Thermal power generation fire coal control optimization analysis method and system

Abstract

The invention discloses a thermal power generation fire coal control optimization analysis method and system, and relates to the technical field of thermal power generation fire coal control, and the analysis method and system comprise the steps: determining at least one to-be-adjusted target operation unit according to the load demand generating capacity, and building a demand schedule based on the determined target generating capacity of the target operation unit; calculating the differential generating capacity of each target operation unit and endowing the target operation unit with a first mark; the combustion optimization mode of the corresponding target operation unit is analyzed according to the difference generating capacity, the combustion optimization mode comprises optimization of the combustion cost and the coal blending mode, when the coal blending mode is optimized, the quality parameter of the coal blending mode is determined with the combustion cost as the constraint condition, coal blending combustion is carried out based on the determined quality parameter, and the coal blending mode is optimized. Recording a combustion result of the target operation unit; and endowing the combustion result of each target operation unit with a second mark, wherein the second mark is used for reflecting the achievement value characteristics of the target operation unit in the demand schedule. According to the analysis method and system, the power generation adjustment plan is established based on the determined target operation unit, fire coal optimization consideration is carried out from the combustion cost and the coal blending mode based on the planning content, and therefore the suitability of the target operation unit as a planned object is judged, matching consideration of planning object selection is achieved, and the adaptability of the target operation unit as the planned object is improved. And the combustion efficiency can be ensured by combining the combustion cost and the coal blending mode.

Description

Technical Field

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

[0002] The coal combustion technology in thermal power plants is a process based on maintaining stable combustion conditions to generate the required heat to drive the unit and ultimately generate electric energy. This process involves multiple systems and control strategies to ensure efficient, safe, and environmentally friendly power production requirements. Since different power load demands require the unit to generate or reach the corresponding power generation, so as to meet the load usage demands in various scenarios, the control and optimization adjustment of coal combustion for operating units is the most critical link.

[0003] Currently, for the optimization of coal combustion control, most consider using the principle of distributed control to monitor each device in the thermal power generation system in real time. Through the monitoring and analysis of parameters such as the temperature, pressure, and vibration of key devices, the real-time control of the coal combustion process is achieved, so as to achieve the purpose of optimization and adjustment. However, in the process of optimization and adjustment, due to different actual working conditions, there will be problems of mismatched coal combustion control effects. For example, when facing high power load demands, usually the way to increase the coal input for each coal-fired unit is adopted to meet the demand for generating more electricity. On the one hand, it is whether the unit can bear the increased combustion burden without systemic risks; on the other hand, blindly increasing the coal quantity may lead to problems such as too high coal combustion costs, unreasonable coal blending, and reduced coal combustion efficiency. Therefore, it is necessary to optimize and analyze from the combustion costs and coal blending factors of different units, so as to find a more reasonable coal combustion control analysis method for different units to bear load demands.

[0004] In view of this, the present application is specifically proposed. Summary of the Invention

[0005] The purpose of the present invention is to provide an optimization analysis method and system for coal combustion control in thermal power generation. The analysis method and system establish a power generation adjustment plan based on the determined target operating unit, and consider coal combustion optimization from the aspects of combustion costs and coal blending modes, so as to judge the suitability of the target operating unit as the planned object. It not only has the consideration of the matching of the planned object selection, but also can ensure the combustion efficiency by combining the consideration of combustion costs and coal blending modes.

[0006] The embodiments of the present invention are implemented as follows: First aspect, a method for optimizing and analyzing coal combustion control in thermal power generation, comprising the following steps: determining at least one target operating unit to be adjusted according to the power generation amount required by the load demand, and establishing a demand schedule based on the target power generation amount of the determined target operating unit, wherein the target power generation amount refers to the target power generation data that the target operating unit needs to achieve; calculating the differential power generation amount of each target operating unit, and assigning a first label to the target operating unit based on the absolute value of the differential power generation amount, where the first label is used to reflect the target value characteristics of the target operating unit in the demand schedule, and the differential power generation amount refers to the difference between the initial load power of the target operating unit and the target power generation amount; analyzing the combustion optimization method of the corresponding target operating unit according to the differential power generation amount, where the combustion optimization method includes the optimization of combustion cost and coal blending mode. When performing coal blending mode optimization, determining the quality parameters of the coal blending mode with the combustion cost as a constraint condition, and performing coal blending combustion based on the determined quality parameters, and recording the combustion result of the target operating unit; assigning a second label to the combustion result of each target operating unit, where the second label is used to reflect the achievement value characteristics of the target operating unit in the demand schedule.

[0007] In an optional implementation manner, the determining the quality parameters of the coal blending mode with the combustion cost as a constraint condition includes the following steps: determining a basic parameter set of the coal blending; and establishing a calculation model with the combustion result data as the target and the combustion cost as the constraint condition based on the basic parameter set; performing linear or nonlinear solution on the calculation model, and taking the obtained result as the quality parameter; where the combustion result refers to the combustion amount data converted with the differential power generation amount as the calculation target.

[0008] In an optional implementation manner, the determining the basic parameter set of the coal blending includes the following steps: obtaining the historical combustion environment parameters and real-time combustion environment parameters of the target operating unit, statistically analyzing the historical basic parameters under the historical combustion environment parameters to obtain a historical statistical result; determining the real-time basic parameters under the real-time combustion environment parameters as the real-time statistical result; screening the real-time statistical result based on the historical statistical result, and all the obtained basic parameters are used as the constituent basis of the basic parameter set.

[0009] In an optional implementation manner, calculating the influence of the real-time basic parameters on the real-time combustion environment parameters and performing truth-preserving processing on the real-time combustion environment parameters to obtain truth-preserving combustion environment parameters; judging the similarity between the truth-preserving combustion environment parameters and the historical combustion environment parameters, adjusting the historical statistical result based on the obtained similarity result, and screening the real-time statistical result based on the adjusted historical statistical result.

[0010] In an alternative embodiment, the impact cost of each basic parameter is calculated. The impact cost includes an operating impact cost and an associated impact cost. The basic parameters with an impact cost higher than a first preset value are screened out, and the remaining basic parameters form the basis of the set of basic parameters. Among them, the operating impact cost refers to the operating cost generated by the target operating unit when this basic parameter is selected, and the associated impact cost refers to the operating cost generated by the synergistic effect of this basic parameter on the remaining basic parameters on the target operating unit when this basic parameter is selected.

[0011] In an alternative embodiment, after screening out the basic parameters with an impact cost higher than the preset value, the following steps are further included: calculating the total cost of the screened basic parameters, and retaining the basic parameters with a total cost lower than a second preset value. Among them, the total cost includes the impact cost and the raw material cost.

[0012] In an alternative embodiment, determine the combustion result of the unadjusted operating unit based on the basic parameters and assign a third mark, and the third mark is used as the judgment basis for whether to assign the first mark and the second mark to this operating unit.

[0013] In an alternative embodiment, it further includes the step of adjusting the first mark based on the second mark: performing a reliability calculation on the target operating units listed in the demand schedule to obtain a reliability calculation result; adjusting the first mark of the target operating units based on the reliability calculation result, and the adjustment includes any one of modifying the target value, canceling the target value, or retaining the target value. The reliability calculation refers to comparing and judging the achieved value and the achievement rate of the target operating unit.

[0014] In an alternative embodiment, in the adjustment method, after canceling the achieved value, the following steps are further included: performing a reliability calculation on the operating unit assigned the third mark, and judging whether this operating unit can replace the target operating unit with the canceled achieved value and enter the demand schedule according to the calculation result.

[0015] In a second aspect, a thermal power generation coal combustion control optimization analysis system includes: A first determination unit, which is used to determine at least one target operating unit to be adjusted according to the power generation amount required by the load demand, and establish a demand schedule based on the target power generation amount of the determined target operating unit. Among them, the target power generation amount refers to the target power generation data that the target operating unit needs to reach. A first calculation unit, which is used to calculate the differential power generation amount of each target operating unit, and assign a first mark to this target operating unit based on the absolute value of the differential power generation amount. The first mark is used to reflect the first feature of the demand schedule. Among them, the differential power generation amount refers to the difference between the initial load power of the target operating unit and the target power generation amount. A second calculation unit, which is configured to analyze the combustion optimization method for a corresponding target operating unit according to the differential power generation amount, where the combustion optimization method includes the optimization of combustion cost and coal blending mode. When performing the optimization of the coal blending mode, the quality parameters of the coal blending mode are determined with the combustion cost as a constraint condition, and coal blending combustion is performed based on the determined quality parameters, and the combustion result of the target operating unit is recorded; A first processing unit, which is configured to assign a second tag to the combustion result of each target operating unit, and the second tag is used to feedback the adjustment method of the corresponding first tag on the demand schedule.

[0016] The beneficial effects of the embodiments of the present invention are: The thermal power generation coal combustion control optimization analysis method and system provided by the embodiments of the present invention pre-determine the target operating units to be adjusted, and perform combustion optimization based on the difference between the required power generation amount of the target operating unit and the current power generation amount, ensuring that the required combustion power generation standard can be achieved. In this optimization process, the consideration of combustion cost and coal blending mode is combined to ensure the low-cost and high-efficiency operation mode of the unit. The actual optimization result is used to assign identification tags, so as to determine whether subsequent adjustment of the target operating unit is required.

[0017] Generally speaking, the thermal power generation coal combustion control optimization analysis method and system provided by the embodiments of the present invention can consider the adaptability of the object on the basis of selecting the target operating unit and consider the compatibility of cost and coal blending combustion in the optimization process, so as to achieve the purpose of safe, efficient and cost-controlled combustion power generation, and provide a reference analysis basis for the subsequent coal combustion power generation control optimization strategy for different power load demand scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0019] Figure 1 It is a flowchart of the main steps of the analysis method provided by the embodiments of the present invention; Figure 2 For Figure 1 It is a flowchart of one of the sub-steps S300 of the shown main steps; Figure 3 For Figure 2 It is a flowchart of the specific steps of the shown sub-step S300; Figure 4 It is a flowchart of the main steps of the analysis method provided by another embodiment of the present invention; Figure 5 For Figure 4 The flowchart of one sub-step S500 of the main steps shown; Figure 6 The modular schematic diagram of the analysis system provided by the embodiment of the present invention.

[0020] Icons: 600 - analysis system; 610 - first determination unit; 620 - first calculation unit; 630 - second calculation unit; 640 - first processing unit; 650 - second processing unit. Specific embodiments

[0021] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Generally, the components of the embodiments of the present invention described and illustrated herein can be arranged and designed in various different configurations.

[0022] Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed present invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0023] It should be understood that the "system", "device" and / or "module" used in the present invention is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the said words can be replaced by other expressions.

[0024] As shown in the present invention and the claims, unless the context clearly indicates an exception, words such as "a", "an", "one" and / or "the" are not specifically singular, but may also include the plural. Generally, the terms "comprising" and "including" only indicate the inclusion of the clearly identified steps and elements, and these steps and elements do not constitute an exclusive list, and the method or device may also include other steps or elements.

[0025] Flowcharts are used in the present invention to illustrate the operations performed by the systems according to the embodiments of the present application. It should be understood that the operations before or after do not necessarily have to be executed precisely in sequence. On the contrary, they can be executed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or several operations can be removed from these processes. Embodiment

[0026] In thermal power generation operations, the coal-fired power generation amount is adjusted instantaneously according to different electricity load demands at the load end. Generally, the power generation amounts of all currently operating units (per unit time period) are collected in real time, and are distributed to each unit according to the total power generation requirement for corresponding increases in power generation. This mode will bring some problems in the practical process. For example, for the dispatched units, can they bear the increased power generation load? How much can they bear? What remedial measures should be taken if the target power generation amount is not achieved after bearing the load? In the practical process, it is generally allocated according to the calibration parameters and historical operation parameters of each unit by the background decision-maker. For example, for large-power units, a greater load increase demand is dispatched; for units prone to failure shutdowns, little or no load increase demand is allocated; for units with high regulation response sensitivity, the load increase demand is allocated first; for those that do not meet the standards during operation, other units are adjusted to bear the load. The above optimization allocation mode not only relies heavily on manual experience judgment, but also only makes a rough estimate of the actual operation of the units, and a more reasonable optimization control strategy cannot be established.

[0027] To address the above problems, we use a distributed control system (sensor network) to real-time monitor the real-time operating conditions of each operating unit, such as current / historical power generation amounts, current / historical operating environments, current / historical combustion modes, and current / historical combustion failure rates, etc., so as to establish an optimization control strategy with more data reliability. Although the above improved method can improve the optimization reliability of the control strategy, when implementing the strategy, whether the unit can reach the required load power generation amount as expected, or how to adjust if the required power generation amount is not reached, or even if the required load power generation amount is reached, resulting in a sharp increase in combustion costs or a decrease in actual combustion efficiency, are all problems that need to be further considered and solved at present. For this reason, we propose a method for optimizing the analysis of coal-fired control in thermal power generation, which can take into account the adaptability of the target object as well as the optimization of costs and coal blending modes to promote the establishment of a control strategy, so as to achieve a higher optimization control mode with safety, efficiency, and controllable costs.

[0028] For details, please refer to Figure 1 , a method for optimizing the analysis of coal-fired control in thermal power generation provided in this embodiment includes the following steps: S100: Determine at least one target operating unit to be adjusted according to the power generation amount required by the load demand, and establish a demand schedule based on the target power generation amount of the determined target operating unit, where the target power generation amount refers to the target power generation data that the target operating unit needs to achieve; this step means determining the required power generation amount to be adjusted and achieved under different load demand environments, and then selecting at least one operating unit as the target operating unit to be adjusted according to the specific size of this demand. The adjustment method can be to increase or decrease the load power generation amount. In this embodiment, taking the increase of the load power generation amount as an example, the selected target operating units are used to establish a preliminary strategy based on the target power generation amount (the target power generation data that the target operating unit needs to achieve), that is, to establish a demand schedule. This demand schedule is mainly used to feedback the content such as the number, initial power generation amount, target power generation amount, and actual power generation amount of the target operating unit. The data such as the number, initial power generation amount, and target power generation amount can be first reflected on the demand schedule for subsequent steps.

[0029] S200: Calculate the differential power generation amount of each target operating unit, and assign a first label to the target operating unit based on the absolute value of the differential power generation amount. The first label is used to reflect the target value characteristics of the target operating unit in the demand schedule, where the differential power generation amount refers to the difference between the initial load power amount and the target power generation amount of the target operating unit; this step means identifying and marking the target (differential power generation amount) that the target operating unit needs to adjust, so as to initially mark the load increase amount assigned to each target operating unit. This initial mark is the first label, recording the achievement value situation of the target operating unit (that is, the power generation data that needs to be increased / decreased), and feeding it back on the demand schedule for use as the medium processing object for subsequent adjustments.

[0030] S300: Analyze the combustion optimization method of the corresponding target operating unit according to the differential power generation amount. The combustion optimization method includes the optimization of combustion cost and coal blending mode. When optimizing the coal blending mode, determine the quality parameters of the coal blending mode with the combustion cost as the constraint condition, and carry out coal blending combustion based on the determined quality parameters, and record the combustion result of the target operating unit; this step means selecting different combustion optimization methods based on the above differential power generation amount, that is, different target operating units need to meet different differential power generation amounts, and different differential power generation amounts require different combustion optimization methods based on the target operating unit. On the one hand, it is to match the combustion optimization method according to the differences in the characteristics of the target operating unit itself, and on the other hand, different combustion optimization methods are required due to different achieved targets, so as to ensure the adaptability and efficiency of combustion optimization control.

[0031] Furthermore, the combustion optimization method includes the optimization of combustion cost and coal blending mode, that is, comprehensive consideration is given to both cost and coal blending mode. For example, if only the combustion cost is optimized without changing the coal blending mode, the raw coal with the corresponding cost can be directly blended and burned (this situation is relatively rare and generally occurs along with the change of the coal blending mode); if the coal blending mode is optimized, the quality parameters of the coal blending mode are determined with the combustion cost as the constraint condition, that is, the combustion cost needs to be considered at all times during coal blending, so as to determine various suitable quality parameters of the raw coal to be configured. The quality parameters include parameters such as total moisture, ash content, volatile matter, calorific value, sulfur content, and ash fusibility. The specific content or performance indicators need to be screened and configured before coal combustion. Finally, coal blending combustion is carried out based on the determined quality parameters, and the combustion results of the target operating unit are recorded. It should be noted that the combustion results refer to the electrical energy data converted from the heat generated during the specified operation period after actual coal blending. The increase or decrease of this electrical energy data serves as the basis for comparison with the differential power generation.

[0032] S400: Assign a second label to the combustion result of each of the target operating units. This second label is used to reflect the achievement value characteristics of the target operating units in the demand schedule. This step means that after obtaining the combustion results of each target operating unit, a label identification is made for the value of the combustion result, that is, a second label is assigned to represent the actual combustion amount achieved after combustion optimization. It can be compared with the target value characteristics represented by the first label to feedback the achievement situation of the target operating unit, so as to know whether the combustion optimization of the target operating unit can meet the requirement of filling the differential power generation as expected. It should be noted that the second label also needs to be fed back on the demand schedule, and it can be combined with the first label to facilitate directly controlling the relationship between the target value and the achievement value of the target operating unit, understanding the achievement rate situation, and it can also be used as the object to be processed as a medium for subsequent adjustment.

[0033] Through the above technical solutions, not only can the power generation optimization control strategy be adjusted flexibly in real time according to different load demands, improving the response speed and adaptability of the system, but also a multi-objective optimization mode is achieved by considering cost and efficiency, not only focusing on cost minimization but also taking into account the combustion efficiency requirements. On this basis, by introducing the first and second labels, a quantitative evaluation mechanism is provided, so that decision-makers can intuitively understand the operating status and optimization effect of the unit, facilitating the monitoring and implementation of the adjustment strategy. Therefore, in the whole process of coal combustion control optimization analysis, the overall goals of stronger adaptability, higher safety, and more controllable cost can be achieved.

[0034] The quality parameters are particularly important for coal blending of the target operating unit. Higher combustion efficiency can be achieved by combining the characteristics of the target operating unit, avoiding the problem that the combustion efficiency decreases due to blindly increasing the coal consumption or inappropriate coal blending. In this embodiment, please refer specifically toFigure 2 To determine the quality parameters of the coal blending mode with the combustion cost as the constraint condition, the specific steps are as follows: S310: Determine the basic parameter set of the coal blending; this step means to first determine all the parameters of the raw coal to be configured, and the content and index range of each parameter need to be determined, so as to be able to select the optimal ones from all the parameters. The parameters include ash content (ash forms solid residues after coal combustion, which will increase the transportation cost and reduce the calorific value of coal. High-ash coal will also increase the wear of coal-fired facilities, generate more fly ash, and impose a burden on the environment), volatile matter (volatile matter affects the combustion characteristics of coal. It is released when coal is heated, affecting the ignition speed and flame stability. High-volatile coal is easier to ignite and burns more completely, but may also increase harmful gas emissions), sulfur content (sulfur in coal is a harmful substance, and sulfur dioxide will be generated during coal combustion. Sulfur dioxide not only corrodes metal equipment, but is also one of the main air pollution sources), total moisture (total moisture will reduce the calorific value of coal because additional energy is required to evaporate the moisture during combustion. In addition, high-moisture coal is prone to caking during storage and transportation, affecting operation. Generally speaking, the lower the total moisture of coal, the better. However, in actual applications, the total moisture of coal cannot be very low. For power coal, high total moisture is not conducive to pulverization and combustion, and the received basis low calorific value of coal with high total moisture is lower, directly affecting the quality of coal), calorific value (calorific value is the most important energy index of coal, which determines the combustion efficiency and economic benefits of coal. High-calorific coal can generate more energy under the same weight) and ash fusion temperature (ash fusion temperature determines the melting temperature of ash during coal combustion, which is very important for preventing boiler slagging. If the ash fusion temperature is too low, it may cause equipment blockage and corrosion problems), etc.

[0035] There are also some quality parameters, such as fixed carbon (fixed carbon is the main component of combustible substances in coal. The higher its content, the higher the calorific value of coal and the better the combustion efficiency), reactivity (the reactivity of coal affects its reaction rate with oxygen, and thus affects the combustion efficiency. High-reactivity coal burns faster, but may be difficult to control), particle size distribution (particle size distribution affects the fluidity, bulk density and combustion characteristics of coal. Appropriate particle size distribution can ensure good combustion efficiency and reduce dust emissions), hardness (hardness determines the abrasion resistance of coal and affects its breakage degree during mining, transportation and storage. Coal with too high hardness may be difficult to break) and grindability index (the grindability index indicates the ease of coal being pulverized, which is crucial for preparing coal powder and adjusting the design of the combustion system. Coal that is easy to grind can reduce the energy consumption of pulverization and improve the overall system efficiency), etc.

[0036] By selecting the above basic quality parameters, the basic quality parameters corresponding to the characteristics or contents are thus constituted into a basic parameter set, so as to achieve a balance between combustion efficiency and cost in consideration of cost conditions.

[0037] S320: Based on the basic parameter set, establish a calculation model with the combustion result data as the target and the combustion cost as the constraint condition. Herein, the combustion result refers to the combustion amount data converted with the differential power generation amount as the calculation target. This step means constructing a calculation model with the above basic parameter set. This calculation model refers to the combustion result that needs to be increased or decreased to achieve, and the constraint condition is set as the objective function of the cost upper limit or cost minimization. For example, assume there are several types of coal, and the quality parameters of each type of coal are different, and each also has its own cost. The calculation target is to minimize the total cost of coal blending, while meeting the quality parameters required for unit operation coal blending and achieving the calorific value conversion standard. It can be represented by the following objective function: Z is the total cost, ci is the unit price of the i-th type of coal, and xi is the parameter ratio of the i-th type of coal. Thus, the above calculation model can be solved to obtain the quality parameters that meet the requirements.

[0038] S330: Perform linear or nonlinear solution on the calculation model, and use the obtained result as the quality parameter; this step means that linear or nonlinear solution can be performed on the above calculation model. If it can be simplified to a linear problem, the linear programming method can be used for solution, such as using a linear programming solver, such as the Simplex algorithm or the interior point method. In practical applications, professional optimization software packages can also be used, such as the Optimization Toolbox of MATLAB, the SciPy.optimize module of Python, or commercial software such as Gurobi and CPLEX. If it contains nonlinear relationships, the nonlinear programming method needs to be used, such as the gradient descent method, the genetic algorithm, or the particle swarm optimization algorithm.

[0039] Through the above technical solution, it is possible to determine the quality parameters of coal blending while taking into account both cost and combustion efficiency. These quality parameters (combinations of various components and their ratios) can form the basic parameter set. However, determining these quality parameters requires a huge amount of calculation, especially when selecting the combination method of coal blending to participate in the calculation. In order to reduce the calculation amount and also refer to the historical coal blending combustion situation and data of the target operating unit, in some embodiments, the following method can be adopted. Please refer to Figure 3 , that is, the steps for determining the basic parameter set of coal blending include the following: S311: Obtain the historical combustion environment parameters and real-time combustion environment parameters of the target operating unit. This step represents collecting the historical combustion environment parameters and real-time combustion environment parameters of the target operating unit. The historical environment parameters refer to the combustion environment parameters (such as combustion temperature, humidity, unit power, global efficiency, etc.) of the target operating unit for at least one historical period (the previous / several data collection cycles), and the real-time combustion environment parameters refer to the combustion environment parameters of the target operating unit in the current period (the current data collection cycle).

[0040] S312: Statistically analyze the historical basic parameters under the historical combustion environment parameters to obtain a historical statistical result; this step represents collecting the quality parameters of the coal blending at that time under the corresponding historical combustion environment parameters as the historical basic parameters for subsequent screening reference. S313: Determine the real-time basic parameters under the real-time combustion environment parameters as the real-time statistical result; similarly, this step represents collecting the quality parameters of the coal blending at this time under the corresponding real-time combustion environment parameters as the real-time basic parameters for subsequent screening reference.

[0041] S314: Screen the real-time statistical result based on the historical statistical result, and all the obtained basic parameters serve as the basis for forming the basic parameter set. This step represents merging the above-obtained historical statistical result (the set of historical basic parameters) and real-time statistical result (the set of real-time basic parameters), using the real-time statistical result as the basis and the historical statistical result as a reference, so as to screen out the final basic parameters to form the basic parameter set. Specifically, all the quality parameters that appear in the real statistical result are temporarily retained first. If these quality parameters have appeared in the historical statistical result, they are considered for retention. The higher the frequency of appearance, the stronger the willingness to retain, and they can participate in data calculation first. On the contrary, the lower the frequency of appearance, the later they participate in data calculation. If they do not appear, they are postponed from participating in the calculation (at this time, a certain quality parameter that does not appear is calculated by means of artificial independent configuration). This can not only reduce the calculation load of the unified working time, but also utilize the reliability characteristics under historical data (indicating that the operating unit adapts to this coal blending method. On the one hand, the combustion efficiency can be guaranteed, and on the other hand, the operating safety is higher), so that the screened basic parameters are more adaptable and stable.

[0042] On the basis of the above technical solutions, considering that the use of historical data to screen real-time data can have the characteristics of stability and adaptability, there may be situations in the real-time statistical results that are not very similar to the historical statistical results, resulting in too many quality parameters configured by humans. For example, when the coal blending method is temporarily adjusted or there is a temporary mutation in the combustion (such as starting and stopping before and after a fault or performing different combustion tests, etc.), the real-time combustion environment parameters at this time will be greatly different from the historical combustion environment parameters. Then the historical statistical results will have a high probability of not appearing in the range of real-time statistical results, resulting in relatively few basic parameter items that are finally screened and determined, and it is necessary to manually select undetermined quality parameters to combine, which not only increases the amount of calculation, but also increases randomness. In order to deal with the above problems, it is necessary to adjust the historical statistical results for this situation so that it is more in line with or more within the range of real-time statistical results.

[0043] Specifically, the influence of the real-time basic parameters on the real-time combustion environment parameters is calculated and the real-time combustion environment parameters are normalized to obtain the normalized combustion environment parameters; considering that under different combustion tests or different coal blending combustion conditions, the combustion of raw coal will affect the parameters of the combustion environment, such as increased temperature, increased humidity, etc., it is necessary to obtain the normalized environment combustion parameters after removing such influences. The normalization processing method can be obtained based on a statistical model, that is, using a mature statistical model (it can also be used by machine learning) to obtain the actual value and influence value of each type of quality parameter on the environmental combustion parameters, thereby eliminating the influence value to obtain the normalized combustion environment parameters.

[0044] Then, a similarity judgment is made between the returned combustion environment parameters and the historical combustion environment parameters, that is, the returned combustion environment parameters are compared with the historical combustion environment parameters, the historical combustion environment parameters that meet the similarity standards are used as references, and the historical basic parameters involved in the historical statistical results are adjusted as considerations, that is, the historical statistical results are adjusted based on the obtained similarity results, so that the adjusted corresponding historical basic parameters are used as the basis for real-time basic parameter selection and processing, so as to achieve the purpose of screening the real-time statistical results based on the adjusted historical statistical results, for example, the historical basic parameters corresponding to the historical combustion environment parameters that meet the similarity standards are all used as historical statistical results, and then all or part of them are merged into the real-time basic parameters, so as to fill in and combine the quality parameters of the undetermined items, so as to achieve the purpose of forming an effective basic parameter set to participate in data calculation, reduce data calculation load and the possibility of random combination.

[0045] Based on the above technical solutions, all quality parameters (basic parameters) that make up the basic parameter set need to participate in data calculations. However, considering that after a certain quality parameter or multiple quality parameters are selected and combined, the raw coal composed of the combined basic parameter set may increase the operation burden of the unit during combustion, resulting in the need for more maintenance or maintenance work for post-combustion unit maintenance. That is, different coal blending (proportion of coal blending) methods have different impacts and damages on the unit and its auxiliary equipment, causing changes in operations such as desulfurization, denitrification, and dust removal, and resulting in different maintenance costs, material costs, and power consumption. Therefore, there will be significant changes in operating costs. At this time, it is necessary to pre-screen the basic parameters that will significantly increase the operating costs to avoid being screened out during subsequent calculations with cost as a constraint condition, which may lead to excessive calculation amounts. Therefore, it is necessary to screen the early cost factors after the steps of matching historical combustion environment parameters and restoring historical combustion environment parameters.

[0046] Specifically, calculate the impact cost of each basic parameter. The impact cost includes the operation impact cost and the associated impact cost. Among them, the operation impact cost refers to the operating cost generated by the target operating unit when this basic parameter is selected, and the associated impact cost refers to the operating cost generated by the target operating unit due to the synergistic effect on the remaining basic parameters when this basic parameter is selected. Screen out the basic parameters with an impact cost higher than the first preset value, and the remaining basic parameters serve as the basis for the composition of the basic parameter set. Through the above technical solutions, the operation impact cost and the associated impact cost of each basic parameter can be calculated and determined using an empirical model (which can also be a statistical model or a machine learning model), and then the objects with too high impact costs are excluded and do not participate in the subsequent combination and calculation of the basic parameter set. It should be noted that the first preset value can be an empirical reference value or a standard reference value, which can be selected according to the calculated load requirements.

[0047] On the basis of the above technical solutions, the operating impact cost and associated impact cost of the basic parameters are considered, and the basic parameters with too high impact cost are pre-eliminated. However, considering that although the operating cost of a certain basic parameter is too high, its raw material cost is not high (the proportion of this component in the total cost of the configured raw coal is not high), and it may be within the solution range when participating in the calculation of the target combustion amount in the subsequent cost constraint mechanism. Therefore, the basic parameters with the total cost below a certain upper limit need to be retained to participate in the subsequent calculation. Specifically, after screening out the basic parameters with the impact cost higher than the preset value, the following steps are further included: calculating the total cost of the screened basic parameters, and retaining the basic parameters with the total cost lower than the second preset value, where the total cost includes the impact cost and the raw material cost. Through the foregoing technical solutions, the basic parameters with the total cost lower than the second preset value (an empirical value, selected according to needs) can be retained to form a basic parameter set to participate in the subsequent calculation, ensuring a reasonable calculation amount while obtaining a more satisfactory calculation result that meets the requirements more fully, so as to obtain a better coal blending combustion result subsequently.

[0048] Through the above technical solutions, for the determination of the basic parameter set, a reasonable selection is made with reference to historical data, and scientific screening is carried out for the impact cost and raw material cost, so that the combined basic parameter set has the advantages of reasonable accuracy and controllable calculation load when participating in the subsequent calculation. On this basis, the above selection and screening methods of the basic parameter set can also be used for non-target operating units, that is, units that have not been adjusted, so as to find out whether there are objects that can be used as target operating units in the units that have not been adjusted. To elaborate, since the determination of the target operating unit is initially based on human experience (configured according to the calibration parameters and historical operating parameters of each unit by the background decision-maker), it is not possible to directly determine the most suitable group of target operating units to execute the current load change strategy. Therefore, there may be other operating units that have not been initially determined and are more suitable as participants in this combustion control adjustment under the selection and screening methods of this basic parameter set. Therefore, after determining the target operating unit, the step of preparing the adjustment participant needs to be carried out.

[0049] The details are as follows: Determine the combustion results of the unadjusted operating units based on the basic parameters and assign a third label, which is used as the judgment basis for whether to assign the first label and the second label to the operating units. This step means that among all the operating units, the remaining operating units (unadjusted operating units) other than the target operating units that have not been initially determined adopt the same basic parameters for coal blending combustion, so as to identify and label the obtained combustion results, that is, the third label. This third label enables the unadjusted operating units to be used as a preliminary judgment basis for the target operating units, that is, as the judgment basis for whether to assign the first label and the second label to the unadjusted operating units, so that they can be used as replacements when the combustion results of certain / some objects that have already been used as target operating units do not meet the standards and participate in the load optimization strategy this time.

[0050] Through the foregoing technical solutions, it is possible to consider preliminary replacements without reasonably determining the participating operating units as target operating units, so as to perform operation and maintenance scheduling among all the units in the entire thermal power plant, achieving the purpose of being more intelligent, reasonable, and accurate. Therefore, it is necessary to screen and determine the target operating units that may be replaced. Please refer to Figure 4 and Figure 5 , and it further includes step S500 (including steps S510 and S520 below) of adjusting the first label based on the second label: S510: Perform a reliability calculation on the target operating units listed in the demand schedule to obtain a reliability calculation result. The reliability calculation refers to comparing and judging the target value and achievement rate of the target operating units. This step means performing a reliability calculation on all the target operating units that already exist in the demand schedule and have been initially determined to participate in the control strategy, that is, evaluating whether the target operating units can achieve the target power generation. This evaluation method mainly refers to predictive evaluation, that is, calculating the ratio of the target value and the achievement value, and the obtained result is the reliability calculation result. If the achievement rate exceeds 100%, it proves that the optimal control is reliable. If the achievement rate is less than 100% and lower, it proves that the reliability is lower. In addition, when estimating the achievement value, it is necessary to estimate in combination with the combustion stage, that is, comprehensively consider and perform predictive evaluation for the combustion rising period and the combustion stable period, so as to predict the achievement value and calculate the achievement rate, and finally obtain the reliability calculation result.

[0051] S520: Adjust the first label of the target operating unit based on the reliability calculation result. The adjustment includes any one of modifying the target value, canceling the target value, or retaining the target value. This step means to adjust the target value feature (the first label) of the target operating unit based on the obtained reliability calculation result above. For example, modify the target value, cancel the target value, or retain the target value. If the achievement rate is just right, consider retaining it. If the achievement rate is high or exceeds, consider modifying (increasing or decreasing) it. If the achievement rate is low, consider canceling this operating unit as the target operating unit, and thus consider using the prepared operating unit as a backup.

[0052] Specifically, in the adjustment method, after canceling the achievement value, the following steps are further included: Perform a reliability calculation on the operating unit assigned the third label. This reliability calculation refers to calculating the achievement rate of the achievement value of the target operating unit to be replaced, and judging whether this operating unit can replace the target operating unit with the canceled achievement value and enter the demand schedule according to the calculation result. Through the foregoing technical solution, in the entire thermal power generation system, on the basis of initially selecting the participating objects for the power optimization control strategy, the consideration of optimizing and adjusting the participating objects is also carried out, making the participating objects of the entire control optimization strategy more matching, and being able to achieve the optimization control purpose more efficiently and safely.

[0053] In this embodiment, a thermal power generation coal combustion control optimization analysis system 600 is also provided. Please refer to Figure 6 the modular schematic diagram of the thermal power generation coal combustion control optimization analysis system 600 in, which is mainly used to divide the functional modules of the thermal power generation coal combustion control optimization analysis system 600 according to the embodiments of the above method. For example, each functional module can be divided, or two or more functions can be integrated into one processing module. The above integrated module can be implemented in the form of hardware or in the form of a software functional module. It should be noted that the division of modules in the present invention is illustrative, only a logical function division, and there can be other division methods in actual implementation. For example, in the case of dividing each functional module corresponding to each function, Figure 6 only a system / device schematic diagram is shown. Among them, the thermal power generation coal combustion control optimization analysis system 600 may include a first determination unit 610, a first calculation unit 620, a second calculation unit 630, a first processing unit 640, and a second processing unit 650. The functions of each unit module will be described below.

[0054] The first determination unit 610 is used to determine at least one target operating unit to be adjusted according to the power generation amount required by the load demand, and establish a demand schedule based on the target power generation amount of the determined target operating unit. Among them, the target power generation amount refers to the target power generation data that the target operating unit needs to reach; The first calculation unit 620 is configured to calculate the differential power generation of each of the target operating units, and assign a first label to the target operating unit based on the absolute value of the differential power generation, where the first label is used to reflect the first feature of the demand schedule. The differential power generation refers to the difference between the initial load power of the target operating unit and the target power generation. The second calculation unit 630 is configured to analyze the combustion optimization method of the corresponding target operating unit according to the differential power generation. The combustion optimization method includes the optimization of combustion cost and coal blending mode. When optimizing the coal blending mode, the quality parameters of the coal blending mode are determined with the combustion cost as a constraint condition, and coal blending combustion is performed based on the determined quality parameters, and the combustion result of the target operating unit is recorded. In some embodiments, the second calculation unit 630 is further configured to determine the basic parameter set of the coal blending; and establish a calculation model with the combustion result data as the target and the combustion cost as the constraint condition based on the basic parameter set; perform linear or nonlinear solution on the calculation model, and use the obtained result as the quality parameter; where the combustion result refers to the combustion amount data converted with the differential power generation as the calculation target; and is further configured to obtain the historical combustion environment parameters and the real-time combustion environment parameters of the target operating unit, perform statistics on the historical basic parameters under the historical combustion environment parameters to obtain a historical statistical result; determine the real-time basic parameters under the real-time combustion environment parameters as the real-time statistical result; screen the real-time statistical result based on the historical statistical result, and all the obtained basic parameters are used as the composition basis of the basic parameter set. It is also used to calculate the influence of the real-time basic parameters on the real-time combustion environment parameters and perform truth-preserving processing on the real-time combustion environment parameters to obtain the truth-preserving combustion environment parameters; perform similarity judgment on the truth-preserving combustion environment parameters and the historical combustion environment parameters, adjust the historical statistical result based on the obtained similarity result, and screen the real-time statistical result based on the adjusted historical statistical result; calculate the influence cost of each basic parameter, where the influence cost includes the operation influence cost and the associated influence cost, and screen out the basic parameters with the influence cost higher than the first preset value, and the remaining basic parameters are used as the composition basis of the basic parameter set. And after screening out the basic parameters with the influence cost higher than the preset value, the following steps are further included: calculate the total cost of the screened basic parameters, and retain the basic parameters with the total cost lower than the second preset value.

[0055] The first processing unit 640 is configured to assign a second label to the combustion result of each of the target operating units, where the second label is used to feedback the adjustment method of the corresponding first label on the demand schedule.

[0056] A second processing unit 650 is configured to adjust the first tag based on a second tag: adjust the first tag of the target operating unit based on the reliability calculation result, where the adjustment includes any one of modifying the target value, canceling the target value, or retaining the target value. The reliability calculation refers to comparing and judging the achievement value and achievement rate of the target operating unit. In some embodiments, the second processing unit 650 is further configured to perform a reliability calculation on the operating unit assigned with a third tag, and determine whether the operating unit can replace the target operating unit with the canceled achievement value and enter the demand schedule according to the calculation result.

[0057] In the above embodiments, the more specific working processes of the functional units can refer to the corresponding content disclosed in the foregoing embodiments. In addition, the functional units can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired or wireless manner. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center integrating one or more available media. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, or a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium (such as a solid state disk (SSD)).

[0058] The embodiments of the present application are described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0059] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to work in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including an instruction device that implements the functions specified in one or more of the processes and / or blocks Figure 1 of the processes or blocks Figure 1 specified in one or more of the processes or blocks.

[0060] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, so that the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in one or more of the processes and / or blocks Figure 1 of the processes or blocks Figure 1 specified in one or more of the processes or blocks.

[0061] Obviously, those skilled in the art can make various changes and modifications to the embodiments of the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the embodiments of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to cover these changes and modifications.

Claims

1. A method for optimizing the analysis of coal combustion control in thermal power generation, characterized in that, It includes the following steps: Determine at least one target operating unit to be adjusted according to the power generation amount required by the load demand, and establish a demand schedule based on the target power generation amount of the determined target operating unit, where the target power generation amount refers to the target power generation data that the target operating unit needs to achieve; Calculate the differential power generation amount of each target operating unit, and assign a first label to the target operating unit based on the absolute value of the differential power generation amount. The first label is used to reflect the target value characteristics of the target operating unit in the demand schedule. The differential power generation amount refers to the difference between the initial load power of the target operating unit and the target power generation amount; Analyze the combustion optimization method of the corresponding target operating unit according to the differential power generation amount. The combustion optimization method includes the optimization of combustion cost and coal blending mode. When optimizing the coal blending mode, determine the quality parameters of the coal blending mode with the combustion cost as the constraint condition, carry out coal blending combustion based on the determined quality parameters, and record the combustion result of the target operating unit; Assign a second label to the combustion result of each target operating unit. The second label is used to reflect the achievement value characteristics of the target operating unit in the demand schedule.

2. The optimized analysis method for coal combustion control in thermal power generation according to claim 1, wherein The step of determining the quality parameters of the coal blending mode with the combustion cost as the constraint condition includes the following steps: Determine the basic parameter set of the coal blending; and establish a calculation model with the combustion result data as the target and the combustion cost as the constraint condition based on the basic parameter set; perform linear or nonlinear solution on the calculation model, and use the obtained result as the quality parameter; where the combustion result refers to the combustion amount data converted with the differential power generation amount as the calculation target.

3. The optimized analysis method for coal combustion control in thermal power generation according to claim 2, wherein The step of determining the basic parameter set of the coal blending includes the following steps: Obtain the historical combustion environment parameters and real-time combustion environment parameters of the target operating unit, statistically analyze the historical basic parameters under the historical combustion environment parameters to obtain a historical statistical result; determine the real-time basic parameters under the real-time combustion environment parameters as the real-time statistical result; Screen the real-time statistical result based on the historical statistical result, and all the obtained basic parameters are used as the composition basis of the basic parameter set.

4. The optimized analysis method for coal combustion control in thermal power generation according to claim 3, wherein Calculate the influence of the real-time basic parameters on the real-time combustion environment parameters and perform truth-preserving processing on the real-time combustion environment parameters to obtain the truth-preserving combustion environment parameters; judge the similarity between the truth-preserving combustion environment parameters and the historical combustion environment parameters, adjust the historical statistical result based on the obtained similarity result, and screen the real-time statistical result based on the adjusted historical statistical result.

5. The optimized analysis method for coal combustion control in thermal power generation according to claim 3, wherein Calculate the influence cost of each basic parameter. The influence cost includes the operation influence cost and the associated influence cost. Screen out the basic parameters with the influence cost higher than the first preset value, and the remaining basic parameters are used as the composition basis of the basic parameter set; where the operation influence cost refers to the operation cost generated by the target operating unit when selecting this basic parameter, and the associated influence cost refers to the operation cost generated by the target operating unit due to the synergistic effect on the remaining basic parameters when selecting this basic parameter.

6. The optimized analysis method for coal combustion control in thermal power generation according to claim 5, characterized in that, After screening out the basic parameters whose impact cost is higher than the preset value, the following steps are further included: calculating the total cost of the screened basic parameters, and retaining the basic parameters whose total cost is lower than the second preset value, where the total cost includes the impact cost and the raw material cost.

7. The optimized analysis method for coal combustion control in thermal power generation according to any one of claims 3-6, characterized in that Determine the combustion result of the unadjusted operating unit based on the basic parameters and assign a third mark, where the third mark is used as the judgment basis for whether to assign the first mark and the second mark to the operating unit.

8. The optimized analysis method for coal combustion control in thermal power generation according to claim 7, characterized in that, It further includes the step of adjusting the first mark based on the second mark: Performing a reliability calculation on the target operating units listed in the demand schedule to obtain a reliability calculation result; Adjusting the first mark of the target operating unit based on the reliability calculation result, and the adjustment includes any one of modifying the target value, canceling the target value, or retaining the target value. The reliability calculation refers to comparing and judging the achievement value and achievement rate of the target operating unit.

9. The method for optimizing and analyzing the coal combustion control in thermal power generation according to claim 8, wherein In the adjustment method, after canceling the achievement value, the following steps are further included: performing a reliability calculation on the operating unit assigned the third mark, and judging whether the operating unit can replace the target operating unit with the canceled achievement value and enter the demand schedule according to the calculation result.

10. A coal - fired control optimization analysis system for thermal power generation, characterized in that, It includes: A first determination unit, which is used to determine at least one target operating unit to be adjusted according to the power generation amount required by the load demand, and establish a demand schedule based on the target power generation amount of the determined target operating unit, where the target power generation amount refers to the target power generation data that the target operating unit needs to reach; A first calculation unit, which is used to calculate the differential power generation amount of each target operating unit, and assign a first mark to the target operating unit based on the absolute value of the differential power generation amount. The first mark is used to reflect the first feature of the demand schedule, where the differential power generation amount refers to the difference between the initial load power of the target operating unit and the target power generation amount; A second calculation unit, which is used to analyze the combustion optimization method of the corresponding target operating unit according to the differential power generation amount. The combustion optimization method includes the optimization of combustion cost and coal blending mode. When optimizing the coal blending mode, the quality parameters of the coal blending mode are determined with the combustion cost as the constraint condition, and coal blending combustion is performed based on the determined quality parameters, and the combustion result of the target operating unit is recorded; A first processing unit, which is used to assign a second mark to the combustion result of each target operating unit, and the second mark is used to feedback the adjustment method of the corresponding first mark on the demand schedule.

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