Load response type coal-fired boiler multi-coal-ammonia collaborative blending optimization method and system

Abstract

The application discloses a load response type coal-fired boiler multi-coal-ammonia collaborative blending optimization method and system, and belongs to the technical field of coal-fired boiler combustion optimization and low-carbon power generation. The method generates an initial blending scheme by establishing a coal quality database and a compatibility matrix and adopting a multi-objective optimization algorithm; in combination with real-time load rate data of a unit, the blending ratio is dynamically adjusted through a three-section load interval division model; based on a calorific value compensation model, the ammonia blending amount is calculated to realize calorific value gap compensation, and finally an ammonia-coal collaborative blending scheme is generated and a cost evaluation report is formed. The system is correspondingly configured with data acquisition, scheme generation and optimization, ammonia blending and cost evaluation modules. The application realizes the technical goal of improving the utilization rate of low-quality coal while reducing carbon emissions under variable load conditions of a coal-fired unit.

Description

Technical Field

[0001] This invention relates to the field of combustion optimization and low-carbon power generation technology for coal-fired boilers, specifically to a method and system for optimizing the multi-coal-ammonia synergistic blending of load-response coal-fired boilers. Background Technology

[0002] Coal-fired power plants can achieve significant economic and resource benefits by increasing the blending ratio of low-quality coal: on the one hand, low-quality coal is 30%-50% cheaper than high-quality bituminous coal, and increasing the blending ratio can reduce fuel costs; on the other hand, its poor coking properties prevent it from being used in the metallurgical industry, allowing power plants to achieve energy cascade utilization. During periods of coal market volatility, this strategy can also reduce dependence on high-calorific-value coal and enhance the resilience of the fuel supply chain. However, the inherent defects of low-quality coal severely restrict its large-scale application; its low calorific-value characteristics bring technical challenges such as increased coal consumption for power generation and deterioration of pulverizing system efficiency.

[0003] When power plants blend low-quality coal, traditional blending methods use the calorific value of the coal entering the furnace as a rigid constraint, relying solely on static parameters such as coal quality as constraints. This makes the calculation of the upper limit of low-quality coal proportion inaccurate. Furthermore, the optimization process for blended coal does not couple key boiler operating parameters such as load rate, leading to deviations between the blending scheme and the actual operating state of the equipment.

[0004] Currently, research on ammonia-blended combustion in coal-fired boilers mainly focuses on nitrogen oxide emission reduction mechanisms (such as the selection of ammonia blending locations) and carbon emission reduction potential (such as replacing part of the coal through the calorific value of ammonia with zero carbon characteristics), but systematic research is lacking on the calorific value compensation function of ammonia fuel and its coupling effect with the synergistic utilization of low-quality coal. Summary of the Invention

[0005] This application provides a method and system for optimizing the multi-coal-ammonia co-blending of load-responsive coal-fired boilers, which optimizes the economy and environmental protection of the multi-coal-ammonia co-blending scheme.

[0006] This invention provides an optimization method for multi-coal-ammonia synergistic blending in load-responsive coal-fired boilers, the method comprising:

[0007] Key characteristic parameters of different coal types are obtained through sampling analysis, and a coal quality database is established based on the obtained key characteristic parameters of coal quality.

[0008] Based on the coal quality database, a coal type compatibility matrix is ​​constructed according to the compatibility rules, and constraints are set to quantify the mixing compatibility of different coal types. Prioritizing low-quality coal, a multi-objective optimization algorithm is adopted, with the minimum cost of the blended coal as the primary objective. Based on the constraints, a blending scheme is obtained, generating the first blending scheme.

[0009] Based on the first blending scheme, real-time load rate data of the unit is introduced, and the first blending scheme is adjusted through a load rate-driven dynamic adjustment mechanism to generate a second blending scheme.

[0010] The combustion calorific value gap under the second blending scheme is calculated based on the calorific value compensation model, and the amount of ammonia added is calculated to obtain the ammonia-coal co-firing scheme and generate the third blending scheme.

[0011] Based on the third blending scheme, a cost model is established, and a cost assessment report for the blending scheme is generated.

[0012] In the above method, optionally, the key coal quality characteristic parameters include linear parameters and nonlinear parameters, wherein: the linear parameters include net calorific value on an as-received basis, sulfur content on a dry basis, ash content on an air-dried basis, and volatile matter on a dry ash-free basis; and the nonlinear parameters include ash fusion point.

[0013] In the above method, optionally, the matching rules include weighted summation and fuzzy mathematical evaluation processing, the linear parameters adopt the matching rules of weighted summation; the nonlinear parameters adopt the matching rules of fuzzy mathematical evaluation processing; the constraints include sulfur content constraints, combustion efficiency constraints and ash fusion characteristic constraints.

[0014] Optionally, in the above method, the first blending scheme includes the basic proportion of the blended coal, and the mass fraction of different coal types in the first blending scheme is defined as the benchmark proportion ε.

[0015] Optionally, in the above method, the load-rate-driven dynamic adjustment mechanism includes:

[0016] Based on the unit's rated load, a three-stage load factor division model is constructed:

[0017] Low load operating range: L%≤L min ;

[0018] Medium load operating range: L min <L%<L max ;

[0019] High load operating range: L%≥L max ;

[0020] Where L% is the real-time load factor, L% = ×100%, where P is the real-time output of the unit collected by the DCS. r This is the rated load of the unit; L min The low-load boundary point, determined through hot-state testing, requires ensuring that the furnace temperature does not fall below the critical ignition point of low-quality coal; L max The high-load boundary point is determined through hot-state testing, and it is necessary to ensure that the flue gas velocity does not cause the heated surface to overheat or coke.

[0021] The first blending scheme is dynamically adjusted based on the unit's load rate range:

[0022] When L% is in the low load range, the amount of low-quality coal blended is reduced by multiplying the benchmark ratio ε by the load reduction correction factor k1; when L% is in the medium load range, the benchmark ratio ε remains unchanged; when L% is in the high load range, the amount of low-quality coal blended is increased by multiplying the benchmark ratio ε by the load increase correction factor k2.

[0023] Among them, k1 and k2 are calibrated through unit hot-state experiments and regression analysis of operating data, satisfying 0≤k1<1, k2>1;

[0024] Set a load hysteresis interval to prevent frequent switching:

[0025] In the direction of load increase, when L% switches from the low load range to the medium load range, L% > L must be satisfied. min Duration ≥ M; When L% switches from the medium load range to the high load range, L% ≥ L must be satisfied. max Duration ≥ M. In the load reduction direction, when L% switches from the high-load operating range to the medium-load operating range, L% < L. max Duration ≥ M; When L% switches from the medium load range to the low load range, L% ≤ L must be satisfied. min Duration ≥ M.

[0026] Where M is the unit's thermal inertia time constant, which is calibrated through a step response test based on the dynamic characteristics of the unit's thermal system.

[0027] The second blending scheme includes the mass fraction of different coal types in the blended coal after adjustment based on the first blending scheme.

[0028] Optionally, in the above method, the calorific value compensation model includes:

[0029] Calculate the unit calorific value of the blended coal in the second blending scheme:

[0030] Q coal = (x) 2i ·Q i,ar ),

[0031] Among them, Q coal x represents the unit calorific value of the blended coal in the second blending scheme. 2i This represents the mass fraction of the i-th type of coal in the second blending scheme, which must satisfy... x 2i =1;Q i,ar Let be the net calorific value of the i-th type of coal as received;

[0032] Calculate the unit calorific value gap of the blended coal in the second blending scheme:

[0033] =Q design -Q coal ,

[0034] in, Q represents the unit calorific value gap of the blended coal in the second blending scheme. design The unit is designed with the fuel received as a basis for lower heating value;

[0035] when When the value is ≤0, it is determined that the second blending scheme has met the design calorific value requirement, the calorific value compensation process is terminated and the second blending scheme is retained as the third blending scheme output.

[0036] Calculate the ammonia-to-coal mass ratio in the aforementioned ammonia-coal co-firing scheme:

[0037] m NH3 = ,

[0038] Where, m NH3 In the ammonia-coal co-firing scheme, η is the ammonia-coal mass ratio, η is the combustion efficiency coefficient, and its value ranges from 0.92 to 0.95. NH3 The lower heating value of ammonia is generally taken as 18.6 MJ / kg;

[0039] Calculate the mass fraction of ammonia and the mass fraction of different coal types in the ammonia-coal co-firing scheme to generate a third blending scheme:

[0040] r NH3 = ,

[0041] Where, r NH3 This indicates the mass fraction of ammonia in the third blending scheme;

[0042] x 3i = x 2i ·(1- r NH3 ),

[0043] Where, x 3i This represents the mass fraction of the i-th type of coal in the third blending scheme, which must satisfy... x 3i + r NH3 =1;

[0044] The third blending scheme includes the mass fraction of different coal types and the mass fraction of ammonia.

[0045] The third blending scheme uses green ammonia as the blending medium.

[0046] Optionally, in the above method, the cost model includes fuel costs, environmental costs, and carbon emission reduction benefits:

[0047] The fuel cost includes the cost of blended coal and the cost of ammonia;

[0048] The environmental protection costs include the costs of the denitrification system, the desulfurization system, and the dust removal system.

[0049] The carbon emission reduction benefits are calculated by introducing a coal-fired carbon emission factor, based on the reduced amount of coal fed into the furnace due to ammonia-blended combustion and the carbon trading price.

[0050] This invention provides a load-responsive coal-fired boiler multi-coal-ammonia synergistic blending optimization system, the system comprising:

[0051] The data acquisition module obtains key characteristic parameters of different coal types through sampling and analysis methods, and establishes a coal quality database based on the obtained key characteristic parameters.

[0052] The mixed coal blending scheme generation module constructs a coal type compatibility matrix based on a coal quality database, sets constraints, and quantifies the mixing compatibility of different coal types. Prioritizing low-quality coal, a multi-objective optimization algorithm is employed, with the primary objective of minimizing the cost of the mixed coal. Based on the constraints, a mixed coal blending scheme is obtained, generating the first blending scheme.

[0053] The coal blending scheme optimization module, based on the first blending scheme, introduces real-time load rate data of the unit and adjusts the first blending scheme through a load rate-driven dynamic adjustment mechanism to generate a second blending scheme.

[0054] The ammonia blending module calculates the combustion calorific value gap under the second blending scheme based on the calorific value compensation model, calculates the amount of ammonia added, obtains the ammonia-coal co-firing scheme, and generates the third blending scheme.

[0055] The cost assessment module establishes a cost model based on the third blending scheme and generates a cost assessment report for the blending scheme.

[0056] Compared with the prior art, this application has at least the following beneficial effects:

[0057] To address the technical bottlenecks in the utilization of low-quality coal in existing coal-fired boilers, such as calorific value constraints and the lack of synergistic application of ammonia fuel, this invention constructs a systematic solution. First, by establishing a coal quality database and compatibility matrix, and employing a multi-objective optimization algorithm, the principle of prioritizing low-quality coal is strictly followed, effectively reducing the procurement cost of blended coal. Simultaneously, an innovative ammonia blending calorific value compensation mechanism is introduced to achieve a dynamic balance between fuel costs and environmental protection investments.

[0058] To enhance system operational flexibility, this invention establishes a real-time load rate-driven adjustment mechanism, significantly improving the thermal efficiency stability of coal-fired boilers under varying operating conditions and ensuring that blending schemes can quickly respond to grid peak-shaving demands. Furthermore, by deeply coupling a calorific value compensation model with ammonia co-firing technology, it can not only effectively compensate for calorific value gaps but also significantly reduce carbon emissions, promoting low-carbon combustion in coal-fired power units.

[0059] In terms of the evaluation system, this invention constructs a cost model to conduct a full-cycle evaluation of blending schemes, and realizes a precise quantitative evaluation of the synergistic co-firing of multiple coal types and ammonia fuel, providing a scientific decision-making basis for the efficient and low-carbon operation of coal-fired boilers. Attached Figure Description

[0060] Figure 1 A schematic flowchart illustrating a load-response coal-fired boiler multi-coal-ammonia synergistic blending optimization method provided in one embodiment of this application;

[0061] Figure 2 Provided for one embodiment of this application Figure 1 The step-by-step flowchart of step 103 in the document;

[0062] Figure 3 Provided for one embodiment of this application Figure 1 The step-by-step flowchart of step 104 in the document;

[0063] Figure 4 This is a schematic diagram of the structure of a load-response type coal-fired boiler multi-coal-ammonia synergistic blending optimization system provided in one embodiment of this application. Detailed Implementation

[0064] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. However, the following embodiments are only for explaining the present invention, and the scope of protection of the present invention should include all the contents of the claims. Moreover, through the description of the following embodiments, those skilled in the art can fully implement all the contents of the claims of the present invention.

[0065] Optimization method for multi-coal-ammonia synergistic blending in load-responsive coal-fired boilers, such as Figure 1 As shown, the above method includes steps 101-105.

[0066] Step 101: Obtain key characteristic parameters of different coal types through sampling analysis methods, and establish a coal quality database based on the obtained key characteristic parameters.

[0067] Representative samples of different coal types were obtained through standardized sampling procedures, and key characteristic parameters of the coal samples were obtained based on industrial analysis, elemental analysis and calorific value determination, including net calorific value on received basis, sulfur content on dry basis, ash content on air-dried basis, volatile matter on dry ash-free basis, ash fusion point and total moisture content, and a coal quality database was established.

[0068] The key coal quality parameters include linear parameters and nonlinear parameters. The linear parameters include net calorific value on an as-received basis, sulfur content on a dry basis, ash content on an air-dried basis, volatile matter on a dry ash-free basis, and total moisture content. The nonlinear parameters include ash fusion point.

[0069] Among them, the volatile matter on the dry ash-free basis affects the combustion stability, the lower heating value on the received basis determines the energy output, the sulfur content on the dry basis and the ash content on the air-dried basis affect environmental protection indicators, and the ash melting point reflects the tendency to slagging.

[0070] Step 102: Based on the coal quality database, construct a coal type compatibility matrix according to the compatibility rules, and set constraints to quantify the mixing compatibility of different coal types. Prioritizing low-quality coal, a multi-objective optimization algorithm is adopted, with the minimum cost of the blended coal as the primary objective. Based on the above constraints, a blending scheme is obtained, generating the first blending scheme.

[0071] The matching rules include weighted summation and fuzzy mathematical evaluation processing. The linear parameters adopt the weighted summation matching rules, and the nonlinear parameters adopt the fuzzy mathematical evaluation processing.

[0072] The constraints include sulfur content constraints, combustion efficiency constraints, and ash fusion characteristics constraints.

[0073] In some embodiments, the constraint is:

[0074] Sulfur constraint:

[0075] ≤α%,

[0076] in, This represents the mass fraction of the i-th type of coal. ; The dry basis sulfur content represents the sulfur content of the i-th type of coal; α is the maximum dry basis sulfur content threshold allowed for coal to be fed into the furnace by the unit.

[0077] Combustion efficiency constraints:

[0078] ≥β%

[0079] in, Let represent the dry ash-free volatile matter of the i-th type of coal, and β be the minimum dry ash-free volatile matter threshold allowed for coal to be fed into the furnace by the unit;

[0080] ≤γ%,

[0081] in, γ represents the air-dried ash content of the i-th type of coal, and γ is the maximum air-dried ash content threshold allowed for coal to be fed into the furnace by the unit.

[0082] Ash fusion characteristics constraints:

[0083] S t ≥θ℃,

[0084] Among them, S t This indicates the softening temperature of the coal ash entering the furnace, where θ is the threshold temperature for the softening temperature of the coal ash entering the furnace, set according to the furnace type.

[0085] The maximum dry basis sulfur content threshold, the minimum dry ash-free volatile matter threshold, the maximum air-dry basis ash content threshold, and the ash softening temperature threshold of the coal allowed to be fed into the boiler are determined by a combination of verification of the burner design parameters of the coal-fired boiler and combustion stability tests.

[0086] In some embodiments, the inferior coal meets any of the following conditions:

[0087] The received lower heating value is > [X] MJ / kg;

[0088] Air-dried basis ash content > [Y]%;

[0089] Sulfur content on dry basis > [Z]%;

[0090] Total moisture > [W]%;

[0091] Among them, [X], [Y], [Z], and [W] are the critical values ​​of the corresponding parameters in the unit fuel technical specifications. Coal types that do not meet any of the above conditions are defined as power coal.

[0092] The objective functions of the multi-objective optimization algorithm include the mixed coal cost function, sulfur content function, combustion efficiency function, and ash fusion characteristic function. The main objective is to minimize the cost of the mixed coal. By setting thresholds, the sulfur content function, combustion efficiency function, and ash fusion characteristic function are transformed into the constraints.

[0093] In some embodiments, the cost function for mixed coal is:

[0094] C total = ,

[0095] Among them, C total p represents the cost of mixed coal. i This represents the unit price of the i-th type of coal.

[0096] By gradually reducing the mass fraction of the thermal coal and increasing the mass fraction of the inferior coal, a first blending scheme with the lowest cost and the highest mass fraction of inferior coal is finally obtained under the premise of meeting the constraints.

[0097] The first blending scheme includes the mass fraction of different coal types in the blended coal. The mass fraction of different coal types in the first blending scheme is defined as the benchmark ratio, denoted by ε.

[0098] Step 103: Based on the first blending scheme, real-time load rate data of the unit is introduced, and the first blending scheme is adjusted through a load rate-driven dynamic adjustment mechanism to generate a second blending scheme.

[0099] In some embodiments, see Figure 2 The load-rate driven dynamic adjustment mechanism includes the following steps:

[0100] Step 1031: Based on the unit's rated load, construct a three-stage load factor division model:

[0101] Low load operating range: L%≤L min ;

[0102] Medium load operating range: L min <L%<L max ;

[0103] High load operating range: L%≥L max ;

[0104] Where L% is the real-time load factor, L% = ×100%, where P is the real-time output of the unit collected by the DCS. r This is the rated load of the unit; L min The low-load boundary point, determined through hot-state testing, requires ensuring that the furnace temperature does not fall below the critical ignition point of low-quality coal; L max The high-load boundary point is determined through hot-state testing, and it is necessary to ensure that the flue gas velocity does not cause the heated surface to overheat or coke.

[0105] Step 1032: Dynamically adjust the first blending scheme based on the load rate range of the unit:

[0106] When L% is in the low load range, the amount of low-quality coal blended is reduced by multiplying the benchmark ratio ε by the load reduction correction factor k1; when L% is in the medium load range, the benchmark ratio ε remains unchanged; when L% is in the high load range, the amount of low-quality coal blended is increased by multiplying the benchmark ratio ε by the load increase correction factor k2.

[0107] Among them, k1 and k2 are calibrated through unit hot-state experiments and regression analysis of operating data, satisfying 0≤k1<1, k2>1;

[0108] Step 1033: Set the load hysteresis interval to prevent frequent switching.

[0109] In the direction of load increase, when L% switches from the low load range to the medium load range, L% > L must be satisfied. min Duration ≥ M; When L% switches from the medium load range to the high load range, L% ≥ L must be satisfied. max Duration ≥ M. In the load reduction direction, when L% switches from the high-load operating range to the medium-load operating range, L% < L. max Duration ≥ M; When L% switches from the medium load range to the low load range, L% ≤ L must be satisfied. min Duration ≥ M.

[0110] Where M is the unit's thermal inertia time constant, which is calibrated through a step response test based on the dynamic characteristics of the unit's thermal system.

[0111] The second blending scheme includes the mass fraction of different coal types in the blended coal after adjustment based on the first blending scheme.

[0112] Step 104: Calculate the calorific value gap of the mixed coal under the second blending scheme based on the calorific value compensation model, and calculate the amount of ammonia added to obtain the ammonia-coal co-firing scheme and generate the third blending scheme.

[0113] In some embodiments, see Figure 3 This includes the following steps:

[0114] Step 1041, calculate the unit calorific value of the blended coal in the second blending scheme:

[0115] Q coal = (x) 2i ·Q i,ar ),

[0116] Among them, Q coal x represents the unit calorific value of the blended coal in the second blending scheme. 2i This represents the mass fraction of the i-th type of coal in the second blending scheme, which must satisfy... x 2i =1;Q i,ar Let be the net calorific value of the i-th type of coal as received;

[0117] Step 1042, calculate the unit calorific value gap of the blended coal in the second blending scheme:

[0118] =Q design -Q coal ,

[0119] in, Q represents the unit calorific value gap of the blended coal in the second blending scheme. design The unit is designed with the fuel received as a basis for lower heating value;

[0120] when When the value is ≤0, it is determined that the second blending scheme has met the design calorific value requirement, the calorific value compensation process is terminated, and the second blending scheme is retained as the third blending scheme for output.

[0121] Step 1043, calculate the ammonia-coal mass ratio in the ammonia-coal co-firing scheme:

[0122] m NH3 = ,

[0123] Where, m NH3 η represents the ammonia-to-coal mass ratio in the ammonia-coal co-firing scheme, η is the combustion efficiency coefficient, and its value ranges from 0.92 to 0.95. NH3 The lower heating value of ammonia is generally taken as 18.6 MJ / kg;

[0124] Step 1044: Calculate the mass fraction of ammonia and the mass fraction of different coal types in the ammonia-coal co-firing scheme to generate the third blending scheme:

[0125] r NH3 = ,

[0126] Where, r NH3 This indicates the mass fraction of ammonia in the third blending scheme;

[0127] x 3i = x 2i ·(1- r NH3 ),

[0128] Where, x 3i This represents the mass fraction of the i-th type of coal in the third blending scheme, which must satisfy... x 3i + r NH3 =1;

[0129] The third blending scheme includes the mass fraction of different coal types and the mass fraction of ammonia.

[0130] The third blending scheme uses green ammonia as the co-firing medium. Its production process is driven by renewable energy electricity. Raw materials are obtained through water electrolysis to produce hydrogen and air separation to produce nitrogen. Ammonia is industrially prepared using a catalytic synthesis process. The entire process complies with low-carbon emission standards.

[0131] In some embodiments, an upper limit for ammonia blending needs to be set: experimental studies have shown that within a specific range of ammonia blending ratios, the boiler can maintain a stable combustion state without significant reduction in thermal efficiency. When the system detects that the ammonia blending ratio has reached the preset upper limit, it will automatically adjust the fuel blending scheme: reducing the proportion of low-quality coal blended and correspondingly increasing the amount of high-calorific-value thermal coal blended to ensure that the calorific value of the fuel entering the furnace meets the unit's operating requirements.

[0132] In some embodiments, a feedback regulation mechanism is established based on real-time monitoring of flue gas composition. When the oxygen concentration in the flue gas exceeds a set threshold, the system will automatically optimize the ammonia injection position to the main combustion zone of the boiler, utilize the high-temperature reducing environment to achieve in-situ control of nitrogen oxides, and reduce the operating load of the downstream denitrification system.

[0133] Step 105: Based on the third blending scheme, establish a cost model and generate a blending scheme cost assessment report.

[0134] In some embodiments, the cost model includes fuel costs, environmental costs, and carbon reduction benefits:

[0135] P total = P fuel + P env - R carbom ,

[0136] Among them, P total P represents the total cost of the third blending scheme. fuel P represents fuel cost. env R represents environmental costs. carbom This indicates the benefits of carbon emission reduction.

[0137] Fuel costs include the cost of blended coal and the cost of ammonia:

[0138] P fuel = (x) 3i ·p i )+ r NH3 ·p NH3 ,

[0139] Where, x 3i p represents the mass fraction of the i-th type of coal in the third blending scheme. i Let r represent the unit price of the i-th type of coal. NH3 p represents the mass fraction of ammonia in the third blending scheme. NH3 This represents the unit price of ammonia.

[0140] In some embodiments, the unit price of coal needs to be adjusted according to the real-time coal price fluctuation coefficient:

[0141] p i =p base,i ·(1+νi ),

[0142] Where, p base,i Let ν be the benchmark coal price for the i-th type of coal. i Let be the real-time coal price fluctuation coefficient for the i-th type of coal.

[0143] Environmental costs include the costs of the denitrification system, the desulfurization system, and the dust removal system.

[0144] P env =P SCR +P FGD +P ESP ,

[0145] Among them, P SCR P represents the cost of a denitrification system. FGD P represents the cost of the desulfurization system. ESP This indicates the cost of the dust removal system;

[0146] In some embodiments, P SCR The acceleration rate of reducing agent (liquid ammonia or urea) is collected through the DCS system, combined with the tax-inclusive delivered price published on the commodity trading platform, and a catalyst activity decay correction coefficient is introduced for dynamic calculation; P FGD Based on real-time parameters of the limestone-gypsum wet desulfurization process, including absorbent feed rate, process water consumption, and plant power consumption of the desulfurization system, the sales revenue of desulfurization by-products is deducted simultaneously for accounting purposes; P ESP The calculation is based on the high-voltage electric field power of the electrostatic precipitator, the electrical energy metering value of the dust removal system, and the unit price of the fly ash transportation and disposal contract, and a correction coefficient for ash characteristics is introduced for verification.

[0147] Carbon emission reduction benefits are calculated by introducing a coal-fired carbon emission factor, based on the reduced amount of coal fed into the furnace due to ammonia-blended combustion and the carbon trading price:

[0148] R carbom = ·b·EF coal ·P carbon ,

[0149] Where b represents the unit's standard coal consumption for power generation, EF coal P represents the carbon emission factor from coal combustion. carbon The carbon trading price is represented by a moving average price.

[0150] when When the value is ≤0, the carbon emission reduction benefit is zero.

[0151] All cost items in this model are calculated based on the cost per unit of electricity generated.

[0152] Load-responsive coal-fired boiler multi-coal-ammonia synergistic blending optimization system, such as Figure 4 As shown, it includes a data acquisition module 401, a mixed coal blending scheme generation module 402, a mixed coal blending scheme optimization module 403, an ammonia blending module 404, and a cost assessment module 405.

[0153] The data acquisition module 401 obtains key characteristic parameters of different coal types through sampling and analysis methods, and establishes a coal quality database based on the obtained key characteristic parameters.

[0154] The mixed coal blending scheme generation module 402 constructs a coal type compatibility matrix based on a coal quality database, sets constraints, and quantifies the mixing compatibility of different coal types. Prioritizing low-quality coal, a multi-objective optimization algorithm is employed, with the primary objective of minimizing the cost of the mixed coal. Based on the constraints, a mixed coal blending scheme is obtained, generating the first blending scheme.

[0155] The coal blending scheme optimization module 403, based on the first blending scheme, introduces real-time load rate data of the unit and adjusts the first blending scheme through a load rate-driven dynamic adjustment mechanism to generate a second blending scheme.

[0156] The ammonia blending module 404 calculates the combustion calorific value gap under the second blending scheme based on the calorific value compensation model, calculates the amount of ammonia added, obtains the ammonia-coal co-firing scheme, and generates the third blending scheme.

[0157] Cost assessment module 405 establishes a cost model based on the third blending scheme and generates a cost assessment report for the blending scheme.

[0158] The above description is merely a specific embodiment of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for optimizing the blending of multiple coal types and ammonia in a load-responsive coal-fired boiler, characterized in that, The method includes: Key characteristic parameters of different coal types are obtained through sampling analysis, and a coal quality database is established based on the obtained key characteristic parameters of coal quality. Based on the coal quality database, a coal type compatibility matrix is ​​constructed according to the compatibility rules, and constraints are set to quantify the mixing compatibility of different coal types. Taking inferior coal as the priority, a multi-objective optimization algorithm is adopted, with the minimum cost of mixed coal as the main objective. Based on the constraints, a mixed coal blending scheme is obtained, and a first blending scheme is generated. Based on the first blending scheme, real-time load rate data of the unit is introduced, and the first blending scheme is adjusted through a load rate-driven dynamic adjustment mechanism to generate a second blending scheme. The combustion calorific value gap under the second blending scheme is calculated based on the calorific value compensation model, and the amount of ammonia added is calculated to obtain the ammonia-coal co-firing scheme and generate the third blending scheme. Based on the third blending scheme, a cost model is established, and a cost assessment report for the blending scheme is generated. The load-rate driven dynamic adjustment mechanism includes: Based on the unit's rated load, a three-stage load factor division model is constructed: Low load operating range: L%≤L min ; Medium load operating range: L min <L%<L max ; High load operating range: L%≥L max ; Where L% represents the real-time load factor, L% = ×100%, where P is the real-time output of the unit collected through the distributed control system. r This is the rated load of the unit; L min The low-load operating condition boundary point, determined through hot-state testing, requires ensuring that the furnace temperature does not fall below the critical ignition point of low-quality coal; L max The high-load operating condition boundary point is determined through hot-state testing, and it is necessary to ensure that the flue gas velocity does not cause the heated surface to overheat or coke. The first blending scheme is dynamically adjusted based on the unit's load rate range: When L% is in the low load range, the amount of low-quality coal blended is reduced by multiplying the benchmark ratio ε by the load reduction correction factor k1; when L% is in the medium load range, the benchmark ratio ε remains unchanged; when L% is in the high load range, the amount of low-quality coal blended is increased by multiplying the benchmark ratio ε by the load increase correction factor k2. Among them, k1 and k2 are calibrated through unit hot-state experiments and regression analysis of operating data, satisfying 0≤k1<1, k2>1; Set a load hysteresis interval to prevent frequent switching: In the direction of load increase, when L% switches from the low load range to the medium load range, L% > L must be satisfied. min Duration ≥ M; When L% switches from the medium load range to the high load range, L% ≥ L must be satisfied. max Duration ≥ M; In the load reduction direction, when L% switches from the high load range to the medium load range, L% < L max Duration ≥ M; When L% switches from the medium load range to the low load range, L% ≤ L must be satisfied. min Duration ≥ M; Where M is the unit's thermal inertia time constant, which is calibrated through a step response test based on the dynamic characteristics of the unit's thermal system; The second blending scheme includes the mass fraction of different coal types in the blended coal adjusted based on the first blending scheme; The calorific value compensation model includes: Calculate the unit calorific value of the blended coal in the second blending scheme: Q coal = (x 2i ·Q i,ar ), Among them, Q coal x represents the unit calorific value of the blended coal in the second blending scheme. 2i This represents the mass fraction of the i-th type of coal in the second blending scheme, which must satisfy... x 2i =1;Q i,ar Let be the net calorific value of the i-th type of coal as received; Calculate the unit calorific value gap of the blended coal in the second blending scheme: =Q design -Q coal , in, Q represents the unit calorific value gap of the blended coal in the second blending scheme. design The unit is designed with the fuel received as a basis for lower heating value; when When the value is ≤0, it is determined that the second blending scheme has met the design calorific value requirement, the calorific value compensation process is terminated and the second blending scheme is retained as the third blending scheme output; Calculate the ammonia-to-coal mass ratio in the aforementioned ammonia-coal co-firing scheme: m NH3 = ， Where, m NH3 Q represents the ammonia-to-coal mass ratio in the ammonia-coal co-firing scheme, η represents the combustion efficiency coefficient, with a value ranging from 0.92 to 0.95, and Q... NH3 The lower heating value of ammonia is generally taken as 18.6 MJ / kg; Calculate the mass fraction of ammonia and the mass fraction of different coal types in the ammonia-coal co-firing scheme to generate a third blending scheme: r NH3 = ， Where, r NH3 This indicates the mass fraction of ammonia in the third blending scheme; x 3i = x 2i ·(1- r NH3 ), Where, x 3i The mass fraction of the i-th type of coal in the third blending scheme must satisfy the following conditions: x 3i + r NH3 =1; The third blending scheme includes the mass fraction of different coal types and the mass fraction of ammonia.

2. The method according to claim 1, characterized in that, The key coal quality parameters include linear parameters and nonlinear parameters, wherein: The linear parameters include received lower heating value, dry sulfur content, air-dried ash content, dry ash-free volatile matter, and total moisture content, and the nonlinear parameters include ash melting point.

3. The method according to claim 2, characterized in that, The matching rules include weighted summation and fuzzy mathematical evaluation processing. The linear parameters adopt the matching rules of weighted summation, and the nonlinear parameters adopt the matching rules of fuzzy mathematical evaluation processing. The constraints include sulfur content constraints, combustion efficiency constraints, and ash fusion characteristic constraints, wherein: The sulfur content constraint is: ≤α%， in, This represents the mass fraction of the i-th type of coal. ; The dry basis sulfur content represents the sulfur content of the i-th type of coal; α is the maximum dry basis sulfur content threshold allowed for coal to be fed into the furnace by the unit. The combustion efficiency constraint is: ≥β%， in, Let represent the dry ash-free volatile matter of the i-th type of coal, and β be the minimum dry ash-free volatile matter threshold allowed for coal to be fed into the furnace by the unit; ≤γ%, in, γ represents the air-dried ash content of the i-th type of coal, and γ is the maximum air-dried ash content threshold allowed for coal to be fed into the furnace by the unit. The constraint on ash fusion characteristics is: S t ≥θ℃, Among them, S t This indicates the softening temperature of the coal ash entering the furnace, where θ is the threshold temperature for the softening temperature of the coal ash entering the furnace, set according to the furnace type.

4. The method according to claim 1, characterized in that, The objective functions of the multi-objective optimization algorithm include the mixed coal cost function, sulfur content function, combustion efficiency function, and ash fusion characteristic function. The main objective is to minimize the cost of the mixed coal. By setting thresholds, the sulfur content function, combustion efficiency function, and ash fusion characteristic function are transformed into the constraints.

5. The method according to claim 1, characterized in that, The first blending scheme includes the basic proportion of the blended coal, and the mass fraction of different coal types in the first blending scheme is defined as the benchmark proportion ε.

6. The method according to claim 1, characterized in that, The cost model includes fuel costs, environmental costs, and carbon reduction benefits.

7. A load-responsive coal-fired boiler multi-coal-ammonia synergistic blending optimization system, characterized in that, The system is used to implement the method of claim 1, comprising: The data acquisition module obtains key characteristic parameters of different coal types through sampling and analysis methods, and establishes a coal quality database based on the obtained key characteristic parameters; The mixed coal blending scheme generation module constructs a coal type compatibility matrix based on a coal quality database, sets constraints, quantifies the mixing compatibility of different coal types, prioritizes inferior coal, adopts a multi-objective optimization algorithm, sets the minimum cost of mixed coal as the main objective, obtains mixed coal blending schemes based on constraints, and generates the first blending scheme. The coal blending scheme optimization module, based on the first blending scheme, introduces real-time load rate data of the unit and adjusts the first blending scheme through a load rate-driven dynamic adjustment mechanism to generate a second blending scheme; The ammonia blending module calculates the combustion calorific value gap under the second blending scheme based on the calorific value compensation model, calculates the amount of ammonia added, obtains the ammonia-coal co-firing scheme, and generates the third blending scheme. The cost assessment module establishes a cost model based on the third blending scheme and generates a cost assessment report for the blending scheme.

Images