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

Abstract

The invention discloses a load response type coal-fired boiler multi-coal-type-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 comprises the following steps: establishing a coal quality database and a compatibility matrix, and generating an initial blending scheme by adopting a multi-objective optimization algorithm; combining real-time load rate data of the unit, and dynamically adjusting the blending proportion through a three-section type load interval division model; and calculating the ammonia doping amount based on the calorific value compensation model to realize calorific value notch compensation, and finally generating an ammonia-coal collaborative blending combustion scheme and forming a cost evaluation report. The system is correspondingly provided with a data acquisition module, a scheme generation and optimization module, an ammonia blending module and a cost evaluation module. According to the method, the technical aims of improving the utilization rate of inferior coal and reducing carbon emission under the variable-load working condition of the coal-fired unit are achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of coal-fired boiler combustion optimization and low-carbon power generation, in particular to a load-responsive coal-fired boiler multi-coal-ammonia collaborative blending optimization method and system. BACKGROUND

[0002] A coal-fired power plant can obtain significant economic and resource benefits by increasing the proportion of inferior coal blending: on the one hand, the price of inferior coal is 30%-50% lower than that of high-quality bituminous coal, so increasing the proportion of inferior coal blending can reduce fuel costs; on the other hand, its poor coking characteristics make it unsuitable for use in the metallurgical industry, and the use of the power plant can achieve energy cascade utilization. During the period of coal market fluctuations, 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 inferior coal severely restrict its large-scale application, and its low-calorific-value characteristics pose technical challenges such as increased power generation coal consumption and deteriorated pulverizing system efficiency.

[0003] When a power plant blends inferior coal, the traditional blending method takes the calorific value of the coal fed into the furnace as a hard constraint, and only uses static parameters such as coal quality parameters as constraint conditions, which makes the calculation of the upper limit of the proportion of inferior coal inaccurate when blending inferior coal. At the same time, key boiler operating parameters such as load rate are not coupled in the mixed coal blending optimization process, resulting in a deviation between the blending scheme and the actual operating state of the equipment.

[0004] Currently, research on coal-fired boiler blending with ammonia focuses on nitrogen oxide emission reduction mechanisms (such as the selection of ammonia blending position) and carbon emission reduction potential (such as replacing part of the coal by the calorific value of ammonia, which is a zero-carbon fuel), but there is a lack of systematic research on the calorific value compensation function of ammonia fuel and its coupling effect with inferior coal. SUMMARY

[0005] The present application provides a load-responsive coal-fired boiler multi-coal-ammonia collaborative blending optimization method and system, which optimizes the economic and environmental performance of the multi-coal-ammonia collaborative blending scheme.

[0006] The present application provides a load-responsive coal-fired boiler multi-coal-ammonia collaborative blending optimization method, which comprises:

[0007] Obtain key characteristic parameters of different coal types by sampling analysis method, and establish a coal quality database according to the obtained key characteristic parameters of coal quality;

[0008] Based on the coal quality database, construct a coal type compatibility matrix according to the compatibility rules, set constraint conditions, and quantify the mixing compatibility of different coal types. According to the principle of giving priority to inferior coal, use a multi-objective optimization algorithm, set the lowest cost of mixed coal as the main objective, obtain a mixed coal blending scheme based on the constraint conditions, and generate a first blending scheme;

[0009] Based on the first blending scheme, introduce the unit real-time load rate data, adjust the first blending scheme through the load rate driven dynamic adjustment mechanism, and generate a second blending scheme;

[0010] Based on the heat value compensation model, calculate the combustion heat value gap under the second blending scheme, and calculate the ammonia blending amount to obtain an ammonia-coal collaborative blending scheme, and generate a third blending scheme;

[0011] Based on the third blending scheme, establish a cost model to form a blending scheme cost evaluation report.

[0012] In the above method, optionally, the coal quality key characteristic parameters include linear parameters and nonlinear parameters, wherein: the linear parameters include received low calorific value, dry basis sulfur content, air dry ash content, and dry ash-free volatile matter, and the nonlinear parameters include ash melting point;

[0013] In the above method, optionally, the compatibility rules include weighted summation and fuzzy mathematical evaluation processing, the linear parameters adopt the weighted summation compatibility rule, the nonlinear parameters adopt the fuzzy mathematical evaluation processing compatibility rule, and the constraint conditions include sulfur content constraint, combustion efficiency constraint, and ash melting characteristic constraint.

[0014] In the above method, optionally, the first blending scheme includes the basic proportioning of mixed coal, and the mass fraction of different coal types in the first blending scheme is defined as the reference proportioning ε.

[0015] In the above method, optionally, the load rate driven dynamic adjustment mechanism includes:

[0016] Based on the unit rated load, a three-section load rate division model is constructed:

[0017] Low load working condition interval: L%≤L min ;

[0018] Medium load working condition interval: L min <L%<L max ;

[0019] High load working condition interval: L%≥L max ;

[0020] Wherein, L% is the real-time load rate, L%= ×100%, P is the unit real-time output collected by DCS, P r is the unit rated load; L min is the low load boundary point, which is determined through a hot state test, and needs to ensure that the furnace temperature is not lower than the critical ignition point of poor-quality coal; L max is the high load boundary point, which is determined through a hot state test, and needs to ensure that the flue gas flow rate does not cause the superheating of heating surfaces or coking.

[0021] Based on the load rate range of the unit, dynamically adjust the first blending scheme:

[0022] When L% is in the low-load operating range, the amount of inferior coal blended is reduced by multiplying the base ratio ε by the load reduction correction coefficient k1; when L% is in the medium-load operating range, the base ratio ε is maintained unchanged; when L% is in the high-load operating range, the amount of inferior coal blended is increased by multiplying the base ratio ε by the load increase correction coefficient k2.

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

[0024] Set the load hysteresis range to prevent frequent switching:

[0025] In the load increasing direction, when L% switches from the low load range to the medium load range, it is necessary to meet L% ≥ L min Duration ≥ M; when L% switches from the medium load range to the high load range, it is necessary to meet L% ≥ L max Duration ≥ M. In the load reduction direction, when L% switches from the high load range to the medium load range, it is necessary to meet L% ≤ L max Duration ≥ M; when L% switches from the medium load range to the low load range, L%≤L min Duration ≥M.

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

[0027] The second blending scheme includes the mass fractions of different types of coal in the mixed coal adjusted based on the first blending scheme.

[0028] In the above method, optionally, the calorific value compensation model includes:

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

[0030] Q coal = (x 2i Q i,ar ),

[0031] Among them, Q coal is the unit calorific value of the mixed coal in the second blending scheme, x 2i Indicates the mass fraction of the i-th coal in the second blending scheme, which must satisfy x 2i =1;Q i,ar is the received low calorific value of the i-th coal;

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

[0033] =Q design -Q coal ,

[0034] in, is the unit calorific value gap of the mixed coal in the second blending scheme, Q design The fuel design for the unit is based on the low calorific value received;

[0035] when When ≤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-coal mass ratio in the ammonia-coal co-combustion scheme:

[0037] m NH3 = ,

[0038] Among them, m NH3 is the ammonia-coal mass ratio in the ammonia-coal synergistic combustion scheme, η is the combustion efficiency coefficient, ranging from 0.92 to 0.95, Q NH3 It is the low calorific value of ammonia, generally 18.6MJ / kg;

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

[0040] r NH3 = ,

[0041] Among them, r NH3 represents the mass fraction of ammonia in the third blending scheme;

[0042] x 3i = x 2i ·(1- r NH3 ),

[0043] Among them, x 3i Indicates the mass fraction of the i-th coal in the third blending scheme, which must satisfy x 3i + r NH3 =1;

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

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

[0046] In the method, optionally, the cost model comprises fuel cost, environmental protection cost and carbon emission reduction benefit.

[0047] The fuel cost comprises mixed coal cost and ammonia cost.

[0048] The environmental protection cost comprises denitration system cost, desulfurization system cost and dust removal system cost.

[0049] The carbon emission reduction benefit is calculated by introducing a coal-fired carbon emission factor, based on the reduced amount of coal entering the furnace and the carbon trading price.

[0050] The present application provides a load-responsive coal-fired boiler multi-coal-ammonia collaborative blending optimization system, which comprises:

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

[0052] A mixed coal blending scheme generation module, based on the coal quality database, constructs a coal type compatibility matrix and sets constraint conditions to quantify the mixing compatibility of different coal types. Based on the principle of giving priority to inferior coal, a multi-objective optimization algorithm is used to set the lowest mixed coal cost as the main target, and a mixed coal blending scheme is obtained based on the constraint conditions to generate a first blending scheme.

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

[0054] An ammonia blending module calculates the combustion heat value gap under the second blending scheme based on a heat value compensation model, and calculates the ammonia blending amount to obtain an ammonia-coal collaborative blending scheme to generate a third blending scheme.

[0055] A cost evaluation module establishes a cost model based on the third blending scheme to form a blending scheme cost evaluation report.

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

[0057] In view of the technical bottlenecks such as heat value constraint and lack of ammonia fuel collaborative application in the process of using inferior coal in existing coal-fired boilers, the present application constructs a systematic solution. First, by building a coal quality database and a compatibility matrix, a multi-objective optimization algorithm is used to strictly follow the principle of giving priority to inferior coal, effectively reducing the mixed coal procurement cost. At the same time, the ammonia blending heat value compensation mechanism is innovatively introduced to realize the dynamic balance of fuel cost and environmental protection investment.

[0058] To improve the system operation flexibility, the application establishes a real-time data driven adjustment mechanism of unit load rate, significantly enhances the thermal efficiency stability of the coal-fired boiler under variable working conditions, and ensures that the blending scheme can quickly respond to the grid peak shaving demand. In addition, by deeply coupling the heat value compensation model and the ammonia blending combustion technology, not only the heat value gap can be effectively made up, but also the carbon emissions can be greatly reduced, and the coal-fired unit realizes low-carbon combustion.

[0059] In terms of evaluation system, the application builds a cost model to evaluate the blending scheme in the whole cycle, realizes the precise quantitative evaluation of multi-coal and ammonia fuel collaborative blending combustion, and provides scientific decision basis for efficient and low-carbon operation of the coal-fired boiler. BRIEF DESCRIPTION OF DRAWINGS

[0060] Figure 1 The flowchart of the multi-coal and ammonia collaborative blending optimization method of the load response type coal-fired boiler provided by an embodiment of the application is shown in the figure.

[0061] Figure 2 The sub-step flowchart of step 103 in the method of the multi-coal and ammonia collaborative blending optimization method of the load response type coal-fired boiler provided by an embodiment of the application is shown in the figure. Figure 1

[0062] The sub-step flowchart of step 104 in the method of the multi-coal and ammonia collaborative blending optimization method of the load response type coal-fired boiler provided by an embodiment of the application is shown in the figure. Figure 3 Figure 1 The structure diagram of the multi-coal and ammonia collaborative blending optimization system of the load response type coal-fired boiler provided by an embodiment of the application is shown in the figure.

[0063] DETAILED DESCRIPTION Figure 4 The application will be described in detail below with reference to the accompanying drawings and specific embodiments. However, the following embodiments are only used to explain the application, and the protection scope of the application should include the entire content of the claims, and through the description of the following embodiments, those skilled in the art can fully realize the entire content of the claims of the application.

[0064] The multi-coal and ammonia collaborative blending optimization method of the load response type coal-fired boiler, as shown in the figure, comprises steps 101-105.

[0065] The multi-coal and ammonia collaborative blending optimization method of the load response type coal-fired boiler, as shown in the figure, comprises steps 101-105. Figure 1

[0066] Step 101: Obtain the key characteristic parameters of different coal types by sampling analysis method, and establish a coal quality database according to the obtained key characteristic parameters.

[0067] Representative samples of different coal types are obtained through a standardized sampling process, and the key characteristic parameters of the coal samples are obtained based on industrial analysis, element analysis and calorific value determination, including received base low-heat value, dry base sulfur content, air dry base ash content, dry ash-free base volatile matter, ash melting point and total moisture, and a coal quality database is established.​​

[0068] wherein the key characteristic parameters of the coal quality include linear parameters and nonlinear parameters, the linear parameters include received low calorific value, dry basis sulfur content, air dry ash content, dry ash-free basis volatile matter, and total moisture, and the nonlinear parameters include ash melting point;

[0069] wherein the dry ash-free basis volatile matter affects combustion stability, the received low calorific value determines energy output, the dry basis sulfur content and the air dry ash content affect environmental protection indexes, and the ash melting point reflects the tendency of slagging.

[0070] In step 102, based on the coal quality database, a coal blending matrix is constructed according to compatibility rules, and constraint conditions are set to quantify the mixing compatibility of different coal types. According to the principle of giving priority to inferior coal, a multi-objective optimization algorithm is used to set the lowest cost of mixed coal as the main target, and based on the above constraint conditions, a mixed coal blending scheme is obtained to generate a first blending scheme.

[0071] The compatibility rules include weighted summation and fuzzy mathematical evaluation processing, and the linear parameters use the weighted summation compatibility rule; and the nonlinear parameters use the fuzzy mathematical evaluation processing.

[0072] The constraint conditions include sulfur content constraint, combustion efficiency constraint, and ash melting property constraint.

[0073] In some embodiments, the constraint conditions are:

[0074] Sulfur content constraint:

[0075] ≤α%,

[0076] wherein, is the mass fraction of the i-th coal, ; is the dry basis sulfur content of the i-th coal, and α is the maximum dry basis sulfur content threshold of the coal allowed into the unit;

[0077] Combustion efficiency constraint:

[0078] ≥β%,

[0079] wherein, is the dry ash-free basis volatile matter of the i-th coal, and β is the minimum dry ash-free basis volatile matter threshold of the coal allowed into the unit;

[0080] ≤γ%,

[0081] wherein, is the air dry ash content of the i-th coal, and γ is the maximum air dry ash content threshold of the coal allowed into the unit;

[0082] Ash fusion characteristics constraints:

[0083] S t ≥θ℃,

[0084] Wherein, S t represents the ash softening temperature of the coal entering the furnace, and θ is the threshold value of the ash softening temperature of the coal entering the furnace set according to the furnace type.

[0085] Wherein, the maximum dry basis sulfur content threshold value of the coal entering the furnace allowed by the unit, the minimum dry ash-free basis volatile matter threshold value of the coal entering the furnace allowed by the unit, the maximum air-dry basis ash content threshold value of the coal entering the furnace allowed by the unit and the ash softening temperature threshold value of the coal entering the furnace are determined by comprehensive determination of the coal-fired boiler burner design parameters verification and combustion stability test.

[0086] In some embodiments, the low-quality coal satisfies any one of the following conditions:

[0087] Received basis low calorific value > [X] MJ / kg;

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

[0089] Dry basis sulfur content > [Z]%;

[0090] Total moisture > [W]%;

[0091] Wherein, [X], [Y], [Z], [W] are the critical values of the corresponding parameters in the unit fuel technical specification, and the coal species that does not satisfy any one of the above conditions is defined as power coal.

[0092] The objective function of the multi-objective optimization algorithm includes a mixed coal cost function, a sulfur content function, a combustion efficiency function and an ash fusion characteristic function, with the lowest mixed coal cost as the main objective, and the sulfur content function, the combustion efficiency function and the ash fusion characteristic function are converted into the constraint conditions by setting threshold values.

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

[0094] C total = ,

[0095] Wherein, C total represents the cost of mixed coal, and p i represents the unit price of the i-th coal.

[0096] Gradually compress the mass fraction of the power coal and increase the mass fraction of the low-quality coal, and finally obtain a first blending scheme with the lowest cost and the largest mass fraction of low-quality coal under the premise of satisfying the constraint conditions.

[0097] The first blending scheme includes mass fractions of different coal types in the mixed coal, and the mass fractions of different coal types in the first blending scheme are defined as a reference ratio, denoted by ε.

[0098] In step 103, based on the first blending scheme, unit real-time load rate data 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, referring to Figure 2 , the load rate driven dynamic adjustment mechanism includes the following steps:

[0100] In step 1031, a three-section load rate division model is constructed based on the unit rated load:

[0101] The low load working condition interval is L%≤L min ;

[0102] The medium load working condition interval is L min <L%<L max ;

[0103] The high load working condition interval is L%≥L max ;

[0104] Wherein, L% is the real-time load rate, L%= × 100%, P is the unit real-time output collected by the DCS, P r is the unit rated load; L min is the low load demarcation point, which is determined through a hot state test, and needs to ensure that the furnace temperature is not lower than the critical point of ignition of poor-quality coal; L max is the high load demarcation point, which is determined through a hot state test, and needs to ensure that the flue gas flow rate does not cause the superheating or coking of the heating surface.

[0105] In step 1032, the first blending scheme is dynamically adjusted based on the load rate interval in which the unit is located:

[0106] When L% is in the low load working condition interval, the amount of poor-quality coal blending is reduced by multiplying the reference ratio ε by a load reduction correction coefficient k1; when L% is in the medium load working condition interval, the reference ratio ε is maintained unchanged; and when L% is in the high load working condition interval, the amount of poor-quality coal blending is increased by multiplying the reference ratio ε by a load increase correction coefficient k2.

[0107] Wherein, k1 and k2 are calibrated through unit hot state experiments and operation data regression analysis, and satisfy 0≤k1<1 and k2>1;

[0108] In step 1033, a load hysteresis interval is set to prevent frequent switching:

[0109] In the load increasing direction, when L% switches from the low load operating interval to the medium load operating interval, L%≥L min must be satisfied, and the duration ≥M; when L% switches from the medium load operating interval to the high load operating interval, L%≥L max must be satisfied, and the duration ≥M. In the load decreasing direction, when L% switches from the high load operating interval to the medium load operating interval, L%≤L max must be satisfied, and the duration ≥M; when L% switches from the medium load operating interval to the low load operating interval, L%≤L min must be satisfied, and the duration ≥M.

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

[0111] The second blending scheme includes mass fractions of different coal types in the mixed coal adjusted based on the first blending scheme.

[0112] Step 104, calculating the heat value gap of the mixed coal under the second blending scheme based on the heat value compensation model, and calculating the ammonia incorporation amount to obtain an ammonia-coal collaborative blending and burning scheme, and generating a third blending scheme.

[0113] In some embodiments, referring to Figure 3 , the method comprises the following steps:

[0114] Step 1041, calculating the unit heat value of the mixed coal of the second blending scheme:

[0115] Q coal = (x 2i ·Q i,ar ),

[0116] wherein Q coal is the unit heat value of the mixed coal of the second blending scheme, x 2i represents the mass fraction of the i-th coal in the second blending scheme, and needs to satisfy x 2i =1; Q i,ar is the received basis low-heat value of the i-th coal;

[0117] Step 1042, calculating the unit heat value gap of the mixed coal of the second blending scheme:

[0118] =Q design -Q coal ,

[0119] wherein, is the unit heat value gap of the mixed coal of the second blending scheme, and Q design is the received basis low-heat value of the unit design fuel.

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

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

[0122] m NH3 = ,

[0123] wherein m NH3 is the ammonia-coal mass ratio in the ammonia-coal collaborative combustion scheme, η is the combustion efficiency coefficient, the value range is 0.92-0.95, Q NH3 is the low heat value of ammonia, generally taking 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 collaborative combustion scheme, and generate a third blending scheme:

[0125] r NH3 = ,

[0126] wherein r NH3 represents the mass fraction of ammonia in the third blending scheme;

[0127] x 3i = x 2i ·(1- r NH3 ),

[0128] wherein x 3i represents the mass fraction of the i-th coal in the third blending scheme, and needs to 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 combustion medium, and its production process is driven by renewable energy power. Raw materials are obtained through water electrolysis hydrogen production and air separation nitrogen production process, and catalytic synthesis process is adopted to realize the industrialized preparation of ammonia. The whole process meets the low carbon emission standard.

[0131] In some embodiments, the ammonia doping upper limit needs to be set: experimental studies have shown that within a certain ammonia doping ratio range, the boiler can maintain a stable combustion state and the thermal efficiency does not decrease significantly. When the system detects that the ammonia doping ratio reaches the preset upper limit, the fuel blending scheme will be automatically adjusted: the blending ratio of low-quality coal is reduced, and the blending amount of high-calorific-value power coal is correspondingly increased, to ensure that the calorific value of the fuel entering the boiler meets the requirements of the unit operation.

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

[0133] Step 105, based on the third blending scheme, a cost model is established to form a blending scheme cost evaluation report.

[0134] In some embodiments, the cost model includes fuel cost, environmental protection cost and carbon emission reduction benefit:

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

[0136] wherein P total represents the total cost of the third blending scheme, P fuel represents the fuel cost, P env represents the environmental protection cost, and R carbom represents the carbon emission reduction benefit.

[0137] The fuel cost includes the cost of mixed coal and ammonia:

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

[0139] wherein x 3i represents the mass fraction of the i-th coal in the third blending scheme, p i represents the unit price of the i-th coal, r NH3 represents the mass fraction of ammonia in the third blending scheme, and p NH3 is the unit price of ammonia.

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

[0141] p i = p base,i ·(1+νi ),

[0142] wherein p base,i is the benchmark coal price of the i-th coal, v i is the real-time coal price fluctuation coefficient of the i-th coal.

[0143] The environmental protection cost item includes the denitration system cost, the desulfurization system cost and the dust removal system cost:

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

[0145] wherein P SCR represents the denitration system cost, P FGD represents the desulfurization system cost, and P ESP represents the dust removal system cost;

[0146] In some embodiments, P SCR is dynamically calculated by collecting the reducing agent (liquid ammonia or urea) injection rate through the DCS system, combining the tax-inclusive ex-works price published by the bulk commodity trading platform, and introducing the catalyst activity decay correction coefficient; P FGD According to the real-time parameters of the limestone-gypsum wet desulfurization process, including the absorbent metering feed amount, process water consumption and desulfurization system auxiliary power consumption, the desulfurization by-product sales income is synchronously deducted for calculation; P ESP The calculation is performed by comprehensively calculating the high-voltage electric field power of the electrostatic precipitator, the dust removal system electric energy metering value and the fly ash transportation disposal contract unit price, and introducing the ash content correction coefficient.

[0147] The carbon emission reduction benefit is calculated by introducing the coal-fired carbon emission factor, based on the reduced amount of coal into the furnace due to ammonia blending combustion and the carbon trading price:

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

[0149] wherein b represents the unit coal consumption of the generating unit, EF coal represents the coal-fired carbon emission factor, and P carbon represents the carbon trading price, which is the sliding average price.

[0150] When ≤0, the carbon emission reduction benefit is taken as zero.

[0151] All cost items of the model are calculated based on the unit power generation cost.

[0152] The load response type coal-fired boiler multi-coal-ammonia collaborative blending optimization system, such asFigure 4 As shown, it comprises 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 evaluation module 405.

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

[0154] The mixed coal blending scheme generation module 402, based on the coal quality database, constructs a coal type compatibility matrix and sets constraint conditions to quantify the mixing compatibility of different coal types. Based on the principle of giving priority to low-quality coal, a multi-objective optimization algorithm is used to set the lowest mixed coal cost as the main target, and a mixed coal blending scheme is obtained based on the constraint conditions to generate a first blending scheme.

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

[0156] The ammonia blending module 404 calculates the combustion heat gap under the second blending scheme based on a heat value compensation model, and calculates the ammonia blending amount to obtain an ammonia-coal collaborative blending scheme to generate a third blending scheme.

[0157] The cost evaluation module 405 establishes a cost model according to the third blending scheme to form a blending scheme cost evaluation report.

[0158] The above is only a specific embodiment of the present application, which enables those skilled in the art to understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features applied herein.

Claims

1. A load-responsive coal-fired boiler multi-coal-ammonia synergistic blending optimization method, characterized in that: The method comprises: Obtain key characteristic parameters of different coal types through sampling and analysis methods, and establish a coal quality database 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. Based on the principle of giving priority to inferior coal, a multi-objective optimization algorithm is adopted, and the primary objective is set to minimize the cost of the mixed coal. Based on the constraints, a mixed coal blending scheme is obtained to generate a first blending scheme; Based on the first blending scheme, the real-time load rate data of the unit is introduced, and the first blending scheme is adjusted through a dynamic adjustment mechanism driven by the load rate 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 ammonia addition amount is calculated to obtain an ammonia-coal synergistic blending scheme, thereby generating a third blending scheme; Based on the third blending scheme, a cost model is established to generate a blending scheme cost assessment report.

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

3. The method according to claim 1, characterized in that The compatibility rule includes weighted summation and fuzzy mathematical evaluation processing. The linear parameters adopt the weighted summation compatibility rule; the nonlinear parameters adopt the fuzzy mathematical evaluation processing compatibility rule; The constraints include sulfur content constraint, combustion efficiency constraint and ash fusion characteristic constraint, where: The sulfur content constraint is: ≤α%， in, represents the mass fraction of the i-th coal, ; represents the dry basis sulfur content of the i-th type of coal; α is the maximum dry basis sulfur content threshold of the coal allowed to be fed into the unit; The combustion efficiency constraint is: ≥β%， in, represents the dry ash-free volatile matter of the i-th coal, and β is the minimum dry ash-free volatile matter threshold of the coal allowed to be fed into the unit; ≤γ%, in, represents the air-dried ash content of the i-th coal, and γ is the maximum air-dried ash content threshold of the coal allowed to be fed into the unit; The ash fusion characteristic constraints are: S t ≥θ℃, Among them, S t It represents the softening temperature of the coal ash entering the furnace, and θ is the softening temperature threshold of the coal ash entering the furnace set according to the furnace type.

4. The method according to claim 1, wherein The objective functions of the multi-objective optimization algorithm include a mixed coal cost function, a sulfur content function, a combustion efficiency function and an ash fusion characteristic function, with the lowest mixed coal cost as the main objective. The sulfur content function, the combustion efficiency function and the ash fusion characteristic function are converted into the constraint conditions by setting thresholds.

5. The method according to claim 1, wherein The first blending scheme includes a basic proportion of mixed coal, and the mass fractions of different coal types in the first blending scheme are defined as a base proportion ε.

6. The method according to claim 1, characterized in that The load rate driven dynamic adjustment mechanism includes: Based on the rated load of the unit, a three-stage load rate division model is constructed: Low load operating range: L%≤L min ; Medium load range: L min <L%<L max ; High load operating range: L%≥L max ; Among them, L% represents the real-time load rate, L%= ×100%, P is the real-time output of the unit collected by the Distributed Control System (DCS), P r is the rated load of the unit; L min It is the low load working condition dividing point, which is determined by hot test. It is necessary to ensure that the furnace temperature is not lower than the critical point of ignition of inferior coal; L max This is the demarcation point for high-load conditions, determined through hot tests. It is necessary to ensure that the flue gas flow rate does not cause overheating or coking on the heating surface. Based on the load rate range of the unit, dynamically adjust the first blending scheme: When L% is in the low-load operating range, the amount of inferior coal blended is reduced by multiplying the base ratio ε by the load reduction correction factor k1; when L% is in the medium-load operating range, the base ratio ε is maintained unchanged; when L% is in the high-load operating range, the amount of inferior coal blended is increased by multiplying the base ratio ε by the load increase correction factor k2; Among them, k1 and k2 are calibrated through unit hot state experiments and operation data regression analysis, satisfying 0≤k1<1, k2>1; Set the load hysteresis range to prevent frequent switching: In the load increasing direction, when L% switches from the low load range to the medium load range, it is necessary to meet L% ≥ L min Duration ≥ M; when L% switches from the medium load range to the high load range, it is necessary to meet L% ≥ L max Duration ≥ M; in the load reduction direction, when L% switches from the high load condition interval to the medium load condition interval, it is necessary to meet L% ≤ L max Duration ≥ M; when L% switches from the medium load range to the low load range, L%≤L min Duration ≥M; Wherein, M is the thermal inertia time constant of the unit, which is calibrated through step response test based on the dynamic characteristics of the unit thermal system; The second blending scheme includes the mass fractions of different types of coal in the mixed coal adjusted based on the first blending scheme.

7. The method according to claim 1, characterized in that The calorific value compensation model includes: Calculate the unit calorific value of the mixed coal in the second blending scheme: Q coal = (x 2i ·Q i,ar ), Among them, Q coal The unit calorific value of the mixed coal in the second blending scheme, x 2i Indicates the mass fraction of the i-th coal in the second blending scheme, which must satisfy x 2i =1;Q i,ar is the received low calorific value of the i-th coal; Calculate the unit calorific value gap of the mixed coal in the second blending scheme: =Q design -Q coal , in, is the unit calorific value gap of the mixed coal in the second blending scheme, Q design The fuel design for the unit is based on the low calorific value received; when When ≤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-coal mass ratio in the ammonia-coal co-combustion scheme: m NH3 = ， Among them, m NH3 represents the mass ratio of ammonia to coal in the ammonia-coal synergistic combustion scheme, η represents the combustion efficiency coefficient, ranging from 0.92 to 0.95, Q NH3 Indicates the low calorific value of ammonia, generally taken as 18.6MJ / kg; Calculate the mass fraction of ammonia and the mass fractions of different coal types in the ammonia-coal synergistic blending scheme to generate a third blending scheme: r NH3 = ， Among them, r NH3 represents the mass fraction of ammonia in the third blending scheme; x 3i = x 2i ·(1- r NH3 ), Among them, x 3i is the mass fraction of the i-th coal in the third blending scheme, which must satisfy x 3i + r NH3 =1; The third blending scheme includes mass fractions of different coal types and mass fractions of ammonia.

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

9. Load-responsive coal-fired boiler multi-coal-ammonia synergistic blending optimization system, characterized by: include: The data acquisition module obtains the 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 the coal quality database, sets constraints, quantifies the mixing compatibility of different coal types, prioritizes inferior coal, adopts a multi-objective optimization algorithm, sets the lowest mixed coal cost as the main goal, obtains the mixed coal blending scheme based on the constraints, and generates the first blending scheme; The mixed coal blending scheme optimization module introduces the real-time load rate data of the unit based on the first blending scheme, and adjusts the first blending scheme through a dynamic adjustment mechanism driven by the load rate to generate a second blending scheme; an ammonia blending module, which calculates the combustion calorific value gap under the second blending scheme based on the calorific value compensation model, and calculates the ammonia blending amount, obtains the ammonia-coal synergistic blending scheme, and generates a third blending scheme; The cost evaluation module establishes a cost model based on the third blending scheme and generates a blending scheme cost evaluation report.

Images