Intelligent coal blending combustion method and system

Abstract

The invention provides an intelligent coal blending combustion method and system, and relates to the technical field of thermal power generation, and the method comprises the steps: collecting combustion parameter information of different coal types, related indexes of each combustor of a boiler corresponding to the combustion parameter information, the consumption amount of each type of fire coal per month, and the minimum load requirement of the boiler; establishing a coal blending combustion DMD model based on the combustion parameter information, the related indexes, the fuel coal consumption amount and the minimum boiler load requirement, wherein the coal blending combustion DMD model is used for minimizing the fuel cost and meeting the boiler load requirement; solving the coal blending combustion DMD model by adopting an Excel planning solving tool, determining the usage amount of each combustor for different coal types, and generating an optimal coal blending scheme; according to the method, the coal blending combustion DMD model based on the combustion parameter information, the related indexes, the fire coal consumption amount and the boiler minimum load requirement is established, and an Excel planning solving tool is adopted for solving, so that the fuel cost can be minimized, and meanwhile, the boiler load requirement is met, and pollutant emission is controlled.

Description

Technical Field

[0001] The present invention relates to the technical field of thermal power generation, and in particular to an intelligent coal blending and combustion method and system. Background Art

[0002] In the development history of the thermal power generation industry, coal blending and combustion technology has gradually received attention. In the early days, thermal power plants mainly relied on a single type of coal for combustion and power generation. With the improvement of energy efficiency and environmental protection requirements, as well as the diversification of coal resource supply, coal blending and combustion technology began to develop; the initial coal blending and combustion methods were relatively simple, mostly based on experience and some basic coal quality indicators for trial mixing; with the development of computer technology and mathematical modeling methods, coal blending and combustion technology has gradually developed in the direction of refinement and intelligence; for example, some studies have begun to use mathematical methods such as linear programming to optimize coal blending plans, considering more coal quality indicators and boiler operating parameters to improve power generation efficiency and reduce pollution emissions; at the same time, with the continuous improvement of sensor technology and data acquisition systems, more accurate and comprehensive coal quality and boiler operation data can be obtained, providing data support for the further development of coal blending and combustion technology;

[0003] Existing coal blending and combustion schemes often fail to minimize costs; some schemes simply consider the price of coal types, but fail to comprehensively consider the impact of multiple factors such as coal quality indicators, boiler operating parameters, and coal consumption on costs; at the same time, some coal blending and combustion schemes cannot meet the various operating requirements of the boiler at the same time; for example, some schemes may meet the requirements of volatile matter, but ignore the limitations of other indicators such as sulfur content and ash content, resulting in problems such as boiler coking and corrosion, affecting the safe and stable operation of the boiler; or when meeting the load requirements of some burners, it is impossible to guarantee the load requirements of the entire boiler, affecting the power generation efficiency;

[0004] Therefore, there is an urgent need in the art for an intelligent coal blending and combustion method and system to solve the above-mentioned problems. Summary of the invention

[0005] The present invention provides an intelligent coal blending and combustion method and system, which aims to minimize fuel costs while meeting boiler load requirements and controlling pollutant emissions by establishing a coal blending and combustion DMD model based on combustion parameter information, relevant indicators, coal consumables and boiler minimum load requirements, and using Excel's planning and solving tools to solve the model.

[0006] In one aspect, the present invention provides an intelligent coal blending and combustion method, comprising:

[0007] Step 1: Collect the combustion parameter information of different types of coal, the relevant indicators of each burner of the boiler corresponding to the combustion parameter information, the monthly consumption of each type of coal and the minimum load requirement of the boiler;

[0008] Step 2: Based on the combustion parameter information, relevant indicators, coal consumption and the minimum load requirement of the boiler, a coal blending DMD model is established to minimize the fuel cost and meet the boiler load requirement;

[0009] Step three, using Excel's planning and solving tool to solve the coal blending DMD model, determine the usage of each burner for different types of coal, and generate an optimal coal blending plan.

[0010] According to an intelligent coal blending and combustion method provided by the present invention, the collecting of combustion parameter information of different types of coal includes:

[0011] Collect the volatile matter of coal j 、Sulfur content j 、Ash content j , calorific value q j and price C j Information, where j represents the type of coal, j∈{1, 2, ..., n};

[0012] The volatile matter f j Used to measure the flammability of coal, the sulfur content j Reflects the potential amount of sulfur dioxide produced when coal is burned. j Reflects the amount of solid impurities remaining after combustion, the calorific value q j Indicates the heat released by the complete combustion of a unit mass of coal. The price C j Used to calculate coal blending costs.

[0013] According to an intelligent coal blending and combustion method provided by the present invention, the relevant indicators corresponding to the combustion parameter information of each burner of the boiler are collected, including:

[0014] For each burner i∈{A, B, ..., N} of the boiler, determine its volatile matter lower limit F i 、Sulfur content upper limit S i 、Ash content upper limit H i And the lower limit of calorific value Q i , to ensure that the coal can burn safely and stably in the burner; at the same time, the overall sulfur content upper limit S and calorific value lower limit Q of the boiler are determined by weighted average to ensure the overall combustion performance and environmental protection requirements of the boiler.

[0015] According to an intelligent coal blending and combustion method provided by the present invention, the collection of the monthly consumable amount of each type of coal and the minimum load requirement of the boiler includes:

[0016] Determine the upper limit M of the amount of coal that can be consumed each month due to the continuous fluctuations in prices and coal supply from suppliers. j, and the minimum load D required by each burner per month based on grid dispatch and company operation plan i ; Upper limit of consumption M j Used to limit the usage of each type of coal in the coal blending plan, minimum load D i It is required to ensure that the boiler can operate normally to meet production needs.

[0017] According to an intelligent coal blending and combustion method provided by the present invention, the process of establishing a coal blending and combustion DMD model includes:

[0018] Set the decision variables, including the amount X of different coal types j used by burner i ij And whether to select the variable Y of this coal type ij , X ij ≥0, Y ij ∈{0,1};Y ij ∈{0, 1}, 0 represents no selection and 1 represents selection;

[0019] Determine the objective function to minimize fuel cost, that is, min∑ j C j X ij ;

[0020] Set constraints, including burner index constraints, boiler overall index constraints, coal supply constraints, and burner coal selection constraints;

[0021] The burner index constraints include volatile content constraints, sulfur content constraints, ash content constraints, and calorific value constraints:

[0022]

[0023] The boiler overall index constraint includes the boiler overall sulfur content constraint and the boiler overall calorific value constraint:

[0024]

[0025] The coal supply constraints include the total monthly usage constraints for each type of coal and the monthly usage constraints for each burner:

[0026]

[0027] The burner coal selection constraints include the number of selected coal types, the amount of coal used after selection, and the coal type selection variable constraints:

[0028]

[0029] Y ij =0,1

[0030] Among them, ∑ j Y ij≤2 represents the maximum number of coals that can be selected for each burner; the upper limit of the amount of coal used after selecting each burner needs to be determined based on the actual situation of the burner;

[0031] The decision variables, objective function and constraint conditions are integrated to generate the coal blending DMD model.

[0032] According to an intelligent coal blending and combustion method provided by the present invention, the process of using Excel planning and solving tool to solve the coal blending and combustion DMD model and determining the usage of each burner for different types of coal includes:

[0033] In Excel, set the cell area corresponding to the decision variables, the cell area corresponding to the objective function, and set the cell area corresponding to the inequality constraint conditions one by one according to the constraint conditions;

[0034] Start the planning and solving tool, input the objective function and constraints, select the simple linear programming method, and calculate the optimal solution that satisfies all constraints, that is, the usage of each burner for different types of coal X ij .

[0035] According to an intelligent coal blending and combustion method provided by the present invention, generating an optimal coal blending scheme includes:

[0036] According to the obtained usage X of each burner for different types of coal ij , determine the distribution of each type of coal in different burners and form the optimal coal mixing plan.

[0037] On the other hand, the present invention provides an intelligent coal blending and combustion system, comprising:

[0038] Data collection module, which is used to collect combustion parameter information of different types of coal, relevant indicators corresponding to the combustion parameter information of each burner of the boiler, the monthly consumption of each type of coal and the minimum load requirement of the boiler;

[0039] A coal blending and combustion DMD model construction module is connected to the data acquisition module, and is used to set decision variables based on the collected data, determine the objective function as minimizing fuel cost, set constraints, and construct a coal blending and combustion DMD model by integrating them;

[0040] A model solving module, which is connected to the coal blending and combustion DMD model building module, uses Excel's planning and solving tool to solve the coal blending and combustion DMD model to determine the usage of each burner for different types of coal;

[0041] The mixed coal scheme generating module is connected to the model solving module, and determines the distribution of each type of coal in different burners according to the usage of different types of coal by each burner obtained by solving, so as to form an optimal mixed coal scheme.

[0042] Compared with the prior art, the beneficial effects of this application are:

[0043] 1. This application establishes a coal blending DMD model, taking into account various factors such as the price of coal, combustion parameter information, relevant indicators of each boiler burner, coal consumption, and minimum boiler load requirements; when determining the coal blending plan, not only the price of coal is taken into account, but also the combustion effect of different coal types in the boiler and the impact on the total cost are considered; for example, by accurately calculating the usage of different coal types to meet the boiler operation requirements, the low combustion efficiency and additional fuel consumption caused by the selection of low-priced coal types that do not meet the requirements are avoided, thereby achieving more accurate cost optimization and effectively reducing fuel costs; based on the model calculation results, the purchase of coal types and quantities of power plants can be guided according to the usage requirements of different coal types, which helps to reasonably arrange procurement plans, avoid inventory backlogs or shortages, improve the efficiency and economy of inventory management, and further reduce overall operating costs;

[0044] 2. When establishing the model, the present application has taken into account in detail the indicators such as the volatile matter, sulfur content, ash content and calorific value of each burner of the boiler, as well as the sulfur content and calorific value of the boiler as a whole, and ensured that the coal blending scheme can meet these indicators at the same time through the set constraints; effectively avoided the problems of boiler coking and corrosion caused by ignoring certain indicators, and ensured the safe and stable operation of the boiler; considering the monthly consumable amount of each type of coal and the minimum load requirement of the boiler, the coal blending scheme determined by the model solution can ensure that each burner can meet the corresponding load requirements and meet the load requirements of the entire boiler, thereby maintaining the normal operation of the boiler and improving the power generation efficiency;

[0045] 3. This application quantifies the impact of factors such as coal volatile matter, sulfur content and nitrogen content on pollutant emissions in the model; by reasonably selecting coal types and determining coal blending ratios, while meeting other conditions, it can effectively reduce NO x , SO2, smoke and other pollutants, meets increasingly stringent environmental protection requirements and helps reduce environmental pollution.

[0046] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0047] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0049] Figure 1 It is a flow chart of an intelligent coal blending and combustion method provided by an embodiment of the present invention;

[0050] Figure 2 It is a structural schematic diagram of an intelligent coal blending and combustion system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0051] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0052] Embodiment 1:

[0053] The embodiment of the present invention provides an intelligent coal blending and combustion method. Figure 1 ,include:

[0054] Step 1: Collect the combustion parameter information of different types of coal, the relevant indicators corresponding to the combustion parameter information of each burner of the boiler, the monthly consumption of each type of coal, and the minimum load requirement of the boiler;

[0055] Step 2: Based on the combustion parameter information, relevant indicators, coal consumption and the minimum load requirement of the boiler, a coal blending DMD model is established to minimize the fuel cost and meet the boiler load requirement;

[0056] Step three, use Excel's planning and solving tool to solve the coal blending DMD model, determine the usage of different types of coal for each burner, and generate the optimal coal blending plan.

[0057] The principle of the above embodiment is: first, by collecting the combustion characteristics of various coals (such as calorific value, volatile matter content, etc.), the response of different burners to these characteristics (such as efficiency, emission level) and key parameters such as the actual available coal quantity and minimum load requirements; then, a dynamic multi-objective decision (DMD) model is established based on the collected data; this model aims to find an optimal solution to ensure that the boiler can work stably under the specified load conditions while minimizing fuel costs; finally, the planning solver in Excel or other similar optimization software is used to solve the DMD model to determine the optimal distribution ratio of each type of coal in each burner, thereby forming the most economical and effective mixed combustion solution.

[0058] The beneficial effects of the above embodiment are: by accurately calculating the optimal usage ratio of various types of coal, the overall operating cost of the power plant, especially the fuel expenditure, can be significantly reduced; reasonable allocation of coal of different properties for mixed burning helps to control pollutant emissions and meet more stringent environmental protection standards.

[0059] In order to further optimize the above embodiment, the combustion parameter information of different types of coal is collected including:

[0060] Collect the volatile matter of coal j 、Sulfur content j 、Ash content j , calorific value q j and price C j Information, where j represents the type of coal, j∈{1, 2, ..., n};

[0061] Volatile matter j Used to measure the flammability of coal, sulfur content j Reflects the potential amount of sulfur dioxide produced when coal is burned, ash content h j Reflects the amount of solid impurities remaining after combustion, calorific value q j Indicates the heat released by the complete combustion of unit mass of coal, price C j Used to calculate coal blending costs.

[0062] It should be noted that the volatile content information can be obtained by selecting representative samples from the coal samples to be analyzed and analyzing them in the laboratory according to relevant standard methods (such as GB / T212-2008 "Industrial Analysis Methods for Coal"); by heating the coal sample, the volatile substances therein are released under specific temperature and conditions, and then measuring the mass of the released substances as a percentage of the original mass of the coal sample, thereby obtaining the volatile content; some advanced thermal power plants are equipped with online monitoring equipment, such as analyzers based on laser induced breakdown spectroscopy (LIBS) technology or Fourier transform infrared spectroscopy (FTIR) technology; these devices can measure the volatile content in the coal flow in real time by focusing the laser beam on the surface of the coal flow to excite the elements and molecules in the coal, and then analyzing the resulting spectral signals to determine the volatile content;

[0063] The commonly used method for collecting sulfur information is the Eschka method, which involves mixing a coal sample with an Eschka reagent and burning it at high temperature to convert the sulfur in the coal into water-soluble sulfate. The sulfate content is then determined by chemical titration or colorimetry to obtain the sulfur content in the coal.

[0064] The common method for collecting ash information is to burn the coal sample at high temperature (such as 815℃) to constant weight, and measure the percentage of the mass of the remaining solid matter after burning to the original mass of the coal sample, which is the ash content.

[0065] A certain amount of coal sample is placed in an oxygen bomb calorimeter, the coal sample is ignited in an oxygen-filled environment, the heat released by the complete combustion of the coal sample is measured, and the calorific value information is obtained after a series of correction calculations;

[0066] For price information, regular surveys can be conducted on the coal market to understand the market price trends of different types of coal; price information can be obtained through direct communication with coal suppliers, querying coal trading market data, paying attention to industry reports, etc.; or the company's own procurement department can comprehensively calculate the actual purchase price of different types of coal based on factors such as the contract price signed with suppliers, transportation costs, and storage costs, and use this to calculate the coal blending cost.

[0067] In order to further optimize the above embodiment, the relevant indicators corresponding to the combustion parameter information of each burner of the boiler are collected, including:

[0068] For each burner i∈{A, B, ..., N} of the boiler, determine its volatile matter lower limit F i 、Sulfur content upper limit S i 、Ash content upper limit H i And the lower limit of calorific value Q i , to ensure that the coal can burn safely and stably in the burner; at the same time, the overall sulfur content upper limit S and calorific value lower limit Q of the boiler are determined by weighted average to ensure the overall combustion performance and environmental protection requirements of the boiler.

[0069] It should be noted that volatile matter is the flammable component in coal. A sufficient volatile matter content can ensure that the coal can ignite quickly and burn stably in the burner. If the volatile matter is too low, the coal may be difficult to ignite, or the combustion process may be unstable, resulting in flameout. Generally, the lower limit of volatile matter that can ensure stable combustion is determined based on the designed combustion characteristics of the boiler and previous operating experience. For example, for burners of certain specific designs, through multiple tests and actual operating observations, it is found that when the volatile matter content is lower than a certain value (such as about 30%, which varies depending on the type of burner and the overall design of the boiler), the combustion stability decreases significantly, so this value is set as the lower limit of volatile matter.

[0070] Sulfur will generate acidic gases such as sulfur dioxide during the combustion process, which are corrosive to the metal parts of the burner and boiler. In order to protect the equipment, it is necessary to determine the upper limit of sulfur content based on the material tolerance and expected service life of the equipment. For example, if the main parts of the burner and boiler are made of ordinary carbon steel, considering the corrosion rate of carbon steel in a certain sulfur content environment, the upper limit of sulfur content is set at a reasonable value (such as about 1.5%) to ensure that the equipment will not be damaged due to excessive corrosion during operation.

[0071] Ash will form solid residues during the combustion process. If the ash content is too high, it is easy to coke and accumulate ash inside the burner and boiler. Coking will affect the heat transfer efficiency. In severe cases, it may block the burner and pipeline, affecting the normal operation of the boiler. By conducting combustion tests on coals with different ash contents, observing the coking and ash accumulation, and combining the design structure and operating parameters of the boiler, the upper limit of ash content that can avoid serious coking and ash accumulation problems is determined. For example, for boilers with certain specific structures, when the ash content exceeds 30%, the coking and ash accumulation phenomena are significantly aggravated, so the upper limit of ash content is set near this value.

[0072] The calorific value is an indicator of the ability of coal to provide heat. In order to ensure that the boiler can obtain enough heat to maintain normal operation and achieve the designed thermal power output, the lower limit of the calorific value needs to be determined; the lower limit of the calorific value that can meet the heat demand is calculated based on the designed thermal power of the boiler, the expected operating load, and the combustion efficiency. For example, if the designed thermal power of the boiler is a certain value, considering the loss of combustion efficiency, it is determined through calculation that when the calorific value is lower than a certain value (such as about 4000 kcal / kg), it cannot meet the heat demand for normal operation of the boiler, so this value is set as the lower limit of the calorific value;

[0073] The load distribution of each burner during boiler operation is an important factor in determining the weight; burners with larger loads have a greater impact on the overall operating conditions of the boiler, so they should have a larger weight in the weighted average calculation; for example, if a burner bears 30% of the total boiler load, then its weight may be set to 0.3 when calculating indicators such as the overall sulfur content upper limit and calorific value lower limit of the boiler; the contribution of different burners to the combustion process is also different; for example, some burners may be located in critical positions and have a significant impact on flame shape, temperature distribution, etc. Even if their load is relatively small, they should be given appropriate weight in the weighted average.

[0074] In order to further optimize the above embodiment, the monthly consumption of each type of coal and the minimum load requirement of the boiler are collected, including:

[0075] Determine the upper limit M of the amount of coal that can be consumed each month due to the continuous fluctuations in prices and coal supply from suppliers. j , and the minimum load D required by each burner per month based on grid dispatch and company operation plan i ; Upper limit of consumption M j Used to limit the usage of each type of coal in the coal blending plan, minimum load D i It is required to ensure that the boiler can operate normally to meet production needs.

[0076] It should be noted that the upper limit of the consumable amount M j and minimum load D i All of them are collected and determined based on actual operating conditions and will not be elaborated on in detail.

[0077] In order to further optimize the above embodiment, the process of establishing a coal blending DMD model includes:

[0078] Set the decision variables, including the amount X of different coal types j used by burner i ij And whether to select the variable Y of this coal type ij , X ij ≥0, Y ij ∈{0,1};Y ij ∈{0, 1}, 0 represents no selection and 1 represents selection;

[0079] Determine the objective function to minimize fuel cost, that is, min∑ j C j x ij ;

[0080] Set constraints, including burner index constraints, boiler overall index constraints, coal supply constraints, and burner coal selection constraints;

[0081] Burner index constraints include volatile content constraints, sulfur content constraints, ash content constraints, and calorific value constraints:

[0082]

[0083] The overall boiler index constraints include overall boiler sulfur content constraints and overall boiler calorific value constraints:

[0084]

[0085] The coal supply constraints include the monthly total usage constraints for each type of coal and the monthly usage constraints for each burner:

[0086]

[0087] The burner coal selection constraints include the number of selected coal types, the amount of coal used after selection, and the coal type selection variable constraints:

[0088]

[0089] Y ij =0,1

[0090] Among them, ∑ j Y ij ≤2 represents the maximum number of coals that can be selected for each burner; the upper limit of the amount of coal used after selecting each burner needs to be determined based on the actual situation of the burner;

[0091] The decision variables, objective function and constraints are integrated to generate the DMD model of coal blending.

[0092] It should be noted that the constraints include the requirement that the volatile content is greater than or equal to the limit, the sulfur content is less than or equal to the limit, the ash content is less than or equal to the limit, the calorific value is greater than or equal to the limit, the total sulfur content of each burner of the boiler is less than or equal to the total sulfur content limit of the boiler, the total calorific value of each burner of the boiler is greater than or equal to the total calorific value limit of the boiler, the total monthly usage of each type of coal is less than or equal to the total supply, the monthly coal usage of each burner is greater than or equal to the minimum load of the burner coal, each burner can select a maximum of two types of coal for combustion, and each burner can select coal usage less than or equal to the monthly supply.

[0093] By combining the decision variables, objective functions and these constraints, a coal blending DMD model can be constructed. This model can find the optimal coal blending solution under various practical conditions and requirements, minimize fuel costs and meet boiler operation requirements.

[0094] In order to further optimize the above embodiment, the DMD model of coal blending and combustion is solved by using Excel's planning and solving tool. The process of determining the usage of each burner for different types of coal includes:

[0095] In Excel, set the cell area corresponding to the decision variables, the cell area corresponding to the objective function, and set the cell area corresponding to the inequality constraint conditions one by one according to the constraint conditions;

[0096] Start the planning and solving tool, input the objective function and constraints, select the simple linear programming method, and calculate the optimal solution that satisfies all constraints, that is, the usage of each burner for different types of coal X ij .

[0097] It should be noted that the cell area corresponding to the decision variable: for the decision variable X ij (the amount of different coal types used by burner i) and Y ij (Whether to select this type of coal), you can select a suitable cell range in the Excel worksheet to represent it; for example, if there are 5 burners (A, B, C, D, E) and 5 types of coal (1, 2, 3, 4, 5), you can select a 5*5 cell range (such as A1:E5) to represent X ij , another cell range of the same size or different size (as needed) (such as F1:J5) to represent; set the initial values ​​of these cells to reasonable guess values ​​(such as 0 or other empirical values) so that the planning solver can perform iterative calculations based on this;

[0098] Cell range corresponding to the objective function: In Excel, you can select a single cell to calculate the value of the objective function; enter the formula in this cell and reference X ij Cell range and C j The corresponding coal price cell area (another cell area can be set up to store price information) is used to calculate the total cost;

[0099] Set the cell areas corresponding to the inequality constraints one by one according to the constraints: For various constraints, you need to set the corresponding inequalities in Excel; for example, for the volatile content constraint, you can set an inequality for each burner; assuming f j Stored in the N1:O5 cell range, F i Stored in the cell range P1:Q5, X ij The volatile matter constraint inequality for burner A can be entered in a new cell (such as R1) as follows: j f j X ij ≥F i Similarly, for other constraints (sulfur content, ash content, calorific value, overall boiler index, coal supply, burner coal selection, etc.), you need to set the corresponding inequality cell area in Excel according to their respective formulas;

[0100] In the "Data" tab of Excel, add the planning and solving tool through "Add-ins"; after adding successfully, click the "Planning and Solving" button to start the tool; in the planning and solving parameter dialog box, set the target cell to the previously set target function cell (such as K1), and select the target as "minimum value"; then add the inequality cell area corresponding to the various constraints set previously in the "Constraint" area; for example, add the volatile content constraint inequality cell area (such as R1) to the constraint area, and add the inequality cell areas of other constraints in turn to ensure that all constraints are added correctly; select the solution method as "simple linear programming" because the coal blending DMD model is based on linear programming; the planning and solving tool will iteratively calculate according to the input objective function and constraints; during the calculation process, it will continuously adjust the decision variable cell area (X ij and Y ij ) until the optimal solution that satisfies all constraints is found; when the calculation is completed, the value in the decision variable cell area is the usage of each burner for different types of coal.

[0101] In order to further optimize the above embodiment, generating the optimal coal blending scheme includes:

[0102] According to the obtained usage X of each burner for different types of coal ij , determine the distribution of each type of coal in different burners and form the optimal coal mixing plan.

[0103] It should be noted that when determining the distribution situation, some factors in actual operation need to be considered; for example, the coal feeding method of different burners, the transportation characteristics of coal types, etc.; if the transportation characteristics of a certain type of coal are poor, it may be necessary to make appropriate adjustments to its distribution in different burners to ensure a smooth coal feeding process; the optimal coal blending scheme formed can be directly applied to the coal blending and combustion operations of thermal power plants; operators can accurately control the addition amount and order of different types of coal in each burner according to the requirements of the scheme, so as to achieve the goal of minimizing costs and meeting boiler operation requirements.

[0104] Embodiment 2:

[0105] The embodiment of the present invention provides an intelligent coal blending and combustion system. Figure 2 ,include:

[0106] Data collection module, which is used to collect combustion parameter information of different types of coal, relevant indicators corresponding to the combustion parameter information of each burner of the boiler, the monthly consumption of each type of coal and the minimum load requirement of the boiler;

[0107] A coal blending and combustion DMD model building module is connected to the data acquisition module, and sets decision variables based on the collected data, determines the objective function as minimizing fuel cost, sets constraints, and builds a coal blending and combustion DMD model by integrating them;

[0108] A model solving module is connected to the coal blending and combustion DMD model building module, and uses Excel's planning and solving tool to solve the coal blending and combustion DMD model to determine the usage of each burner for different types of coal;

[0109] The mixed coal scheme generation module is connected to the model solving module. According to the usage of different types of coal for each burner obtained by solving the solution, the distribution of each type of coal in different burners is determined to form an optimal mixed coal scheme.

[0110] It should be noted that the data acquisition module can collect the combustion parameter information of different types of coal (such as volatile matter, sulfur content, ash content, calorific value and price), the relevant indicators of each burner of the boiler (lower limit of volatile matter, upper limit of sulfur content, upper limit of ash content and lower limit of calorific value, as well as the upper limit of sulfur content and lower limit of calorific value of the boiler as a whole), the monthly consumable amount of each type of coal and the minimum load requirements of the boiler; and ensure the accuracy of the data through a variety of collection methods (such as laboratory analysis, online monitoring equipment, etc.);

[0111] The DMD model building module for coal blending and combustion scientifically sets decision variables (the amount of different types of coal used by the burner and whether to select the coal type) and objective function (minimizing fuel cost) based on the collected data; this setting meets the actual needs of coal blending and combustion, and can find the most cost-effective coal blending scheme through model solving; the model building module reasonably sets constraints, including burner index constraints (ensuring safe and stable combustion of coal in the burner), boiler overall index constraints (ensuring overall boiler operation performance and environmental protection requirements), coal supply constraints (considering coal supply and load requirements) and burner coal selection constraints (in line with actual operational feasibility); these constraints are comprehensive and reasonable, enabling the model to solve the optimal coal blending scheme under various actual conditions;

[0112] The model solving module uses Excel's planning and solving tool to solve the coal blending DMD model. This solution method has high efficiency. By setting the relevant cell area in Excel and entering the objective function and constraints, the optimal solution that meets all constraints can be quickly obtained, that is, the usage of different coal types for each burner.

[0113] By operating according to the optimal coal blending plan, the goals of minimizing costs and meeting boiler operation requirements can be effectively achieved.

[0114] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned 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 invention.

Claims

1. An intelligent coal blending and combustion method, characterized in that: include: Step 1: Collect the combustion parameter information of different types of coal, the relevant indicators of each burner of the boiler corresponding to the combustion parameter information, the monthly consumption of each type of coal and the minimum load requirement of the boiler; Step 2: Based on the combustion parameter information, relevant indicators, coal consumption and the minimum load requirement of the boiler, a coal blending DMD model is established to minimize the fuel cost and meet the boiler load requirement; Step three, using Excel's planning and solving tool to solve the coal blending DMD model, determine the usage of each burner for different types of coal, and generate an optimal coal blending plan.

2. The intelligent coal blending and combustion method according to claim 1 is characterized in that: The acquisition of combustion parameter information of different types of coal includes: Collect the volatile matter of coal j 、Sulfur content j 、Ash content j , calorific value q j and price C j Information, where j represents the type of coal, j∈{1, 2, ..., n}; The volatile matter f j Used to measure the flammability of coal, the sulfur content j Reflects the potential amount of sulfur dioxide produced when coal is burned. j Reflects the amount of solid impurities remaining after combustion, the calorific value q j Indicates the heat released by the complete combustion of a unit mass of coal. The price C j Used to calculate coal blending costs.

3. The intelligent coal blending and combustion method according to claim 2 is characterized in that: The relevant indicators corresponding to the combustion parameter information of each burner of the boiler include: For each burner i∈{A, B, ..., N} of the boiler, determine its volatile matter lower limit F i 、Sulfur content upper limit S i 、Ash content upper limit H i And the lower limit of calorific value Q i , to ensure that the coal can burn safely and stably in the burner; at the same time, the overall sulfur content upper limit S and calorific value lower limit Q of the boiler are determined by weighted average to ensure the overall combustion performance and environmental protection requirements of the boiler.

4. The intelligent coal blending and combustion method according to claim 3 is characterized in that: The collection of monthly coal consumption and boiler minimum load requirements includes: Determine the upper limit M of the amount of coal that can be consumed each month due to the continuous fluctuations in prices and coal supply from suppliers. j , and the minimum load D required by each burner per month based on grid dispatch and company operation plan i ; Upper limit of consumption M j Used to limit the usage of each type of coal in the coal blending plan, minimum load D i It is required to ensure that the boiler can operate normally to meet production needs.

5. The intelligent coal blending and combustion method according to claim 4 is characterized in that: The process of establishing the coal blending DMD model includes: Set the decision variables, including the amount X of different coal types j used by burner i ij And whether to select the variable Y of this coal type ij , X ij ≥0, Y ij ∈{0,1};Y ij ∈{0, 1}, 0 represents no selection and 1 represents selection; Determine the objective function to minimize fuel cost, that is, min∑ j C j X ij ; Set constraints, including burner index constraints, boiler overall index constraints, coal supply constraints, and burner coal selection constraints; The burner index constraints include volatile content constraints, sulfur content constraints, ash content constraints, and calorific value constraints: The boiler overall index constraint includes the boiler overall sulfur content constraint and the boiler overall calorific value constraint: The coal supply constraints include the total monthly usage constraints for each type of coal and the monthly usage constraints for each burner: The burner coal selection constraints include the number of selected coal types, the amount of coal used after selection, and the coal type selection variable constraints: Y ij ＝0，1 Among them, ∑ j Y ij ≤2 represents the maximum number of coals that can be selected for each burner; the upper limit of the amount of coal used after selecting each burner needs to be determined based on the actual situation of the burner; The decision variables, objective function and constraint conditions are integrated to generate the coal blending DMD model.

6. The intelligent coal blending and combustion method according to claim 5 is characterized in that: The process of using the Excel planning and solving tool to solve the coal blending DMD model and determine the usage of each burner for different types of coal includes: In Excel, set the cell area corresponding to the decision variables, the cell area corresponding to the objective function, and set the cell area corresponding to the inequality constraint conditions one by one according to the constraint conditions; Start the planning and solving tool, input the objective function and constraints, select the simple linear programming method, and calculate the optimal solution that satisfies all constraints, that is, the usage of each burner for different types of coal X ij .

7. The intelligent coal blending and combustion method according to claim 6 is characterized in that: The optimal coal blending scheme comprises: According to the obtained usage X of each burner for different types of coal ij , determine the distribution of each type of coal in different burners and form the optimal coal mixing plan.

8. An intelligent coal blending and combustion system, based on the method described in claims 1 to 7, characterized in that: include: Data collection module, which is used to collect combustion parameter information of different types of coal, relevant indicators corresponding to the combustion parameter information of each burner of the boiler, the monthly consumption of each type of coal and the minimum load requirement of the boiler; A coal blending and combustion DMD model construction module is connected to the data acquisition module, and is used to set decision variables based on the collected data, determine the objective function as minimizing fuel cost, set constraints, and construct a coal blending and combustion DMD model by integrating them; A model solving module, which is connected to the coal blending and combustion DMD model building module, uses Excel's planning and solving tool to solve the coal blending and combustion DMD model to determine the usage of each burner for different types of coal; The mixed coal scheme generating module is connected to the model solving module, and determines the distribution of each type of coal in different burners according to the usage of different types of coal by each burner obtained by solving, so as to form an optimal mixed coal scheme.

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