Thermal power plant coal blending combustion proportion optimization decision-making method based on feasible region model

Abstract

The invention relates to the technical field of thermal power plant operation optimization, and discloses a thermal power plant coal blending combustion proportion optimization decision method based on a feasible region model, comprising the following steps: step 1, constructing a basic model of a coal blending combustion proportion feasible region; step 2, determining a first type of feasible region boundary function based on a coal blending combustion constraint condition, and determining a second type of feasible region boundary function based on a coal blending total proportion constraint condition; the coal blending combustion constraint condition comprises coal blending calorific value, coal blending volatile component, coal blending sulfur content, moisture, ash content and single coal proportion; 3, determining a coal blending combustion proportion feasible region under the condition of multiple coal types by adopting a vertex search-based space projection algorithm; and 4, setting a target function, and outputting an optimal blending combustion ratio based on the coal blending combustion ratio feasible region. According to the method, rapid calculation of the optimal blending combustion proportion and rapid adjustment of the blending combustion proportion facing sudden change of working conditions can be achieved, and thermal power plant operation personnel are accurately assisted in performing blending combustion proportion optimization decision making in real time.

Description

Technical Field

[0001] The present invention relates to the technical field of operation optimization of thermal power plants, and particularly to an optimization decision-making method for blending ratio of coal blending in thermal power plants based on a feasible region model. Background Art

[0002] Traditional coal-fired thermal power enterprises are facing unprecedented challenges. On the supply side, the installed capacity and on-grid electricity of clean energy such as wind power and photovoltaic power continue to grow, squeezing the market share of thermal power enterprises. On the cost side, the tight balance in the coal market has led to long-term volatile operation of coal prices. Coupled with the significant increase in coal consumption per unit of electricity under low-load conditions of thermal power units, the profit space of thermal power enterprises has been further compressed, exacerbating the potential loss risk. The above factors have continuously pressured the overall profitability of thermal power enterprises. Against this background, effectively controlling the power generation cost has become the key for thermal power enterprises to enhance their market competitiveness.

[0003] Statistical data shows that the coal cost of thermal power plants usually accounts for more than 70% of the entire power generation cost. Therefore, controlling the coal combustion cost is the most effective way to reduce the power generation cost of thermal power enterprises. Common coal combustion cost control technologies include coal blending and coal washing. Considering the current situation of coal resource distribution and market situation in China, coal blending has become the main means for most domestic thermal power plants to reduce coal combustion cost. As a flexible fuel management technology, coal blending is to blend different types and qualities of coal into mixed coal according to specific ratios, and through optimizing the chemical composition, physical properties and combustion characteristics of steam coal, realize complementary coal quality, so as to achieve the purpose of improving power generation efficiency and reducing the coal consumption cost of thermal power plants.

[0004] The core of the coal blending technology lies in realizing the rapid and accurate decision-making of the blending ratio, while ensuring the safe operation of the boiler and auxiliary systems, and taking into account the goals of improving combustion efficiency and controlling pollutant emissions. However, although certain achievements have been made in the existing research on optimizing the coal blending ratio, there are still the following two deficiencies: 1) Existing methods mostly focus on obtaining a single optimal solution, only outputting the optimal blending ratio under specific working conditions, and failing to provide the complete distribution information of the adjustable blending ratio under this working condition, making it difficult to provide sufficient operation optimization and emergency decision-making basis for operators. 2) When the operating conditions (such as environmental protection requirements) change suddenly, existing methods need to reconstruct the optimization model and solve it with intelligent algorithms, which takes a long time to calculate and is difficult to generate a feasible coal blending plan in time to guide the coal type switching, and cannot meet the rapid response requirements for sudden changes in working conditions in actual production. Summary of the Invention

[0005] The present invention aims to provide an optimization decision-making method for the blending ratio of coal blending in thermal power plants based on a feasible region model, which can realize the rapid calculation of the optimal blending ratio and the rapid adjustment of the blending ratio for sudden changes in working conditions, and assist the operation personnel of thermal power plants to make optimization decisions on the blending ratio in real time and accurately.

[0006] The basic solution provided by the present invention is: an optimization decision method for the blending ratio of coal in a thermal power plant based on a feasible region model, comprising the following steps:

[0007] Step 1, constructing a basic model of the feasible region of the blending ratio of coal;

[0008] Step 2, determining the first type of feasible region boundary function based on the coal blending combustion constraints, and determining the second type of feasible region boundary function based on the total coal blending ratio constraint; the coal blending combustion constraints include the calorific value of the blended coal, the volatile matter of the blended coal, the sulfur content of the blended coal, the moisture content, the ash content, and the proportion of single coal types;

[0009] Step 3, using a space projection algorithm based on vertex search to determine the feasible region of the blending ratio of coal in the case of multiple coal types;

[0010] Step 4, setting an objective function, and outputting the optimal blending ratio based on the feasible region of the blending ratio of coal.

[0011] The working principle and advantages of the present invention are as follows:

[0012] An optimization decision method for the blending ratio of coal in a thermal power plant based on a feasible region model according to the present invention, in view of the problems mentioned in the background art, draws on the analysis idea of the domain model of the power system, and innovatively proposes an optimization decision method based on the feasible region model, which can realize the rapid calculation of the optimal blending ratio and the rapid adjustment of the blending ratio facing sudden changes in working conditions, and can provide a new technical path for the optimization of coal blending combustion in thermal power plants, with high practical application benefits. The key points are as follows:

[0013] This solution draws on the analysis paradigm of the power system security domain. By constructing a feasible region model of the blending ratio of coal, the complex multi-constraint optimization problem is transformed into a problem of searching for a feasible solution domain in geometric space, making the optimization process have a strict theoretical basis and being able to accurately and efficiently find the optimal solution. Among them, the space projection algorithm using vertex search in this solution can reduce the multi-dimensional constraint space to an analytic dimension, and the extreme points of the polyhedron vertices are found to characterize the boundary of the feasible region. Compared with traditional optimization algorithms such as the simplex method, the calculation efficiency is improved.

[0014] Moreover, this solution realizes the systematic integration of all-factor constraints in blended coal combustion by constructing a dual boundary function system. The first type of boundary function covers core coal quality indicators such as calorific value, volatile matter, sulfur content, moisture content, and ash content. By establishing a linear weighted model for each indicator, it accurately reflects the influence mechanism of the blending ratio of different coal types on combustion efficiency, pollutant emissions, and equipment safety. Taking the volatile matter constraint as an example, by establishing a quantitative relationship model between the volatile matter content and combustion stability, it can effectively prevent combustion oscillation problems caused by too low volatile matter. The second type of boundary function focuses on the constraint of the blending ratio of coal types, including both the engineering limit of the upper limit of single coal type combustion and the normalization requirement of the total blending ratio. Through the integration of multi-dimensional constraints, the optimization results can simultaneously meet multiple objectives such as environmental protection emissions, boiler efficiency, and equipment life, improving the comprehensiveness and accuracy of optimization decisions.

[0015] In addition, in the specific application of this solution, by collecting real-time data on the coal quality of the coal entering the furnace and related indicator requirements, the constraint conditions can be updated online and the boundary of the feasible region can be reconstructed. When sudden changes in operating conditions occur (such as the interruption of the supply of a certain coal type), based on the vertex search algorithm, the range of the feasible region can be re-determined within a short time, and the blending ratio of the blended coal can be quickly adjusted to quickly generate an alternative blended coal solution. This solution has a certain degree of dynamic adjustability, can break through the limitations of traditional static optimization models, and makes the decision-making of the blending ratio have self-adaptive characteristics. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic flow chart of the method of an embodiment of the method for optimizing the blending ratio of blended coal combustion in a thermal power plant based on a feasible region model of the present invention;

[0017] Figure 2 It is a schematic diagram of the feasible region of the blending ratio of blended coal combustion in an embodiment of the method for optimizing the blending ratio of blended coal combustion in a thermal power plant based on a feasible region model of the present invention;

[0018] Figure 3 It is a schematic diagram of the modeling of the boundaries of two types of feasible regions of the blending ratio of blended coal combustion in an embodiment of the method for optimizing the blending ratio of blended coal combustion in a thermal power plant based on a feasible region model of the present invention;

[0019] Figure 4 It is a schematic flow chart of the construction process of the feasible region of the blending ratio of blended coal combustion based on vertex search in an embodiment of the method for optimizing the blending ratio of blended coal combustion in a thermal power plant based on a feasible region model of the present invention;

[0020] Figure 5 It is a schematic flow chart of the rapid solution of the optimal blending ratio based on the feasible region of the blending ratio of blended coal combustion in an embodiment of the method for optimizing the blending ratio of blended coal combustion in a thermal power plant based on a feasible region model of the present invention;

[0021] Figure 6Schematic diagram of a rapid adjustment method for the blending ratio of blended coal in a thermal power plant facing sudden changes in operating conditions, which is an embodiment of a method for optimizing and making decisions on the blending ratio of blended coal in a thermal power plant based on a feasible region model;

[0022] Figure 7 Schematic diagram of the relationship between modules of an optimization decision-making system for the blending ratio of blended coal in a thermal power plant based on a feasible region model, which is an embodiment of a method for optimizing and making decisions on the blending ratio of blended coal in a thermal power plant based on a feasible region model;

[0023] Figure 8 Schematic diagram of the feasible region of the blending ratio corresponding to three types of coal in an example of an embodiment of a method for optimizing and making decisions on the blending ratio of blended coal in a thermal power plant based on a feasible region model;

[0024] Figure 9 Schematic diagram of the optimal blending ratio with the lowest cost per unit calorific value as the goal in an example of an embodiment of a method for optimizing and making decisions on the blending ratio of blended coal in a thermal power plant based on a feasible region model;

[0025] Figure 10 Schematic diagram of the comparison of the calculation times of the present invention and the traditional particle swarm optimization algorithm in an example of an embodiment of a method for optimizing and making decisions on the blending ratio of blended coal in a thermal power plant based on a feasible region model;

[0026] Figure 11 Schematic diagram of the adjustment result of the blending ratio in an example of an embodiment of a method for optimizing and making decisions on the blending ratio of blended coal in a thermal power plant based on a feasible region model. Detailed implementation manners

[0027] The following is a further detailed description through specific implementation manners:

[0028] The embodiment is basically as shown in the appendix Figure 1 as follows: A method for optimizing and making decisions on the blending ratio of blended coal in a thermal power plant based on a feasible region model includes the following steps:

[0029] Step 1, construct a basic model of the feasible region of the blending ratio of blended coal.

[0030] Combined with the actual number of coal types in each power plant, determine the basic model of the feasible region of the blending ratio of blended coal.

[0031] Specifically, the feasibility of the blended coal blending scheme depends on whether its key parameters (including sulfur content, moisture content, ash content, and calorific value, etc.) meet the technical requirements of unit operation. For this reason, the feasible region boundary g(M) of the blending ratio under this operating condition can be determined by establishing the equality and inequality constraint conditions of the blending parameters. Based on g(M), the entire blending ratio space can be divided into two regions: the feasible region and the infeasible region, and any blending ratio within the feasible region meets the technical requirements.

[0032] Here, taking the co - firing of two types of coal as an example for specific illustration, the example explanation is as follows Figure 2 as shown

[0033] It can be seen from Figure 2 that in the case of co - firing two types of coal, the inequality constraints of the coal blending parameters will limit the blending ratio within the two - dimensional plane region enclosed by multiple feasible region boundaries, while the equality constraints will further limit the blending ratio to a straight line. Therefore, after comprehensively considering various constraint conditions, the feasible region of the coal blending ratio in this case is the intersection region of the two - dimensional plane and the one - dimensional straight line. Further, for the co - firing of three or more types of coal, the corresponding feasible region will then become the intersection region of the hyper - polyhedron and the hyper - plane

[0034] Through the construction of the feasible region of the coal blending ratio, the operating personnel can make more effective optimization decisions on the blending ratio based on different optimization objectives

[0035] Step 2: Determine the first - type feasible region boundary function based on the coal blending and firing constraint conditions, and determine the second - type feasible region boundary function based on the total coal blending ratio constraint conditions, as shown Figure 3 ; The coal blending and firing constraint conditions include the calorific value of the blended coal, volatile matter of the blended coal, sulfur content of the blended coal, moisture, ash content, and the proportion of single coal types

[0036] The calorific value is a key index to measure the quality of the blended coal. A higher calorific value of the blended coal is beneficial to reducing the power generation cost, while too low a calorific value will lead to insufficient combustion temperature in the boiler furnace, and at the same time increase the flue gas emission loss and coal consumption per kilowatt - hour. Correspondingly, the first - type feasible region boundary function corresponding to the calorific value of the blended coal is g1(M):

[0037] g1(M) = M1Q coal,1 + M2Q coal,2 + … + M k Q coal,k - Q mix,min ;

[0038] In the formula, M1, M2, …, M k are respectively the proportions of the 1st, 2nd, …, kth types of coal in the blended coal; Q coal,1 , Q coal,2 , …, Q coal,k are respectively the calorific values of the 1st, 2nd, …, kth types of coal, with the unit of kJ / kg; Q mix,min is the predetermined lower limit value of the calorific value of the blended coal, with the unit of kJ / kg

[0039] As a key parameter affecting the combustion characteristics, the volatile content of blended coal directly impacts combustion stability. When the volatile content is too high, it will lead to flame elongation and furnace coking problems; while when the volatile content is insufficient, it will cause unstable combustion. Correspondingly, the first type of feasible region boundary functions corresponding to the volatile content of the blended coal include: the upper limit function g2(M) of the volatile content of the blended coal and the lower limit function g3(M) of the volatile content of the blended coal:

[0040] g2(M) = M1H coal,1 + M2H coal,2 + … + M k H coal,k - H mix,min ;

[0041] g3(M) = H mix,max -(M1H coal,1 + M2H coal,2 + … + M k H coal,k );

[0042] In the formula, H coal,1 , H coal,2 , …, H coal,k are the volatile content values of the 1st, 2nd, …, kth types of coal respectively, with the unit of %; H mix,max and H mix,min are the upper limit value and the lower limit value of the volatile content of the blended coal respectively, with the unit of %.

[0043] Too high a sulfur content in the blended coal will exacerbate the low-temperature corrosion in the tail flue and cause environmental pollution problems; too high a moisture content will reduce the proportion of combustible components in the blended coal and increase the coal consumption cost; while too high an ash content will weaken the bonding performance of the blended coal, easily cause slagging phenomena and damage the furnace structure. Therefore, there are upper limit restrictions for all these three indicators, and three feasible region boundary functions corresponding to the inequality constraints of the sulfur content, moisture content, and ash content of the blended coal can be established accordingly. Specifically, the first type of feasible region boundary functions corresponding to the sulfur content, moisture content, and ash content of the blended coal are g4(M), g5(M), and g6(M) respectively:

[0044] g4(M) = S mix,max -(M1S coal,1 + M2S coal,2 + … + M k S coal,k );

[0045] g5(M) = W mix,max -(M1W coal,1 + M2W coal,2 + … + M k W coal,k );

[0046] g6(M) = Dmix,max -(M1D coal,1 +M2D coal,2 +…+M k D coal,k ));

[0047] In the formula, S coal,1 , S coal,2 , …, S coal,k are the sulfur content values of the 1st, 2nd, …, kth types of coal, with the unit of %; W coal,1 , W coal,2 , …, W coal,k are the moisture content values of the 1st, 2nd, …, kth types of coal, with the unit of %; D coal,1 , D coal,2 , …, D coal,k are the ash content values of the 1st, 2nd, …, kth types of coal, with the unit of %; S mix,max , W mix,max and D mix,max are the upper limit values of the sulfur content, moisture content, and ash content of the blended coal, with the unit of %.

[0048] The blending ratios of each type of coal need to satisfy the non - negativity constraint and the upper - limit constraint. Correspondingly, the boundary functions of the first - type feasible region corresponding to the single - coal - type ratios include: the function g7(M) based on the non - negativity constraint and the function g8(M) based on the upper - limit constraint:

[0049] g7(M) = 1 - M i i = 1, 2, …, k;

[0050] g8(M) = M i i = 1, 2, …, k.

[0051] The boundary functions g1(M) to g8(M) of the feasible region need to satisfy g l (M)≥0, where l = 1, 2, …, 8.

[0052] The sum of the blending ratios of all types of coal should be equal to 1. Correspondingly, the boundary function of the second - type feasible region based on the total blending - ratio constraint condition is g9(M):

[0053] g9(M) = M1 + M2 + … + M k - 1;

[0054] g9(M) satisfies g9(M) = 0.

[0055] Step 3: Use the spatial projection algorithm based on vertex search to determine the feasible region of the blending ratio for multi - coal - type cases. Specifically, it includes the following sub - steps:

[0056] S3.1. When the number of coal types for blended coal k ≥ 3, the feasible region of the blending ratio is a complex set formed by the intersection of a hyperpolyhedron and a hyperplane in a high-dimensional space.

[0057] Initialize the hyperpolyhedron Θ * , whose vertices are jointly composed of the origin of coordinates and the optimal vertices of the k-dimensional coordinate axes.

[0058] Among them, the optimal vertex on the i-th (i = 1, 2,..., k) coordinate axis can be solved through the following linear programming model:

[0059] max

[0060] s.t. g l (M) ≥ 0 l = 1, 2,..., 8;

[0061] In the formula, V region is the optimal vertex on the k-dimensional coordinate axis; is the unit basis vector in the direction of the i-th coordinate axis.

[0062] In addition, for the case of blending two types of coal (k = 2), the feasible region of the blending ratio can be characterized as a line segment in a two-dimensional plane, and the existing technology can be directly applied to solve it.

[0063] S3.2. Search for new vertices on each face of Θ * to expand the feasible region.

[0064] The new vertex can be obtained by solving the following linear programming model, which aims to find the vertex farthest from the internal point V * of Θ ip :

[0065]

[0066] s.t.

[0067] g l (M) ≥ 0 l = 1, 2,..., 8;

[0068] In the formula, H j is the height of the j-th face of Θ * ; H max is the maximum height among all faces of Θ * ; G j is the normal vector of the j-th face; ||G j || is the L2 norm of G j ; is the optimal solution of this linear programming model, that is, the newly determined vertex.

[0069] S3.3. Use the new vertex and the original vertices to jointly construct a new sub-region and update Θ* ;

[0070] S3.4. Repeat the execution of S3.2 and S3.3 until the volume change of the newly generated superpolyhedron is less than the threshold compared to the change value in the previous round, and terminate the iteration.

[0071] Specifically,

[0072] In the formula, U bf and U af are the volumes of the superpolyhedron in the previous round and this round respectively; ò is the convergence coefficient (i.e., the threshold).

[0073] S3.5. Calculate the intersection point of the superpolyhedron Θ * and the hyperplane g9(M) = 0, so as to determine the feasible region of the blending ratio. As Figure 4 shown.

[0074] Step 4. Set the objective function, and based on the feasible region of the blending ratio of blended coal, output the optimal blending ratio.

[0075] The objective function includes the optimization function F ue with the minimum usage of the first type of coal and the second type of coal as the goal and the optimization function F ef with the lowest cost per unit calorific value as the goal:

[0076] minF ue = M1 + M2;

[0077]

[0078] In the formula, R coal,1 , R coal,2 , …, R coal,k are the cost values of the first, second, …, kth types of coal respectively, with the unit of kg / yuan.

[0079] After determining the feasible region of the blending ratio of blended coal, the range of the optimal blending ratio solution has been determined. The adaptive grid search method can be directly used for iterative optimization until the termination condition is met, and the optimal blending ratio is output.

[0080] Specifically, as Figure 5 shown, first, use Latin hypercube sampling to uniformly sample E grid points {M op,1 , M op,2 , ……, M op,E} from the feasible region of the blending ratio of blended coal established above. Calculate the objective function F ue or F ef corresponding to each grid point, select the first C optimal grid points as candidate grid points, and determine the optimal objective function value and the corresponding blending ratio in this round.

[0081] Then, perform step-by-step grid contraction optimization. Process each of the previously determined C optimal grid points one by one. With each candidate grid point as the center and a contraction radius of o, construct C sub-regions. Within each sub-region, regenerate E new grid points by using Latin hypercube sampling again. Calculate the objective function value of each grid point within the sub-region, and select the optimal solution for each sub-region. Furthermore, determine the optimal objective function value for the current grid contraction round.

[0082] Judge whether any one of the following three termination conditions is satisfied. The termination conditions include: whether the current grid contraction round reaches the predetermined maximum iteration round Iter, whether the sub-region grid width is less than the preset threshold, and whether the number of candidate points to be selected is less than 1. If one of them is yes, it is determined that the termination condition is satisfied, and the optimization result is output. If none of them is yes, it is determined that the termination condition is not satisfied, and then perform another step-by-step grid contraction optimization. And in the next iteration, select C - 2 new candidate grid points from the C sub-regions, continue the region contraction and optimization until the termination condition is satisfied.

[0083] In addition, when the operating condition suddenly changes, quickly adjust the blending ratio of coal co-firing to respond to the real-time operating condition requirements and ensure that the unit operation meets the new constraint conditions; as Figure 6 shown, the process of adjusting the blending ratio is equivalent to a geometric process of moving the current blending ratio to the boundary of the new feasible region, and its mathematical model is expressed as follows:

[0084] min d(M af ,M bf );

[0085] s.t.

[0086]

[0087] In the formula, M af and M bf are the blending ratios of coal co-firing before and after adjustment respectively; is the feasible region of coal co-firing blending constructed based on the new constraint conditions, i.e., the new feasible boundary; by solving the minimization of d(M af ,M bf ), the adjusted blending ratio of coal co-firing is obtained.

[0088] Among them, when solving, the adaptive grid search method can also be used for solving.

[0089] This embodiment also provides a modular architecture of an optimization decision system for the blending ratio of coal co-firing in a thermal power plant established based on the method proposed in the present invention, as Figure 7As shown, it can provide technical support for thermal power enterprises to optimize the blending ratio decision-making. The modular architecture includes: an input module for collecting coal type characteristic data and index requirement data of thermal power plants; a boundary function modeling and feasible region solving module for executing Steps 1 to 3; a feasible region output display module for executing Step 4; an objective function modification module for modifying the objective function; and an output module for summarizing and outputting the coal blending implementation plan.

[0090] A method for optimizing the blending ratio decision-making of coal blending in thermal power plants based on a feasible region model provided in this embodiment first proposes a feasible region modeling method for coal blending ratio based on a linear boundary function and a vertex search algorithm, and establishes a mathematical model of the feasible region of coal blending ratio in thermal power plants; secondly, in combination with the grid search method, a fast calculation method for the optimal blending ratio based on the feasible region is proposed; in addition, the process of adjusting the blending ratio is equivalent to a geometric process of moving the current blending ratio to the boundary of the new feasible region, and a fast adjustment method for the coal blending ratio under the condition of sudden change in working conditions is proposed; applying this solution can assist the operation personnel of thermal power plants to optimize the blending ratio decision-making in real time and accurately.

[0091] Furthermore, taking a certain power plant's blending of 3 types of coal as an example, the feasibility and effectiveness of the method for optimizing the blending ratio decision-making of coal blending in thermal power plants proposed in the present invention are verified. Among them, the characteristic parameters of various coal types and the characteristic parameter requirements of the blended coal are shown in Table 1 below.

[0092] Table 1 Introduction to Coal Type Characteristic Parameters

[0093]

[0094] Through computer program calculation and visualization processing, the feasible region of coal blending that meets the above constraints can be obtained, and the results are as follows Figure 8 As shown, Region I is a hyperpolyhedron, and Region II is a hyperplane.

[0095] Based on the established feasible region of coal blending, with the goal of minimizing the cost per unit calorific value, the method proposed in the present invention can quickly solve the optimal blending ratio that meets the given coal blending parameter constraints, and the optimization results are as Figure 9 shown.

[0096] When blending coal according to the above ratio, the lowest cost per unit calorific value is 1.09×10-5 yuan / kJ. It should be noted that the comparison of the calculation time of the fast calculation method proposed in the present invention and the traditional particle swarm algorithm is as Figure 10 shown. From Figure 10 it can be seen that the calculation time used for the optimal blending ratio decision-making by using the method proposed in the present invention is less than 1 minute, and this calculation efficiency fully meets the real-time calculation requirements on site, verifying the effectiveness and engineering practicability of the proposed method.

[0097] On this basis, assuming that it is necessary to increase the calorific value of the blended coal to 22500 kg / kJ, at this time, through computer program compilation and solution, the blending ratios of each coal type after adjustment are obtained, specifically as Figure 11 shown.

[0098] When coal is blended and burned according to the above ratios, the volatile matter, sulfur content, moisture content, and ash content of the blended coal are 22.01%, 1.13%, 10.34%, and 17.33% respectively, all of which meet the requirements of the given characteristic parameters, proving the effectiveness of the rapid adjustment method for the blending ratio proposed in the present invention.

[0099] The above example analysis proves that: the method proposed in the present invention can effectively assist the operating personnel of thermal power plants in making optimal decisions on blending ratios and has good engineering practical value.

[0100] The above are only embodiments of the present invention. Specific structures and common knowledge such as characteristics that are well-known in the art are not described in detail here. Those of ordinary skill in the art know all the common general technical knowledge in the technical field to which the invention belongs before the application date or the priority date, can know all the prior arts in this field, and have the ability to apply the conventional experimental means before this date. Those of ordinary skill in the art can, under the inspiration given in this application, combine their own abilities to improve and implement this solution. Some typical well-known structures or well-known methods should not be an obstacle for those of ordinary skill in the art to implement this application. It should be pointed out that for those skilled in the art, without departing from the structure of the present invention, several deformations and improvements can still be made, and these should also be regarded as the protection scope of the present invention, and these will not affect the implementation effect of the present invention and the practicality of the patent.

Claims

1. An optimization decision-making method for the blending ratio of coal in a thermal power plant based on a feasible region model, characterized in that, It includes the following steps: Step 1, construct the basic model of the feasible region of the blending ratio of blended coal; Step 2, determine the first - type feasible region boundary function based on the blended coal combustion constraints, and determine the second - type feasible region boundary function based on the total blending ratio constraint of blended coal; the blended coal combustion constraints include the calorific value of blended coal, volatile matter of blended coal, sulfur content of blended coal, moisture, ash content, and the proportion of single coal type; Step 3, use the space projection algorithm based on vertex search to determine the feasible region of the blending ratio of blended coal in the case of multiple coal types; Step 4, set the objective function, and based on the feasible region of the blending ratio of blended coal, output the optimal blending ratio.

2. The optimization decision-making method for the blending ratio of coal blending in thermal power plants based on the feasible region model according to claim 1, characterized in that, The first - type feasible region boundary function corresponding to the calorific value of blended coal is g1(M): g1(M) = M1Q coal,1 + M2Q coal,2 + … + M k Q coal,k - Q mix,min ; Wherein, M1, M2, …, M k are the proportions of the 1st, 2nd, …, kth coal types in the blended coal respectively; Q coal,1 , Q coal,2 , …, Q coal,k are the calorific values of the 1st, 2nd, …, kth coal types respectively, with the unit of kJ / kg; Q mix,min is the lower limit value of the calorific value of the predetermined blended coal, with the unit of kJ / kg.

3. The optimization decision-making method for the blending ratio of coal in a thermal power plant based on a feasible region model according to claim 1, wherein The first - type feasible region boundary functions corresponding to the volatile matter of blended coal include: the upper - limit function of the volatile matter of blended coal g2(M) and the lower - limit function of the volatile matter of blended coal g3(M): g2(M) = M1H coal,1 + M2H coal,2 + … + M k H coal,k - H mix,min ; g3(M) = H mix,max -(M1H coal,1 +M2H coal,2 +…+M k H coal,k ); wherein, M1, M2, …, M k are respectively the proportions of the 1st, 2nd, …, kth types of coal in the blended coal; H coal,1 , H coal,2 , …, H coal,k are respectively the volatile matter contents of the 1st, 2nd, …, kth types of coal, in %; H mix,max and H mix,min are the upper limit value and the lower limit value of the volatile matter of the predetermined blended coal, in %.

4. A method for optimizing and making decisions on the blending ratio of coal in a thermal power plant based on a feasible region model according to claim 1, characterized in that, The first - type feasible region boundary functions corresponding to the sulfur content, moisture, and ash content of blended coal are g4(M), g5(M), and g6(M) respectively: g4(M) = S mix , max-(M1S coal , 1 + M2S coal , 2 + … + M k s coal , k); g5(M) = W mix,max -(M1W coal,1 +M2W coal,2 +…+M k W coal,k ); g6(M) = D mix,max -(M1D coal,1 +M2D coal,2 +…+M k D coal,k ); wherein, M1, M2, …, M k are respectively the proportions of the 1st, 2nd, …, kth types of coal in the blended coal; S coal,1 , S coal,2 , …, S coal,k are respectively the sulfur content values of the 1st, 2nd, …, kth types of coal, in %; W coal,1 , W coal,2 , …, W coal,k are respectively the moisture content values of the 1st, 2nd, …, kth types of coal, in %; D coal,1 , D coal,2 , …, D coal,k are respectively the ash content values of the 1st, 2nd, …, kth types of coal, in %; S mix,max , W mix,max and D mix,max are the upper limit values of the sulfur content, moisture content, and ash content of the predetermined blended coal, in %.

5. A method for optimizing the blending ratio decision of coal blending in a thermal power plant based on a feasible region model according to claim 1, characterized in that, The first - type feasible region boundary functions corresponding to the proportion of single coal type include: the function g7(M) based on non - negativity constraint and the function g8(M) based on upper - limit constraint: g7(M) = 1 - M i i = 1, 2, …, k; g8(M) = M i i = 1, 2, …, k; where M1, M2, …, M k are the proportions of the 1st, 2nd, …, kth types of coal in the blended coal, respectively.

6. The optimization decision-making method for the blending ratio of coal in a thermal power plant based on a feasible region model according to claim 1, wherein The second - type feasible region boundary function is g9(M): g9(M) = M1 + M2 + … + M k -1; where M1, M2, …, M k are the proportions of the 1st, 2nd, …, kth types of coal in the blended coal, respectively; g9(M) satisfies g9(M) = 0.

7. A method for optimizing the blending ratio decision of coal blending in thermal power plants based on a feasible region model according to claim 6, characterized in that, The said Step 3 includes the following sub - steps: S3.1, when the number of coal types k of blended coal ≥ 3, the feasible region of the blending ratio of blended coal is a set formed by the intersection of a hyper - polyhedron and a hyper - plane in a high - dimensional space; Initialize the hyperpolyhedron Θ * , whose vertices are jointly composed of the origin of coordinates and the optimal vertices of the k-dimensional coordinate axes; S3.2, search for new vertices on each face of Θ * to expand the feasible region; S3.

3. Use the new vertex and the original vertices to jointly construct a new sub-region and update Θ * ; S3.4, repeat S3.2 and S3.3 until the volume change of the newly generated hyper - polyhedron is less than the threshold compared with the change value of the previous round, and terminate the iteration; S3.5, Calculation of the hyperpolyhedron Θ * The intersection with the hyperplane g9(M) = 0, thus determining the feasible region of the blending ratio 8. A method for optimizing and making decisions on the blending ratio of coal in a thermal power plant based on a feasible region model according to claim 1, characterized in that, The objective function includes an optimization function F with the objective of minimizing the usage amounts of the first-class coal type and the second-class coal type ue and an optimization function F with the objective of minimizing the cost per unit calorific value ef : minF ue = M1 + M2; wherein, R coal,1 , R coal,2 , …, R coal,k are the cost values of the 1st, 2nd, …, kth types of coal respectively, with the unit of kg / yuan.

9. The optimization decision-making method for the blending ratio of coal in a thermal power plant based on a feasible region model according to claim 1, wherein In Step 4, after determining the feasible region of the blending ratio of blended coal, perform iterative optimization through the adaptive grid search method until the termination condition is met, and output the optimal blending ratio; The said termination conditions include: whether the current grid contraction round reaches the predetermined maximum iteration round Iter, whether the grid width of the sub - region is less than the preset threshold, and whether the number of candidate points is less than 1. If any one of them is yes, it is determined that the termination condition is met; if none of them is yes, it is determined that the termination condition is not met.

10. A method for optimizing the blending ratio decision of coal blending in a thermal power plant based on a feasible region model according to claim 1, characterized in that, In Step 4, it also includes: When the operating condition suddenly changes, perform rapid adjustment of the blending ratio of blended coal; the process of adjusting the blending ratio is equivalent to a geometric process of moving the current blending ratio to the boundary of the new feasible region, and its mathematical model is expressed as follows: min d(M af ,M bf ); where M af and M bf are the blending ratios of coal blending combustion before and after adjustment respectively; is the feasible region of coal blending combustion constructed based on the new constraint condition, i.e., the new feasible boundary; By solving the minimization of d(M af , M bf ), the adjusted blending ratio of coal blending combustion is obtained.

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