Large silo coal blending analysis system

Abstract

The invention relates to a large silo coal blending analysis system, and belongs to the technical field of coking. The system comprises a silo coal resource access statistics module, a switching analysis module, a coal blending ratio difference analysis module, a coal blending and coke quality prediction module and an information display module. Firstly, the silo coal seam distribution condition is established, then the coal seam resource switching condition is analyzed and judged according to the discharging weight, the accumulated coal blending execution deviation rate is calculated, and the coal blending amount and the switching condition of the next period are estimated; the blended coal and coke quality prediction module predicts the quality and analyzes the change condition according to the actual coal blending ratio and the like; and the information display module stores and visually displays the result. The system has strong analysis and prediction capabilities, and can comprehensively and deeply analyze yesterday coal blending conditions, scientifically predict coke quality, intelligently plan today coal blending by means of accumulated coal blending execution deviation rates, simply, conveniently and quickly grasp dynamics and trends, guide coal blending and quality control, improve product quality, reduce cost and enhance competitiveness.

Description

Technical Field

[0001] This invention belongs to the field of coking technology and relates to a large silo coal blending and analysis system. Background Technology

[0002] In the steel production sector, coal blending and coking are crucial steps, and their quality directly affects the quality of coke, which in turn impacts the final quality of steel products and production cost control. Traditional coal storage methods often employ open-air storage yards or ordinary warehouses. These methods have numerous drawbacks, such as requiring large land areas, which undoubtedly increases land costs and makes land acquisition more difficult for enterprises in the current context of increasingly scarce land resources. Furthermore, open-air storage makes coal susceptible to weathering, leading to coal loss and quality degradation. Additionally, the severe dust generated during loading, unloading, and storage causes significant pollution to the surrounding environment, contradicting the concept of green development.

[0003] With technological advancements and increasingly stringent environmental regulations, large silos have emerged as an advanced coal storage facility. Large silos offer significant advantages: their compact structure and small footprint allow for the storage of large quantities of coal within limited space, effectively improving land use efficiency and reducing land costs for businesses. Furthermore, their enclosed structure effectively prevents coal from being affected by external environmental factors during storage, reducing coal loss and quality changes, while also significantly minimizing dust pollution, thus meeting environmental protection requirements. Therefore, they are now widely used in major steel mills across China.

[0004] However, in actual use, large silos have also revealed some problems, posing challenges to the production and operation of steel plants. In steel production, to meet different production needs and product quality requirements, it is necessary to flexibly adjust the coal blending scheme according to the actual situation, which involves frequent switching of coal types or batches within the silos. However, large silos currently have significant shortcomings in coal management, lacking effective tracking methods. Because it is impossible to accurately grasp information such as the type, batch, and specific storage location of the coal within the silos, it is difficult to precisely select the required coal during blending, and can only rely on experience for rough estimation, resulting in difficulty in accurately controlling the coal blending ratio.

[0005] This situation leads to significant fluctuations in coke quality, sometimes failing to meet stable production standards and impacting the quality stability of steel products. To ensure product quality, steel mills often have to raise coal blending standards and increase the proportion of high-quality coal, resulting in a substantial increase in coal blending costs. This not only increases production costs and reduces economic efficiency but also, to some extent, limits the flexibility and market competitiveness of steel production. Therefore, developing a technology or system capable of effectively tracking coal type and batch information within large silos is of great significance for stabilizing coke quality, reducing coal blending costs, and improving the production efficiency of steel enterprises. Summary of the Invention

[0006] In view of this, the purpose of the present invention is to provide a large silo coal blending analysis system, which tracks the coal seam at the bottom of the silo and combines it with the actual coal blending situation to realize the tracking and analysis of batch switching of coal blending in the silo, and at the same time realizes the prediction of coke quality of coal blending in the silo, thereby facilitating the rapid understanding of batch switching in the silo and guiding coal blending.

[0007] To achieve the above objectives, the present invention provides the following technical solution: A large-scale silo coal blending analysis system includes a silo coal resource inflow and outflow statistics module, a silo coal resource switching analysis module, a silo coal blending ratio difference analysis module, a silo blended coal and coke quality prediction module, and an information display module. The silo coal resource entry and exit statistics module counts the coking coal entering each silo, establishes the coal seam distribution of coal resources inside the silo based on the batch number of the coking coal entering the silo, and counts the coal discharge weight of each silo according to a preset time period. The silo coal resource switching analysis module determines whether the coal resource type of the bottom coal seam in the silo has switched in the previous statistical period based on the coal feeding weight and coal seam distribution inside the silo, thus obtaining the silo coal resource switching status. In addition, the silo coal resource switching analysis module also calculates the cumulative coal blending execution deviation rate based on the coal blending ratio in the previous several statistical periods, estimates the coal blending amount for the next statistical period based on the cumulative coal blending execution deviation rate, and then predicts whether the coal resource type inside the silo will switch in the next statistical period. The silo coal blending ratio difference analysis module analyzes the differences in silo coal blending ratios within the previous statistical period based on the coal feeding weight of different silos within the previous statistical period. The silo blended coal and coke quality prediction module predicts the quality of blended coal and coke based on the coal feeding weight and the actual coal blending ratio in the silo during the previous statistical period. The silo blended coal and coke quality prediction module can also predict the quality of blended coal and coke by combining the estimated actual coal blending ratio, and analyze and predict the changes in the quality of blended coal and coke.

[0008] All modules transmit their respective analysis results to the information display module for information storage and display.

[0009] Furthermore, the silo coal resource entry and exit statistics module, based on silo inventory, establishes a coal seam distribution tracking system within silos by classifying consecutive batches of coking coal entering the same silo as a single coal seam. When coal resources enter a corresponding silo, the bottom coal seam formed by the first batch of coking coal entering a silo is defined as... Its weight is The second batch of coking coal entering the silo formed a coal seam that was... Its weight is And so on, let's assume the coal seam in a single silo is divided into... Layer, number The weight of coal in the layer is recorded as Let the silo's stock be . ,have This establishes the coal seam distribution in all silos. When coal resources flow out of a silo, a statistical period is set. The weight of the material inside is This results in the material feeding weight data for different statistical periods.

[0010] Furthermore, the switching judgment process of the silo coal resource switching analysis module for the previous statistical period is as follows: First, determine the number of coal seam levels in the silo: initial settings. =1, Judgment conditions Is it true? If so, then determine the current situation. The value is the number of coal seam levels in the selected silo; otherwise, = +1, Repeat the above process until... Establishment, determination of the number of coal seam levels in the silo. ; If the number of levels If the coal seam resource has changed, it is considered that the coal seam resource has changed; otherwise, it is considered that the coal seam resource has not changed. This is based on the number of coal seam levels used in the lower part of the silo. In addition, the batch number of each coal seam is obtained to track the switching of batch numbers in the coal feeding group of the silo and the corresponding weight changes.

[0011] Furthermore, the silo coal blending ratio difference analysis module calculates the difference between the actual coal blending ratio and the planned coal blending ratio as follows: Set in the previous statistical period Inside, a total of There are 10 silos, and the unloading weight of each silo is recorded as follows: For silos In fact, its actual coal blending ratio is:

[0012] Setting up silos In the previous statistical period The planned coal blending ratio within the area is For silos In the previous statistical period within .

[0013] Furthermore, the silo coal and coke quality prediction module can predict the quality of coal blending based on the actual coal blending ratio in the previous statistical period. The quality of blended coal and coke for the previous statistical period is calculated based on the corresponding quality indicators for each silo. Let the quality indicator corresponding to silo m be... ,in, The quality indicators of blended coal include ash content (Ad), sulfur content (St,d), G value, and Y value. The calculation method is as follows:

[0014] coke quality The calculation method is as follows:

[0015] in, This represents the conversion coefficients for the corresponding indicators ash content (Ad) and sulfur content (St,d).

[0016] Furthermore, the silo coal resource switching analysis module also updates the coal seam situation within the silo based on the resource switching analysis results of the previous statistical period; assuming the silo... Contains coal seams It is known that the coal seam level for silo m was determined in the previous statistical period. and the corresponding material weight Then the coal seam number of silo m at the current moment is updated to Reset coal seam levels Among them, coal seams The batch number at that time corresponds to the level in the previous statistical period. Batch number, coal seam Remaining coal resources weight at that time Coal seam The batch number at that time corresponds sequentially to the hierarchy within the previous statistical period. The batch number at that time, and the remaining coal resource weight corresponding to the batch number remains unchanged.

[0017] Furthermore, the silo coal resource switching analysis module also predicts whether the silo coal seam resource type will switch in the next statistical period. The process includes: First, calculate the cumulative coal blending performance deviation rate of the silos; set the coal blending performance deviation rate of the silos within a statistical period as follows:

[0018] in, Indicates the statistical period The actual coal blending ratio inside, Indicates the statistical period The planned coal blending ratio within the scope; Then, the cumulative coal blending execution deviation rate is calculated based on the coal blending execution deviation rate over several statistical periods. :

[0019] in, Indicates the preceding One statistical period, For the front The first statistical period One statistical period, For the first Coal blending execution deviation rate within a statistical period For the first The duration of each statistical period; Then, based on the cumulative coal blending deviation rate within a certain period, the estimated coal blending quantity for the silo is calculated. Estimated coal blending volume for each silo Represented as:

[0020] in, Indicates the first The planned coal blending ratio for each silo Indicates the first Cumulative coal blending deviation rate of each silo This indicates the planned total coal allocation. Based on the updated coal seam resource distribution, predict whether the resources of each seam will switch in the next statistical period: First, determine the number of coal seam levels in the silo to be selected. Initial settings =1, Judgment conditions Is it true? If so, then determine the current situation. The value is the number of coal seam levels in the selected silo; otherwise, = +1, Repeat the above process until... Establishment, determination of the number of coal seam levels in the silo. ; like Then predict the silo The coal seam resource type within the silo will switch; otherwise, the prediction silo will be affected. The coal seam resource type within the silo will not change; based on the number of coal seams used in the lower part of the silo and the corresponding batch number of each coal seam, the switching of the batch number of coal feeding group and the weight change in the next statistical period are predicted.

[0021] Furthermore, the silo-based coal and coke quality prediction module also performs individual silo analysis for the next statistical period. Predictive quality indicators The process is as follows: First, calculate the estimated actual coal blending ratio for each silo. Represented as:

[0022] Let the first The quality index of the layer is The corresponding prediction quality index The calculation process is as follows:

[0023] Calculate the silos in sequence The predicted quality indicators are: air-dried ash content (Ad), dried ash-free volatile matter (Vdaf), dried total sulfur content (St,d), adhesion index (G), and maximum thickness of the adhesive layer (Y). ,Right now , , , , .

[0024] Furthermore, the silo coal and coke quality prediction module predicts the quality indicators for the next statistical period. and the estimated actual coal blending ratio Predicting the quality of blended coal :

[0025] Then, based on the quality of the blended coal Predicting coke quality .

[0026] Furthermore, the information display module receives the results from the silo coal resource inflow and outflow statistics module, the silo coal resource switching analysis module, the silo coal blending ratio difference analysis module, the silo blended coal and coke quality prediction module, and the information display module, and then visualizes the information.

[0027] The beneficial effects of this invention are as follows: The system of this invention possesses powerful data analysis and prediction capabilities, enabling comprehensive and in-depth automatic analysis of yesterday's silo coal blending. On one hand, it can accurately identify the switching points and patterns of silo coal blending varieties, clearly presenting the alternating use of different coal varieties; on the other hand, it can accurately calculate the actual coal blending ratio of the silos, providing detailed and accurate data support for the production process. Simultaneously, relying on advanced algorithm models, the system scientifically predicts and analyzes the quality of yesterday's coke production, anticipating potential quality fluctuations.

[0028] This invention also enables intelligent planning of today's silo coal blending operations by utilizing the crucial indicator of the cumulative coal blending deviation rate. Based on the deviation rate data, it automatically analyzes and determines the switching strategy for today's silo coal blending types, ensuring the scientific and rational nature of the type switching. Simultaneously, it accurately predicts the quality of today's blended coal and the final coke quality, providing forward-looking guidance for production personnel.

[0029] The large-scale silo coal blending analysis system of this invention enables simple and rapid monitoring of the dynamic switching of coal blending varieties in silos, as well as the changing trends in the quality of blended coal and coke. This helps production personnel adjust coal blending plans in a timely manner, optimize production processes, effectively guide coal blending and coke quality control, thereby improving product quality, reducing production costs, and enhancing the company's competitiveness in the market.

[0030] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0031] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein: Figure 1 This is a schematic diagram of the overall structure of the large silo coal blending analysis system according to an embodiment of the present invention. Detailed Implementation

[0032] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0033] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual pictures. They should not be construed as limiting the invention. To better illustrate the embodiments of the invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0034] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that if terms such as "upper," "lower," "left," "right," "front," and "rear" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present invention. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0035] Please see Figure 1 This is a large-scale silo coal blending analysis system.

[0036] Example This embodiment describes in detail a large silo coal blending analysis system, such as... Figure 1 As shown, it includes a silo coal resource inflow and outflow statistics module, a silo coal resource switching analysis module, a silo coal blending ratio difference analysis module, a silo blended coal and coke quality prediction module, and an information display module. The silo coal resource entry and exit statistics module counts the coking coal entering each silo, establishes the coal seam distribution of coal resources inside the silo based on the batch number of the coking coal entering the silo, and counts the coal discharge weight of each silo according to a preset time period. The silo coal resource switching analysis module determines whether the coal resource type of the bottom coal seam in the silo has switched in the previous statistical period based on the coal feeding weight and coal seam distribution inside the silo, thus obtaining the silo coal resource switching status. In addition, the silo coal resource switching analysis module also calculates the cumulative coal blending execution deviation rate based on the coal blending ratio in the previous several statistical periods, estimates the coal blending amount for the next statistical period based on the cumulative coal blending execution deviation rate, and then predicts whether the coal resource type inside the silo will switch in the next statistical period. The silo coal blending ratio difference analysis module analyzes the differences in silo coal blending ratios within the previous statistical period based on the coal feeding weight of different silos within the previous statistical period. The silo blended coal and coke quality prediction module predicts the quality of blended coal and coke based on the coal feeding weight and the actual coal blending ratio in the silo during the previous statistical period. The silo blended coal and coke quality prediction module can also predict the quality of blended coal and coke by combining the estimated actual coal blending ratio, and analyze and predict the changes in the quality of blended coal and coke.

[0037] All modules transmit their respective analysis results to the information display module for information storage and display.

[0038] It is worth mentioning that the previous and next statistical periods described in this invention can be understood as two adjacent statistical periods. Typically, the coal resource entry and exit statistics module for silos uses a "shift" or "day" as a statistical period. In this embodiment, a "day" is selected as the statistical period for detailed explanation. The previous statistical period is yesterday, and the next statistical period is today.

[0039] In this embodiment, the silo coal resource entry and exit statistics module uses the silo inventory as a basis. It classifies coal seams as those formed by consecutive batches of coking coal entering the same silo. The module works backward from the most recent batch until the last batch's entry volume is greater than or equal to the silo inventory, thus tracking the coal seam distribution within the silo and knowing the distribution of coal seams at the bottom of the silo and the weight of each seam. Specifically, when coal resources enter a corresponding silo, the bottommost coal seam formed by the first batch of coking coal entering a silo is defined as... Its weight is The second batch of coking coal entering the silo formed a coal seam that was... Its weight is And so on, let's assume the coal seam in a single silo is divided into... Layer, number The weight of coal in the layer is recorded as Let the silo's stock be . ,have This establishes the distribution of coal seams in all silos.

[0040] When coal resources flow out of a silo, a statistical period is set. The weight of the material inside is This results in the material feeding weight data for different statistical periods.

[0041] In this embodiment, the silo coal resource switching analysis module analyzes the switching status of the lower coal seam of the silo based on the silo discharge weight of the previous day, and derives the batch number of the coal used for silo blending from this analysis; specifically, its switching judgment... The process involves: First, determining the number of coal seam levels in the silo: Initial settings. =1, Judgment conditions Is it true? If so, then determine the current situation. The value is the number of coal seam levels in the selected silo; otherwise, = +1,; Repeat the above process until... Establishment, determination of the number of coal seam levels in the silo. If the number of levels If the coal seam resource has changed, it is considered that the coal seam resource has changed; otherwise, it is considered that the coal seam resource has not changed. Based on the number of coal seams used in the lower part of the silo and the corresponding batch number of each coal seam, the batch number change of the coal blending and feeding group in the silo and the corresponding weight change can be known.

[0042] In this embodiment, based on the coal seam tracking of the silo, it is assumed that on October 2, 2025, the bottom batch number of No. 1 silo is ZPT20250528001 (coking coal 01), with Ad 10.68%, Vdaf 21.58%, St,d 1.58%, G 79, Y14mm, and a stock of 1600 tons; the upper batch number of ZPT20250528001 is ZPT20250529002 (coking coal 02), with ZPT20250529002 having Ad 10.89%, Vdaf 23.79%, St,d 1.62%, G 76, Y13mm, and a corresponding weight of 2000 tons.

[0043] Assuming the unloading weight of silo #1 on October 2, 2025 is 1170 tons, then the initial... =1, S=S1=1600 tons, the coal quantity of a single silo is S0=1170 tons, therefore Established on October 2, 2025, the No. 1 silo only contained the bottom coal seam, with the corresponding batch number ZPT20250528001, and no coal seam resource switching was performed.

[0044] Assuming the unloading weight of silo #1 on October 2, 2025 is 1800 tons, then the initial... =1, S=S1=1600 tons, the coal distribution of a single silo is S0=1800 tons; This is not true; therefore, = +1, ,Right now =2, S=S1+S2=1600 tons+2000 tons=3600 tons, at this time Established on October 2, 2025, the No. 1 silo will use the bottom two coal seams for material feeding, with corresponding batch numbers ZPT20250528001 and ZPT20250529002. The coal seam resources for No. 1 silo feeding will be switched on October 2, 2025; the specific switching details are shown in Table 1 below. Table 1

[0045] Therefore, the silo coal resource switching analysis module concludes that: for the switching of the type of coal in silo #1, coking coal 01 is switched to coking coal 02, and batch number ZPT20250528001 is switched to ZPT20250529002. After the switching, Ad +0.21%, Vdaf +2.21%, St,d +0.04%, G -3, Y -1.

[0046] In this embodiment, the silo coal blending ratio difference analysis module calculates the actual coal blending ratio of the silos based on the material weight fed into silos of different coal tower numbers (coke oven numbers). If the difference between the actual coal blending ratio and the planned coal blending ratio is ≥1%, it is determined that the silo coal blending ratio needs to be adjusted. Specifically, this is set based on the previous statistical period. within, share There are 10 silos, and the unloading weight of each silo is recorded as follows: For silos In fact, its actual coal blending ratio is:

[0047] Setting up silos In the previous statistical period The planned coal blending ratio within the area is For silos In the previous statistical period within .

[0048] Assuming the planned coal blending ratio, actual coal weight, actual coal blending ratio, and coal blending ratio difference for silos #1-#8 on October 2, 2025 are shown in Table 2 below: Table 2

[0049] As shown in the table above, the analysis by the silo blending ratio adjustment module of the silo coal blending analysis system concludes that: the planned coal blending ratio of silo #3 is 15%, and the actual coal blending ratio is 13.74%, which is -1.26% compared to the planned coal blending ratio; the planned coal blending ratio of silo #4 is 10%, and the actual coal blending ratio is 11.37%, which is +1.37% compared to the planned coal blending ratio.

[0050] In this embodiment, the silo blended coal and coke quality prediction module predicts the blending ratio based on the actual blending ratio of the silo blended coal the previous day. and silo unloading weight Predicting the quality of blended coal (such as ash content Ad, sulfur content St,d, G value, Y value, etc.). Specifically, let the quality index corresponding to silo m be... ,in, The quality indicators of blended coal include ash content (Ad), sulfur content (St,d), G value, and Y value. The calculation method is as follows:

[0051] In this embodiment, it is assumed that the actual coal blending ratios of silos #1-#8 and the corresponding masses of the silos on October 2, 2025 are shown in Table 3 below: Table 3

[0052] Predicting the quality of blended coal: Ad=(10.79%*10.68%+19.18%*9.4%+13.74%*9.5%+11.37%*10.98%+9.04%*7.45%+15.88%*10.51%+14.09%*9.8%+5.91%*9.76) / 100%=9.81%.

[0053] Similarly, other quality indicators of the blended coal are predicted, as shown in Table 4 below: Table 4

[0054] The silo-based coal and coke quality prediction module also predicts coke quality. Based on the quality of the blended coal, it predicts coke quality such as ash content (Ad) and sulfur content (St,d). The calculation method is as follows:

[0055] in, This represents the conversion coefficient between the ash content (Ad) and sulfur content (St,d). For example, in this embodiment, coke Ad = 9.81% * 1.3 = 12.75%; coke St,d = 0.89% * 0.9 = 0.80%.

[0056] In this embodiment, the silo coal resource switching analysis module also predicts whether the silo coal seam resource type will switch in the next statistical period. The process includes: First, calculate the cumulative coal blending performance deviation rate of the silos; set the coal blending performance deviation rate of the silos within a statistical period as follows:

[0057] in, Indicates the statistical period The actual coal blending ratio inside, Indicates the statistical period The planned coal blending ratio within the scope.

[0058] In this embodiment, it is assumed that the planned coal blending ratio, actual coal weight, and actual coal blending ratio of silos 1#-8# on October 2, 2025 are shown in Table 5 below: Table 5

[0059] The actual coal blending ratio of silo #1 is 1170 / 10844*100%=10.79%. Similarly, the actual coal blending ratios of silos #2-#8 can be calculated.

[0060] The deviation rate of coal blending in silo #1 is calculated as follows: (10.79 - 10) / 10 * 100% = 7.9%. Similarly, the deviation rate of coal blending in silos #2-#8 can be calculated, as shown in Table 6 below: Table 6

[0061] Then, the cumulative coal blending execution deviation rate is calculated based on the coal blending execution deviation rate over several statistical periods. :

[0062] in, Indicates the preceding One statistical period, For the front The first statistical period One statistical period, For the first Coal blending execution deviation rate within a statistical period For the first The duration of each statistical period.

[0063] In this embodiment, it is assumed that the coal blending deviation rate of silos #1-#8 from September 29th to October 2nd, 2025 is shown in Table 7 below: Table 7

[0064] Cumulative coal blending deviation rate of Silo #1 = (900*7.9% + 1000*6.2% + 950*8.2% + 950*7.5%) / (900 + 1000 + 950 + 950) = 7.4% Similarly, the cumulative coal blending deviation rate of silos #2-#8 can be calculated, as shown in Table 8 below. Table 8

[0065] The estimated coal blending quantity for the silo is calculated based on the cumulative coal blending deviation rate over a certain period. Then, the... Estimated coal blending volume for each silo Represented as:

[0066] The estimated coal blending quantity for all silos Represented as:

[0067] in, Indicates the first The planned coal blending ratio for each silo Indicates the first Cumulative coal blending deviation rate of each silo This indicates the planned total coal allocation.

[0068] In this embodiment, assuming a daily coke production of 8,000 tons and a coal consumption of 1.35 tons per ton of coke, the planned daily coal consumption is 10,800 tons. Coal blending technicians formulate a coal blending plan, as shown in Table 9 below: Table 9

[0069] Estimated coal blending quantity: Assuming the planned daily consumption is taken as the planned daily coal blending amount, the estimated coal blending amount for silo #1, calculated from the table above, is 10% * (1 + 7.4%) * 10800 = 1160 tons. Similarly, the estimated coal blending amounts for silos #2-#8 can be calculated, as shown in Table 10 below: Table 10

[0070] The estimated total coal blending volume = ∑ estimated coal blending volume per silo, therefore the estimated total coal blending volume = 10824 tons.

[0071] Estimated coal blending ratio: Estimated coal blending ratio = Estimated coal blending amount per silo / Estimated coal blending amount * 100% Based on the table above, the estimated coal blending ratio for each silo is calculated as follows: Estimated coal blending ratio for silo #1 = 1160 / 10824 * 100% = 10.72% Similarly, the estimated coal blending ratios for silos #2-#8 can be calculated, as shown in Table 11 below: Table 11

[0072] The silo coal resource switching analysis module also updates the coal seam situation within the silo based on the resource switching analysis results of the previous statistical period; (The silo is then set up.) Contains coal seams It is known that the coal seam level for silo m was determined in the previous statistical period. and the corresponding material weight Then the coal seam number of silo m at the current moment is updated to Reset coal seam levels Among them, coal seams The batch number at that time corresponds to the level in the previous statistical period. Batch number, coal seam Remaining coal resources weight at that time Coal seam The batch number at that time corresponds sequentially to the hierarchy within the previous statistical period. The batch number at that time, and the remaining coal resource weight corresponding to the batch number remains unchanged.

[0073] Based on the updated coal seam resource distribution, predict whether the resources of each seam will switch in the next statistical period: First, determine the number of coal seam levels in the silo to be selected. The specific process is as follows: Initial setup =1, Judgment conditions Is it true? If so, then determine the current situation. The value is the number of coal seam levels in the selected silo; otherwise, = +1, Repeat the above process until... Establishment, determination of the number of coal seam levels in the silo. ;like Then predict the silo The coal seam resource type within the silo will switch; otherwise, the prediction silo will be affected. The type of coal seam resources within the silo will not change. Based on the quantity of coal seams used in the lower part of the silo and the corresponding batch number of each coal seam, the batch number switching and weight changes of the coal blending and feeding group in the silo can be determined.

[0074] Based on the coal seam tracking data of the silos, assuming that on October 2, 2025, the bottommost batch of coal in silo #1 has the batch number ZPT20250528001 (coking coal 01), with Ad 10.68%, Vdaf 21.58%, St,d 1.58%, G 79, Y 14mm, and a stock volume of 1600 tons; the upper batch number of ZPT20250528001 is ZPT20250529002 (coking coal 02), with Ad 10.89%, Vdaf 23.79%, St,d 1.62%, G 76, Y 13mm, and a corresponding weight of 2000 tons.

[0075] On October 2, 2025, the unloading weight of silo #1 was 1170 tons, therefore the initial... =1, S=S1=1600 tons, the coal blending amount of No.1 silo S0=1170 tons; Therefore, the coal in the lower part of Silo #1 was not switched. On October 3, 2025, the lower part of Silo #1 contained ZPT20250528001 (coking coal 01), with a remaining coal content of 1600 tons - 1170 tons = 430 tons.

[0076] The updated batch number corresponding to k=1 is ZPT20250528001 (coking coal 01), with a remaining coal volume of 1600 tons - 1170 tons = 430 tons. Coal switching prediction for silos on October 3, 2025: Estimated coal blending for silo #1: 1160 tons. Therefore, S0 = 1160 tons. Initial... =1, S=S1=430 tons, the coal blending amount of No.1 silo S0=1160 tons; This is not true; therefore, = +1, ,Right now =2, S=S1+S2=430 tons+2000 tons=2430 tons, at this time Established on October 3, 2025, the No. 1 silo will be equipped with two coal seams for material feeding, with the corresponding batch numbers of the coal seams being ZPT20250528001 and ZPT20250529002.

[0077] ZPT20250528001 Usage Weight = S1 = 430 tons ZPT20250529002 Usage Weight = S0 - S1 = 1160 tons - 430 tons = 730 tons Therefore, the predicted coal quality for use in Silo #1 on October 3, 2025 is shown in Table 12 below: Table 12

[0078] Predicted Ad = (430*10.68%+730*10.89%) / (430+730) = 10.81%.

[0079] Similarly, other predicted quality indicators for silo #1 can be calculated: Vdaf 22.97%, St,d 1.61%, G 77.11%, and Y 13.37mm. Based on the above method, the predicted quality of other silos under the estimated coal blending weight is predicted, as shown in Table 13 below: Table 13

[0080] The silo-based blended coal and coke quality prediction module also predicts and determines the blended coal quality for the next statistical period. The specific process includes: Calculation of estimated actual coal blending ratio for a single silo. Represented as:

[0081] Let the first The quality index of the layer is The corresponding prediction quality index The calculation process is as follows:

[0082] Calculate the silos in sequence The predicted quality indicators are: air-dried ash content (Ad), dried ash-free volatile matter (Vdaf), dried total sulfur content (St,d), adhesion index (G), and maximum thickness of the adhesive layer (Y). ,Right now , , , , .

[0083] The quality prediction of blended coal is achieved by estimating the actual coal blending ratio and the predicted quality indicators of the silos.

[0084] The silo-based blended coal and coke quality prediction module can also predict changes in blended coal quality. Based on the blended coal quality of the previous day and the predicted blended coal quality for today, it determines the differences in various quality indicators of the blended coal.

[0085] In this embodiment, the quality of the blended coal is determined based on the estimated blending ratio and the predicted quality under the estimated blending weight.

[0086] The estimated coal blending ratios and estimated coal blending weights of silos 1#-8# as of October 3, 2025 are shown in Table 14 below: Table 14

[0087] Predicting the quality of blended coal: Ad = (10.72%*10.81%+13.85%*9.4%+13.78%*9.5%+9.96%*10.98%+7.27%*7.45%+15.90%*10.51%+16.75%*9.8%+11.77%*9.76) / 100% = 9.87%. Similarly, other quality indicators for the blended coal are predicted, as shown in Table 15 below: Table 15

[0088] The silo-based coal and coke quality prediction module can also predict the coke quality changes for the next statistical period, i.e., today. Based on the coke quality of the previous statistical period (the day before yesterday) and the predicted coke quality for today, it determines the differences in various quality indicators of the coke. Predicted coke quality: Coke Ad = 9.87% * 1.3 = 12.83%. Coke St,d = 0.90% * 0.9 = 0.81%.

[0089] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A large-scale silo coal blending analysis system, characterized in that: The system includes a silo coal resource inflow and outflow statistics module, a silo coal resource switching analysis module, a silo coal blending ratio difference analysis module, a silo blended coal and coke quality prediction module, and an information display module. The silo coal resource entry and exit statistics module counts the coking coal entering each silo, establishes the coal seam distribution of coal resources inside the silo based on the batch number of the coking coal entering the silo, and counts the coal discharge weight of each silo according to a preset time period. The silo coal resource switching analysis module determines whether the coal resource type of the bottom coal seam in the silo has switched in the previous statistical period based on the coal feeding weight and coal seam distribution inside the silo, thus obtaining the silo coal resource switching status. In addition, the silo coal resource switching analysis module also calculates the cumulative coal blending execution deviation rate based on the coal blending ratio in the previous several statistical periods, estimates the coal blending amount for the next statistical period based on the cumulative coal blending execution deviation rate, and then predicts whether the coal resource type inside the silo will switch in the next statistical period. The silo coal blending ratio difference analysis module analyzes the differences in silo coal blending ratios within the previous statistical period based on the coal feeding weight of different silos within the previous statistical period. The silo blended coal and coke quality prediction module predicts the blended coal quality and coke quality based on the coal feeding weight and actual coal blending ratio in the silo during the previous statistical period. The silo blended coal and coke quality prediction module can also predict the blended coal quality and coke quality by combining the estimated actual coal blending ratio, and analyze and predict the changes in blended coal quality and coke quality. All modules transmit their respective analysis results to the information display module for information storage and display.

2. The large silo coal blending analysis system according to claim 1, characterized in that: The silo coal resource entry and exit statistics module is based on silo inventory. It tracks the distribution of coal seams within a silo by classifying consecutive batches of coking coal entering the same silo as a single coal seam. When coal resources enter a corresponding silo, the bottommost coal seam of that silo formed by the first batch of coking coal entering that silo is defined as... Its weight is The second batch of coking coal entering the silo formed a coal seam that was... Its weight is And so on, let's assume the coal seam in a single silo is divided into... Layer, number The weight of coal in the layer is recorded as Let the silo's stock be . ,have This establishes the coal seam distribution in all silos. When coal resources flow out of a silo, a statistical period is set. The weight of the material inside is This results in the material feeding weight data for different statistical periods.

3. The large silo coal blending analysis system according to claim 2, characterized in that: The switching judgment process for the silo coal resource switching analysis module within the previous statistical period is as follows: First, determine the number of coal seam levels in the silo: initial settings. =1, Judgment conditions Is it true? If so, then determine the current situation. The value is the number of coal seam levels in the selected silo; otherwise, = +1, Repeat the above process until... Establishment, determination of the number of coal seam levels in the silo. ; If the number of levels If the coal seam resource has changed, it is considered that the coal seam resource has changed; otherwise, it is considered that the coal seam resource has not changed. This is based on the number of coal seam levels used in the lower part of the silo. In addition, the batch number of each coal seam is obtained to track the switching of batch numbers in the coal feeding group of the silo and the corresponding weight changes.

4. The large silo coal blending analysis system according to claim 3, characterized in that: The silo coal blending ratio difference analysis module calculates the difference between the actual coal blending ratio and the planned coal blending ratio as follows: Set in the previous statistical period Inside, a total of There are 10 silos, and the unloading weight of each silo is recorded as follows: For silos In fact, its actual coal blending ratio is: Setting up silos In the previous statistical period The planned coal blending ratio within the area is For silos In the previous statistical period within .

5. The large silo coal blending analysis system according to claim 4, characterized in that: The silo coal and coke quality prediction module can predict the quality of coal blending based on the actual coal blending ratio in the previous statistical period. The quality of blended coal and coke for the previous statistical period is calculated based on the corresponding quality indicators for each silo. Let the quality indicator corresponding to silo m be... ,in, The quality indicators of blended coal include ash content (Ad), sulfur content (St,d), G value, and Y value. The calculation method is as follows: coke quality The calculation method is as follows: in, This represents the conversion coefficients for the corresponding indicators ash content (Ad) and sulfur content (St,d).

6. The large silo coal blending analysis system according to claim 3, characterized in that: The silo coal resource switching analysis module also updates the coal seam situation within the silo based on the resource switching analysis results of the previous statistical period; (The silo is then set up.) Contains coal seams It is known that the silos were identified in the previous statistical period. coal seam hierarchy and the corresponding material weight Then the coal seam number of silo m at the current moment is updated to Reset coal seam levels Among them, coal seams The batch number at that time corresponds to the level in the previous statistical period. Batch number, coal seam The remaining coal resources weight at that time Coal seam The batch number at that time corresponds sequentially to the hierarchy within the previous statistical period. The batch number at that time, and the remaining coal resource weight corresponding to the batch number remains unchanged.

7. A large silo coal blending analysis system according to claim 6, characterized in that: The silo coal resource switching analysis module also predicts whether the silo coal seam resource type will switch in the next statistical period. The process includes: First, calculate the cumulative coal blending performance deviation rate of the silos; set the coal blending performance deviation rate of the silos within a statistical period as follows: in, Indicates the statistical period The actual coal blending ratio inside, Indicates the statistical period The planned coal blending ratio within the scope; Then, the cumulative coal blending execution deviation rate is calculated based on the coal blending execution deviation rate over several statistical periods. : in, Indicates the preceding One statistical period, For the front The first statistical period One statistical period, For the first Coal blending execution deviation rate within a statistical period For the first The duration of each statistical period; Then, based on the cumulative coal blending deviation rate within a certain period, the estimated coal blending quantity for the silo is calculated. Estimated coal blending volume for each silo Represented as: in, Indicates the first The planned coal blending ratio for each silo Indicates the first Cumulative coal blending deviation rate of each silo This indicates the planned total coal allocation. Based on the updated coal seam resource distribution, predict whether the resources of each seam will switch in the next statistical period: First, determine the number of coal seam levels in the silo to be selected. Initial settings =1, Judgment conditions Is it true? If so, then determine the current situation. The value is the number of coal seam levels in the selected silo; otherwise, = +1, Repeat the above process until... Establishment, determination of the number of coal seam levels in the silo. ; like Then predict the silo The coal seam resource type within the silo will switch; otherwise, the prediction silo will be affected. The coal seam resource type within the silo will not change; based on the number of coal seams used in the lower part of the silo and the corresponding batch number of each coal seam, the switching of the batch number of coal feeding group and the weight change in the next statistical period are predicted.

8. A large silo coal blending analysis system according to claim 7, characterized in that: The silo-based coal and coke quality prediction module also performs individual silo analysis for the next statistical period. Predictive quality indicators The process is as follows: First, calculate the estimated actual coal blending ratio for each silo. Represented as: Let the first The quality index of the layer is The corresponding prediction quality index The calculation process is as follows: Calculate the silos sequentially The predicted quality indicators are: air-dried ash content (Ad), dried ash-free volatile matter (Vdaf), dried total sulfur content (St,d), adhesion index (G), and maximum thickness of the adhesive layer (Y). ,Right now , , , , .

9. A large silo coal blending analysis system according to claim 8, characterized in that: The silo coal and coke quality prediction module predicts the quality indicators for the next statistical period. and the estimated actual coal blending ratio Predicting the quality of blended coal : Then, based on the quality of the blended coal Predicting coke quality .

10. A large silo coal blending analysis system according to claim 1, characterized in that: The information display module receives the results from the silo coal resource inflow and outflow statistics module, the silo coal resource switching analysis module, the silo coal blending ratio difference analysis module, the silo blended coal and coke quality prediction module, and the information display module, and then visualizes the information.

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