worst performance indicator" is the maximum value.

[0021] Furthermore, the determination of the optimal coal powder ratio combination described in S6 specifically involves selecting the level with the highest average comprehensive score under each factor level and combining them to form the optimized coal blending scheme.

[0022] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention achieves a scientific and systematic approach to coal blending, fundamentally overcoming the blindness of experience-based coal blending. By introducing mathematical optimization orthogonal design, the complex multi-factor, multi-level coal blending problem is transformed into a structured and analyzable experimental sequence. This systematic approach, unlike the scattered adjustments of existing technologies, enables the scientific arrangement of experiments. With a minimal number of experiments (e.g., 9 experiments can represent 81 full factor combinations), it efficiently reveals the intrinsic influence and interaction of different coal types and their proportions on the final mixed coal powder performance, thereby elevating coal blending from "experience-driven" to "scientific decision-making."

[0023] 2. This invention resolves the core contradiction of balancing cost and performance, maximizing overall benefits. By establishing a comprehensive evaluation system encompassing technical indicators (such as fixed carbon, effective calorific value, and combustion rate) and economic indicators (such as coal blending cost), and by standardizing and summing the scores of each indicator, this invention successfully unifies multiple mutually restrictive objectives into a quantifiable comprehensive score. As shown in Example 1, the optimized scheme (22% thermal coal, 18% Yangquan coal, and 60% local bituminous coal) achieves a comprehensive score as high as 33.95 while maintaining a coal blending cost of only 998-1013 yuan / ton, thus achieving a balance between low cost and high performance. This is because the method can accurately identify the coal type (such as local bituminous coal) that contributes most to performance at a low cost level and determine its optimal blending ratio, something that no existing single-objective-oriented method can achieve.

[0024] 3. This invention significantly broadens the scope of use for inexpensive coal types and alternative resources, reducing raw material costs and resource constraints. Through orthogonal experiments and range analysis, this invention can clarify the order of importance and optimal addition range of various coal types (including inexpensive and alternative resources) on overall performance. For example, in several embodiments, the lower-priced "local long-bituminous coal" was identified as a key influencing factor and assigned a higher proportion (e.g., 55% in Embodiment 4), while the impact of some anthracite coals was relatively weak. This allows enterprises to boldly and scientifically increase the proportion of inexpensive and non-mainstream resources while ensuring overall performance, significantly reducing coal blending costs and dependence on specific high-quality coal types.

[0025] 4. Improved coal blending accuracy and the predictive reliability of in-furnace performance, ensuring stable and smooth blast furnace operation. This invention uses at least four key indicators (covering composition, physicochemical properties, and in-furnace behavior) and a relative scoring formula based on the best / worst measured values ​​to make the evaluation results closer to the actual needs of blast furnace production. The assessment of indicators directly reflecting in-furnace performance, such as "effective calorific value (Qexisting)" and "combustion rate (BR)," ensures that the optimized scheme not only meets the composition requirements before entering the furnace but also predicts and guarantees its actual performance in the furnace. This avoids furnace condition fluctuations caused by poor coal powder utilization, providing core technical support for achieving "low-consumption and high-efficiency" blast furnace smelting.

[0026] 5. The optimization process is efficient, intuitive, and easy to promote and apply in enterprises. The optimization process of this invention is based on intuitive orthogonal arrays and range analysis, with simple calculations and clear results (the order of factor importance A1B1C1D3 can be directly obtained), requiring no advanced programming knowledge. This greatly reduces the technical threshold and implementation cost, enabling technical personnel in general manufacturing enterprises to master and implement it, significantly enhancing the universality and industrial application prospects of this method.

[0027] In summary, this invention, by creatively combining orthogonal design with a multi-index comprehensive evaluation system, successfully solves a series of technical problems that have long existed in existing coal blending methods, such as systematic deficiencies, cost-performance contradictions, limited resource utilization, low prediction accuracy, and high application thresholds. Detailed Implementation

[0028] The following detailed description of specific embodiments of the present invention is not intended to limit the scope of the invention: An optimized method for blending pulverized coal for blast furnace injection can be implemented through the following steps: Step 1: Determine the raw materials for coal blending The raw materials for preparing mixed pulverized coal for blast furnace injection consist of two or more different types of pulverized coal, mainly including anthracite, bituminous coal, lean coal, semi-lean coal, lignite, peat, and various alternative coal types. The alternative coal types may consist of one or more of the following: semi-coke, upgraded coal, CDQ powder (dry quenching coke dust), water-quenched coke powder, blast furnace dust, and biomass fuel; wherein the blast furnace dust may include one or more of the following: ore trough dust, furnace top dust, and furnace front dust; and the biomass fuel may include one or more of the following: charcoal, straw, fruitwood husks, and carbonized rice husks.

[0029] Step 2: Designing Orthogonal Experiments Mathematical optimization orthogonal design was employed to arrange the coal blending experiment. Specifically, different types of pulverized coal were used as experimental factors, and the proportions of each type of pulverized coal were used as levels. Based on the types of pulverized coal actually selected and the number of levels investigated, an appropriate orthogonal array was chosen for the experimental design; specifically, an L-array could be used. 9 (3) 4 L8 (2) 7 ) or L8 (4 1 ×2 4 Experimental design methods were used to develop a complete coal blending test plan.

[0030] Step 3: Testing and evaluating performance indicators After completing the experimental design, mixed pulverized coal samples were prepared one by one according to the orthogonal array scheme, and various assessment indicators were tested and analyzed. The assessment indicators should include at least four items, which can be selected from the following indicators and can be added or removed according to actual conditions: fixed carbon (C), ash (A), sulfur (S), grindability index (HGI), calorific value (Q), effective calorific value in the furnace (Qavailable), ash fusion performance (ST), fluidity index (LI), jetting index (SI), reactivity (R), pulverized coal combustion rate in the furnace (BR), coal-coke replacement ratio (CKR), and coal blending cost (COST). All test results must be statistically recorded.

[0031] Step 4: Indicator Scoring and Comprehensive Evaluation To optimize the coal blending scheme, the various assessment indicators obtained in step three need to be standardized and scored. The optimal value for a single assessment indicator is set at 10 points, and the worst value at 0 points. The remaining indicator values ​​are converted to scores between 0 and 10 using the following formula: Individual score = After assigning scores to all assessment indicators, the scores of each indicator under each experimental scheme are summed to obtain the comprehensive score of the scheme.

[0032] Step 5: Range Analysis and Solution Development Based on the comprehensive score calculated in step four, range analysis is performed using the adopted orthogonal array. By analyzing the average comprehensive score of each factor (pulverized coal) at different levels, the order of influence of different types of pulverized coal factors is identified, and the optimal blending ratio of each type of pulverized coal is determined accordingly. Finally, the optimal blending scheme for blast furnace injection of mixed pulverized coal is formulated.

[0033] Example 1 (A steel plant selected three types of pulverized coal for blending) In this coal blending process, many factors such as the existing coal powder types, purchase quantities and costs of a certain steel plant were taken into account. The existing coal types of the steel plant were used as the basic coal types for blending. After selecting two types of anthracite (thermal coal and Yangquan coal) and one type of bituminous coal (local long bituminous coal), the factors and coal powder blending ratio design are shown in Table 1-1.

[0034] Table 1-1 Factor Level Table Choose L 9 (3) 4 An orthogonal array was used to design the experiment. The evaluation indicators were: ash content (A), grindability index (HGI), effective calorific value in the furnace (Qo), combustion rate (BR), coal-coke replacement ratio (CKR), and coal blending cost (COST). Each indicator was assigned a score and a comprehensive score was calculated. The experimental scheme and results are shown in Table 1-2.

[0035] Table 1-2 Experimental Scheme and Results Note: A represents thermal coal, B represents empty coal, C represents Yangquan coal, and D represents local long bituminous coal.

[0036] Coal blending experiments were conducted using three types of coal—thermal coal, Yangquan coal, and local long-bituminous coal—as influencing factors. After optimization analysis, the order of importance of the influencing factors for each coal type was Yangquan coal, local long-bituminous coal, and thermal coal. Among them, thermal coal had a relatively weak influence in this coal blending scheme. The optimized coal blending scheme obtained through mathematical optimization design is shown in Table 1-3 (for ease of industrial implementation, the coal blending ratio was slightly adjusted to an integer).

[0037] Table 1-3 Optimized Coal Blending Scheme The above optimized design scheme shows that, under the above factors and level selection, the optimal coal blending scheme should be 22% thermal coal, 18% Yangquan coal, and 60% local long-bituminous coal.

[0038] Example 2 (A steel plant selected three types of pulverized coal for blending) In this coal blending process, many factors such as the existing coal powder types, purchase quantities and costs of a certain steel plant were taken into account. The existing coal types of the steel plant were used as the basic coal types for blending. After selecting two types of anthracite (power coal and washed mixed coal) and one type of bituminous coal (local long bituminous coal), the factors and coal powder blending ratio design are shown in Table 2-1.

[0039] Table 2-1 Factor Level Table Choose L 9 (3) 4An orthogonal array was used to design the experiment. The evaluation indicators were: ash content (A), grindability index (HGI), effective calorific value in the furnace (Q_available), combustion rate (BR), coal-coke replacement ratio (CKR), and coal blending cost (COST). Each indicator was assigned a score and a comprehensive score was calculated. The experimental scheme and results are shown in Table 2-2.

[0040] Table 2-2 Experimental Scheme and Results Note: A represents thermal coal, B represents empty coal, C represents washed mixed coal, and D represents local bituminous coal. The coal blending experiment was conducted using three types of coal—thermal coal, washed mixed coal, and local long-bituminous coal—as influencing factors. After optimization analysis, the order of importance of the influencing factors for each coal type was as follows: washed mixed coal, local long-bituminous coal, and thermal coal. Among them, thermal coal had a relatively weak influence in this coal blending scheme. The optimized coal blending scheme obtained through mathematical optimization design is shown in Table 2-3 (for ease of industrial implementation, the coal blending ratio was slightly adjusted to an integer).

[0041] Table 2-3 Optimized Coal Blending Scheme The above optimized design scheme shows that, under the above factors and level selection, the optimal coal blending scheme should be 38% thermal coal, 20% washed mixed coal, and 42% local long bituminous coal.

[0042] Example 3 (A steel plant selected four types of pulverized coal for blending) In this coal blending process, many factors such as the existing coal powder types, purchase quantities and costs of a certain steel plant were taken into account. The existing coal types of the steel plant were used as the basic coal types for blending. After selecting 3 types of anthracite (thermal coal, washed mixed coal, and Yangquan coal) and 1 type of bituminous coal (local long bituminous coal), the factors and coal powder blending ratio design are shown in Table 3-1.

[0043] Table 3-1 Factor Level Table Choose L 9 (3) 4 An orthogonal array was used to design the experiment. The evaluation indicators were: ash content (A), grindability index (HGI), effective calorific value in the furnace (Qo), combustion rate (BR), coal-coke replacement ratio (CKR), and coal blending cost (COST). Each indicator was assigned a score and a comprehensive score was calculated. The experimental scheme and results are shown in Table 3-2.

[0044] Table 3-2 Experimental Scheme and Results Note: A represents thermal coal, B represents washed mixed coal, C represents Yangquan coal, and D represents local bituminous coal.

[0045] The coal blending experiment was conducted using four types of coal as influencing factors: thermal coal, washed mixed coal, Yangquan coal, and local long-bituminous coal. After optimization analysis, the order of importance of the influencing factors for each coal type was as follows: washed mixed coal, local long-bituminous coal, Yangquan coal, and thermal coal. Among them, thermal coal had a relatively weak influence in this coal blending scheme. The optimized coal blending scheme obtained through mathematical optimization design is shown in Table 3-3 (for ease of industrial implementation, the coal blending ratio was slightly adjusted to an integer).

[0046] Table 3-3 Optimized Coal Blending Scheme The above optimized design scheme shows that, under the above factors and level selection, the optimal coal blending scheme should be 22% thermal coal, 10% washed mixed coal, 24% Yangquan coal, and 38% local long bituminous coal.

[0047] Example 4 (A steel plant selected four types of pulverized coal for blending) In this coal blending process, many factors such as the existing coal powder types, purchase quantities and costs of a certain steel plant were taken into account. The existing coal types of the steel plant were used as the basic coal blending coal types. The coal blending scheme selected 3 types of anthracite (thermal coal, washed mixed coal, and Xiyang coal) and 1 type of bituminous coal (local long bituminous coal). The factors and coal powder blending ratio design are shown in Table 4-1.

[0048] Table 4-1 Factor Level Table Choose L 9 (3) 4 An orthogonal array was used to design the experiment. The evaluation indicators were: ash content (A), grindability index (HGI), effective calorific value in the furnace (Q_available), combustion rate (BR), coal-coke replacement ratio (CKR), and coal blending cost (COST). Each indicator was assigned a score and a comprehensive score was calculated. The experimental scheme and results are shown in Table 4-2.

[0049] Table 4-2 Experimental Scheme and Results Note: A represents thermal coal, B represents washed mixed coal, C represents Xiyang coal, and D represents local long bituminous coal.

[0050] The coal blending experiment was conducted using four types of coal as influencing factors: thermal coal, washed mixed coal, Xiyang coal, and local long-bituminous coal. After optimization analysis, the order of importance of the influencing factors for each coal type was as follows: washed mixed coal, local long-bituminous coal, thermal coal, and Xiyang coal. Among them, Xiyang coal had a relatively weak influence in this coal blending scheme. The optimized coal blending scheme obtained through mathematical optimization design is shown in Table 4-3 (for ease of industrial implementation, the coal blending ratio is slightly adjusted to an integer).

[0051] Table 4-3 Optimized Coal Blending Scheme The above optimized design scheme shows that, under the above factors and level selection, the optimal coal blending scheme should be 20% thermal coal, 15% washed mixed coal, 10% Xiyang coal, and 55% local long bituminous coal.

[0052] Example 5 (A steel plant selected 5 types of pulverized coal for blending) In this coal blending process, many factors such as the existing coal powder types, purchase quantities and costs of a certain steel plant were taken into account. The existing coal types of the steel plant were used as the basic coal blending coal types. After selecting four types of anthracite (power coal, washed mixed coal, Yangquan coal and Xiyang coal) and one type of bituminous coal (Mengfa bituminous coal), the fixed proportion of bituminous coal was 45%. The factors and coal powder blending ratio design are shown in Table 5-1.

[0053] Table 5-1 Factor Level Table Choose L 9 (3) 4 An orthogonal array was used to design the experiment. The evaluation indicators were: ash content (A), grindability index (HGI), effective calorific value in the furnace (Qo), combustion rate (BR), coal-coke replacement ratio (CKR), and coal blending cost (COST). Each indicator was assigned a score and a comprehensive score was calculated. The experimental scheme and results are shown in Table 5-2.

[0054] Table 5-2 Experimental Scheme and Results Note: A represents thermal coal, B represents washed mixed coal, C represents Yangquan coal, and D represents Xiyang coal.

[0055] The coal blending experiment was conducted using four types of coal—thermal coal, washed mixed coal, Yangquan coal, and Xiyang coal—as influencing factors. After optimization analysis, the order of importance of the influencing factors for each coal type was as follows: washed mixed coal, thermal coal, Yangquan coal, and Xiyang coal. Among them, Xiyang coal had a relatively weak influence in this coal blending scheme. The optimized coal blending scheme obtained through mathematical optimization design is shown in Table 5-3 (for ease of industrial implementation, the coal blending ratio was slightly adjusted to an integer).

[0056] Table 5-3 Optimized Coal Blending Scheme The above optimized design scheme shows that, under the above factors and level selection, the optimized coal blending scheme should be 19% thermal coal, 8% washed mixed coal, 15% Yangquan coal, 13% Xiyang coal, and 45% Mengfa bituminous coal.

[0057] Example 6 (A steel plant selected 5 types of pulverized coal for blending) In this coal blending process, many factors such as the existing coal powder types, purchase quantities and costs of a certain steel plant were taken into account. The existing coal types of the steel plant were used as the basic coal blending coal types. The coal blending scheme selected 4 types of anthracite (washed mixed coal, Yangquan coal, Xiyang coal and Lu'an white coal) and 1 type of bituminous coal (Mengfa bituminous coal). The fixed proportion of bituminous coal was 45%. The factors and coal powder blending ratio design are shown in Table 6-1.

[0058] Table 6-1 Factor Level Table Choose L 9 (3) 4 An orthogonal array was used to design the experiment. The evaluation indicators were: ash content (A), grindability index (HGI), effective calorific value in the furnace (Q_available), combustion rate (BR), coal-coke replacement ratio (CKR), and coal blending cost (COST). Each indicator was assigned a score and a comprehensive score was calculated. The experimental scheme and results are shown in Table 6-2.

[0059] Table 6-2 Experimental Scheme and Results Note: A represents washed mixed coal, B represents Yangquan coal, C represents Xiyang coal, and D represents Lu'an white coal.

[0060] Coal blending experiments were conducted using four types of coal—washed mixed coal, Yangquan coal, Xiyang coal, and Lu'an white coal—as influencing factors. After optimization analysis, the order of importance of the influencing factors for each coal type was Lu'an white coal, washed mixed coal, Xiyang coal, and Yangquan coal. Among them, Yangquan coal had a relatively weak influence in this coal blending scheme. The optimized coal blending scheme obtained through mathematical optimization design is shown in Table 6-3.

[0061] Table 6-3 Optimized Coal Blending Scheme The above optimized design scheme shows that, under the above factors and level selection, the optimized coal blending scheme should be 12% washed mixed coal, 19% Yangquan coal, 10% Xiyang coal, 14% Lu'an white coal, and 45% Mengfa bituminous coal.

[0062] The above six embodiments fully demonstrate the application of the method of the present invention in various practical scenarios, including different quantities of coal types, different combinations of coal types, and a fixed proportion of bituminous coal. Through a systematic process of "factor level design → orthogonal experiment → multi-index detection and scoring → comprehensive scoring → range analysis → determination of the optimal solution," the scientific nature, universality, and effectiveness of the method are fully demonstrated, providing a precise, efficient, and economical optimization solution for blast furnace pulverized coal injection.

[0063] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A multi-index orthogonal optimization coal blending method for blast furnace pulverized coal injection, characterized in that, Includes the following steps: S1. Determine the coal blending factors and levels: Select n types of coal powder to be blended as factors, and set m different blending ratios for each type of coal powder as levels, where n≥2 and m≥2; S2. Design an orthogonal experiment: Based on the factors and levels determined in step S1, select the appropriate orthogonal array to arrange a set of coal blending test schemes; S3. Determine the assessment indicators and conduct the tests: Select at least 4 assessment indicators for evaluating the overall performance of the mixed coal powder, and prepare the mixed coal powder according to the test plan in step S2, and test the values ​​of each assessment indicator for each sample. S4. Assigning scores to assessment indicators: Standardize the values ​​of each assessment indicator obtained in step S3 and convert the actual value of each indicator into a single score between 0 and 10. S5. Calculate the overall score: Sum all the individual scores obtained in step S4 to obtain the overall score for each test plan; S6. Determine the optimal coal blending scheme: Based on the comprehensive score of each test scheme obtained in step S5, perform range analysis to determine the order of importance of each coal powder influencing factor, and determine the optimal coal powder ratio combination accordingly, thus obtaining the optimal coal blending scheme.

2. The multi-index orthogonal optimization coal blending method for blast furnace pulverized coal injection according to claim 1, characterized in that, The pulverized coal mentioned in S1 includes two or more of the following: anthracite, bituminous coal, lean coal, semi-lean coal, lignite, and peat.

3. The multi-index orthogonal optimization coal blending method for blast furnace pulverized coal injection according to claim 2, characterized in that, The pulverized coal mentioned in S1 also includes alternative coal types, which are one or more of the following: semi-coke, upgraded coal, dry quenching coke dust, water quenching coke powder, blast furnace dust, and biomass fuel.

4. The multi-index orthogonal optimization coal blending method for blast furnace pulverized coal injection according to claim 2, characterized in that, The biomass fuel is one or more of the following: charcoal, straw, fruitwood shells, and carbonized rice husks.

5. The multi-index orthogonal optimization coal blending method for blast furnace pulverized coal injection according to claim 1, characterized in that, The orthogonal array mentioned in S2 is L 9 (3) 4 L8 (2) 7 ) or L8 (4 1 ×2 4 ).

6. The multi-index orthogonal optimization coal blending method for blast furnace pulverized coal injection according to claim 1, characterized in that, The assessment indicators described in S3 include at least four of the following: fixed carbon, ash content, sulfur content, grindability index, calorific value, effective calorific value in the furnace, ash fusion properties, fluidity index, jetting index, reactivity, combustion rate, coal-coke replacement ratio, and coal blending cost of the mixed pulverized coal.

7. The multi-index orthogonal optimization coal blending method for blast furnace pulverized coal injection according to claim 1, characterized in that, The formula used for standardization in S4 is: For indicators where a larger value is better, the "optimal performance indicator" is the maximum value and the "worst performance indicator" is the minimum value; for indicators where a smaller value is better, the "optimal performance indicator" is the minimum value and the "worst performance indicator" is the maximum value.

8. The multi-index orthogonal optimization coal blending method for blast furnace pulverized coal injection according to claim 1, characterized in that, The determination of the optimal coal powder ratio combination described in S6 specifically involves selecting the level with the highest average comprehensive score under each factor level and combining them to form the optimized coal blending scheme.

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