Coal bunker and coal mill external hanging depth coal blending control system based on cascade optimization strategy

Abstract

The application provides a coal bunker and coal mill external hanging depth coal blending control system based on a cascade optimization strategy, and relates to the technical field of power plant energy saving and environmental protection. The system comprises a server, a communication module, a primary optimization coal blending module, a secondary optimization coal blending module, a model solving module, a relational database interface and a real-time database interface module arranged on the server. The primary optimization coal blending module establishes a primary coal blending mathematical optimization model by taking the coal type and proportion of each coal bunker as an optimization variable. The secondary optimization coal blending module establishes a secondary coal blending mathematical optimization model by taking the operation combination and optimal output of each coal mill as an optimization variable on the basis of the primary optimization coal blending. The model solving module realizes modeling and solving of the primary coal blending mathematical optimization model and the secondary coal blending mathematical optimization model, solves the coal bunker mixed coal blending scheme, the coal mill optimal combination and the optimal output point of the mill operation scheme, and further realizes control and adjustment of the coal bunker and the coal mill.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power plant energy saving and environmental protection, and particularly relates to a coal bunker and coal mill externally hung depth coal blending control system based on a cascade optimization strategy. BACKGROUND

[0002] At present, the power generation system in China mainly includes a parallel power generation system composed of thermal power, photovoltaic power, wind power, hydropower and nuclear power. By the end of 2022, thermal power accounted for 67%, which is still the main force of power generation in China and will not change in the short term. Optimizing energy structure and improving clean and efficient utilization of coal are long-term measures to achieve the dual carbon goal. The thermal power generating units in China were designed for a specific single coal type during the construction period. The unit burning this single coal type has good safety, environmental protection and economy. However, the domestic coal market is complex and changeable, and more and more coal-fired power plants have to purchase some non-design coal; in addition, the continuous rise in coal prices also forces power generation enterprises to purchase some low-quality coal to reduce power generation costs. Therefore, a coal yard of a power generation enterprise usually has about 10 coal types with different coal quality parameters. Based on the change of power load, it is very important to dynamically and reasonably blend coal in the coal yard every day. Through reasonable design of the coal quality parameters of blended coal, the adaptability of the boiler to blended coal can be improved, the fuel efficiency can be improved, and the pollution emission can be reduced.

[0003] There are generally three ways of coal blending and burning in power plants in China: 1) coal yard blending, that is, blending coal in the special coal yard of the power plant according to the demand of load and environmental protection indicators, and then transporting it to the power plant. This method is beneficial to the comprehensive dispatching of power coal and can achieve high-precision coal blending, but the investment and maintenance cost of the coal yard is large, and it lacks flexibility and real-time performance for the power plant; 2) coal bunker blending before the furnace, that is, different coal types are pre-blended in the coal storage bunker of the power plant according to a certain proportion, and then sent to the furnace for combustion. The number of coal types participating in blending in a single coal bunker determines the number of stacker-reclaimers and feeding conveyors, and generally no more than two coal types per bunker. This method is widely used in enterprises; 3) in-furnace blending, also known as separate-mill blending or separate-layer blending, that is, different single coals enter different coal mills for pulverizing, and then are sent to different burners for in-furnace blending. This is the most convenient way to use, but the different coals cannot be uniformly mixed, and the combustion stability and environmental protection cannot be effectively guaranteed.

[0004] Domestic thermal power enterprises have generally carried out coal blending work, and under the premise of ensuring the safety of the unit, environmental protection and driving the planned load (power generation load, heat supply load and steam supply load) of the boiler, the optimal coal blending cost is pursued. Taking the coal bunker form coal blending as an example, according to the process equipment, the coal blending work should include three stages of coal blending of the coal bunker, mixed coal operation of the coal mill and mixed coal combustion of the boiler. The coal blending stage of the coal bunker is to take the coal type and proportion of each coal bunker as the optimization variable, and at present, the enterprises mainly rely on the expert manual experience mode to carry out the coal blending work of the coal bunker. After the coal blending scheme of the coal bunker is specified and the coal bunker is added, the mixed coal operation stage of the coal mill should take the operation combination and output (coal feeding rate) of each coal mill as the optimization variable, and at present, the enterprises also mainly rely on the expert manual experience to determine the combination operation of each mill (stop or cut mill), and the optimal combination output of the coal mill is rarely researched, and basically the traditional coal mill output and load are simply adjusted according to the function. In addition, the work of further optimizing the coal blending process in combination with the burner structure and parameters of the boiler is less researched.

[0005] At present, the coal blending process of the domestic thermal power enterprises generally relies on the expert manual experience and the simple calculation mode combined with the Excel table, and the biggest disadvantage of the coal blending of the coal bunker relying on the manual experience is that it is difficult to guarantee the optimality and rapidity of the coal blending scheme, and the professional software of the computer optimization technology is urgently needed. In addition, theoretically, the research on the cascade optimization method is lacking, and at present, the coal blending method whether it is coal yard coal blending, coal bunker coal blending or in-furnace coal blending is theoretically based on the single-model single-link optimization technology, and the research on the multi-link cascade optimization method of the coal bunker coal preparation, the coal mill operation and the combustion in the boiler is rarely explored, and the multi-link process optimization of the coal blending is lacking.

[0006] The coal bunker and the coal mill are externally hung in the depth coal blending control system based on the cascade optimization strategy, on the basis of the primary optimization of the coal blending of the coal bunker, the secondary optimization of the coal blending is realized by adjusting the combination of the coal mill and the output point of each coal mill, so that the fuel of the coal mill set optimally matches the planned load demand, precise coal blending is realized, and the energy saving and environmental protection purposes are achieved. SUMMARY

[0007] The technical problem solved by the present application is to provide a coal bunker and a coal mill externally hung in the depth coal blending control system based on the cascade optimization strategy, on the basis of the primary optimization of the coal blending of the coal bunker, the secondary optimization of the coal blending is realized by adjusting the combination of the coal mill and the output point of each coal mill, so that the fuel of the coal mill set optimally matches the planned load demand, precise coal blending is realized, and the energy saving and environmental protection purposes are achieved.

[0008] To solve the above technical problems, the technical scheme adopted by the present application is: a coal bunker and coal mill external hanging depth coal blending control system based on cascade optimization strategy, comprising a server, a communication module, and a coal bunker-based primary optimization coal blending module, a coal mill-based secondary optimization coal blending module, a model solving module, a relational database interface module, and a real-time database interface module formed by software programming and arranged on the server;

[0009] The relational database interface module reads corresponding coal quality parameters from an SQL Server relational database storing coal quality parameter information of each single coal type in the coal yard, and provides coal yard storage coal basic data for the primary optimization coal blending;

[0010] The real-time database interface module reads corresponding operating parameter real-time data from an OPENPlant real-time database storing unit operating parameter real-time data, and provides unit real-time data basis for the secondary optimization coal blending;

[0011] The coal bunker-based primary optimization coal blending module establishes a primary coal blending mathematical optimization model with the coal type and proportion of each coal bunker as optimization variables in the coal blending and mixing stage of the coal bunker; at the same time, the primary optimization coal blending module transmits the coal type and proportion of the blended coal of each coal bunker to the upper computer of the original reclaimer control system through the communication module, to coordinate the collaborative coal feeding of the two reclaimers;

[0012] The coal mill-based secondary optimization coal blending module establishes a secondary coal blending mathematical optimization model with the operating combination and optimal output of each coal mill as optimization variables in the coal blending operation stage of the coal mill on the basis of the primary optimization coal blending; at the same time, the secondary optimization coal blending module transmits the optimal combination and optimal output point of the coal mill to realize the compensation adjustment of the coal feeding rate of the original coal mill control system;

[0013] The model solving module realizes the modeling and solving of the primary coal blending mathematical optimization model and the secondary coal blending mathematical optimization model on the basis of the established primary coal blending mathematical optimization model and secondary coal blending mathematical optimization model, solves the coal blending and mixing scheme of the coal bunker, the coal mill optimal combination and optimal output point of the coal mill operation scheme, and further realizes the control and adjustment of the coal bunker and the coal mill.

[0014] Preferably, the coal bunker-based primary coal blending optimization module takes the coal blending combustion meeting the calorific value, sulfur, volatile matter, and ash content indicators as constraint conditions, and takes the minimum coal blending economy indicator as the objective function to establish the primary coal blending mathematical optimization model, as shown in the following formula:

[0015]

[0016] In the formula, minf(x) is the minimum cost of the blended coal, c jis the cost of the jth single coal, i.e. the unit price of standard coal, x j is the blending ratio of the jth single coal, and n is the total number of coal types in the blended coal;

[0017] According to the actual situation of the equipment and coal types of the enterprise, a part of the coal quality parameter characteristic indexes of the blended coal are selected as the constraint conditions; the enterprise has limitations on the net calorific value, actual sulfur content, actual ash content and actual volatile content of the blended coal entering the furnace, and thus the constraint conditions of the blended coal are:

[0018]

[0019] In the formula, h j , s j , a j , and v j respectively represent the calorific value, sulfur content, ash content and volatile content of each coal type; H, S, A and V respectively represent the upper limit or lower limit of the calorific value, sulfur content, ash content and volatile content of the blended coal scheme; according to the actual situation of the enterprise, other special constraint conditions of the coal quality characteristic indexes and the characteristics and needs of the enterprise itself can also be added to the constraint conditions.

[0020] Preferably, the secondary coal blending optimization module based on the coal mill takes the real-time fuel cost as the target, introduces the operating parameter variables of each coal mill in combination with the actual situation, and makes the real-time fuel cost of power generation lowest under the premise of meeting safety and environmental protection, and the objective function of the secondary coal blending mathematical optimization model is as follows:

[0021]

[0022] In the formula, U i is the i th coal mill operating parameter, U i takes 0 to represent shutdown and takes 1 to represent operation; x i is the coal feeding rate of the i th coal mill; P i is the unit price of the blended coal in the i th coal bunker; and N is the number of coal mills of the unit.

[0023] Preferably, the secondary coal blending mathematical optimization model established by the secondary coal blending optimization module based on the coal mill meets the following constraint conditions:

[0024] (1) The real-time boiler load determined according to the output of each coal mill and the coal type information of the corresponding coal bunker meets the sum of the planned electric load and the steam and heat supply load at this time, and the load constraint is as follows:

[0025]

[0026] In the formula, h i is the calorific value of the blended coal in the coal bunker corresponding to the i th coal mill; M h is the coal consumption of the standard coal under this working condition; F d and Fr , F q are electric load, steam supply load and heat supply load respectively;

[0027] The sulfur content in the coal entering the furnace per hour is less than the maximum desulfurization capacity of the power plant at this time, and this constraint condition is:

[0028]

[0029] In the formula, s i is the sulfur content of the mixed coal in the coal bunker corresponding to the i th coal mill; S t is the maximum desulfurization capacity of the desulfurization equipment;

[0030] In order to avoid slagging in the furnace, the ash content of the mixed coal entering the furnace is limited, and this constraint condition is expressed as:

[0031]

[0032] In the formula, a i is the ash content of the mixed coal in the coal bunker corresponding to the i th coal mill; A is the total amount of mixed coal ash entering the furnace;

[0033] The volatile matter of the mixed coal entering the furnace is limited, and the constraint is as follows:

[0034]

[0035] In the formula, v i is the volatile matter of the mixed coal in the coal bunker corresponding to the i th coal mill; V is the total amount of mixed coal volatile matter entering the furnace;

[0036] Each coal mill has an upper and lower output limit, in order to make the coal mill run within the conventional output range, the following elastic constraint equation is established:

[0037]

[0038] In the formula, V i max and V i min are the maximum and minimum outputs of the i th coal mill, respectively, and the elastic range of V i min V i min is the lower limit of V i min , and is the upper limit of V i min .

[0039] ​Preferably, the coal mill-based secondary coal blending optimization module converts the secondary coal blending mathematical optimization model with elastic constraints into a non-elastic constraint optimization model.

[0040] The elastic constraint condition in the elastic constraint equation (8) is fuzzified, and the membership function of the coal mill coal feeding rate in this interval is defined as follows:

[0041]

[0042] In the formula, A i (x i ) is a fuzzy set of the elastic constraint of the coal mill coal feeding rate;

[0043] If the elastic constraint of equation (8) takes , let the optimal value of the corresponding ordinary linear programming of the optimization model be z0; if the elastic constraint of equation (8) takes V i min ≤x i ≤V i max , let the optimal value of the corresponding ordinary linear programming of the optimization model be z1, z1<z0; let d0=z0-z1, then d0>0, which is the stretching index of the objective function in the fuzzy linear programming; the objective function of the fuzzy linear programming is fuzzified, and the membership function of the indicator function is:

[0044]

[0045] In the formula,

[0046] The following conclusions are obtained from the definitions of A i (x i ) and G(t0):

[0047] 1) For any λ∈[0,1], there is

[0048]

[0049] In the formula,

[0050] 2) For any λ∈[0,1], there is

[0051]

[0052] In the formula, G(t0(x)) is the membership function of the fuzzy set of the objective function;

[0053] In order to solve the secondary coal blending mathematical optimization model (3) and facilitate calculation, let

[0054]

[0055] wherein A(x) is the intersection of all fuzzy sets of the coal mill coal feeding rate elastic constraints;

[0056] Since the symmetric fuzzy discrimination regards the objective function and all constraints equally, to make all fuzzy constraints satisfied as much as possible and the objective function optimal as much as possible, it is required that x * satisfies A i (x * )≥λ and G(x * )≥λ, and λ reaches the maximum value. According to the conclusions 1 and 2, the quadratic coal blending mathematical optimization model with elastic constraints is converted into the following non-elastic constraint optimization model:

[0057] maxλ (14)

[0058]

[0059] wherein the other conventional constraints are equations (4), (5), (6), and (7);

[0060] Setting the optimal solution of the non-elastic constraint optimization model (14) as x * and λ * , the quadratic coal blending mathematical optimization model with elastic constraints can be converted into a model that can be solved by using the two-stage simplex method, and equations (3) and (14) are equivalent, and the optimal solution of the quadratic coal blending mathematical optimization model with elastic constraints is x * , and the optimal value is t=t(x * ).

[0061] Preferably, the specific method for solving the primary coal blending mathematical optimization model and the quadratic coal blending mathematical optimization model by the model solving module by using the two-stage simplex method is as follows:

[0062] Step S1: adding slack variables and surplus variables to make the primary and quadratic coal blending optimization models into standard linear programming models;

[0063] Step S2: based on the unit basis number n (n m-n ] T of the standard linear programming model as a standard allowable basis;

[0064] Step S3: for the m-n artificial vectors, the objective function of the standard linear programming model is changed into u1+u2+u3+...+u m-n , and then a one-stage simplex algorithm is used for calculation; m-nWhen equal to 0, a basic allowable solution is obtained as a new standard allowable base, and a one-stage simplex method is used to solve the objective function of the original once-coal blending mathematical optimization model or the twice-coal blending mathematical optimization model, so that the optimal solution of the once-coal blending optimization model or the twice-coal blending optimization model is obtained.

[0065] Preferably, an analog data frame is designed between the external coal blending control adjustment system and the original control system for transmitting the coal mill group coal feeding rate optimization value, an analog request frame is used for requesting analog signals, a mill switching on-off quantity data frame is used for transmitting the running and shutdown signals of the coal mill group, and a heartbeat on-off quantity request frame is used for verifying whether the system is online.

[0066] The original control system sends a heartbeat on-off quantity request frame to the external coal blending control adjustment system, the external coal blending control adjustment system responds to the analog heartbeat signal through the program designed digital quantity on-off signal (0, 1), and the original control system detects whether the communication of the external system is normal through the heartbeat frame response; when the original control system does not detect the heartbeat frame response for more than 30s, the external system is automatically removed, and the original control system is switched.

[0067] The coal bunker and coal mill external hanging deep coal blending control system based on the cascade optimization strategy has the following advantages: (1) the coal blending process of a thermal power enterprise generally relies on expert artificial experience and combines with an Excel table simple calculation mode, and rarely uses optimization technology, and the biggest disadvantage of artificial experience blending is that it is difficult to ensure the optimization of the mixed coal result, the coal bunker automatic once coal blending module and the model solving module based on the optimization model are established, the coal blending efficiency is greatly improved, and the coal blending cost is significantly reduced.

[0068] (2) the coal bunker coal blending does not consider the disadvantage of mill operation combination optimization, the twice optimization coal blending module and the model solving module based on the optimization model are proposed to select a reasonable mill operation combination and optimal output point, the simple function relationship between the coal feeding rate and the load of the original control system is optimized, and the coal blending cost is further saved.

[0069] (3) the linear fuzzy programming optimization model with elastic constraints and the model solving module are established according to the actual demand of the mill vibration prevention. Under the condition of meeting the safe operation of the mill, the lower limit space of the coal feeding rate required by the dynamic load is further optimized.

[0070] (4) The modules proposed in the application are implemented on an external server, the external server is connected with the original control system in a communication mode, and the original control system hardware and program are not modified, only the original manual experience mode is modified into an external computer control and adjustment implementation mode based on an optimization algorithm; the external server adopts heartbeat pulse and disturbance-free switching to ensure system safety, and the original control system can also operate normally once communication failure occurs.

[0071] (5) In view of the disadvantages of not considering the optimization of the operation combination of the coal mill in the primary coal distribution of the coal bunker in front of the furnace and the actual demand of variable load, the application first proposes progressive coal distribution of the coal bunker and the coal mill based on a cascade optimization model, thereby providing a new solution for realizing accurate coal distribution and reducing fuel cost for a thermal power enterprise.

[0072] (6) On the basis of the primary coal distribution of the coal bunker in front of the furnace, the application first proposes secondary optimization coal distribution technology based on an optimization model to select a reasonable operation combination of the coal mill and an optimal output point, thereby optimizing the simple function relationship between the coal feeding rate and the load of the original control system of the enterprise and further reducing the fuel cost.

[0073] (7) In the secondary optimization coal distribution process, considering the actual demand of vibration prevention of the coal mill, the application first establishes a fuzzy linear programming optimization coal distribution model with elastic constraints and a solving method thereof, thereby further optimizing the adaptability of the combination and the output point of the coal mill to variable load under the condition of meeting the safe operation of the coal mill.

[0074] The coal bunker and coal mill external distribution control and adjustment system based on the cascade optimization model proposed in the application optimizes the coal distribution and blending process of the power plant in combination with the primary coal distribution optimization model, the secondary coal distribution optimization model and the fuzzy linear programming technology. In view of the disadvantages of not considering the operation combination and the optimal output point of the coal mill in the coal blending and distribution commonly applied in the thermal power plant at present, the application proposes the coal bunker and coal mill external distribution control and adjustment system based on the cascade optimization model, thereby optimizing the simple function relationship between the coal feeding rate and the load of the original control system of the enterprise and further reducing the fuel cost. In addition, considering the actual demand of vibration prevention of the coal mill, the fuzzy linear programming optimization coal distribution model with elastic constraints and the solving method thereof are established, thereby further optimizing the adaptability of the combination and the output point of the coal mill to variable load under the condition of meeting the safe operation of the coal mill.

[0075] In summary, the application has great economic value and social value. BRIEF DESCRIPTION OF DRAWINGS

[0076] Figure 1 The physical architecture schematic diagram of the progressive coal distribution of the coal bunker and the coal mill based on the cascade optimization model provided for the embodiments of the application is shown in the figure.

[0077] Figure 2The principle and structure schematic diagram of the coal bunker and the coal mill progressive coal blending based on the cascade optimization model provided by the embodiment of the present application, wherein (a) is a principle diagram, and (b) is a structure schematic diagram;

[0078] Figure 3 The coal feeding rate x of the i-th coal mill provided by the embodiment of the present application i The fuzzy membership function of t0 provided by the embodiment of the present application;

[0079] Figure 4 The fuzzy membership function of t0 provided by the embodiment of the present application;

[0080] Figure 5 The flow chart of the two-stage simplex solution method provided by the embodiment of the present application;

[0081] Figure 6 The communication flow chart of the coal bunker and the coal mill external hanging deep coal blending control system based on the cascade optimization strategy provided by the embodiment of the present application;

[0082] Figure 7 The implementation architecture diagram of the coal bunker and the coal mill external hanging deep coal blending control system based on the cascade optimization strategy provided by the embodiment of the present application;

[0083] Figure 8 The coal blending software interface diagram based on the optimization model provided by the embodiment of the present application;

[0084] Figure 9 The coal blending mill setting interface diagram based on the optimization model provided by the embodiment of the present application. DETAILED DESCRIPTION

[0085] The specific embodiments of the present application are described in further detail below in combination with the drawings and the embodiments. The following embodiments are used to illustrate the present application, but not to limit the scope of the present application.

[0086] In the embodiment, the coal bunker and the coal mill external hanging deep coal blending control system based on the cascade optimization strategy, as shown in Figure 1 and 2 includes a server, a communication module, and a coal bunker-based primary optimization coal blending module, a coal mill-based secondary optimization coal blending module, a model solution module, a relational database interface module and a real-time database interface module formed by software programming and set on the server;

[0087] The relational database interface module is established based on ADO.NET, reads corresponding coal quality parameters from the SQL Server relational database storing the coal quality parameter information (such as heat value, sulfur, moisture, volatile matter, ash content) of each single coal type in the coal yard, and provides the coal yard coal storage basic data for the primary optimization coal blending;

[0088] The real-time database interface module is established based on OPAPI.NET, reads real-time data of operation parameters from a storage unit group (such as load, coal mill coal feeding rate, coal level meter, etc.), and provides real-time data of the unit group for secondary optimization of coal blending;

[0089] The coal bunker-based primary optimization coal blending module, under the premise of ensuring safety and environmental protection of the power generating unit group and constraints of maximum planned load (power generation load, heat supply load and steam supply load) of the boiler, takes the optimal coal blending cost of the coal bunker as the target, and establishes a primary coal blending mathematical optimization model with the coal type and proportion of each coal bunker as the optimization variable in the coal blending stage of the coal bunker; meanwhile, the primary optimization coal blending module transmits the coal type and proportion of the blended coal of each coal bunker to the upper computer of the original reclaimer control system through the communication module (network communication based on Ethernet), so as to coordinate the collaborative coal feeding of the two reclaimers.

[0090] The secondary optimization coal blending module based on the coal mill, under the premise of ensuring safety and environmental protection of the power generating unit group and constraints of dynamic planned load (non-maximum planned load) of the boiler, takes the optimal coal mill operation cost as the target, and establishes a secondary coal blending mathematical optimization model with the operation combination and optimal output (coal feeding rate) of each coal mill as the optimization variable in the coal blending stage of the coal mill; meanwhile, the secondary optimization coal blending module transmits the optimal combination and optimal output point of the coal mill to the original coal mill control system through the communication module (communication based on serial port) in the form of 485 bus and RTU mode based on Modbus protocol, so as to realize compensation adjustment of the coal feeding rate of the original coal mill control system.

[0091] The model solving module, based on the established primary coal blending mathematical optimization model and secondary coal blending mathematical optimization model, realizes modeling and solving of the primary coal blending mathematical optimization model and the secondary coal blending mathematical optimization model through software programming, solves the coal blending scheme of the coal bunker and the coal mill operation scheme of the optimal combination and optimal output point of the coal mill, and further realizes control and adjustment of the coal bunker and the coal mill.

[0092] The coal bunker-based primary coal blending optimization module takes the coal blending combustion meeting the calorific value, sulfur, volatile matter and ash content indicators as the constraint condition, and takes the minimum economic indicator of the blended coal as the objective function, and establishes a primary coal blending mathematical optimization model, as shown in the following formula:

[0093]

[0094] In the formula, minf(x) is the minimum cost of the blended coal, c j is the cost of the jth single coal, i.e. the unit price of standard coal, x j is the blending proportion of the jth single coal, and n is the total number of coal types in the blended coal.

[0095] According to the actual situation of the enterprise equipment and coal type, part of the coal quality parameter characteristic indexes of the mixed coal are selected as the constraint conditions; the enterprise has a limit on the net calorific value, actual sulfur content, actual ash content and actual volatile content of the mixed coal, and the constraint conditions of the mixed coal are:

[0096]

[0097] In the formula, h j ,s j ,a j ,v j respectively represent the calorific value, sulfur content, ash content and volatile content of each coal type; H, S, A and V respectively represent the upper limit or lower limit of the calorific value, sulfur content, ash content and volatile content of the mixed coal scheme; according to the actual situation of the enterprise, the slagging characteristics, burnout characteristics, grindability and other coal quality characteristic indexes and other special constraint conditions (such as priority of special coal type, ash melting point, virtual coal type) of the enterprise itself and demand can also be added to the constraint conditions. After the optimization model of the pre-furnace coal blending is established, the coal blending scheme of the first coal blending of the coal bunker can be obtained by using the optimization algorithm, and the coal bunker scheme is formulated based on the scheme, and then the coal bunker is added to complete the first coal blending task.

[0098] In this embodiment, for the first high load optimization coal blending, 7 coal types are stacked in the experimental coal yard, and each coal bunker has no more than 2 coal types, the power generation high load is 310MW, and the heating load and the steam supply load are 10MW respectively. Based on the maximum desulfurization capacity of the unit, the total sulfur upper limit of the first optimization coal blending model of the unit can be calculated as 0.87, and the volatile content and ash content upper limits are set as 35 and 25. At this time, the boiler load (330MW) is in a high load operation mode, the coal mill runs A mill, B mill and D mill, and the maximum coal feeding rate of each mill is shown in Table 1. The first optimization model of the formula (1) and (2) and its constraints are solved by using the two-stage simplex algorithm to obtain the first optimal high load coal blending scheme of the ABD coal bunker in Table 2.

[0099] On the basis of the above first high load optimization coal blending, for the medium-high electric load of 210MW, the heating load and the steam supply load are also 10MW, and the C mill is configured, and the corresponding constraint parameters and the high load remain the same. At this time, the boiler load 230MW is in a medium-high load operation mode, the coal mill runs A mill, B mill and C mill, and the first optimization model of the formula (1) and (2) is also used and the two-stage simplex algorithm can obtain the first optimal medium-high load coal blending scheme of the ABC coal bunker in Table 2.

[0100] Table 1 Coal mill and parameter configuration

[0101] Coal mill number A mill B mill C mill D mill Coal mill function Continuous Continuous Medium-high reserve High load reserve Maximum output (T / h) 52 54 53 55 Minimum output range (T / h) 26-30 26-30 26-30 26-30

[0102] Table 2 Actual first coal blending scheme of the thermal power plant

[0103]

[0104]

[0105] The secondary coal blending optimization module based on the coal mill takes real-time fuel cost as the target, introduces each coal mill operation parameter variable in combination with the actual situation, and makes the real-time fuel cost of power production lowest under the premise of meeting safety and environmental protection. The objective function of the secondary coal blending mathematical optimization model is as follows:

[0106]

[0107] In the formula, U i is the i-th coal mill operation parameter, wherein 0 represents shutdown and 1 represents operation; x i is the coal feeding rate of the i-th coal mill; P i is the mixed coal unit price in the i-th coal bunker; and N is the number of coal mills of the unit.

[0108] The secondary coal blending mathematical optimization model established by the secondary coal blending optimization module based on the coal mill meets the following constraint conditions:

[0109] (1) The real-time boiler load determined according to the output of each coal mill and the coal type information of the corresponding coal bunker meets the sum of the planned electric load and the steam and heat supply load at this time. The load constraint is as follows:

[0110]

[0111] In the formula, h i is the mixed coal calorific value in the i-th coal bunker corresponding to the coal mill; M h is the coal consumption of the standard coal under the working condition; F d , F r , and F q are the electric load, steam load and heat load of the boiler load, respectively.

[0112] The environmental protection requirement cannot be ignored in power production. The sulfur content in the coal entering the furnace per hour should be less than the maximum desulfurization capacity of the power plant at this time. This constraint condition is as follows:

[0113]

[0114] In the formula, s i is the mixed coal sulfur content in the i-th coal bunker corresponding to the coal mill; and S t is the maximum desulfurization capacity of the desulfurization equipment.

[0115] In order to avoid slagging in the furnace, the ash content of the mixed coal entering the furnace is limited. This constraint condition is expressed as:

[0116]

[0117] In the formula, a i is the ash content of the mixed coal in the coal bunker corresponding to the i-th coal mill; A is the total amount of the ash content limit of the mixed coal entering the furnace;

[0118] To ensure the safety of combustion in the furnace and avoid deflagration in the furnace, the volatile matter of the mixed coal entering the furnace is limited, and the constraint is as follows:

[0119]

[0120] In the formula, v i is the volatile matter of the mixed coal in the coal bunker corresponding to the i-th coal mill; V is the total amount of the volatile matter limit of the mixed coal entering the furnace;

[0121] Each coal mill has an upper limit and a lower limit of the output, and to make the coal mill run within the conventional output range, the following elastic constraint equation is established:

[0122]

[0123] In the formula, V i max and V i min are the maximum and minimum outputs of the i-th coal mill, respectively, and V i min The elastic range of V V i min is the lower limit of V i min , and is the upper limit of V i min ;

[0124] Considering that too small coal feeding rate of the coal mill can cause vibration of the mill, in order to avoid this hazard, the in equation (8) is a fuzzy constraint condition, indicating that the constraint has a certain elasticity, and the elastic range on the left side of the constraint is If the coal feeding rate of the coal mill is greater than or equal to , the engineer is "satisfied", and between , the satisfaction of the engineer gradually decreases, and less than V i min , the engineer is not satisfied.

[0125] The secondary coal blending optimization module based on the coal mill converts the secondary coal blending mathematical optimization model with elastic constraints into a non-elastic constraint optimization model;

[0126] Since the equation (8) has elastic constraint conditions, the traditional two-stage simplex method cannot be directly used for solving. Therefore, the elastic constraint conditions in the elastic constraint equation (8) are fuzzified, and the membership function of the coal feeding rate of the coal mill in this interval is defined as follows:

[0127]

[0128] Where, A i (x i ) is the fuzzy set of the elastic constraint of the coal feeding rate of the coal mill, and the membership function of the elastic constraint of x i is as shown in Figure 3 Figure.

[0129] If the elastic constraint of the equation (8) takes , let the optimal value of the corresponding ordinary linear programming of the optimization model be z0; if the elastic constraint of the equation (8) takes V i min ≤x i ≤V i max , let the optimal value of the corresponding ordinary linear programming be z1. Generally speaking, there should be z1 < z0, otherwise the stretching index of the fuzzy constraint condition in the fuzzy linear programming does not work. Denote d0 = z0 - z1, then d0 > 0, which is the stretching index of the objective function in the fuzzy linear programming; the objective function of the fuzzy linear programming is fuzzified, and the membership function of the objective function is:

[0130]

[0131] Where, Its graph is as shown in Figure 4 Figure.

[0132] From the definitions of A i (x i ) and G(t0), the following conclusions are obtained:

[0133] 1) For any λ ∈ [0, 1], there is

[0134]

[0135] Where,

[0136] 2) For any λ ∈ [0, 1], there is

[0137]

[0138] Where, G(t0(x)) is the membership function of the fuzzy set of the objective function;

[0139] To solve the secondary coal blending mathematical optimization model (3) and facilitate calculation, let

[0140]

[0141] In the formula, A(x) is the intersection of the fuzzy sets of all coal mill feed rate elastic constraints. In this example, m=4 represents mills A, B, C, and D.

[0142] Since symmetric fuzzy discrimination treats the objective function and all constraints equally, to satisfy all fuzzy constraints as much as possible and to optimize the objective function as much as possible, x must be... * Satisfy A i (x * )≥λ and G(x) * Given that ) ≥ λ, and λ reaches its maximum value, based on conclusions 1 and 2, the quadratic coal blending mathematical optimization model with elastic constraints is transformed into the following inelastic constraint optimization model:

[0143] maxλ (14)

[0144]

[0145] In the formula, the other conventional constraints are equations (4), (5), (6), and (7);

[0146] The optimal solution of the inelastic constraint optimization model (14) is set to x. * and λ * Then the mathematical optimization model of secondary coal blending with elastic constraints can be transformed into a two-stage simplex solution, and (3) and (14) are equivalent. The optimal solution is then x * The optimal value is t = t(x) * ).

[0147] The coal blending schemes formulated above based on high and medium-high loads are carried out under the condition of maximum coal feed rate of the coal mill. As the load changes, the coal feed rate changes accordingly. There are few studies on secondary coal blending based on primary coal blending to find the optimal mill combination and the optimal matching coal feed rate. In this embodiment, for low load of 130MW, the optimal rate combination is given based on the coal mill optimization model (3) to (14), as shown in Table 3 for low load output. From the results of the secondary optimization coal blending experiment, it can be concluded that the coal feed rates of mills A, B and C are 28.00T / h, 29.08T / h and 28.54T / h, respectively, all within the fuzzy range of 26-30, making the coal blending scheme more optimized and economical, and also within the allowable range of avoiding vibration.

[0148] Table 3. Secondary Optimization Scheme for Coal Mill Combination under Fuzzy Decision-Making

[0149] Coal mill number A mill B mill C mill D mill Objective function High load output (T / h) 52.00 54.00 —— 55.00 175458 Medium-high load output (T / h) 52.00 54.00 53.00 —— 166139 Low load output (T / h) 28.00 29.08 28.54 —— 89464

[0150] Due to the needs of actual engineering problems, there are inequality constraints and equality constraints in the previously established primary coal blending optimization model and secondary coal blending optimization model. The primary and secondary coal blending optimization models can be transformed into standard linear programming models by adding slack variables and surplus variables. Since the constraint matrices in the transformed standard linear programming models do not contain an m-order (m is the number of constraint equations) identity matrix, it indicates that there is no initial admissible basis at this moment. In order to make an m-order identity matrix exist in the constraint matrix, that is, to have an m-order standard admissible basis, based on the number of unit bases n (n < m) in the standard model, a new artificial vector u = [u1, u2,..., u m-n T is introduced, and then the following two-phase simplex method is used for solution.

[0151] Therefore, the specific method for the model solution module to solve the primary coal blending mathematical optimization model and the secondary coal blending mathematical optimization model using the two-phase simplex method is as follows:

[0152] Step S1: Transform the primary and secondary coal blending optimization models into standard linear programming models by adding slack variables and surplus variables;

[0153] Step S2: Based on the number of unit bases n (n < m) in the standard linear programming model, add an m-order artificial vector u = [u1, u2,..., u m-n T to the standard linear programming model as the standard admissible basis;

[0154] Step S3: For the m - n artificial vectors, change the objective function of the standard linear programming model to u1 + u2 + u3 +... + u m-n , and then use the one-phase simplex algorithm for calculation; this process is the first stage of the two-phase simplex method;

[0155] Step S4: When the objective function u1 + u2 + u3 +... + u m-n is equal to 0 (if not equal to 0, it means there is no solution), at this time, a basic admissible solution is obtained as the new standard admissible basis, and then the objective function of the original primary coal blending mathematical optimization model or secondary coal blending mathematical optimization model is solved by the one-phase simplex method. This process is the second stage of the two-phase simplex method, and the optimal solution of the primary coal blending optimization model or secondary coal blending optimization model is obtained. The above solution program process is as Figure 5 shown.

[0156] ​​The external coal distribution control and regulation system is designed with analog data frames to transmit the optimized coal feeding rate of the coal mill, analog request frames to request analog signals, mill start-up and stop switch data frames to transmit the operation and shutdown signals of the coal mill, and heartbeat switch request frames to verify whether the system is online.

[0157] To ensure the safety of system operation and to avoid affecting production, the original control system sends a heartbeat switch request frame to the external coal blending control and regulation system. The external coal blending control and regulation system simulates a heartbeat signal by responding to a digital switch signal (0,1) programmed in the program. The original control system then uses the heartbeat frame response to detect whether the communication of the external system is normal. If the original control system does not detect a heartbeat frame response for more than 30 seconds, it automatically disconnects the external system and switches back to the original control system, ensuring the safety and normal operation of the power plant's original control system.

[0158] The external coal blending control and regulation system, based on a cascade optimization model, uses primary and secondary coal blending modules and a solution module to calculate the coal type ratio, optimal combination of pulverizers, and output points in the coal bunker. This data is then transmitted to the original control system via a communication module, enabling automatic adjustment of bunker filling, pulverizer combination, and coal feed rate. Considering communication processing capabilities, the pulverizer feed rate adjustment control cycle is set to 15 seconds to ensure the external coal blending control and regulation system has sufficient time to complete online calculations and process adjustments. The communication process is as follows: Figure 6 As shown.

[0159] In this embodiment, the stacker-reclaimer control system and the coal mill control system coordinate, optimize, and control the two stacker-reclaimers, two feeding conveyors, four coal bunkers, and four coal mills. Based on a control network and Ethernet network, and supported by relational and real-time databases, the external coal blending control and regulation system operates physically independently of the original control system, under safe conditions and without affecting power plant production. The external coal blending control and regulation system connects to the original control system via communication, without modifying the original control system's hardware or programs; it simply replaces the original manual experience-based approach with an algorithm-based, automated external computer implementation. The external coal blending control and regulation system uses heartbeat pulses and bumpless switching to ensure system safety. In the event of communication failures, the original control system can continue to operate normally and safely.

[0160] The implementation of primary coal blending is as follows: 1) The maximum load, maximum medium-high load, and constraint boundary conditions of the boiler are read through the relational database interface module; 2) The coal quality parameters of each single coal type are read through the relational database through the interface module; 3) Based on the working range of the two reclaimers, the coal type combination of each coal yard is cyclically calculated through the coal yard matching module; 4) The coal type and proportion of coal blending in each coal bunker are calculated based on the primary optimization model module and the solution module; 5) The external system coordinates the adjustment of the set value of the reclaimer control system through communication. The reclaimer control system uses the PID algorithm to control the coordinated work of the two reclaimers to feed coal to the coal bunker in proportion and complete the primary coal blending task.

[0161] Implementation of secondary coal blending: 1) Read the coal bunker blending scheme from the relational database through the real-time database interface module; 2) Read the real-time load of the real-time database by time period through the interface module; 3) Calculate the optimal combination and output point of the coal mills through the secondary optimization model and solution module; 4) Switch the operating status of the mills according to the optimal mill combination (implementation requires confirmation by the operator); 5) The external system connects to the coal mill control system via communication, and adjusts the coal feed rate (output) of the coal mills through the offset to complete the secondary coal blending task. Its system architecture and interface are shown below. Figure 7-9 .

[0162] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope defined by the claims of the present invention.

Claims

1. A deep coal blending control system for coal bunkers and coal mills based on a cascade optimization strategy, characterized in that: It includes a server, a communication module, and application modules set on the server through software programming, including a primary coal blending module based on a coal bunker, a secondary coal blending module based on a coal mill, a model solving module, a relational database interface module, and a real-time database interface module. The relational database interface module reads the corresponding coal quality parameters from the SQL Server relational database that stores coal quality parameter information for each type of coal in the coal yard, providing basic coal storage data for a single coal blending optimization. The real-time database interface module reads the corresponding real-time operating parameter data from the OPENPlant real-time database that stores real-time data of unit operating parameters, providing a real-time data foundation for secondary optimization of coal blending. The primary coal blending module based on the coal bunker establishes a primary coal blending mathematical optimization model with the coal type and proportion of each coal bunker as optimization variables during the coal blending stage. At the same time, the primary coal blending module transmits the coal type and proportion of each coal bunker to the host computer of the original material reclaimer control system through the communication module to coordinate the collaborative coal feeding of the two material reclaimers. The secondary coal blending module based on the coal mill establishes a secondary coal blending mathematical optimization model based on the primary coal blending and the operation combination and optimal output of each coal mill as optimization variables during the coal mixing operation stage of the coal mill. At the same time, the secondary coal blending module transmits the coal mill operation plan with the optimal combination and optimal output point of the coal mill through the communication module, so as to realize the compensation and adjustment of the coal feed rate of the original coal mill control system. The model solving module, based on the established primary coal blending mathematical optimization model and secondary coal blending mathematical optimization model, realizes the modeling and solving of the primary coal blending mathematical optimization model and the secondary coal blending mathematical optimization model, and solves the coal blending scheme of the coal bunker, the optimal combination of coal mills and the coal mill operation scheme of the optimal output point, thereby realizing the control and regulation of the coal bunker and the coal mill. The external deep coal blending control system is designed with analog data frames to transmit the optimized coal feeding rate value of the coal mill unit, analog request frames to request analog signals, mill start-up and stop switch data frames to transmit the operation and shutdown signals of the coal mill unit, and heartbeat switch request frames to verify whether the system is online. The original control system sends a heartbeat switch request frame to the external deep coal blending control system. The external deep coal blending control system simulates a heartbeat signal by responding with a digital switch signal (0,1) programmed in the program. The original control system then uses the heartbeat frame response to detect whether the communication of the external deep coal blending control system is normal. If the original control system does not detect a heartbeat frame response for more than 30 seconds, it automatically disconnects the external deep coal blending control system and switches back to the original control system.

2. The coal bunker and coal mill external deep coal blending control system based on cascade optimization strategy according to claim 1, characterized in that: The primary coal blending optimization module based on the coal bunker uses the calorific value, sulfur content, volatile matter content, and ash content of the blended coal combustion as constraints, and establishes a primary coal blending mathematical optimization model with the minimum economic index of the blended coal as the objective function, as shown in the following formula: (1)； In the formula, To minimize the cost of blended coal, It is the cost of the j-th single coal, i.e., the standard coal price per unit. It is the blending ratio of the j-th single coal, and n is the total number of coal types in the blend; Based on the actual conditions of the enterprise's equipment and coal type, a portion of the coal quality parameters of the blended coal are selected as constraints. Given that the enterprise imposes restrictions on the actual calorific value, actual sulfur content, actual ash content, and actual volatile matter of the blended coal fed into the furnace, the constraints for the blended coal are as follows: (2)； In the formula, These represent the calorific value, sulfur content, ash content, and volatile matter values ​​for each type of coal; These represent the upper or lower limits of the calorific value, sulfur content, ash content, and volatile matter of the coal blending scheme, respectively. Depending on the actual situation of the enterprise, coal quality characteristic indicators and other special constraints based on the enterprise's own characteristics and needs can also be added to the constraints.

3. The coal bunker and coal mill external deep coal blending control system based on cascade optimization strategy according to claim 2, characterized in that: The secondary coal blending optimization module based on coal mills aims at real-time fuel cost. It incorporates operating parameter variables of each coal mill based on actual conditions, and minimizes the real-time fuel cost of power production while meeting safety and environmental protection requirements. The objective function of the secondary coal blending mathematical optimization model is as follows: (3)； In the formula, For the first Operating parameters of a coal mill A value of 0 represents shutdown, and a value of 1 represents operation. For the first The coal feed rate of each coal mill; For the first The unit price of mixed coal (standard coal) in each coal bunker; This refers to the number of coal mills in the unit.

4. The coal bunker and coal mill external deep coal blending control system based on cascade optimization strategy according to claim 3, characterized in that: The secondary coal blending mathematical optimization model established by the coal mill-based secondary coal blending optimization module satisfies the following constraints: (1) The real-time boiler load determined based on the output of each coal mill and the coal type information of the corresponding coal bunker must meet the sum of the planned electrical load and the steam and heating load at this time. The load constraints are as follows: (4)； In the formula, For the first The calorific value of the mixed coal in the coal bunker corresponding to each coal mill; This refers to the standard coal consumption under this operating condition. , , These are the electrical load, steam supply load, and heating load of the boiler, respectively. The sulfur content of the coal fed into the furnace per hour must be less than the power plant's maximum desulfurization capacity at that time. This constraint is: (5)； In the formula, The sulfur content of the mixed coal in the coal bunker corresponding to the i-th coal mill; This represents the maximum desulfurization capacity of the desulfurization equipment. To prevent slagging in the furnace, the ash content of the coal mixed with the furnace must be limited. This constraint is expressed as follows: (6)； In the formula, The ash content of the mixed coal in the coal bunker corresponding to the i-th coal mill; The total ash content of mixed coal entering the furnace is limited; The volatile matter content of the mixed coal fed into the furnace is limited, and the constraints are as follows: (7)； In the formula, Let be the volatile matter content of the mixed coal in the coal bunker corresponding to the i-th coal mill; The total amount of volatile matter in mixed coal fed into the furnace is limited; Each coal mill has its own upper and lower output limits. To ensure that the coal mill operates within its normal output range, the following elastic constraint equations are established: (8)； In the formula, and Let be the maximum and minimum outputs of the i-th coal mill, respectively. The elastic range is , for The lower limit, for The upper limit.

5. The coal bunker and coal mill external deep coal blending control system based on cascade optimization strategy according to claim 4, characterized in that: The secondary coal blending optimization module based on the coal mill converts the secondary coal blending mathematical optimization model with elastic constraints into an optimization model with inelastic constraints. By fuzzifying the elastic constraint conditions in the elastic constraint equation (8), the membership function of the coal mill feed rate in this interval is defined as follows: (9)； In the formula, For the fuzzy set of elastic constraints on the coal feed rate of the coal mill; If the elastic constraint of equation (8) is taken Let the optimal value of the ordinary linear programming corresponding to the optimization model be... If the elastic constraint of equation (8) is taken as Let the optimal value of the corresponding ordinary linear programming problem be... , ;remember ,but , where is the scaling index of the objective function in fuzzy linear programming; the objective function of fuzzy linear programming is fuzzified, and the membership function of the scaling index is: (10)； In the formula, ; Depend on and The definition yields the following conclusions: 1) For any Then there is (11)； In the formula, ; 2) For any ,have (12)； In the formula, The fuzzy set membership function is the objective function. To solve the secondary coal blending mathematical optimization model (3) and facilitate calculation, let (13)； In the formula, The intersection of the fuzzy sets of all elastic constraints on the coal feed rate of coal mills; Since symmetric fuzzy discrimination treats the objective function and all constraints equally, to satisfy all fuzzy constraints as much as possible and to optimize the objective function as much as possible, it is required that... satisfy and , and make Having reached the maximum value, based on conclusions 1 and 2, the quadratic coal blending mathematical optimization model with elastic constraints is transformed into the following inelastic constraint optimization model: (14)； In the formula, the other conventional constraints are equations (4), (5), (6), and (7); The optimal solution of the inelastic constraint optimization model (14) is set as follows: and Then, the quadratic coal blending mathematical optimization model with elastic constraints can be transformed into a two-stage simplex solution, and equations (3) and (14) are equivalent. The optimal solution of the quadratic coal blending mathematical optimization model with elastic constraints is then... The optimal value is .

6. The coal bunker and coal mill external deep coal blending control system based on cascade optimization strategy according to claim 5, characterized in that: The specific method for solving the primary coal blending mathematical optimization model and the secondary coal blending mathematical optimization model using the two-stage simplex method in the model solving module is as follows: Step S1: By adding slack variables and residual variables, the primary and secondary coal blending optimization models are transformed into standard linear programming models; Step S2: Based on the number of unit bases \(n\) (\(n < m\)) of the standard linear programming model, add an \(m\)-order artificial vector to the standard linear programming model as the standard admissible basis; Step S3: For m-n artificial vectors, transform the objective function of the standard linear programming model into u1+u2+u3+...+u m-n Then, the one-stage simplex algorithm is used for calculation; Step S4: In the objective function u1+u2+u3+...+u m-n When the value equals 0, a basic allowable solution is obtained as a new standard allowable basis. Then, the objective function of the original primary coal blending mathematical optimization model or the secondary coal blending mathematical optimization model is solved by the one-stage simplex method to obtain the optimal solution of the primary coal blending optimization model or the secondary coal blending optimization model.

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