Power plant multi-coal blending optimization method and device, electronic equipment and storage medium

Abstract

This application relates to a method, apparatus, electronic equipment, and storage medium for optimizing multi-coal blending in power plants. The method includes: selecting coal types from a coal type database that meet preset blending restrictions and a first preset minimum price condition as initial main coal types; generating actual blending ratios of the initial main coal type and new coal types using a preset step-by-step iteration; determining a first blending ratio of the initial main coal type and new coal types that meets the first preset optimal condition when all safety indices meet preset safety verification conditions; determining a blending ratio that meets the preset final optimal condition based on the first and second blending ratios; and controlling the power plant to execute blending optimization actions corresponding to the blending ratio of the preset final optimal condition. This solves the problems of related technologies in multi-coal blending optimization, such as difficulty in balancing safety and economy, disordered blending optimization logic, low iterative convergence efficiency, and poor scenario adaptability.

Description

Technical Field

[0001] This application relates to the field of coal blending technology for coal-fired power generation, and in particular to a method, apparatus, electronic equipment and storage medium for optimizing the blending of multiple coal types in a power plant. Background Technology

[0002] Coal-fired power generation is the main mode of electricity supply in my country, and the rationality of the coal blending scheme directly determines the safety, environmental protection, and economy of boiler operation. With the diversification of coal supply channels for power plants, blending of multiple coal types has become the norm.

[0003] In related technologies, coal blending optimization in power plants mainly relies on human experience judgment or a single-objective linear weighted method. In some scenarios, intelligent optimization algorithms such as genetic algorithms and particle swarm optimization are used to find the optimal blending ratio. By setting the objective function and constraints, the appropriate coal blending ratio is calculated iteratively.

[0004] However, when optimizing coal blending for multiple coal types, the relevant technologies suffer from problems such as difficulty in achieving a balance between safety and economy, disordered logic in the optimization of blending ratios, low iterative convergence efficiency, and poor adaptability to high-dimensional scenarios, which urgently need to be improved. Summary of the Invention

[0005] This application provides a method, apparatus, electronic equipment, and storage medium for optimizing multi-coal blending in power plants. It addresses issues in related technologies where random proportioning trials or single-objective optimization fail to balance safety and economy, leading to boiler operational safety hazards (such as insufficient mill output and excessive ash / slag) and increased cost per kilowatt-hour. Furthermore, the lack of fixed steps in proportioning adjustments results in disordered iterations, low convergence efficiency, and difficulty in adapting to complex proportioning optimizations of n types of coal. Additionally, during multi-coal iterations, the relative proportions of the original optimal proportions are not maintained, leading to chaotic optimization directions and an inability to achieve global cost optimization.

[0006] The first aspect of this application provides a method for optimizing the blending of multiple coal types in a power plant, comprising the following steps: establishing a coal type database based on coal quality data of multiple coal types in the power plant, and selecting coal types that meet preset blending restrictions and a first preset minimum price condition from the coal type database as initial main coal types; selecting coal types that meet a second preset minimum price condition from the coal type database as new coal types, and generating actual blending ratios of the initial main coal type and the new coal type using a preset blending ratio stepwise iteration; based on the actual blending ratios, detecting whether various safety indices of the power plant meet preset safety verification pass conditions, and optimizing the blending ratios of the power plant based on the preset safety verification pass conditions. When the preset safety verification pass conditions are met, the current cost per kilowatt-hour of the power plant is calculated, and the first coal blending ratio of the initial main coal type and the new coal type that meets the first preset optimal condition is determined based on the current cost per kilowatt-hour. When the various safety indices do not meet the preset safety verification pass conditions, the current cost per kilowatt-hour is greater than or equal to the previous cost per kilowatt-hour, or the coal blending ratio meets the preset limit condition, the second coal blending ratio that meets the second preset optimal condition is determined, and the coal blending ratio that meets the preset final optimal condition is determined based on the first coal blending ratio and the second coal blending ratio, and the power plant is controlled to perform coal blending optimization actions corresponding to the coal blending ratio of the preset final optimal condition.

[0007] Optionally, in one embodiment of this application, controlling the power plant to perform a coal blending optimization action corresponding to the preset final optimal condition includes: detecting whether the coal types in the coal type database meet the preset all-introduction conditions, and when the coal types in the coal type database meet the preset all-introduction conditions, traversing the coal blending ratios in all coal blending stages including the first coal blending ratio and the second coal blending ratio; determining the coal blending ratio that meets the preset final optimal condition based on the coal blending ratios in all coal blending stages, and determining the coal blending ratio of the power plant based on the coal blending ratio that meets the preset final optimal condition; adjusting the coal quality, combustion state, and environmental protection operation parameters of the power plant according to the coal blending ratio, generating the coal blending optimization action of the power plant, and executing the coal blending optimization action.

[0008] Optionally, in one embodiment of this application, the step of adjusting the actual coal blending ratio of the initial main coal type and the new coal type by adjusting the preset coal blending ratio in a step-by-step iterative manner includes: detecting whether the quantity of the new coal type meets a first preset quantity condition; in response to the quantity of the new coal type meeting the first preset quantity condition, adjusting the initial coal blending ratio of the initial main coal type and the new coal type in a step-by-step iterative manner based on the preset blending ratio until the initial coal blending ratio meets a preset iteration stop condition, and then determining the actual coal blending ratio.

[0009] Optionally, in one embodiment of this application, the step of adjusting the actual coal blending ratio of the initial main coal type and the new coal type by adjusting the steps according to the preset coal blending ratio further includes: detecting whether the quantity of the new coal type meets the second preset quantity condition; in response to the condition that the quantity of the new coal type meets the second preset quantity condition, keeping the relative proportions between all original coal types unchanged, and replacing the initial main coal type with the newly introduced coal type step by step based on the preset blending ratio steps to determine the actual coal blending ratio.

[0010] Optionally, in one embodiment of this application, detecting whether at least one safety index of the power plant meets the preset safety verification pass conditions includes: acquiring at least one safety index including coal mill output, ash and slag production, primary air fan output, and induced draft fan output, and detecting whether the at least one safety index exceeds a preset rated range; in response to the at least one safety index not exceeding the preset rated range, determining that the at least one safety index meets the preset safety verification pass conditions; otherwise, determining that the at least one safety index does not meet the preset safety verification pass conditions.

[0011] Optionally, in one embodiment of this application, the step of calculating the current cost per kilowatt-hour of the power plant when each safety index meets the preset safety verification pass condition, and determining the first coal blending ratio of the initial main coal type and the new coal type to meet the first preset optimal condition based on the current cost per kilowatt-hour, includes: when at least one safety index meets the preset safety verification pass condition, calculating the current cost per kilowatt-hour of the power plant based on coal type price, coal type consumption, coal blending ratio, boiler efficiency, and target environmental treatment cost; responding to the situation where the current cost per kilowatt-hour is less than the cost per kilowatt-hour of the previous stage, and the coal blending ratio does not meet the preset limit condition, iteratively adjusting the actual coal blending ratio of the initial main coal type and the new coal type until the actual coal blending ratio meets the preset iterative adjustment stop condition, and then determining the first coal blending ratio of the initial main coal type and the new coal type to meet the first preset optimal condition.

[0012] Optionally, in one embodiment of this application, establishing a coal type database based on coal quality data of multiple coal types in a power plant includes: acquiring the coal quality data including industrial analysis data, elemental analysis data, Hardgrove grindability index, ash fusion characteristic temperature, ash composition analysis data, pulverized coal explosiveness index, and pulverized coal ignition temperature; and establishing the coal type database based on the coal quality data.

[0013] A second aspect of this application provides a multi-coal blending optimization device for a power plant, comprising: a first screening module, configured to establish a coal type database based on coal quality data of multiple coal types in the power plant, and screen coal types that meet preset blending restrictions and a first preset minimum price condition from the coal type database as initial main coal types; a second screening module, configured to screen coal types that meet the second preset minimum price condition from the coal type database as new coal types, and iteratively generate the actual blending ratio of the initial main coal type and the new coal type using a preset blending ratio stepwise; and a determination module, configured to detect whether various safety indices of the power plant meet preset safety verification pass conditions based on the actual blending ratio, and determine whether the safety indices meet the preset safety verification pass conditions. When all indices meet the preset safety verification conditions, the current cost per kilowatt-hour of the power plant is calculated, and the first coal blending ratio of the initial main coal type and the new coal type that meets the first preset optimal condition is determined based on the current cost per kilowatt-hour. The optimization module is used to determine the second coal blending ratio that meets the second preset optimal condition when the various safety indices do not meet the preset safety verification conditions, the current cost per kilowatt-hour is greater than or equal to the previous cost per kilowatt-hour, or the coal blending ratio meets the preset limit condition. Based on the first coal blending ratio and the second coal blending ratio, the coal blending ratio that meets the preset final optimal condition is determined, and the power plant is controlled to perform the coal blending optimization action corresponding to the coal blending ratio of the preset final optimal condition.

[0014] Optionally, in one embodiment of this application, the optimization module includes: a traversal unit, configured to detect whether the coal types in the coal type database meet the preset all-introduction conditions, and when the coal types in the coal type database meet the preset all-introduction conditions, traverse the coal blending ratios in all coal blending stages including the first coal blending ratio and the second coal blending ratio; a first determination unit, configured to determine the coal blending ratio that meets the preset final optimal conditions based on the coal blending ratios in all coal blending stages, and determine the coal blending ratio of the power plant based on the coal blending ratio that meets the preset final optimal conditions; and an optimization unit, configured to adjust the coal quality, combustion state, and environmental protection operation parameters of the power plant according to the coal blending ratio, generate the coal blending optimization action of the power plant, and execute the coal blending optimization action.

[0015] Optionally, in one embodiment of this application, the second screening module includes: a first detection unit, used to detect whether the quantity of the new coal type meets a first preset quantity condition; and an adjustment unit, used to, in response to the condition that the quantity of the new coal type meets the first preset quantity condition, to iteratively adjust the initial coal blending ratio of the initial main coal type and the new coal type based on the preset blending ratio stepwise adjustment until the initial coal blending ratio meets the preset iteration stop condition, and then determine the actual coal blending ratio.

[0016] Optionally, in one embodiment of this application, the second screening module further includes: a second detection unit, used to detect whether the quantity of the new coal type meets a second preset quantity condition; and a replacement unit, used to, in response to the condition that the quantity of the new coal type meets the second preset quantity condition, maintain the relative proportions between all existing coal types unchanged, and, based on the preset proportioning ladder, replace the initial main coal type step by step with the newly introduced coal type to determine the actual coal blending ratio.

[0017] Optionally, in one embodiment of this application, the determining module includes: an acquisition unit, configured to acquire at least one safety index including coal mill output, ash production, primary air fan output, and induced draft fan output, and detect whether the at least one safety index exceeds a preset rated range; and a determination unit, configured to determine that the at least one safety index meets the preset safety verification pass condition if the at least one safety index does not exceed the preset rated range, otherwise, determine that the at least one safety index does not meet the preset safety verification pass condition.

[0018] Optionally, in one embodiment of this application, the determining module includes: a calculation unit, configured to calculate the current cost per kilowatt-hour of the power plant based on coal price, coal consumption, coal blending ratio, boiler efficiency, and target environmental treatment cost when at least one safety index meets the preset safety verification pass condition; and a second determining unit, configured to iteratively adjust the actual coal blending ratio of the initial main coal type and the new coal type in response to the situation where the current cost per kilowatt-hour is less than the cost per kilowatt-hour of the previous stage and the coal blending ratio does not meet the preset limit condition, until the actual coal blending ratio meets the preset iterative adjustment stop condition, and then determine the first coal blending ratio of the initial main coal type and the new coal type that meets the first preset optimal condition.

[0019] Optionally, in one embodiment of this application, the first screening module includes: a data acquisition unit, used to acquire the coal quality data including industrial analysis data, elemental analysis data, Hardgrove grindability index, ash fusion characteristic temperature, ash composition analysis data, pulverized coal explosiveness index, and pulverized coal ignition temperature; and a building unit, used to build the coal type database based on the coal quality data.

[0020] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the multi-coal blending optimization method for power plants as described in the above embodiments.

[0021] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for optimizing the blending of multiple coal types in a power plant.

[0022] This application first selects the coal type that meets the blending target constraints and has the lowest price as the initial main coal type. Then, other coal types with increasing prices are introduced sequentially, using a fixed ratio adjustment ladder. Based on the optimal ratio in the previous stage, the original main coal type is gradually replaced with the newly introduced coal type. Safety index verification and economic parameter calculation are carried out step by step. Iterative convergence is achieved through comparison of cost per kilowatt-hour, until safety is limited or cost per kilowatt-hour increases. Finally, the optimal coal blending ratio of n coal types is output. This can maximize the reduction of cost per kilowatt-hour while ensuring stable boiler operation, adapting to multi-coal supply scenarios in power plants and improving the economic benefits of coal-fired power generation. Thus, it solves the problems of related technologies in multi-coal blending optimization, such as difficulty in balancing safety and economy, disordered optimization logic, low iterative convergence efficiency, and poor adaptability to high-dimensional scenarios.

[0023] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0024] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart illustrating a multi-coal blending optimization method for power plants according to an embodiment of this application. Figure 2 This is an overall flowchart of a multi-coal blending optimization method for a power plant according to an embodiment of this application; Figure 3 This is a binary iterative flowchart of a multi-coal blending optimization method for power plants according to an embodiment of this application; Figure 4 This is an n-ary iterative flowchart of a multi-coal blending optimization method for power plants according to an embodiment of this application; Figure 5 This is a schematic diagram of a multi-coal blending optimization device for a power plant according to an embodiment of this application; Figure 6 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0025] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0026] The following describes a method, apparatus, electronic device, and storage medium for optimizing multi-coal blending in power plants, based on embodiments of this application, with reference to the accompanying drawings. Addressing the issues mentioned in the background art regarding the difficulty in achieving a balanced approach between safety and economy in multi-coal blending optimization, disordered optimization logic, low iterative convergence efficiency, and poor adaptability to high-dimensional scenarios, this application provides a method for optimizing multi-coal blending in power plants. In this method, the coal type that meets the blending target constraints and has the lowest price is first selected as the initial main coal type. Then, other coal types with increasing prices are introduced sequentially, using a fixed ratio adjustment ladder. Based on the optimal ratio of the previous stage, the original main coal type is gradually replaced with newly introduced coal types. Safety index verification and economic parameter calculation are performed sequentially. Iterative convergence is achieved through comparison of per-kilowatt-hour costs until safety is limited or per-kilowatt-hour costs increase. Finally, the optimal blending ratio of n coal types is output. This method maximizes the reduction of per-kilowatt-hour costs while ensuring stable boiler operation, adapts to multi-coal supply scenarios in power plants, and improves the economic efficiency of coal-fired power generation. This solves the problems that related technologies face when optimizing coal blending for multiple coal types, such as difficulty in balancing safety and economy, disordered logic in ratio optimization, low iterative convergence efficiency, and poor adaptability to high-dimensional scenarios.

[0027] Specifically, Figure 1 This is a flowchart illustrating a multi-coal blending optimization method for power plants provided in an embodiment of this application.

[0028] like Figure 1 As shown, the multi-coal blending optimization method for this power plant includes the following steps: In step S101, a coal type database is established based on the coal quality data of multiple coal types in the power plant, and coal types that meet the preset coal mixing restriction conditions and the first preset minimum price condition are selected from the coal type database as the initial main coal types.

[0029] It is understood that the preset coal blending restrictions in this application embodiment include, but are not limited to, coal quality indicators, environmental emission standards, and combustion stability requirements. The preset coal blending restrictions also include at least one of the following: lower limit of calorific value, upper limit of ash content, volatile matter range, and upper limit of sulfur content. The coal type for the first preset minimum price condition can be the coal type with the lowest price among the preset coal blending restrictions. The application can combine power plant boiler design parameters, environmental emission standards, and coal supply requirements to set coal blending restrictions as a benchmark for initial selection of the main coal type, including but not limited to: coal quality indicators: calorific value ≥ Q0 kcal / kg, ash content ≤ A0%, volatile matter in the V1%~V2% range, sulfur content ≤ S0%; environmental standards: NO... x Initial emission concentration ≤ N0 mg / m³, initial SO2 emission concentration ≤ S1 mg / m³; combustion stability: burnout rate ≥ 98%, slagging index ≤ 0.6.

[0030] In actual implementation, such as Figure 2 As shown, the embodiments of this application can obtain data including boiler rated evaporation capacity, furnace cross-sectional heat load, furnace volumetric heat load, burner type, coal mill output range, primary air temperature, primary air velocity, secondary air temperature, secondary air velocity, furnace outlet flue gas temperature, furnace outlet oxygen content, desulfurization system inlet SO2 concentration limit, and denitrification system inlet NO. x Basic data on coal blending for coal-fired power units, such as concentration limits and dust concentration limits at the inlet of dust collectors, are used to establish boiler economic evaluation and safety evaluation modules related to the quality of coal entering the furnace. A coal type database is established based on the coal quality data of available coal types in the power plant. All coal types that meet the preset coal blending restrictions, including coal quality indicators, environmental emission standards, and combustion stability requirements, are selected from the coal type database. The coal type with the lowest price among all coal types that meet the preset coal blending restrictions is selected as the initial main coal type, i.e., the first main coal type x1.

[0031] This application embodiment can select coal types that meet the preset coal blending restrictions and the first preset minimum price condition from the coal type database as the initial main coal types, and further form a coal type combination for subsequent blending ratio iteration optimization, providing basic coal type objects for tiered coal blending ratio adjustment and safety and economic dual criterion verification.

[0032] Optionally, in one embodiment of this application, a coal type database is established based on coal quality data of multiple coal types in a power plant, including: acquiring coal quality data containing industrial analysis data, elemental analysis data, Hardgrove grindability index, ash fusion characteristic temperature, ash composition analysis data, pulverized coal explosiveness index, and pulverized coal ignition temperature; and establishing a coal type database based on the coal quality data.

[0033] Specifically, the embodiments of this application can acquire coal quality data of coal types available for power plants, including industrial analysis data, elemental analysis data, Hardgrove Grindability Index (HGI), ash fusion characteristic temperature, ash composition analysis data, pulverized coal explosiveness index (Kst), pulverized coal ignition temperature (Ti), and prices converted to standard coal equivalent. A coal type database is established based on the coal quality data of coal types available for power plants, thereby providing support for screening coal types that meet preset blending restrictions.

[0034] In step S102, coal types that meet the second preset minimum price condition are selected from the coal type database as new coal types, and the actual coal blending ratio of the initial main coal type and the new coal type is generated by iterative step-by-step generation of preset blending ratio.

[0035] It is understood that the coal type for the second preset minimum price condition in this application embodiment can be the coal type with the lowest price in the coal type database.

[0036] In this embodiment, the coal types that have not been selected from the candidate coal types can be extracted from the coal type database in ascending order of price as the i-th economic coal type x. i (i≥2), this application selects the coal type with the lowest price from the coal type database as the new coal type, thereby forming a coal type combination for subsequent ratio iteration optimization, providing a basic coal type object for the step-by-step coal blending ratio adjustment and the verification of safety and economic dual criteria.

[0037] Optionally, in one embodiment of this application, adjusting the initial main coal type and the actual coal type by adjusting the step-by-step iteration based on the preset coal blending ratio includes: detecting whether the quantity of the new coal type meets the first preset quantity condition; in response to the quantity of the new coal type meeting the first preset quantity condition, adjusting the initial coal blending ratio of the initial main coal type and the new coal type by step-by-step iteration based on the preset blending ratio until the initial coal blending ratio meets the preset iteration stop condition, and then determining the actual coal blending ratio.

[0038] It is understood that the first preset quantity condition in the embodiments of this application can be a condition that the quantity of coal is 2; the preset proportion step can be a fixed proportion adjustment step of 5%; and the preset iteration stop condition can be a condition that stops the iteration after the proportion meets the requirements.

[0039] In actual implementation, such as Figure 3 As shown in the embodiment of this application, coal types that have not been selected from the candidate coal types can be selected as the i-th economic coal type x in order of price from low to high. i(i≥2), progressively carry out iterative optimization of multi-coal type ratios. This application can detect whether the number of new coal types is 2. When i=2, a binary ratio iteration is performed, using x1 as the initial benchmark and 5% as the fixed ratio adjustment step, adjusting the initial main coal type and the new coal type ratio, gradually increasing the proportion of x2. The initial ratio is x1:x2=95%:5%. The ratio in the nth iteration (n≥1) is x1:x2=(100-5n)%:5n% (5n%≤100%), and 5n%<100% (i.e., n<20, ensuring the ratio is non-negative). This continues until the coal ratio meets the preset iteration stopping condition, at which point the actual coal ratio is determined. Furthermore, this application can calculate the economic parameters of x1 (boiler efficiency, NO). x The emission concentrations (including SO2 emission concentrations) were analyzed, and the cost per kilowatt-hour (C0) of the coal when burned alone was estimated. From the database of single coal types for power plants, coal types that met all the above-mentioned blended coal target constraints were selected. The coal type with the lowest price delivered to the plant was extracted as the primary coal type x1. Based on the industrial analysis data of x1, its economic parameters (boiler efficiency η0, NO2 emission concentrations) were calculated using a boiler thermal calculation model. x Given emission concentrations N0 and S0, estimate the cost per kilowatt-hour (C0) of x1 when burned alone using the following formula:

[0040] Where P1 is the delivered price of x1 (yuan / ton), b0 is the standard coal consumption of x1 (g / kWh), and C is the environmental treatment cost calculated based on N0 and S0 (yuan / kWh). The embodiments of this application uniformly adopt a fixed ratio adjustment step of 5%, which makes the iterative process converge in an orderly manner. Compared with random trial calculations or non-fixed steps, it greatly improves the efficiency of ratio optimization and shortens the decision-making cycle.

[0041] Optionally, in one embodiment of this application, the actual coal blending ratio of the initial main coal type and the new coal type is generated by adjusting the step-by-step iteration with a preset coal blending ratio, further includes: detecting whether the quantity of the new coal type meets a second preset quantity condition; in response to the condition that the quantity of the new coal type meets the second preset quantity condition, keeping the relative proportions between all original coal types unchanged, and replacing the initial main coal type with the newly introduced coal type step by step based on the preset blending ratio step to determine the actual coal blending ratio.

[0042] It is understood that the second preset quantity condition in the embodiments of this application can be a condition that the quantity of coal is greater than or equal to 3.

[0043] In actual implementation, such as Figure 4As shown, this embodiment of the application can detect whether the quantity of the new coal type meets the second preset quantity condition. When i≥3 and n≥3, an n-element ratio iteration is performed to obtain the coal blending ratio of the initial main coal type and the new coal type in the previous stage (i-1 coal types). The optimal blending ratio M in the previous stage (i-1 coal types) is then used. (i-1) Based on this, keep M (i-1) The relative proportions of all existing coal types remain unchanged, with a fixed adjustment step of 5%, gradually using the i-th economic coal type x. i Complete replacement of M (i-1) The original main coal type (i.e., the first initial main coal type x1) is used to form a new blend: x1:x2:…:x i-1 :x i = k·R1:k·R2:…:k·R (i-1) :(100-k)% Where R1:R2:…:R (i-1) For M (i-1) The relative proportions of all existing coal types in the mixture, where k represents the proportions of the existing coal types (x1~x2). (i-1) The total retention percentage starts at 100%, maintaining the relative proportions among all existing coal types. Each iteration decreases by 5% in increments (k = 100%, 95%, 90%...0%), gradually replacing the initial main coal type with newly introduced coal types to determine the actual coal blending ratio. Here, (100-k)% represents the percentage of the i-th economic coal type x. i The proportion of the incorporation gradually increases as k decreases, increasing by 5% each time.

[0044] In this embodiment of the application, when i≥3, if the original main coal type x1 is completely replaced (k=0%), and the current cost per kilowatt-hour is still lower than the previous one, then the current ratio (x2:…:x) is used. i Using this as the new benchmark, we will continue to introduce the next type of coal and gradually replace the new main coal type (i.e., the coal type with the highest proportion in the original mix).

[0045] Where i≥3, the original main coal type x1 is completely replaced (k=0%), and the current cost per kilowatt-hour is still lower than the cost of the previous iteration, then the current ratio (x2:…:x) is used. i Using this as a new benchmark, we will continue to introduce the next type of coal and gradually replace the new main coal type (i.e., the coal type with the highest proportion in the current blend) to ensure the continuity of the iteration logic.

[0046] The iterative logic of this application follows the principle of maintaining the original optimal ratio and replacing the main coal in stages, ensuring that each iteration is based on the optimal solution of the previous stage, avoiding confusion in the optimization direction, and achieving the optimal global cost per kilowatt-hour.

[0047] In step S103, based on the actual coal blending ratio, it is detected whether the various safety indices of the power plant meet the preset safety verification conditions. When the various safety indices meet the preset safety verification conditions, the current cost per kilowatt-hour of the power plant is calculated. Based on the current cost per kilowatt-hour, the first coal blending ratio of the initial main coal type and the new coal type that meets the first preset optimal condition is determined.

[0048] It is understood that the preset safety verification pass conditions in the embodiments of this application can be the conditions for passing the safety verification, such as the output of the coal mill, the amount of ash and slag produced, the output of the primary air fan and the induced draft fan, the fly ash concentration, and the amount of slag. The first coal blending ratio of the first preset optimal condition can be the optimal coal blending ratio recorded when the current coal type iteration is terminated in the current iteration stage (only the combination of the current main coal and the current new coal type).

[0049] In actual implementation, this application embodiment can, based on the actual coal blending ratio, detect whether the power plant's coal mill output, ash and slag production, primary air fan and induced draft fan output, fly ash concentration, and slag quantity meet safety requirements and pass safety verification. Upon passing safety verification, the current cost per kilowatt-hour of the power plant is calculated. That is, this application first passes all verification items, and then calculates the economic parameters of the current blending ratio (boiler efficiency η). n NO x Emission concentration N n SO2 emission concentration S n ), and estimate the cost per kilowatt-hour C using the following formula. n :

[0050] Where P is the delivered price of the j-th type of coal (yuan / ton), b is the standard coal consumption of the j-th type of coal (g / kWh), α is the proportion of the j-th type of coal in the current blend, and η n Let C be the boiler efficiency based on the current ratio, and C be the efficiency based on N. n S n Calculated environmental treatment costs (RMB / kWh).

[0051] Furthermore, this application determines the optimal coal blending ratio recorded when terminating the current coal type iteration within the current iteration stage (only the combination of the current main coal and the current new coal type) based on the current cost per kilowatt-hour, providing support for subsequent iteration methods.

[0052] The formula for estimating the cost per kilowatt-hour is: Cost per kilowatt-hour = (Coal price of each type of coal × Standard coal consumption of the corresponding coal type) / (Boiler efficiency × 1000) + Environmental treatment cost, where the environmental treatment cost is based on the NO under the current ratio. x SO2 emission concentration calculation.

[0053] This application embodiment can calculate the economic parameters of the current allocation ratio and estimate the cost per kilowatt-hour C after the safety index has been verified.n By verifying safety and economy as dual criteria, and adopting a dual criterion of prioritizing safety and comparing economy, safety is verified first and then cost is calculated. This ensures that the optimal output ratio maximizes the reduction of cost per kilowatt-hour while ensuring stable boiler operation, thus balancing safety and economy.

[0054] It should be noted that the preset security verification pass conditions can be set by those skilled in the art according to the actual situation, and no specific restrictions are imposed here.

[0055] Optionally, in one embodiment of this application, detecting whether at least one safety index of the power plant meets the preset safety verification pass conditions includes: obtaining at least one safety index including coal mill output, ash and slag production, primary air fan output and induced draft fan output, and detecting whether at least one safety index exceeds a preset rated range; in response to the case that at least one safety index does not exceed the preset rated range, determining that at least one safety index meets the preset safety verification pass conditions, otherwise, determining that at least one safety index does not meet the preset safety verification pass conditions.

[0056] It is understood that, in the embodiments of this application, the preset rated range of at least one safety index can be: the output of the coal mill does not exceed 95% of the rated output, the amount of ash and slag produced does not exceed the rated processing capacity of the boiler slag removal system, the output of the primary air fan and the induced draft fan does not exceed 90% of the rated output, the fly ash concentration does not exceed 90% of the rated output of the electrostatic precipitator, and the amount of slag does not exceed 90% of the maximum output of the slag removal system.

[0057] In this embodiment, for each iteration of the new mix ratio, a safety index is first calculated to obtain at least one safety index including the output of the coal mill, the amount of ash and slag produced, the output of the primary air fan, and the output of the induced draft fan. The system checks whether the output of the coal mill exceeds 95% of the rated output, whether the amount of ash and slag produced exceeds the rated capacity of the boiler slag removal system, whether the output of both the primary air fan and the induced draft fan exceeds 90% of the rated output, whether the fly ash concentration exceeds 90% of the rated output of the electrostatic precipitator, and whether the amount of slag exceeds 90% of the maximum output of the slag removal system. If at least one safety index does not exceed the preset rated range, it is determined that at least one safety index meets the preset safety verification pass condition. Otherwise, if at least one safety index exceeds the preset rated range, it is determined that at least one safety index does not meet the preset safety verification pass condition, and the iteration of the current coal type is terminated.

[0058] Optionally, in one embodiment of this application, when each safety index meets the preset safety verification pass conditions, the current cost per kilowatt-hour of the power plant is calculated, and the first coal blending ratio of the initial main coal type and the new coal type that meets the first preset optimal condition is determined based on the current cost per kilowatt-hour. This includes: when at least one safety index meets the preset safety verification pass conditions, calculating the current cost per kilowatt-hour of the power plant based on coal type price, coal type consumption, coal blending ratio, boiler efficiency, and target environmental treatment cost; responding to the situation where the current cost per kilowatt-hour is less than the cost per kilowatt-hour of the previous stage, and the coal blending ratio does not meet the preset limit condition, iteratively adjusting the actual coal blending ratio of the initial main coal type and the new coal type until the actual coal blending ratio meets the preset iterative adjustment stop condition, and then determining the first coal blending ratio of the initial main coal type and the new coal type that meets the first preset optimal condition.

[0059] It should be noted that, in order to improve iteration efficiency and avoid invalid iterations during the iteration process, a supplementary rule is set: if the cost per kilowatt-hour decrease is less than 0.1‰ for three consecutive iterations, the iteration of the current coal type is terminated in advance, and the optimal ratio of the current stage is recorded. There is no need to continue to reduce k or increase the proportion of the new coal type. This supplementary rule for terminating the iteration avoids invalid iterations, further improves optimization efficiency, and meets the needs of power plants for real-time coal allocation decisions.

[0060] It is understood that the preset iterative adjustment stop condition in the embodiments of this application can be the condition that the current cost per kilowatt-hour is greater than or equal to the cost per kilowatt-hour of the previous iteration.

[0061] In this embodiment, in response to at least one safety index meeting a preset safety verification pass condition, the current cost per kilowatt-hour (C) of the power plant is calculated based on coal price, coal consumption, coal blending ratio, boiler efficiency, and target environmental treatment cost. n Compared to the current cost per kilowatt-hour C n The cost per kilowatt-hour C compared to the previous iteration n-1 At the current cost per kilowatt-hour C n Less than the levelized cost of electricity (C) of the previous iteration n-1 (C) n <C n-1 If the target coal blending ratio has not reached its limit (5n%≤100% or k≥0%), continue to the next iteration, iteratively adjusting the actual coal blending ratio of the initial main coal type and the new coal type until the actual coal blending ratio meets the preset iteration adjustment stop condition, that is, the current cost per kilowatt-hour is greater than or equal to the cost per kilowatt-hour of the previous iteration. At this point, determine the first coal blending ratio of the initial main coal type and the new coal type that meets the first preset optimal condition, terminate the iteration of the current coal type, and record the optimal blending ratio M of the current stage. i M2 Where n is a positive integer ≥ 2, the number of coal types to be selected is not less than n, and the i-th economic coal type xi The price of (i≥2) is not higher than the price of other unselected coal types.

[0062] In step S104, when the various safety indices do not meet the preset safety verification pass conditions, the current cost per kilowatt-hour is greater than or equal to the previous cost per kilowatt-hour, or the coal blending ratio meets the preset limit conditions, a second coal blending ratio that meets the second preset optimal conditions is determined. Based on the first coal blending ratio and the second coal blending ratio, a coal blending ratio that meets the preset final optimal conditions is determined, and the power plant is controlled to perform coal blending optimization actions corresponding to the coal blending ratio of the preset final optimal conditions.

[0063] It is understood that the second coal blending ratio that satisfies the second preset optimal condition in the embodiments of this application can be the optimal coal blending ratio retained in this stage after the current coal type iteration cycle ends.

[0064] In actual implementation, this application embodiment can be implemented even when various safety indices do not meet the preset safety verification pass conditions (coal mill output exceeds 95% of rated output, ash and slag production exceeds the rated processing capacity of boiler slag removal system, primary air fan and induced draft fan outputs both exceed 90% of rated output, fly ash concentration exceeds 90% of electrostatic precipitator rated output, slag amount exceeds 90% of maximum output of slag removal system), and the current cost per kilowatt-hour is greater than or equal to the previous cost per kilowatt-hour (C). n ≥C n-1 When the coal blending ratio meets the preset limit conditions, all blending ratio records that have passed safety verification within the current iteration cycle are retrieved. The cost per kilowatt-hour corresponding to each blending ratio is compared, and the blending ratio with the lowest cost per kilowatt-hour is selected. This determines the optimal coal blending ratio (second coal blending ratio) remaining in this stage after the current coal type iteration cycle ends. This application determines the final optimal coal blending ratio based on the first and second coal blending ratios and controls the power plant to perform coal blending optimization actions corresponding to the final optimal coal blending ratio. This allows for flexible expansion to optimize the blending ratio of n types of coal, adapting to the multi-coal supply scenario of power plants. It solves the problem that existing technologies cannot achieve large-scale multi-coal optimization, has strong versatility, maximizes the reduction of cost per kilowatt-hour while ensuring stable boiler operation, adapts to the multi-coal supply scenario of power plants, and improves the economic benefits of coal-fired power generation.

[0065] Optionally, in one embodiment of this application, controlling the power plant to perform a coal blending optimization action corresponding to a preset final optimal condition includes: detecting whether the coal types in the coal type database meet the preset all-introduction conditions, and when the coal types in the coal type database meet the preset all-introduction conditions, traversing the coal blending ratios in all coal blending stages including the first coal blending ratio and the second coal blending ratio; determining the coal blending ratio that meets the preset final optimal condition based on the coal blending ratios in all coal blending stages, and determining the coal blending ratio of the power plant based on the coal blending ratio that meets the preset final optimal condition; adjusting the coal quality, combustion state, and environmental protection operation parameters of the power plant according to the coal blending ratio, generating the coal blending optimization action of the power plant, and executing the coal blending optimization action.

[0066] In actual implementation, the embodiments of this application can detect whether all new coal types have been introduced, and after all new coal types have been introduced, calculate the minimum cost per kilowatt-hour corresponding to the optimal coal blending ratio of the current initial main coal type and the new coal type in the current stage, match the optimal coal blending ratio of multiple coal types according to the minimum cost per kilowatt-hour, determine the coal blending ratio of the power plant according to the optimal coal blending ratio of multiple coal types, adjust the coal quality, combustion state and environmental protection operation parameters of the power plant according to the coal blending ratio, generate the coal blending optimization action of the power plant, and execute the coal blending optimization action.

[0067] In this application, after not all new coal types have been introduced, it determines whether to introduce the (i+1)th coal type. If not all candidate coal types have been introduced, i = i+1, and coal types are introduced again in ascending order of price. If all candidate coal types have been introduced, the entire iteration process terminates. This application iterates through the optimal ratios in all iteration stages, extracts the coal blending ratio with the lowest cost per kilowatt-hour, and uses it as the optimal blending ratio for the n coal types. Simultaneously, it outputs the boiler efficiency and NO under this ratio. x Key parameters such as SO2 emission concentration, coal mill output, ash and slag volume, and fan output provide a basis for power plant coal blending decisions.

[0068] Furthermore, the embodiments of this application can be used to further describe the present invention in detail with reference to specific embodiments (n=3 types of coal).

[0069] Example: Optimization of coal blending ratio for n=3 types of coal A 600MW coal-fired power plant has eight types of coal to be selected. It plans to select three types (x1, x2, x3) for coal blending optimization. The specific implementation steps are as follows: 1. Set target constraints for coal blending Calorific value ≥4000 kcal / kg, ash content ≤20%, volatile matter 30%~40%, sulfur content ≤1.0%; burnout rate ≥98%, slagging index ≤0.6.

[0070] 2. Select the primary coal type x1 and calculate its basic parameters. From 8 candidate coal types, a total of 3 coal types meeting all constraints are screened out, with the prices respectively: Coal Type A (860 RMB / ton), Coal Type B (890 RMB / ton), Coal Type C (870 RMB / ton); the lowest-priced Coal Type A is selected as the first main coal type x1.

[0071] Calculate the economic parameters of x1: boiler efficiency η0=94.8%, NO x =360 mg / m³, SO2=820 mg / m³; the estimated power generation cost C0=0.288 RMB / kWh.

[0072] 3. Introduce the second economic coal type x2, and carry out binary proportion iteration (with a 5% step) The second economic coal type x2 is selected as the lowest-priced Coal Type D among all candidate coal types (790 RMB / ton, calorific value 3500 kcal / kg, ash content 5%, volatile matter 50%, sulfur content 0.4%). The proportion of x2 is increased in 5% steps, and the iteration process is as follows: (1) n=1 (x1:x2=95%:5%): Safety verification is passed (mill output 87%, ash and slag amount 89%, primary air fan 84%, induced draft fan 85%), the power generation cost C1=0.283 RMB / kWh < C0, and the iteration continues; n=2 (x1:x2=90%:10%): Safety verification is passed, the power generation cost C2=0.279 RMB / kWh < C1, and the iteration continues; (iterate sequentially, the proportion of x2 increases by 5% each time).

[0073] (2) n=6 (x1:x2=70%:30%): Safety verification is passed, and the power generation cost C6=0.271 RMB / kWh (the current lowest).

[0074] (3) n=7 (x1:x2=65%:35%): Safety verification: induced draft fan output 91% (>90%), not passed, the binary iteration is terminated; the binary optimal proportion M2 is recorded: x1:x2=70%:30% (power generation cost 0.271 RMB / kWh), where R1:R2=7:3.

[0075] Introduce the third economic coal type x3, and carry out ternary proportion iteration (with a 5% step). The third economic coal type x3 is selected as the lowest-priced Coal Type E excluding x1 and x2 (820 RMB / ton, calorific value 3800 kcal / kg, ash content 8%, volatile matter 48%, sulfur content 0.5%); based on M2, keep the relative ratio of x1:x2=7:3 unchanged, take 5% as the step, gradually replace x1 with x3, k decreases from 100%, and the iteration process is as follows: (1) k=100% (x1:x2:x3=70%:30%:0%): that is M2, the power generation cost C0'=0.271 RMB / kWh.

[0076] (2) k=95% (x1:x2:x3=66.5%:28.5%:5%): Safety verification is passed, the levelized electricity cost C1'=0.268 yuan / kWh < C0', and the iteration continues.

[0077] (3) k=90% (x1:x2:x3=63%:27%:10%): Safety verification is passed, the levelized electricity cost C2'=0.265 yuan / kWh < C1', and the iteration continues; (iterating in sequence, k decreases by 5% each time, and the proportion of x3 increases by 5%).

[0078] (4) k=75% (x1:x2:x3=52.5%:22.5%:25%): Safety verification is passed, and the levelized electricity cost C5'=0.261 yuan / kWh (the current minimum).

[0079] (5) k=70% (x1:x2:x3=49%:21%:30%): Safety verification: the output of the coal mill is 96% (>95%), which is not passed, the ternary iteration is terminated, and the ternary optimal ratio M3 is recorded: x1:x2:x3=52.5%:22.5%:25% (the levelized electricity cost is 0.261 yuan / kWh).

[0080] 4. Output the optimal ratio All 3 coal types with the lowest price among all candidate coal types have been introduced, and all iterations are terminated; comparing the optimal ratios of the binary and ternary iterations, the ternary ratio M3 has the lowest levelized electricity cost and passes the safety verification, which is determined as the optimal coal blending ratio of the 3 coal types; the key parameters under this ratio are output synchronously: boiler efficiency 93.1%, NO x =375 mg / m³, SO2=880 mg / m³, coal mill output 94%, ash and slag amount 93%, primary fan output 87%, induced draft fan output 88%.

[0081] The present application adopts the logic of initial main coal type screening, sequentially introducing new coal types according to price, 5% step replacement, and iteration based on dual criteria of safety and economy, which can be flexibly extended to the ratio optimization of n types of coal. Each iteration is based on the optimal solution of the previous stage, ensuring the global cost is optimal. The setting of the fixed 5% step makes the iteration process standardized with high convergence efficiency and avoids disordered trial calculation. Meanwhile, the verification principle of safety priority ensures that the coal blending scheme meets the actual operation requirements of the power plant, taking both safety and economy into account, and can be widely applied to multi-coal-type coal blending decisions of various coal-fired power plants.

[0082] The multi-coal blending optimization method for power plants proposed in this application first selects the coal type with the lowest price that meets the blending target constraints as the initial main coal type. Then, other coal types with increasing prices are introduced sequentially, using a fixed ratio adjustment ladder. Based on the optimal ratio in the previous stage, the original main coal type is gradually replaced with the newly introduced coal type. Safety index verification and economic parameter calculation are carried out step by step. Iterative convergence is achieved through comparison of per-kilowatt-hour cost until safety is limited or per-kilowatt-hour cost increases. Finally, the optimal blending ratio of n coal types is output. This method can maximize the reduction of per-kilowatt-hour cost while ensuring stable boiler operation, adapting to multi-coal supply scenarios in power plants and improving the economic benefits of coal-fired power generation. Thus, it solves the problems of related technologies in multi-coal blending optimization, such as difficulty in balancing safety and economy, disordered ratio optimization logic, low iterative convergence efficiency, and poor adaptability to high-dimensional scenarios.

[0083] Next, referring to the accompanying drawings, a multi-coal blending optimization device for power plants according to an embodiment of this application is described.

[0084] Figure 5 This is a schematic diagram of the structure of a multi-coal blending optimization device for a power plant, according to an embodiment of this application.

[0085] like Figure 5 As shown, the multi-coal blending optimization device 10 of the power plant includes: a first screening module 100, a second screening module 200, a determination module 300, and an optimization module 400.

[0086] Specifically, the first screening module 100 is used to establish a coal type database based on the coal quality data of multiple coal types in the power plant, and to screen out coal types that meet the preset blending restriction conditions and the first preset minimum price condition from the coal type database as the initial main coal types.

[0087] The second screening module 200 is used to select coal types that meet the second preset minimum price condition from the coal type database as new coal types, and to generate the actual coal blending ratio of the initial main coal type and the new coal type by using a preset blending step iteration.

[0088] The determination module 300 is used to detect whether the various safety indices of the power plant meet the preset safety verification conditions based on the actual coal blending ratio, and when the various safety indices meet the preset safety verification conditions, calculate the current cost per kilowatt-hour of the power plant, and determine the first coal blending ratio of the initial main coal type and the new coal type that meets the first preset optimal condition based on the current cost per kilowatt-hour.

[0089] The optimization module 400 is used to determine the second coal blending ratio that meets the second preset optimal condition when various safety indices do not meet the preset safety verification pass conditions, the current cost per kilowatt-hour is greater than or equal to the previous cost per kilowatt-hour, or the coal blending ratio meets the preset limit condition. Based on the first coal blending ratio and the second coal blending ratio, the optimization module 400 determines the coal blending ratio that meets the preset final optimal condition and controls the power plant to perform the coal blending optimization action corresponding to the coal blending ratio of the preset final optimal condition.

[0090] Optionally, in one embodiment of this application, the optimization module 400 includes: a traversal unit, a first determination unit, and an optimization unit.

[0091] The traversal unit is used to detect whether the coal types in the coal type database meet the preset conditions for all coal types to be introduced. When the coal types in the coal type database meet the preset conditions for all coal types to be introduced, it traverses the coal blending ratios in all coal blending stages, including the first coal blending ratio and the second coal blending ratio.

[0092] The first determining unit is used to determine the coal blending ratio that meets the preset final optimal conditions based on the coal blending ratio in all coal blending stages, and to determine the coal blending ratio of the power plant based on the coal blending ratio that meets the preset final optimal conditions.

[0093] The optimization unit is used to adjust the coal quality, combustion state, and environmental operation parameters of the power plant according to the coal blending ratio, generate coal blending optimization actions for the power plant, and execute the coal blending optimization actions.

[0094] Optionally, in one embodiment of this application, the second screening module 200 includes a first detection unit and an adjustment unit.

[0095] The first detection unit is used to detect whether the quantity of the new coal type meets the first preset quantity condition.

[0096] The adjustment unit is used to adjust the initial coal blending ratio of the initial main coal type and the new coal type based on the preset blending ratio step iteration when the quantity of the new coal type meets the first preset quantity condition, until the initial coal blending ratio meets the preset iteration stop condition, and then determine the actual coal blending ratio.

[0097] Optionally, in one embodiment of this application, the second screening module 200 further includes a second detection unit and a replacement unit.

[0098] The second detection unit is used to detect whether the quantity of the new coal type meets the second preset quantity condition.

[0099] The replacement unit is used to maintain the relative proportions of all original coal types unchanged when the quantity of new coal types meets the second preset quantity condition. Based on the preset proportioning ladder, the unit replaces the initial main coal type with the newly introduced coal type step by step to determine the actual coal blending ratio.

[0100] Optionally, in one embodiment of this application, the determining module 300 includes: an acquisition unit and a determination unit.

[0101] The acquisition unit is used to acquire at least one safety index, including coal mill output, ash and slag production, primary air fan output and induced draft fan output, and to detect whether at least one safety index exceeds the preset rated range.

[0102] The determination unit is used to determine that at least one safety index meets the preset safety verification pass conditions if at least one safety index does not exceed the preset rated range; otherwise, it determines that at least one safety index does not meet the preset safety verification pass conditions.

[0103] Optionally, in one embodiment of this application, the determining module 300 includes: a calculation unit and a second determining unit.

[0104] The calculation unit is used to calculate the current cost per kilowatt-hour of the power plant based on coal price, coal consumption, coal blending ratio, boiler efficiency, and target environmental protection treatment cost, when at least one safety index meets the preset safety verification conditions.

[0105] The second determining unit is used to iteratively adjust the actual coal blending ratio of the initial main coal type and the new coal type in response to the situation where the current cost per kilowatt-hour is less than the cost per kilowatt-hour of the previous stage and the coal blending ratio does not meet the preset limit condition, until the actual coal blending ratio meets the preset iterative adjustment stop condition, and then determine the first coal blending ratio of the initial main coal type and the new coal type that meets the first preset optimal condition.

[0106] Optionally, in one embodiment of this application, the first screening module 100 includes a data acquisition unit and a setup unit.

[0107] The data acquisition unit is used to acquire coal quality data, including industrial analysis data, elemental analysis data, Hardgrove grindability index, ash fusion characteristic temperature, ash composition analysis data, pulverized coal explosive index, and pulverized coal ignition temperature.

[0108] Establish a unit to create a coal type database based on coal quality data.

[0109] It should be noted that the explanation of the above-mentioned embodiment of the multi-coal blending optimization method for power plants also applies to the multi-coal blending optimization device of the power plant in this embodiment, and will not be repeated here.

[0110] The multi-coal blending optimization device for power plants proposed in this application first selects the coal type that meets the blending target constraints and has the lowest price as the initial main coal type. Then, other coal types with increasing prices are introduced sequentially, using a fixed ratio adjustment ladder. Based on the optimal ratio in the previous stage, the original main coal type is gradually replaced with the newly introduced coal type. Safety index verification and economic parameter calculation are carried out step by step. Iterative convergence is achieved through comparison of the cost per kilowatt-hour until safety is limited or the cost per kilowatt-hour increases. Finally, the optimal blending ratio of n coal types is output. This device can maximize the reduction of the cost per kilowatt-hour while ensuring stable boiler operation, adapting to multi-coal supply scenarios in power plants and improving the economic benefits of coal-fired power generation. This solves the problems of related technologies in multi-coal blending optimization, such as difficulty in balancing safety and economy, disordered ratio optimization logic, low iterative convergence efficiency, and poor adaptability to high-dimensional scenarios.

[0111] Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: The memory 601, the processor 602, and the computer program stored on the memory 601 and capable of running on the processor 602.

[0112] When the processor 602 executes the program, it implements the multi-coal blending optimization method for power plants provided in the above embodiments.

[0113] Furthermore, electronic devices also include: Communication interface 603 is used for communication between memory 601 and processor 602.

[0114] The memory 601 is used to store computer programs that can run on the processor 602.

[0115] The memory 601 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0116] If the memory 601, processor 602, and communication interface 603 are implemented independently, then the communication interface 603, memory 601, and processor 602 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 6 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0117] Optionally, in a specific implementation, if the memory 601, processor 602, and communication interface 603 are integrated on a single chip, then the memory 601, processor 602, and communication interface 603 can communicate with each other through an internal interface.

[0118] The processor 602 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0119] This embodiment also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for optimizing the blending of multiple coal types in a power plant.

[0120] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0121] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0122] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0123] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0124] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0125] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0126] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0127] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for optimizing the blending of multiple coal types in a power plant, characterized in that, Includes the following steps: A coal type database is established based on coal quality data of multiple coal types in the power plant, and coal types that meet the preset coal blending restriction conditions and the first preset minimum price condition are selected from the coal type database as the initial main coal types. The coal types that meet the second preset minimum price condition are selected from the coal type database as new coal types, and the actual coal blending ratio of the initial main coal type and the new coal type is generated by iterative step-by-step generation of the preset blending ratio. Based on the actual coal blending ratio, it is detected whether the various safety indices of the power plant meet the preset safety verification conditions. When the various safety indices meet the preset safety verification conditions, the current cost per kilowatt-hour of the power plant is calculated. Based on the current cost per kilowatt-hour, the first coal blending ratio of the initial main coal type and the new coal type that meets the first preset optimal conditions is determined. When the various safety indices do not meet the preset safety verification pass conditions, the current cost per kilowatt-hour is greater than or equal to the previous cost per kilowatt-hour, or the coal blending ratio meets the preset limit conditions, a second coal blending ratio that meets the second preset optimal conditions is determined. Based on the first coal blending ratio and the second coal blending ratio, a coal blending ratio that meets the preset final optimal conditions is determined, and the power plant is controlled to perform coal blending optimization actions corresponding to the coal blending ratio of the preset final optimal conditions.

2. The method according to claim 1, characterized in that, The control of the power plant to perform coal blending optimization actions corresponding to the preset final optimal conditions includes: Detect whether the coal types in the coal type database meet the preset all-introduction conditions, and when the coal types in the coal type database meet the preset all-introduction conditions, traverse the coal blending ratios in all coal blending stages that include the first coal blending ratio and the second coal blending ratio. The coal blending ratio that satisfies the preset final optimal condition is determined based on the coal blending ratio in all the coal blending stages, and the coal blending ratio of the power plant is determined based on the coal blending ratio that satisfies the preset final optimal condition. The coal quality, combustion state, and environmental operation parameters of the power plant are adjusted according to the blending ratio of the coal type, generating the coal blending optimization action of the power plant, and then the coal blending optimization action is executed.

3. The method according to claim 1, characterized in that, The step-by-step iterative generation of the actual coal blending ratios of the initial main coal type and the new coal type using a preset coal blending ratio includes: Check whether the quantity of the new coal type meets the first preset quantity condition; In response to the condition that the quantity of the new coal type meets the first preset quantity condition, the initial coal blending ratio of the initial main coal type and the new coal type is adjusted iteratively based on the preset blending ratio until the initial coal blending ratio meets the preset iteration stop condition, and then the actual coal blending ratio is determined.

4. The method according to claim 3, characterized in that, The step-by-step iterative generation of the actual coal blending ratios of the initial main coal type and the new coal type using a preset coal blending ratio also includes: Check whether the quantity of the new coal type meets the second preset quantity condition; In response to the condition that the quantity of the new coal type meets the second preset quantity condition, the relative proportions among all existing coal types remain unchanged. Based on the preset proportioning steps, the initial main coal type is replaced step by step with the newly introduced coal type to determine the actual coal blending ratio.

5. The method according to claim 1, characterized in that, The detection of whether at least one safety index of the power plant meets the preset safety verification pass conditions includes: Obtain at least one safety index including coal mill output, ash and slag production, primary air fan output, and induced draft fan output, and detect whether the at least one safety index exceeds a preset rated range; If the at least one security index does not exceed the preset rated range, it is determined that the at least one security index meets the preset security verification pass condition; otherwise, it is determined that the at least one security index does not meet the preset security verification pass condition.

6. The method according to claim 5, characterized in that, When all safety indices meet the preset safety verification pass conditions, the current cost per kilowatt-hour of the power plant is calculated, and the first coal blending ratio of the initial main coal type and the new coal type that meets the first preset optimal condition is determined based on the current cost per kilowatt-hour, including: When at least one safety index meets the preset safety verification pass conditions, the current cost per kilowatt-hour of the power plant is calculated based on coal price, coal consumption, coal blending ratio, boiler efficiency and target environmental treatment cost. In response to the current cost per kilowatt-hour being less than the cost per kilowatt-hour in the previous stage, and the coal blending ratio not meeting the preset limit condition, the actual coal blending ratio of the initial main coal type and the new coal type is iteratively adjusted until the actual coal blending ratio meets the preset iterative adjustment stop condition, and then the first coal blending ratio of the initial main coal type and the new coal type that meets the first preset optimal condition is determined.

7. The method according to claim 1, characterized in that, The establishment of a coal type database based on coal quality data of multiple coal types in power plants includes: Acquire the coal quality data, which includes industrial analysis data, elemental analysis data, Hardgrove grindability index, ash fusion characteristic temperature, ash composition analysis data, pulverized coal explosive index, and pulverized coal ignition temperature; The coal type database is established based on the coal quality data.

8. A multi-coal blending optimization device for power plants, characterized in that, include: The first screening module is used to establish a coal type database based on the coal quality data of multiple coal types in the power plant, and to screen out coal types that meet the preset coal mixing restriction conditions and the first preset minimum price condition from the coal type database as the initial main coal types. The second screening module is used to screen out coal types that meet the second preset minimum price condition from the coal type database as new coal types, and generate the actual coal blending ratio of the initial main coal type and the new coal type by iteratively generating the blending ratio of the initial main coal type and the new coal type according to the preset blending ratio step. The determination module is used to detect whether the various safety indices of the power plant meet the preset safety verification conditions based on the actual coal blending ratio, and when the various safety indices meet the preset safety verification conditions, calculate the current cost per kilowatt-hour of the power plant, and determine the first coal blending ratio of the initial main coal type and the new coal type that meets the first preset optimal conditions based on the current cost per kilowatt-hour; The optimization module is used to determine a second coal blending ratio that meets a second preset optimal condition when the various safety indices do not meet the preset safety verification pass conditions, the current cost per kilowatt-hour is greater than or equal to the previous cost per kilowatt-hour, or the coal blending ratio meets a preset limit condition; determine a coal blending ratio that meets a preset final optimal condition based on the first coal blending ratio and the second coal blending ratio; and control the power plant to perform coal blending optimization actions corresponding to the coal blending ratio of the preset final optimal condition.

9. An electronic device, characterized in that, include: The device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the multi-coal blending optimization method for a power plant as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the multi-coal blending optimization method for power plants as described in any one of claims 1-7.

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