Sintering raw material ratio calculation method and related equipment

Abstract

The invention discloses a sintering raw material ratio calculation method and related equipment, relates to the field of smelting, and mainly aims to solve the problem that a traditional method is difficult to process a sintering raw material ratio under a multi-constraint condition, so that a decision is slow. The method comprises the following steps: acquiring basic information of sintering raw materials and constraint information of a sintering raw material ratio; determining a ratio combination scheme based on the basic information of the sintering raw materials and the constraint information of the ratio of the sintering raw materials; and selecting an optimal solution from the matching combination scheme. The method is used for the sintering raw material ratio calculation process.

Description

Technical Field

[0001] The present invention relates to the field of smelting, and in particular to a method for calculating the proportion of sintering raw materials and related equipment. Background Art

[0002] Ironmaking is a critical process in steel mills, and sintered ore is one of its primary raw materials. Sintered ore is produced by mixing iron ore, return ore (recycled sintering scrap), fuel (such as coke), and flux (such as limestone) in a specific proportion and then sintering them at high temperatures into agglomerates. The quality of sintered ore directly affects the cost of molten iron and smelting efficiency.

[0003] However, traditional evaluation methods rely solely on a single metric. For example, when evaluating the quality of sintering raw materials, they often focus solely on price or iron content. Consequently, traditional manual calculations or simple models struggle to handle the complex combination of multiple constraints, resulting in slow decision-making. Summary of the Invention

[0004] In view of the above problems, the present invention provides a method for calculating the sintering raw material ratio and related equipment, the main purpose of which is to solve the problem that traditional methods are difficult to handle the sintering raw material ratio under multiple constraints, resulting in slow decision-making.

[0005] To solve at least one of the above technical problems, in a first aspect, the present invention provides a method for calculating a sintering raw material ratio, the method comprising:

[0006] Obtain basic information of sintering raw materials and constraint information of sintering raw material ratio;

[0007] Determining a ratio combination scheme based on the basic information of the sintering raw materials and the constraint information of the sintering raw material ratio;

[0008] Select the optimal solution from the above-mentioned combination schemes.

[0009] Optional,

[0010] Basic information of the sintering raw materials, including chemical composition, price and inventory;

[0011] The constraint information of the sintering raw material ratio includes: upper and lower limits of the ratio, component constraints, and mineral ratio constraints.

[0012] Optionally, selecting the optimal solution from the ratio combination schemes includes:

[0013] Constructing a linear programming model for each ore proportioning combination scheme, wherein the linear programming model takes the lowest ore proportioning cost as the objective function;

[0014] Solve the linear programming model corresponding to each ratio combination scheme;

[0015] Obtain the optimal solution based on the solution results of each linear programming model.

[0016] Optionally, the process constraints of the linear programming model include burning amount constraint, ratio constraint, hematite ratio constraint, limonite ratio constraint, magnetite ratio constraint, main ore ratio constraint, Brazilian ore ratio constraint, composition constraint, alkali metal content constraint, taste constraint, alkalinity constraint, inlet SO2 concentration constraint and inventory constraint.

[0017] Optionally, the method further includes:

[0018] Get user filtering conditions;

[0019] Obtain a matching combination scheme in the linear programming model that meets the user screening condition.

[0020] Optionally, the proportion combination scheme includes the dry material amount, wet material amount, ore type ratio and sintered ore composition of the sintering raw materials.

[0021] Optionally, the method further includes:

[0022] In the case where there is a new combination and ratio combination scheme, obtaining a corresponding linear programming model of the new combination and ratio combination scheme;

[0023] The optimal solution is determined based on the comparison results of the solution results of the linear programming model corresponding to the newly added combination and matching scheme and the solution results of the linear programming model corresponding to the original combination and matching scheme.

[0024] In a second aspect, an embodiment of the present invention further provides a sintering raw material ratio calculation device, comprising:

[0025] An acquisition unit, used to acquire basic information of sintering raw materials and constraint information of sintering raw material ratio;

[0026] a determining unit, configured to determine a ratio combination scheme based on the basic information of the sintering raw materials and the constraint information of the sintering raw material ratio;

[0027] A selection unit is used to select the optimal solution from the matching combination schemes.

[0028] To achieve the above object, according to a third aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored program, wherein when the program is executed by a processor, the steps of the above-mentioned method for calculating the sintering raw material ratio are implemented.

[0029] In order to achieve the above-mentioned purpose, according to the fourth aspect of the present invention, there is provided an electronic device, comprising at least one processor and at least one memory connected to the processor; wherein the above-mentioned processor is used to call the program instructions in the above-mentioned memory to execute the steps of the above-mentioned sintering raw material ratio calculation method.

[0030] Through the above technical solution, considering that traditional methods rely on historical data or fixed parameters and cannot cope with dynamic scenarios such as raw material price fluctuations (such as sudden changes in iron ore futures prices) or inventory crises (such as temporary shortages of flux), which can easily lead to the failure of the solution, this application can dynamically set filtering conditions based on production needs, and flexibly adjust the filtering conditions between cost, quality, environmental protection and other goals, and then achieve scientific decision-making through dynamic data integration, multi-constraint modeling and efficient calculation.

[0031] Correspondingly, the sintering raw material ratio calculation device, equipment and computer-readable storage medium provided in the embodiments of the present invention also have the above-mentioned technical effects.

[0032] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are specifically listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present invention. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0034] Figure 1 A schematic flow chart of a method for calculating a sintering raw material ratio according to an embodiment of the present invention is shown;

[0035] Figure 2 A schematic block diagram showing the composition of a sintering raw material ratio calculation device provided by an embodiment of the present invention is shown;

[0036] Figure 3 A schematic block diagram of the composition of an electronic device for calculating sintering raw material ratios provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0037] Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0038] In order to solve the problem that traditional methods are difficult to handle the sintering raw material ratio under multiple constraints, resulting in slow decision-making, the embodiment of the present invention provides a sintering raw material ratio calculation method, such as Figure 1 As shown, the method includes:

[0039] S101, obtaining basic information of sintering raw materials and constraint information of sintering raw material ratio;

[0040] In one embodiment,

[0041] Basic information of the sintering raw materials, including chemical composition, price and inventory;

[0042] The constraint information of the sintering raw material ratio includes: upper and lower limits of the ratio, component constraints, and mineral ratio constraints.

[0043] For example, the chemical composition of sintering raw materials, including data on 23 components such as TFe (total iron content), SiO2 (silicon dioxide), and CaO (calcium oxide), is collected in real time from the steel mill's MES system, procurement system, and inventory system. Price refers to the purchase price per unit of the raw materials, provided by the procurement system. Inventory quantity refers to the real-time inventory quantity of each raw material, collected through the inventory system.

[0044] The aforementioned chemical composition, including indicators such as TFe, SiO2, and CaO, is used to calculate the sinter's metallurgical properties (such as iron grade) and environmental indicators (such as sulfur content). The aforementioned price represents the purchase price and the book cost of the raw materials in stock, used in the objective function calculation. The aforementioned inventory quantity represents the dynamically updated available raw material quantity, with the input quantity limited by the inventory constraint formula.

[0045] For example, the upper and lower limits of the sintering raw material ratio are the user-entered allowable range of raw material ratios. Composition constraints are the upper and lower limits of the content of each chemical component (such as TFe and SiO2) in the sintered ore product. Ore ratio constraints are the proportion limits of hematite, limonite, and other minerals in the finished product.

[0046] Specifically, the system regularly pulls the raw material composition table from the MES system, the price list from the procurement system, and the real-time inventory data from the inventory system through the API interface. The upper and lower limits of the ratio are converted into the decision variable range of the linear programming model, the composition constraints are converted into linear inequalities, and the mineral ratio constraints are calculated by summarizing the raw material classification (hematite, limonite, etc.).

[0047] Through the above scheme, this application introduces 13 types of constraints such as upper and lower limits of proportions, mineral proportions, and environmental indicators (such as SO2 emissions) to ensure that the mineral proportioning scheme meets both process requirements and environmental regulations. For example, the alkali metal content constraint can prevent the problem of sinter ore pulverization caused by the addition of high sodium and potassium raw materials. This application uses the comprehensive collection of chemical composition, price, and inventory to enable the model to simultaneously optimize economic cost (price) and technical feasibility (ingredients, inventory), overcoming the defects of single-dimensional evaluation of traditional methods. This application classifies constraints such as proportions, ingredients, and mineral types into clear mathematical expressions (such as linear inequalities) to facilitate standardized model construction. For example, the mineral proportion constraint quickly generates a summary formula through raw material classification labels, reducing the workload of manual modeling. By incorporating inventory as a hard constraint into the model, it is possible to prevent the generation of unexecutable solutions. For example, when a flux inventory is only 100 tons left, the system automatically excludes combinations that require the use of a large amount of this flux to avoid disconnection between procurement and production. By using upper and lower limit constraints on components such as TFe and SiO2, it is ensured that the iron grade and alkalinity of the sintered ore meet the requirements of blast furnace smelting. For example, too high SiO2 will lead to an increase in slag volume. This method controls it within the process allowable range through composition constraints.

[0048] S102: determining a proportion combination scheme based on the basic information of the sintering raw materials and the constraint information of the sintering raw material proportions;

[0049] S103: Select the optimal solution from the matching combination schemes.

[0050] In one embodiment, selecting the optimal solution from the matching combination schemes includes:

[0051] Constructing a linear programming model for each ore proportioning combination scheme, wherein the linear programming model takes the lowest ore proportioning cost as the objective function;

[0052] Solve the linear programming model corresponding to each ratio combination scheme;

[0053] Obtain the optimal solution based on the solution results of each linear programming model.

[0054] For example, each raw material combination corresponds to a linear programming model, where the decision variable is the raw material quantity within the combination. Constraint equations are automatically generated based on user-entered upper and lower limits on the ratio, composition constraints, and other parameters. The system launches multiple processes based on server performance to simultaneously solve models for multiple combinations. The solution with the lowest total cost among all feasible solutions is the optimal solution, and metrics such as dry-wet material quantity and mineral type ratio are output through a post-processing module.

[0055] Two specific embodiments are shown below:

[0056] Example 1: A ore blending task involves selecting 5 combinations of 10 raw materials. The system generates 252 models and solves them in parallel to determine the cost of each solution (e.g., Solution A costs 1200 / ton, Solution B costs 1150 / ton). Solution B is ultimately selected as the optimal solution.

[0057] Example 2: A combination cannot meet the firing quantity constraint due to insufficient inventory. The model returns no solution and the system automatically eliminates the combination to prevent invalid solutions from entering the screening process.

[0058] Through the above scheme, the linear programming model converts multi-dimensional constraints (such as composition and mineral species ratio) into mathematical equations, and accurately handles nonlinear relationships (such as alkalinity ratio constraints) through the CBC solver to ensure that the solution set strictly meets the process requirements. Multi-process parallel solving makes full use of server computing resources, greatly shortens the calculation time of large-scale combinations, and meets the real-time requirements of production. This application avoids the local optimal trap that traditional heuristic algorithms may fall into by traversing all feasible combinations and selecting the lowest-cost solution, thereby ensuring the optimal economy of the procurement strategy.

[0059] Furthermore, the system automatically pulls the chemical composition, price, and inventory of existing raw materials from the steel plant's information system at regular intervals. The chemical composition and price of newly added raw materials to be evaluated are triggered and collected by the user or an external system. The user inputs parameters such as upper and lower limits of the ratio, composition constraints, and ore ratio constraints through the interactive interface, and the system checks the legality through the data verification module. According to the number of raw materials in stock and the number of raw materials to be evaluated, all possible combination schemes are generated according to the fixed number of ore-matching raw materials. A linear programming model is constructed for each combination scheme, and the lowest ore-matching cost is used as the objective function to obtain a feasible solution. The post-processing module calculates the sinter ore grade, alkalinity and other indicators of each scheme, and the global optimal solution is filtered out in combination with the user's screening conditions.

[0060] This solution, through integration with the steel mill's MES, procurement, and inventory systems, provides real-time access to raw material prices and inventory changes, avoiding the lag inherent in traditional approaches that rely on static data. For example, if inventory of a particular raw material plummets, the system can immediately eliminate combinations that rely on that raw material, ensuring the feasibility of the optimal solution.

[0061] This application avoids local optimality traps by traversing all possible raw material combinations. For example, the traditional tonnage price method may ignore low-priced raw materials with high gangue content. However, this method uses composition constraints and global search to screen out the feasible solution with the lowest overall cost.

[0062] The entire process of data collection, model building, solution, and screening is fully automated, reducing manual calculation errors. For example, the system automatically verifies whether the ratio range entered by the user conflicts with the inventory level, preventing the generation of invalid solutions.

[0063] Two specific embodiments are shown below:

[0064] Example 1: A steel mill needs to purchase new magnetite. The system collects its chemical composition (TFe = 62%, SiO2 = 5%) and price (￥500 / ton). The user enters the upper limit of the ratio (magnetite ≤ 30%) and the composition constraint (TFe ≥ 58%). The system generates five possible combinations of this raw material and selects the one with the lowest cost and that satisfies the TFe constraint as the optimal solution.

[0065] Example 2: The inventory of hematite is running low (1,000 tons remaining). The system automatically adjusts the inventory constraints to eliminate combination solutions that require large amounts of hematite, ensuring that the optimal solution meets actual production sustainability.

[0066] In one embodiment, the process constraints of the linear programming model include burning amount constraint, ratio constraint, hematite ratio constraint, limonite ratio constraint, magnetite ratio constraint, main ore ratio constraint, Brazilian ore ratio constraint, composition constraint, alkali metal content constraint, taste constraint, alkalinity constraint, inlet SO2 concentration constraint and inventory constraint.

[0067] For example, the system dynamically loads corresponding constraints based on the raw material type (e.g., hematite). For example, if the current combination does not contain hematite, the hematite ratio constraint is skipped. Composition constraints are automatically converted to linear inequalities, and basicity constraints are converted to ratio inequalities.

[0068] Two specific embodiments are shown below:

[0069] Example 1: A solution includes hematite and limonite. The system automatically loads the hematite ratio constraint (10%≤ratio≤20%) and the limonite ratio constraint (5%≤ratio≤15%), and checks whether they are satisfied at the same time.

[0070] Example 2: Environmental protection requirements in a certain area: SO2 concentration ≤ 200 mg / m 3 The system calculates the flue gas emission value through the inlet SO2 constraint formula and eliminates the scheme that exceeds the standard.

[0071] This technical solution applies 13 types of constraints, covering key production metrics such as sintering, composition, and environmental protection, ensuring that the ore blending plan meets both process feasibility and regulatory requirements. For example, SO2 concentration constraints mitigate the risk of environmental violations. Constraints are dynamically enabled or disabled based on the raw material mix (e.g., skipping the corresponding constraint when hematite is absent), reducing wasted computation and improving model solution efficiency.

[0072] In one embodiment, the method further comprises:

[0073] Get user filtering conditions;

[0074] Obtain a matching combination scheme in the linear programming model that meets the user screening condition.

[0075] For example, user screening conditions: dynamic filtering conditions input by the front-end, such as sinter grade ≥ 58%, basicity 1.82.2. By performing range screening on the post-processed data, only solutions that meet the user's requirements are retained.

[0076] Specifically, users set thresholds for grade, alkalinity, and other factors on the front-end interface, and the system converts these into filtering rules. A subset of all feasible solutions that meet the criteria is extracted, for example, solutions with a grade ≥ 58% and an alkalinity between 1.8 and 2.2 are selected.

[0077] Two specific embodiments are shown below:

[0078] Example 1: The user sets the sinter grade to be ≥60%. The system selects 20 solutions that meet the requirements from 100 feasible solutions and displays their cost rankings.

[0079] Example 2: The alkalinity requirement is temporarily adjusted to 2.02.5 in production. After the user updates the screening conditions, the system refreshes the list of feasible solutions in real time.

[0080] With the above technical solution, users can dynamically set screening conditions based on production fluctuations (such as blast furnace process adjustments) and quickly lock in the solution that meets current needs, avoiding the time cost of re-solving the model. This application supports users from a technical dimension (further optimize solution selection and improve decision-making precision) by post-processing data such as sintered ore composition and environmental indicators.

[0081] In one embodiment, the proportion combination scheme includes the dry material amount, wet material amount, ore type ratio and sintered ore composition of the sintering raw materials.

[0082] For example, the dry material quantity is the actual effective weight of the raw materials after removing moisture, while the wet material quantity includes the actual amount of raw materials fed, which is directly derived from the feed quantity. This is derived from the decision variable values ​​obtained by solving the linear programming model. The mineral composition ratio is the weight percentage of hematite, limonite, and other minerals in the total dry material, and the same applies to other minerals. The sinter composition is the content of each chemical component in the sintered product.

[0083] Specifically, the system calculates the dry and wet quantities of each raw material based on the linear programming solution. It aggregates the dry material quantities based on the raw material type labels (e.g., hematite, limonite) and calculates the ore mix. The chemical composition of the sintered ore is calculated using the composition constraint formula.

[0084] Among them, the amount of dry material is used to guide the actual feeding (water evaporation needs to be taken into account); the amount of wet material directly corresponds to the raw material procurement and inventory consumption plan; the mineral ratio is used to meet the process requirements (such as the proportion of hematite affects the strength of sintered ore); the composition of sintered ore is passed to downstream processes as a quality indicator.

[0085] Through the above-mentioned plan, the wet material quantity directly corresponds to the actual purchase and input quantity, avoiding the ratio deviation caused by ignoring moisture in traditional methods (such as moisture fluctuations affecting the actual input of dry materials). Through the calculation of ore ratio, the mineral composition of the sintered ore is ensured to meet the requirements of blast furnace smelting (for example, too high a proportion of hematite may reduce the reducibility of the sintered ore). The advance calculation of the sintered ore composition helps the quality department predict product indicators (such as whether TFe meets the standard) and reduce the cost of subsequent process adjustments. Through the dry and wet material quantities, ore ratios, and composition data, a complete production guidance chain is formed to support collaborative decision-making among multiple departments such as procurement, inventory, and production.

[0086] In one embodiment, the method further comprises:

[0087] In the case where there is a new combination and ratio combination scheme, obtaining a corresponding linear programming model of the new combination and ratio combination scheme;

[0088] The optimal solution is determined based on the comparison results of the solution results of the linear programming model corresponding to the newly added combination and matching scheme and the solution results of the linear programming model corresponding to the original combination and matching scheme.

[0089] Exemplarily, these newly added combinations include newly introduced raw materials (e.g., newly purchased magnetite) or newly generated raw material combinations (e.g., existing raw materials plus new raw materials). These combinations are triggered when new raw materials arrive in the warehouse or when the user adjusts the raw material selection range. The process for determining the optimal solution by comparing these combinations is as follows: merging the solution set for the newly added combination with the original solution set, selecting the globally lowest-cost solution, maintaining a historical solution database for the system, and re-ranking the newly added solutions after insertion.

[0090] With this technical solution, the system automatically expands or contracts the combination space when new raw materials are added or eliminated, ensuring that the optimal solution always reflects the latest resource status. The existing solution set does not need to be recalculated; only the new combinations need to be solved incrementally, significantly saving computing resources. By comparing the entire solution set, the system avoids the misselection of suboptimal solutions caused by local data updates. When raw material supply fluctuates (such as when a mine is shut down), alternative solutions can be quickly generated to reduce the risk of production interruptions.

[0091] The following shows a complete specific embodiment of the present application:

[0092] Step 1: Data integration: Regularly collect parameters such as the chemical composition, price, inventory level, etc. of sintering raw materials from the iron and steel enterprise's internal information systems such as the ironmaking MES system, procurement system, and inventory system.

[0093] Step 101: Regularly collect information on existing sintering raw materials (including seven categories, namely hematite, limonite, magnetite, solid waste, flux, solid fuel, and others) from the steel plant's MES system and other information systems, specifically including the unit price, inventory, raw material type, raw material source, moisture content, composition (including TFe, SiO2, CaO, MgO, Al2O3, K2O, Na2O, ZnO, Cl, S, P, TiO2, FeO, C, burnout, PbO, CuO, V2O5, AsO, MnO, Cr2O3), and the number of sintering raw materials N. stock .

[0094] Step 102: Trigger the collection of sintering raw material information to be evaluated for cost-effectiveness from the collection system, including the unit price, raw material type, raw material source, moisture content, composition (including TFe, SiO2, CaO, MgO, Al2O3, K2O, Na2O, ZnO, Cl, S, P, TiO2, FeO, C, burnout, PbO, CuO, V2O5, AsO, MnO, Cr2O3, etc.) of each raw material, and the number N of sintering raw materials to be evaluated for cost-effectiveness. candidate .

[0095] Step 2: The user enters key data (usually only more data needs to be filled in during the first use, and only a small part of the data needs to be adjusted for subsequent use).

[0096] Step 201: The user inputs the upper limit and lower limit of the ratio of each sintering raw material in the stockyard;

[0097] Step 202: The user inputs sintering constraints, including upper and lower limits of the ratios of TFe, SiO2, CaO, MgO, Al2O3, K2O, Na2O, K+Na, ZnO, Cl, S, P, TiO2, C, alkalinity, PbO, CuO, AsO, MnO, and Cr2O3;

[0098] Step 203: The user inputs the upper and lower limits of the proportions of hematite, limonite, magnetite, main ore, and Brazilian ore in the sintered ore product;

[0099] Step 204: The user maintains and confirms the element distribution table for the sintering process, including the proportions of different components in the sintered ore, dust removal ash, and flue gas;

[0100] Step 205: The user maintains and confirms the sintering parameters and processing costs, including the base daily output, sintering return rate, flue gas volume, flue gas temperature, flue gas pressure, exhaust gas SO2 concentration, desulfurization agent CaO content, local atmospheric pressure, sintering ore processing cost, and sintering ore fixed cost;

[0101] Step 206: Data verification: The cost-performance evaluation system verifies the user input and the system-collected information based on fixed rules. If the information is incorrect, an alarm is issued and the user is prompted to fill in the information.

[0102] Step 3: Linear programming model construction and solution: Based on the information obtained from steps 1 and 2 and the user input, a linear programming model for sintering ore matching is constructed.

[0103] Step 301: The amount of each sintering raw material is used as a decision variable for a linear programming problem.

[0104] Step 302: Constraints include: burn quantity constraint, proportion constraint, hematite proportion constraint, limonite proportion constraint, magnetite proportion constraint, main ore proportion constraint, Brazilian ore proportion constraint, composition constraint, alkali metal content constraint, grade constraint, alkalinity constraint, inlet SO2 concentration constraint, and inventory constraint, for a total of 13 types. The constraint formula is as follows:

[0105]

[0106] Among them, xx is XX.

[0107] Step 303: The objective function of the linear programming problem is to minimize the ore allocation price.

[0108]

[0109] Among them, xx is XX.

[0110] Step 304: Call the CBC solver to solve the linear programming problem corresponding to the single sintering ore blending scheme. Based on the server performance used by the system, the solution is solved in parallel through multiple processes to speed up the solution. The solution results include the amount of each sintering raw material and the blending price.

[0111] Step 305: Post-process the results of the ore blending scheme to solve the dry material amount, dry proportion, wet material amount, total wet material amount, wet proportion, inventory usage time, hematite proportion, limonite proportion, magnetite proportion, main ore proportion, Brazilian ore proportion, sintered ore grade, sintered ore FeO content, sintered ore alkalinity, other sintered ore components, and desulfurization inlet SO2 concentration information of the sintered ore under the ore blending scheme.

[0112] Step 4: Dynamic combination generation and combination solution. Based on the raw material information automatically obtained from the information system (including raw materials that have been evaluated for cost-effectiveness and raw materials to be evaluated for cost-effectiveness), all raw material combination plans are dynamically generated. The linear programming model construction and solution method in Step 3 are continuously called to solve the linear programming problem for all combinations.

[0113] Step 401: Data maintenance of pre-solution and cost-effectiveness evaluation results. When the system function is run for the first time, all raw materials have not been evaluated for cost-effectiveness, so all raw materials belong to the raw materials to be evaluated for cost-effectiveness, and there are N types of raw materials. According to the number P of sintering raw materials selected by the user (P is usually a fixed value, and P is less than N), P types of raw materials are selected from the N types of sintering raw materials for sintering ore blending, and there are a total of C(N, P) schemes. For example, there are 10 types of raw materials that have not been evaluated for cost-effectiveness, and the number of raw materials required for a single sintering ore blending is 5, then C(10, 5) = 252 types of batching schemes can be formed. Each scheme is in the form of (1, 2, 3, 4, 5), (1, 2, 3, 4, 6), (6, 7, 8, 9, 10), where the numbers are the raw material codes corresponding to each sintering raw material. Then, step 3 is called to traverse all 252 batching schemes, constructing and solving the linear programming problem. Ultimately, the sintering raw material code combination, sintering raw material input quantity, ore price, and post-processing data for each batching scheme are obtained. The results obtained from this solution are maintained in the database of the sintering raw material cost-effectiveness evaluation system. The above steps only need to be performed once when the system is first run.

[0114] Step 402: Form a combination of raw materials to be evaluated. When M new raw materials require cost-effectiveness evaluation, all raw materials are combined, and the number of possible combinations is C(M+N,P). The combination C(N,P) calculated in step 401 is eliminated. For the remaining C(M+N,P)-C(N,P) batching schemes, call step 3 to traverse and construct and solve the linear programming problem. Ultimately, the sintering raw material code combination, sintering raw material input quantity, batching price, and post-processing data information for each batching scheme are obtained. The results obtained from this solution are maintained in the sintering raw material cost-effectiveness evaluation system database.

[0115] Step 5: Return and display the results.

[0116] Step 501: The user filters the ore blending schemes on the front-end page. For example, if the sinter grade, sinter alkalinity, and hematite ratio are above or below the set screening conditions, the system selects all blending schemes that meet the conditions. Among the schemes that meet the conditions, the one with the lowest Pi value, containing the raw material to be evaluated for cost-effectiveness, is found. The minimum Pi value is Pi_min, which serves as the cost-effectiveness evaluation value for this raw material. In this way, the Pi_min value of all raw materials to be evaluated for cost-effectiveness is found.

[0117] Step 502: The final cost-performance evaluation result is displayed on the front-end page, and detailed ratio schemes and dependencies are marked (for example, the optimal solution for raw material A often coexists with raw material B).

[0118] Furthermore, as a response to the above Figure 1In order to realize the method shown in the figure, the embodiment of the present invention also provides a sintering raw material ratio calculation device for calculating the above Figure 1 This device embodiment corresponds to the aforementioned method embodiment. For ease of reading, this device embodiment will not describe the details of the aforementioned method embodiment one by one, but it should be clear that the device in this embodiment can implement all the contents of the aforementioned method embodiment. Figure 2 As shown, the device includes: an acquisition unit 21, a determination unit 22 and a selection unit 23, wherein

[0119] An acquisition unit 21 is used to acquire basic information of sintering raw materials and constraint information of sintering raw material ratio;

[0120] A determining unit 22 is configured to determine a ratio combination scheme based on the basic information of the sintering raw materials and the constraint information of the sintering raw material ratio;

[0121] The selection unit 23 is used to select the optimal solution from the matching combination schemes.

[0122] The processor contains a kernel, which retrieves the corresponding program unit from memory. One or more kernels can be configured, and by adjusting kernel parameters, a method for calculating the sintering raw material ratio can be implemented. This solves the problem that traditional methods have difficulty handling sintering raw material ratios under multiple constraints, resulting in slow decision-making.

[0123] An embodiment of the present invention provides a computer-readable storage medium, which includes a stored program. When the program is executed by a processor, the method for calculating the sintering raw material ratio is implemented.

[0124] An embodiment of the present invention provides a processor, which is used to run a program, wherein the sintering raw material ratio calculation method is executed when the program is run.

[0125] An embodiment of the present invention provides an electronic device, comprising at least one processor and at least one memory connected to the processor; wherein the processor is configured to call program instructions in the memory to execute the above-mentioned sintering raw material ratio calculation method.

[0126] An embodiment of the present invention provides an electronic device 30, such as Figure 3 As shown, the electronic device includes at least one processor 301, and at least one memory 302 and a bus 303 connected to the processor; wherein the processor 301 and the memory 302 communicate with each other through the bus 303; the processor 301 is used to call the program instructions in the memory to execute the above-mentioned sintering raw material ratio calculation method.

[0127] The intelligent electronic devices in this article can be PCs, PADs, mobile phones, etc.

[0128] The present application also provides a computer program product, which, when executed on a process management electronic device, is suitable for executing a program that initializes the steps of the above-mentioned sintering raw material ratio calculation method.

[0129] It should be noted that, in the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0130] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0131] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0132] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0133] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1A step that specifies a function in one or more boxes.

[0134] The present application also provides a computer program product, which includes computer software instructions. When the computer software instructions are executed on a processing device, the processing device is caused to execute the following Figure 1 This corresponds to the flow of memory control in the embodiment.

[0135] A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that a computer can store or a data storage device such as a server or data center that includes one or more available media integrated. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state drive (SSD)).

[0136] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0137] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0138] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0139] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0140] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0141] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for calculating the ratio of sintering raw materials, characterized in that: include: Obtain basic information of sintering raw materials and constraint information of sintering raw material ratio; Determining a ratio combination scheme based on the basic information of the sintering raw materials and the constraint information of the sintering raw material ratio; Select the optimal solution from the above-mentioned combination schemes.

2. The method according to claim 1, characterized in that Basic information of the sintering raw materials, including chemical composition, price and inventory; The constraint information of the sintering raw material ratio includes: upper and lower limits of the ratio, component constraints, and mineral ratio constraints.

3. The method according to claim 1, characterized in that The selecting the optimal solution from the matching combination schemes includes: Constructing a linear programming model for each ore proportioning combination scheme, wherein the linear programming model takes the lowest ore proportioning cost as the objective function; Solve the linear programming model corresponding to each ratio combination scheme; Obtain the optimal solution based on the solution results of each linear programming model.

4. The method according to claim 3, characterized in that The process constraints of the linear programming model include burning amount constraint, ratio constraint, hematite ratio constraint, limonite ratio constraint, magnetite ratio constraint, main ore ratio constraint, Brazilian ore ratio constraint, composition constraint, alkali metal content constraint, taste constraint, alkalinity constraint, inlet SO2 concentration constraint and inventory constraint.

5. The method according to claim 1, wherein Also includes: Get user filtering conditions; Obtain a matching combination scheme in the linear programming model that meets the user screening condition.

6. The method according to claim 1, wherein The proportion combination scheme includes the dry material amount, wet material amount, ore type proportion and sintered ore composition of the sintering raw materials.

7. The method according to claim 1, characterized in that Also includes: In the case where there is a new combination and ratio combination scheme, obtaining a corresponding linear programming model of the new combination and ratio combination scheme; The optimal solution is determined based on the comparison results of the solution results of the linear programming model corresponding to the newly added combination and matching scheme and the solution results of the linear programming model corresponding to the original combination and matching scheme.

8. A sintering raw material ratio calculation device, characterized in that: Also includes: An acquisition unit, used to acquire basic information of sintering raw materials and constraint information of sintering raw material ratio; a determining unit, configured to determine a ratio combination scheme based on the basic information of the sintering raw materials and the constraint information of the sintering raw material ratio; A selection unit is used to select the optimal solution from the ratio combination scheme.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored program, wherein when the program is executed by a processor, the steps of the method for calculating the sintering raw material ratio according to any one of claims 1 to 7 are implemented.

10. An electronic device, characterized in that: The electronic device includes at least one processor and at least one memory connected to the processor; wherein the processor is used to call program instructions in the memory to execute the steps of the sintering raw material ratio calculation method according to any one of claims 1 to 7.

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