Method and system for evaluating energy efficiency of coal-fired boiler coupled with multi-source clean fuel and blending combustion

Abstract

The application provides a multi-source clean fuel coupling mixed combustion coal-fired boiler energy efficiency evaluation method and system, and belongs to the technical field of boiler energy efficiency evaluation. The method comprises the following steps: obtaining a plurality of energy efficiency evaluation parameters of a target boiler in a multi-source clean fuel coupling mixed combustion process; determining a weight coefficient of each energy efficiency evaluation parameter based on a preset rule. The preset rule comprises the following steps: setting a corresponding weight fluctuation influence factor and a correlation between the weight fluctuation influence factor and the weight coefficient corresponding to each energy efficiency evaluation parameter for each energy efficiency evaluation parameter; and calculating an energy efficiency evaluation value of the multi-source clean fuel coupling mixed combustion coal-fired boiler based on the plurality of energy efficiency evaluation parameters and the weight coefficient. The multi-source clean fuel coupling mixed combustion coal-fired boiler energy efficiency evaluation method and system provided by the application can improve the accuracy of coal-fired boiler energy efficiency evaluation.

Description

Technical Field

[0001] This application belongs to the field of boiler energy efficiency assessment technology, and more specifically, it relates to a method and system for assessing the energy efficiency of coal-fired boilers that use multiple clean fuels in coupled combustion. Background Technology

[0002] Multi-source clean fuel (such as ammonia, hydrogen, alcohol, biomass, etc.) coupled with coal co-firing technology can partially replace fossil fuels and reduce exhaust emissions, thus achieving the dual goals of energy efficiency and pollutant reduction.

[0003] However, due to the significant differences in composition and complex combustion characteristics of various clean fuels, their co-firing with coal involves multiple dynamically changing parameters such as fuel ratio, combustion temperature, and flue gas composition. These parameters interact with each other, resulting in significant complexity and uncertainty in boiler energy efficiency performance. Traditional energy efficiency assessment methods for coal-fired boilers are mostly designed for single fuels and cannot comprehensively reflect the energy efficiency characteristics when multiple fuels are co-firing. Assessments of co-firing with multiple fuels largely rely on previous evaluation criteria, leading to insufficient accuracy in assessing the energy efficiency of coal-fired boilers when co-firing with various clean fuels. Summary of the Invention

[0004] The purpose of this application is to provide a method and system for evaluating the energy efficiency of coal-fired boilers that use multiple clean fuels in combination, so as to improve the accuracy of energy efficiency evaluation of coal-fired boilers.

[0005] A first aspect of this application provides a method for evaluating the energy efficiency of a coal-fired boiler that involves the coupled combustion of multiple clean fuels, including:

[0006] Obtain multiple energy efficiency evaluation parameters of the target boiler during the co-firing process of multiple clean fuels;

[0007] The weight coefficient of each energy efficiency assessment parameter is determined based on preset rules. The preset rules include: setting a corresponding weight fluctuation impact factor for each energy efficiency assessment parameter and the correlation between the weight fluctuation impact factor and the weight coefficient of each energy efficiency assessment parameter.

[0008] The energy efficiency assessment value of a coal-fired boiler with co-firing of multiple clean fuels is calculated based on multiple energy efficiency assessment parameters and weighting coefficients.

[0009] A second aspect of this application provides an energy efficiency evaluation system for a coal-fired boiler that involves the coupled combustion of multiple clean fuels, comprising:

[0010] The parameter acquisition module is used to acquire multiple energy efficiency evaluation parameters of the target boiler during the co-firing process of multiple clean fuels.

[0011] The weight determination module is used to determine the weight coefficient of each energy efficiency assessment parameter based on preset rules. The preset rules include: setting a corresponding weight fluctuation impact factor for each energy efficiency assessment parameter and the correlation between the weight fluctuation impact factor and the weight coefficient of each energy efficiency assessment parameter.

[0012] The energy efficiency assessment module is used to calculate the energy efficiency assessment value of a coal-fired boiler that uses multiple energy efficiency assessment parameters and weighting coefficients in combination with clean fuels from multiple sources.

[0013] A third aspect of this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of the above-described method for evaluating the energy efficiency of a coal-fired boiler with coupled combustion of multiple clean fuels.

[0014] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method for evaluating the energy efficiency of a coal-fired boiler with coupled combustion of multiple clean fuels.

[0015] The beneficial effects of the energy efficiency evaluation method and system for coal-fired boilers with multi-source clean fuel co-firing provided in this application are as follows:

[0016] This application's embodiments determine the weight coefficient of each energy efficiency assessment parameter based on preset rules. These preset rules set a corresponding weight fluctuation influence factor for each energy efficiency assessment parameter, along with the correlation between this factor and each parameter. This considers the importance and dynamic characteristics of different parameters in the multi-source fuel coupling and co-firing process, making the determination of the weight coefficients more reasonable. Secondly, different parameters have varying degrees of influence on energy efficiency, and this influence changes with operating conditions. The weight fluctuation influence factor allows for dynamic adjustment of the weights, more accurately reflecting the impact of each parameter on energy efficiency in actual conditions. This avoids the assessment bias caused by fixed or simply set weights in traditional methods, improving the accuracy of energy efficiency assessment for coal-fired boilers. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating an energy efficiency evaluation method for a coal-fired boiler using multi-source clean fuel co-firing, provided as an embodiment of this application;

[0019] Figure 2 A flowchart illustrating another method for evaluating the energy efficiency of a coal-fired boiler using multi-source clean fuel co-firing, as provided in an embodiment of this application;

[0020] Figure 3 This is a structural block diagram of a coal-fired boiler energy efficiency evaluation system for multi-source clean fuel co-firing provided in an embodiment of this application;

[0021] Figure 4 This is a schematic block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0022] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0023] To make the objectives, technical solutions, and advantages of this application clearer, the following description will be provided in conjunction with the accompanying drawings and specific embodiments.

[0024] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating a method for evaluating the energy efficiency of a coal-fired boiler using a multi-source clean fuel co-firing system, provided in an embodiment of this application. The method can be executed by an electronic device and may include steps S101-S103.

[0025] S101: Obtain multiple energy efficiency evaluation parameters of the target boiler during the co-firing process of multiple clean fuels.

[0026] In this embodiment, the target boiler refers to a specific coal-fired boiler that requires energy efficiency evaluation. Multi-source clean fuels can include at least two of the following types: biomass fuel natural gas, methanol, hydrogen, and clean coal upgraded from coal. Co-firing refers to mixing multiple clean fuels in a certain proportion and then feeding them into the boiler for co-combustion, rather than burning a single fuel alone. The purpose is to optimize combustion efficiency, reduce pollution, or control costs. Energy efficiency evaluation parameters are indicators used to measure the boiler's energy utilization efficiency and overall performance during combustion, and can be at least one of thermal efficiency, pollutant emission reduction rate, carbon emission reduction rate, and fuel cost ratio.

[0027] In this embodiment, the pollutant emission reduction rate is used to evaluate the effect of co-firing on the reduction of pollutant (such as SO2, NO, or particulate matter) emissions. Taking the pollutant emission reduction rate as an example, it can be obtained in the following way:

[0028] First, determine the baseline emissions and measure the pollutant emission concentration (mg / m³) of the target boiler during pure coal combustion. 3 ) and flue gas emissions (m 3 / h), calculate the pollutant emissions under the baseline operating conditions, that is, emission concentration × flue gas emission.

[0029] Secondly, the emissions under co-firing conditions are measured. When multiple clean fuels are coupled and co-firing, the emission concentration and flue gas emission of the same pollutants are measured to calculate the actual emissions under co-firing conditions.

[0030] Finally, the emission reduction rate is calculated as follows: Pollutant emission reduction rate = (Baseline emissions - Blended combustion emissions) ÷ Baseline emissions × 100%. It should be noted that if multiple pollutants are involved, the emission reduction rate for each pollutant can be calculated separately, or the total emission reduction rate can be calculated by combining them according to preset weights. In this embodiment, parameters such as thermal efficiency, carbon emission reduction rate, and fuel cost ratio can be obtained using methods commonly used in the field, and will not be elaborated further in this embodiment. The fuel cost ratio is the ratio of the fuel cost in pure coal combustion to the overall fuel cost after blending, i.e., fuel cost in pure coal combustion / overall fuel cost after blending.

[0031] S102: Determine the weight coefficient of each energy efficiency assessment parameter based on preset rules. The preset rules include: setting a corresponding weight fluctuation impact factor for each energy efficiency assessment parameter and the correlation between the weight fluctuation impact factor and the weight coefficient of each energy efficiency assessment parameter.

[0032] In this embodiment, the preset rules refer to a set of pre-defined logic or standards used to determine the weighting coefficients, which specifies how to dynamically adjust the weights of each parameter according to the actual situation. Its core function is to ensure that the weighting coefficients are not fixed but change with specific factors, making the energy efficiency assessment more closely aligned with real-world scenarios and improving the accuracy of boiler energy efficiency assessments.

[0033] In this embodiment, the weight fluctuation influencing factor is the specific factor that causes the weight coefficient to change. Each energy efficiency assessment parameter corresponds to one or more unique influencing factors. For example, the weight fluctuation influencing factor for thermal efficiency could be boiler load; the influencing factor for pollutant emission reduction rate could be the proportion of high-pollution clean fuels. Correlation refers to the relationship between the weight fluctuation influencing factor and the corresponding weight coefficient of the energy efficiency assessment parameter, which can be divided into positive correlation and negative correlation.

[0034] In this embodiment, considering that the importance of each energy efficiency assessment parameter varies in different scenarios, it is necessary to dynamically adjust the weight coefficients through preset rules rather than using fixed values. Therefore, this embodiment first matches a corresponding weight fluctuation impact factor for each energy efficiency assessment parameter; clarifies the correlation between the impact factor and the parameter; and finally calculates the weight coefficient of the parameter in the current scenario based on the actual value of the impact factor and the correlation.

[0035] S103: Calculate the energy efficiency assessment value of a coal-fired boiler that uses multiple energy efficiency assessment parameters and weighting coefficients in combination with clean fuels from multiple sources.

[0036] In this embodiment, the energy efficiency assessment value refers to the comprehensive quantitative result obtained by weighting multiple assessment parameters. It is used to intuitively reflect the overall energy efficiency level of the multi-source clean fuel co-firing scheme. The higher the value, the better the overall performance of the scheme.

[0037] The essence of this process is to integrate multiple disparate indicators into a single comprehensive indicator. The core logic can be summarized as follows:

[0038] Firstly, parameter standardization can be performed, such as normalizing thermal efficiency, pollutant emission reduction rate, carbon emission reduction rate, and fuel cost ratio.

[0039] Then, a weighted summation calculation is performed. Based on the standardized evaluation parameters and corresponding weight coefficients, the energy efficiency evaluation value is calculated using the weighted summation formula.

[0040] As can be seen from the above, the embodiments of this application determine the weight coefficient of each energy efficiency assessment parameter based on preset rules. These preset rules set a corresponding weight fluctuation influence factor for each energy efficiency assessment parameter, along with the correlation between this factor and each parameter. This considers the importance and dynamic characteristics of different parameters in the multi-source fuel coupling and co-firing process, making the determination of the weight coefficients more reasonable. Secondly, different parameters have varying degrees of influence on energy efficiency, and these influences change with operating conditions. The weight fluctuation influence factor allows for dynamic adjustment of the weights, more accurately reflecting the impact of each parameter on energy efficiency in actual conditions. This avoids the assessment bias caused by fixed or simply set weights in traditional methods, thus improving the accuracy of energy efficiency assessment for coal-fired boilers.

[0041] In one embodiment of this application, the energy efficiency assessment parameters include at least one of the following: thermal efficiency, pollutant emission reduction rate, carbon emission reduction rate, and fuel cost ratio; the preset rules include at least one of the following:

[0042] The weighted fluctuation factors affecting thermal efficiency include: the load of the target boiler, and the load of the target boiler is negatively correlated with the weighting coefficient corresponding to thermal efficiency;

[0043] The weighting factors affecting the pollutant emission reduction rate include: the proportion of high-pollution clean fuels whose combustion pollutant emission rate exceeds the preset threshold among multi-source clean fuels; the proportion of high-pollution clean fuels is positively correlated with the weighting coefficient corresponding to the pollutant emission reduction rate.

[0044] The weighted fluctuation factors affecting the carbon emission reduction rate include: carbon price, and the weight coefficients of carbon price and carbon emission reduction rate are positively correlated.

[0045] The weighting factors affecting the fuel cost ratio fluctuation include: the price of each fuel, and the weighting coefficient of each fuel price is positively correlated with the corresponding fuel cost ratio.

[0046] In this embodiment, thermal efficiency is the ratio of the heat effectively utilized by the boiler to the total calorific value of the fuel, reflecting energy conversion efficiency. Pollutant emission reduction rate refers to the percentage reduction in pollutant emissions (such as sulfur oxides and nitrogen oxides) from the target boiler after blending with clean fuels compared to a benchmark value (such as pollutant emissions from the target boiler when burning only coal), reflecting environmental performance. Carbon emission reduction rate refers to the percentage reduction in carbon emissions compared to a benchmark value after blending, also reflecting environmental performance. Fuel cost ratio refers to the total cost of various clean fuels used in the blending process, reflecting economic efficiency.

[0047] In this embodiment, the preset rules may include at least one of the preset rules for thermal efficiency, preset rules for pollutant emission reduction rate, preset rules for carbon emission reduction rate, and preset rules for fuel cost ratio.

[0048] Among the preset rules for thermal efficiency, the weighted fluctuation factors affecting thermal efficiency include: the target boiler load, which is negatively correlated with thermal efficiency. The principle is as follows: when the boiler is under high load, factors such as combustion stability and heat loss have less sensitivity to thermal efficiency. For example, when running at full load, thermal efficiency is usually close to optimal, with little room for improvement; therefore, other parameters need more attention. At low load, however, incomplete combustion easily leads to a decrease in efficiency, resulting in larger fluctuations in thermal efficiency; therefore, these factors have a higher weight and require more focused evaluation.

[0049] In the preset rules for pollutant emission reduction rates, the weighting factor affecting the fluctuation of pollutant emission reduction rates is the proportion of high-pollution clean fuels among multi-source clean fuels. High-pollution clean fuels are fuels whose pollutant emission rates exceed preset pollution thresholds during combustion, such as some biomass fuels with high calorific value but high sulfur content. The preset pollution thresholds can be set based on experience or conventional judgment methods in the field. The proportion of high-pollution clean fuels is positively correlated with the weight of the pollutant emission reduction rate. The logic is: if the proportion of high-pollution clean fuels in the blended fuels is high, it indicates greater pressure on pollutant control. In this case, the pollutant emission reduction rate indicator needs to be given more attention, and its weight should be increased to ensure that the assessment results reflect environmental risks.

[0050] In the pre-defined rules for carbon emission reduction rates, the weighted fluctuation factor corresponding to the carbon emission reduction rate can be the carbon price, i.e., the price of carbon emission rights trading, reflecting the economic cost of carbon emissions. The correlation is that the weight of carbon price and carbon emission reduction rate is positively correlated. The logic is that when the carbon price rises, the economic cost of carbon emissions increases, therefore the weight of the carbon emission reduction rate needs to be increased to make the assessment results more in line with the actual needs of a low-carbon economy.

[0051] In the preset rules for the fuel cost ratio, the weighting factor for fluctuations in the fuel cost ratio can be the price of each fuel, i.e., the unit price of different clean fuels used in blending. The correlation is that fuel price and the weight of the fuel cost ratio are positively correlated. The logic is that when the price of a certain type of fuel increases, its impact on the total fuel cost ratio increases, and at this time, the weight of the fuel cost ratio needs to be increased to highlight the importance of economic efficiency in energy efficiency assessment.

[0052] As can be seen from the above, this application embodiment uses thermal efficiency, pollutant emission reduction rate, carbon emission reduction rate, and fuel cost ratio as energy efficiency evaluation parameters to evaluate the energy efficiency of coal-fired boilers with multi-source clean fuel co-firing from multiple key dimensions such as energy conversion efficiency, environmental performance, and economy. Thermal efficiency reflects the boiler's energy utilization level and is a core indicator for measuring energy conversion efficiency; pollutant emission reduction rate and carbon emission reduction rate reflect the boiler's environmental performance, meeting the current requirements for strict control of pollutant and carbon emissions; fuel cost ratio focuses on economic efficiency and is crucial for cost control in actual production and operation. By comprehensively considering these parameters, a comprehensive and accurate picture of the boiler's energy efficiency when co-firing multiple fuels can be depicted, avoiding the one-sidedness caused by the single parameter in traditional evaluation methods and improving the accuracy of coal-fired boiler energy efficiency evaluation. This application embodiment sets corresponding weight fluctuation influence factors based on preset rules according to the characteristics of different parameters and clarifies their positive and negative correlation with the parameters, so that the weights can be dynamically adjusted according to actual operating conditions, improving the accuracy of coal-fired boiler energy efficiency evaluation.

[0053] In one embodiment of this application, determining the weighting coefficient for each energy efficiency assessment parameter based on preset rules includes:

[0054] Obtain the weighted fluctuation impact factors corresponding to each energy efficiency assessment parameter;

[0055] The weighting coefficient for each energy efficiency assessment parameter is determined based on the correlation between the weighting fluctuation impact factor and each energy efficiency assessment parameter, specifically including at least one of the following:

[0056] Based on the positive correlation between the load of the target boiler and the weighting coefficients corresponding to the load and thermal efficiency of the target boiler, the weighting coefficient α of the thermal efficiency of the target boiler is determined.

[0057] Based on the positive correlation between the proportion of high-pollution clean fuels and the weighting coefficients corresponding to the pollutant emission reduction rate, the weighting coefficient β of the pollutant emission reduction rate is determined.

[0058] Based on the positive correlation between carbon price and the weighting coefficients corresponding to carbon emission reduction rate, the weighting coefficient γ of carbon emission reduction rate is determined.

[0059] Based on the positive correlation between the prices of each fuel and the weighting coefficients corresponding to the fuel cost ratio, the weighting coefficient δ of the fuel cost ratio is determined.

[0060] In this embodiment, the weighted fluctuation influencing factors can be the target boiler's load, the proportion of high-pollution clean fuels, the carbon price, and the prices of various fuels. The target boiler's load can be obtained based on its built-in operation monitoring system, such as a DCS distributed control system that collects load data in real time. The system records parameters such as the boiler's real-time steam flow and pressure to calculate the load value. If an automatic monitoring system is lacking, the boiler's output data can be manually recorded periodically, such as recording the actual evaporation rate hourly, comparing it with the rated evaporation rate to calculate the load, and storing the manually obtained data in the memory of the electronic device executing this method. When the electronic device needs to retrieve this data, it can access the data in the memory and perform further calculations based on it. The proportion of high-pollution clean fuels, the carbon price, and the prices of various fuels can be obtained using the same method; the carbon price and the prices of various fuels can also be obtained based on network information.

[0061] In this embodiment, the weighting coefficient of each energy efficiency assessment parameter can be determined based on mapping relationships or linear relationships, and the intercept and slope of each linear relationship can be determined based on multiple experiments or experience. The weighting coefficient of the pollutant emission reduction rate can also be determined based on the following formula:

[0062] ,in, The weighting coefficients representing the pollutant emission reduction rate The maximum value of the weighting coefficient representing the pollutant emission reduction rate. The minimum value of the weighting coefficient representing the pollutant emission reduction rate. This indicates the proportion of high-pollution clean fuels. This represents a critical value indicating the proportion of high-polluting fuels. If the proportion of high-polluting fuels reaches this value, it indicates a relatively high proportion of high-polluting fuels. It is a natural constant. This represents the correction factor, which can be equal to 3 to control the rate of curve growth.

[0063] In this embodiment, the formula uses an exponential growth curve, when hour, It has no high-polluting fuels and the lowest emission reduction rate weight; when When it increases, As the proportion of high-polluting fuels gradually increases, the higher the proportion of high-polluting fuels, the faster the weight of emission reduction rates increases. The exponential curve's characteristic of being fast at first and then slowing down fits this scenario: in the low-proportion stage, such as... It is between 0 and 0.2. Rapidly increase to warn of risks; high-percentage stage, such as It is between 0.3 and 0.5. The growth rate was slowed down to avoid excessive weighting that squeezed out the proportion of other parameters.

[0064] In this embodiment, the energy efficiency assessment value of a coal-fired boiler co-firing multiple clean fuels can be calculated based on the following formula: η = α × boiler thermal efficiency + β × pollutant emission reduction rate + γ × carbon emission reduction rate + δ × fuel cost ratio. η represents the energy efficiency assessment value of the coal-fired boiler co-firing multiple clean fuels, and the boiler thermal efficiency, pollutant emission reduction rate, carbon emission reduction rate, and fuel cost ratio in the formula are all numerical values.

[0065] As can be seen from the above, the embodiments of this application take into account the multiple dynamically changing parameters involved in the co-firing process of multi-source clean fuels, such as fuel ratio, combustion temperature, and flue gas composition, resulting in complex and variable operating conditions. The embodiments of this application determine the weighting coefficients based on weighting influence factors, which can comprehensively consider the impact of various energy efficiency assessment parameters under different operating conditions. For example, under different boiler loads, the weight of thermal efficiency will be dynamically adjusted according to the relationship between load and thermal efficiency; when the proportion of high-pollution clean fuels changes, the weight of pollutant emission reduction rate will change accordingly; fluctuations in carbon price and fuel price will also affect the weights of carbon emission reduction rate and fuel cost ratio, enabling the energy efficiency assessment to more comprehensively and accurately reflect the actual energy efficiency of the boiler under different operating conditions, thus improving the accuracy of coal-fired boiler energy efficiency assessment.

[0066] In one embodiment of this application, the energy efficiency evaluation method for a coal-fired boiler with multi-source clean fuel co-firing further includes: determining the standard value of the weight coefficient of each energy efficiency evaluation parameter. The standard value is the value of the weight coefficient of each energy efficiency evaluation parameter when the weight fluctuation influence factor corresponding to each energy efficiency evaluation parameter is within a preset range, wherein the standard value of α is 0.4, the standard value of β is 0.2, the standard value of γ is 0.3, and the standard value of δ is 0.1.

[0067] In this embodiment, the standard value refers to the benchmark value of the weight coefficient when each weight fluctuation influencing factor is within a preset range. The preset range refers to the normal and stable interval of the weight fluctuation influencing factors, such as: boiler load is 80%-100% of the design load, the proportion of high-pollution fuel is ≤10%, and the carbon price is within the normal market fluctuation range, etc. In this case, there is no need to adjust the weight, and the standard value is directly adopted.

[0068] In one embodiment of this application, determining the weighting coefficient for each energy efficiency assessment parameter based on preset rules includes:

[0069] Determine whether the weight fluctuation impact factor corresponding to each energy efficiency assessment parameter is within the preset range;

[0070] In response to the fact that the weight fluctuation impact factor corresponding to the energy efficiency assessment parameter is not within the preset range, the weight coefficient corresponding to the energy efficiency assessment parameter is adjusted according to the preset step size based on the standard value and the correlation between the weight fluctuation impact factor and each energy efficiency assessment parameter.

[0071] In this embodiment, when the weight fluctuation impact factor corresponding to the energy efficiency assessment parameter is within a preset range, the aforementioned positive or negative correlation adjustment can be omitted, and the standard value of the weight coefficient of each energy efficiency assessment parameter can be directly determined as the weight coefficient of the corresponding energy efficiency assessment parameter. When the weight fluctuation impact factor corresponding to the energy efficiency assessment parameter is not within the preset range, the updated weight coefficient can be determined based on the aforementioned mapping relationship, linear relationship, or specific formula, and the weight coefficient can also be adjusted based on a preset step size.

[0072] When adjusting the weighting coefficients for different energy efficiency assessment parameters, the preset step size can be the same or different. The preset step size for different energy efficiency assessment parameters can be set based on experience.

[0073] In this embodiment, the standard value is the default configuration in the evaluation method, applicable to scenarios where the weight fluctuation impact factor is within a normal range. The logic is: under stable operating conditions, priority is given to ensuring energy efficiency and carbon emission reduction, followed by environmental protection and economic efficiency. When the impact factor exceeds the normal range, the weight coefficients will be dynamically adjusted, but the standard value always serves as the benchmark and reference point for adjustment, ensuring a balance between consistency and flexibility in the evaluation.

[0074] It should be noted that the adjusted weights should be normalized to ensure that the sum of the weight coefficients of each energy efficiency assessment parameter is 1.

[0075] As can be seen from the above, the embodiments of this application clearly provide standard values ​​for the weight coefficients of each energy efficiency assessment parameter. These standard values ​​are benchmark values ​​determined when the fluctuation influencing factors of each weight are within a preset range (a normal and stable interval). When the boiler is operating under stable conditions, such as boiler load at 80%-100% of design load, high-pollution fuel proportion ≤10%, and carbon price within the normal market fluctuation range, the standard values ​​are directly used as weight coefficients. This provides a stable reference benchmark for energy efficiency assessment, avoiding fluctuations in assessment results caused by frequent adjustments to weight coefficients under normal operating conditions, reducing unnecessary operations and adjustments, and making the assessment system more stable and reliable.

[0076] In one embodiment of this application, when more than two types of clean fuels are co-fired, the energy efficiency evaluation method for coal-fired boilers with co-fired multiple clean fuels further includes:

[0077] The interaction factor λ is determined based on the following formula;

[0078] λ=1+k1×(ΔT0–ΔT)-k2×(S-S0)+k3×(f / f0-1)

[0079] Wherein, ΔT0 is the cross-sectional temperature difference of a pure coal-fired furnace, ΔT is the measured cross-sectional temperature difference under co-firing conditions, S0 is the coking index of a pure coal-fired furnace, S is the actual coking index of the furnace, f0 is the reference flame pulsation frequency of pure coal-fired furnace, f is the measured main flame pulsation frequency, k1 is the temperature difference gain coefficient, k2 is the coking penalty coefficient, and k3 is the frequency gain coefficient.

[0080] The energy efficiency assessment value is updated based on the interaction factors.

[0081] In this embodiment, when more than two types of clean fuels are co-combined, it indicates that the differences in physicochemical properties between the fuels are greater, and the interactions during combustion are more complex. Therefore, it is necessary to consider the impact of multiple fuel interactions on energy efficiency. Thus, an interaction factor is introduced to quantify the synergistic or antagonistic effects of multiple fuel mixtures during combustion, addressing the shortcomings of traditional assessments that neglect inter-fuel interactions. λ>1 indicates that the interaction improves energy efficiency, λ<1 indicates that the interaction reduces energy efficiency, and λ=1 indicates no effect.

[0082] ΔT0 is the temperature difference across different sections of the boiler furnace during pure coal combustion, reflecting the uniformity of temperature distribution during pure coal combustion; ΔT is the measured temperature difference across the furnace cross-section during multi-fuel blending, reflecting the temperature distribution during blending. S0 is the degree index of furnace coking during pure coal combustion; the larger the value, the more severe the coking; S is the actual coking index during blending. f0 is the reference pulsation frequency of flame combustion during pure coal combustion, reflecting combustion stability; f is the measured main flame pulsation frequency during blending.

[0083] k1, k2, and k3 are correction coefficients. k1 amplifies the effect of temperature difference changes on the interaction, k2 amplifies the effect of coking changes on the interaction, and k3 amplifies the effect of flame frequency changes on the interaction. Among them, k1 is a gain coefficient, which strengthens the positive effect of temperature difference improvement; k2 is a penalty coefficient, which strengthens the negative effect of increased coking; and k3 is a gain coefficient, which strengthens the effect of flame stability changes.

[0084] The formula for the interaction factor λ originates from a mathematical abstraction of the physical essence of multi-fuel coupled combustion. Its core logic is to transform complex nonlinear interactions into quantifiable calculations by decoupling the perturbation effects of three key physical fields (temperature field uniformity, coking risk, and combustion stability). Furthermore, these three indicators are orthogonal and have low correlation, avoiding redundant corrections. All formulas for determining the interaction factor λ involve numerical calculations. k1, k2, and k3 can be determined based on multiple experiments or set empirically.

[0085] The logic of the k1×(ΔT0–ΔT) term is as follows: if ΔT0–ΔT>0, that is, the temperature difference is smaller and the temperature distribution is more uniform during co-firing, this term is positive, λ increases, and the interaction improves energy efficiency; otherwise, λ decreases and the interaction reduces energy efficiency.

[0086] The logic of the -k2×(S-S0) term is as follows: S-S0>0 indicates that coking is more severe when coking is done in blended coal than when burning pure coal. This term is negative, λ decreases, coking worsens and damages energy efficiency, resulting in a negative interactive effect; if S-S0<0, it means that coking is reduced, this term is positive, λ increases, indicating a positive effect.

[0087] The logic of the k3×(f / f0-1) term is as follows: f / f0 represents the ratio of the measured flame frequency to the pure coal reference frequency. If f / f0>1, it means that the combustion is more active, λ increases, and it indicates a positive impact.

[0088] By incorporating the aforementioned interactive factors into the calculation formula for the energy efficiency assessment value of a coal-fired boiler with multi-source clean fuel co-firing, the updated energy efficiency assessment value can be obtained. For example: η=[α×boiler thermal efficiency+β×pollutant emission reduction rate+γ×carbon emission reduction rate+δ×fuel cost ratio]×λ.

[0089] In one embodiment of this application, when only two fuels are blended, the formula for λ can also be:

[0090] ,in, Indicates the interaction factor. This indicates the total number of clean fuel types used in co-firing. . Indicates the first The blending ratio of the fuel, that is, the first The mass percentage of each type of fuel Indicates the first The blending ratio of the fuel, that is, the first The mass percentage of each type of fuel. Indicates the first Type of fuel and the first The interaction coefficient of the fuels. Indicates the first Type of fuel and the first The difference between the actual thermal efficiency improvement and the theoretical thermal efficiency improvement of the mixed combustion of the two fuels. This indicates the baseline thermal efficiency, such as the thermal efficiency when burning pure coal, or the design rated thermal efficiency. Indicates the first Type of fuel and the first The difference between the actual pollutant emissions and the theoretical pollutant emissions from the combustion of a mixture of fuels. This represents the baseline pollutant emissions, such as the pollutant emission concentration during pure coal combustion, or the national standard limit. The theoretical thermal efficiency improvement can be equal to the weighted sum of the thermal efficiencies of a single fuel, and the theoretical pollutant emissions can be equal to the weighted sum of the pollutant emissions of a single fuel.

[0091] In this embodiment, Indicates the first Type of fuel and the first When two fuels are blended and burned, the deviation coefficient relative to the ideal superposition effect of the two fuels burning individually is used to reflect the intensity of their interaction. When This indicates that the mixing of two fuels produces a synergistic effect, such as a more significant increase in thermal efficiency and a greater reduction in pollutant emissions; when : Indicates that the blending effect is an ideal superposition (without interactive effects); when This indicates that mixing two fuels produces antagonistic effects, such as decreased thermal efficiency and increased pollutant emissions.

[0092] In this embodiment, the first result can be obtained through multiple sets of co-firing experiments. Type of fuel and the first The energy efficiency evaluation parameters of various fuels at different blending ratios, such as actual thermal efficiency and actual pollutant emission reduction rate, are compared with the ideal superimposed values. Specifically:

[0093] Step 1: Measure the first... Thermal efficiency of a single fuel (Other parameter values ​​are also possible; this embodiment uses thermal efficiency as an example.) and the first Thermal efficiency of a single fuel ;

[0094] Step 2: According to proportion and Mixed combustion Type of fuel and the first Various fuels were tested to determine their actual thermal efficiency. ;

[0095] Step 3: Calculate the ideal superposition value. ;

[0096] Step 4: Define the interaction coefficient: .

[0097] This embodiment uses thermal efficiency as an example, but it can also be replaced by parameters such as pollutant emission reduction rate and carbon emission reduction rate, taking the average value of multiple experiments as the result. The final value.

[0098] In the above formula, through and Simultaneously considering the synergistic effect of combustion efficiency and environmental protection, through Amplify the interaction between high proportions of fuels, through Distinguish between synergistic / antagonistic relationships between fuels to avoid misjudging the interaction effects of different fuel combinations.

[0099] In this embodiment, considering that only two fuels are coupled and blended, the specific values ​​of the two fuels can be used as a basis for calculation. When two or more fuels are coupled and blended, due to experimental costs and other issues, it is impossible to completely measure the data of each fuel when blended in different proportions. Therefore, the results can be determined through more macroscopic state changes. This method is better suited for assessing the overall combustion state of a mixture of multiple fuels, eliminating the need to distinguish between specific interactions between two fuels. It focuses solely on the deviation between the overall combustion effect and pure coal combustion. In other words, the two formulas are not contradictory but rather quantify the impact of multi-fuel co-firing from different dimensions. When there are more than two fuel types, the overall coupling effect can be indirectly reflected through the macroscopic state of the furnace, without needing to distinguish between individual fuel interactions. This is more suitable for scenarios involving multi-fuel co-firing that require rapid assessment. When there are only two fuel types, the coupling effect can be directly quantified through the specific interactions between fuels. This is more suitable for scenarios with fewer fuel types where precise analysis of the impact of specific fuels on energy efficiency assessment results is required.

[0100] As can be seen from the above, the embodiments of this application take into account that when multiple clean fuels are coupled and there are more than two types of clean fuels, the differences in physicochemical properties between fuels increase significantly, and the interactions during combustion are more complex. Traditional energy efficiency assessment methods often ignore this complex interaction between fuels, resulting in inaccurate and incomplete assessment results. This application introduces an interaction influence factor λ, and uses a specific formula to quantify the synergistic or antagonistic effects of multiple fuels in mixed combustion from three key physical field dimensions: temperature field uniformity (temperature difference in furnace cross section), coking risk (furnace coking index), and combustion stability (flame pulsation frequency). This allows the energy efficiency assessment to more realistically reflect the actual situation of multi-fuel blending, thereby improving the accuracy of energy efficiency assessment for coal-fired boilers.

[0101] In one embodiment of this application, the energy efficiency evaluation method for coal-fired boilers with multi-source clean fuel co-firing further includes at least one of the following:

[0102] Determine whether the energy efficiency assessment value is less than the first threshold; in response to the energy efficiency assessment value being less than the first threshold, reduce the blending ratio of each clean fuel in the coupled combustion according to the set first priority order;

[0103] Determine whether the energy efficiency assessment value is greater than or equal to the second threshold; in response to the energy efficiency assessment value being greater than or equal to the second threshold, increase the blending ratio of each clean fuel in the coupled combustion according to the set second priority order.

[0104] In this embodiment, the first threshold is a critical value for judging whether the energy efficiency meets the standard. When the energy efficiency assessment value is lower than this value, it indicates that the current co-firing scheme has obvious problems (such as low efficiency, excessive pollution, etc.) and needs to be adjusted. The second threshold is a critical value for judging whether the energy efficiency is good. When the energy efficiency assessment value reaches or exceeds this value, it indicates that the current co-firing scheme is effective.

[0105] In this embodiment, the first priority order refers to the order in which each fuel is reduced when the amount of clean fuel blended needs to be reduced. For example, high-cost fuels can be reduced first, followed by fuels with lower emission reduction effects; that is, the blending of fuels with the highest unit calorific value cost is reduced first. The second priority order refers to the order in which each fuel is increased when the amount of clean fuel blended needs to be increased. For example, fuels with higher emission reduction effects can be increased first, followed by low-cost fuels; that is, the proportion of fuels is increased according to their carbon emission reduction benefits. The first threshold can be 0.9, and the second threshold can be 1.1.

[0106] In this embodiment, when the energy efficiency assessment value is less than the first threshold, meaning the overall performance of the current blending scheme is poor, the proportion of clean fuel blending is reduced in the first priority order. This can reduce the negative impact of fuels with low energy efficiency contributions on the combustion process, gradually improving overall energy efficiency. When the energy efficiency assessment value is greater than or equal to the second threshold, meaning the overall performance of the current blending scheme is excellent, the proportion of clean fuel blending can be increased in the second priority order. By increasing fuels with high energy efficiency contributions, the advantages of clean fuels, such as improving emission reduction rates and reducing carbon emissions, can be further amplified. For example, fuels with higher emission reduction effects can be increased first to further amplify the advantages of clean fuels.

[0107] As can be seen from the above, the embodiments of this application set two key indicators, a first threshold and a second threshold, providing a clear and scientific classification standard for the energy efficiency evaluation of coal-fired boilers with multi-source clean fuel co-firing. The first threshold serves as a critical value for judging whether the energy efficiency meets the standard. When the energy efficiency evaluation value is lower than this value, it indicates that there is a problem with the current co-firing scheme and it needs to be adjusted in time. The second threshold can judge whether the energy efficiency is good. When the energy efficiency evaluation value reaches or exceeds this value, it means that the current co-firing scheme is effective and has the potential for further optimization. Therefore, when the energy efficiency evaluation value is less than the first threshold, and it is necessary to reduce the co-firing ratio of each clean fuel in the co-firing, the operation is carried out according to the set first priority order, that is, the fuel with the highest unit calorific value cost is reduced first. For example, high-cost fuels are reduced first, and then fuels with low emission reduction effect are reduced. Taking cost factors into account, by reducing fuels with low energy efficiency contribution and high cost, the negative impact can be reduced while effectively controlling costs. When the energy efficiency assessment value is greater than or equal to the second threshold, and it is necessary to increase the blending ratio of each clean fuel in the coupled combustion, the fuel ratio shall be increased in accordance with the set second priority order, that is, according to the carbon emission reduction benefit. For example, fuels with high emission reduction effect shall be increased first, followed by low cost fuels. The focus is on giving full play to the advantages of clean fuels. By increasing fuels that have high energy efficiency contribution and good emission reduction effect, the advantages of clean fuels shall be further amplified, such as improving the emission reduction rate and reducing carbon emissions.

[0108] In one embodiment of this application, the energy efficiency evaluation method for coal-fired boilers with multi-source clean fuel co-firing further includes:

[0109] In response to the detection that the current combustion parameters meet the preset safety constraints, a blending ratio downgrade command is triggered to execute the corresponding blending ratio downgrade operation.

[0110] The current combustion parameters meet at least one of the preset safety constraints:

[0111] Hydrogen volume concentration >1%, ammonia slip >10ppm, biomass moisture content >30%, and methanol incomplete vaporization rate >5%.

[0112] In this embodiment, the preset safety constraints refer to the critical values ​​of combustion parameters set in advance to ensure the safe and stable operation of the boiler. When the actual parameters exceed these values, safety risks may arise, such as explosions, equipment damage, and combustion runaway. Current combustion parameters refer to indicators related to the combustion state monitored in real time during boiler operation, including hydrogen volume concentration, ammonia slip, biomass moisture content, and methanol incomplete vaporization rate, which directly reflect the safety of the combustion system. The blending ratio downgrade instruction refers to an instruction automatically generated by the system when the safety constraints are triggered, reducing the blending ratio of clean fuels, such as increasing the coal ratio to ensure basic combustion stability. The purpose is to reduce safety hazards by decreasing the amount of high-risk fuels used. The blending ratio downgrade operation refers to the specific actions performed to execute the downgrade instruction, such as adjusting the fuel valve opening or changing the feeding speed, ultimately reducing the proportion of clean fuels in the total fuel and bringing the combustion state back to a safe range.

[0113] In this embodiment, the hydrogen volume concentration is >1%: Hydrogen is a flammable gas. When the hydrogen concentration in the furnace or flue exceeds 1%, it may explode upon contact with an open flame, which is a high-risk indicator. Ammonia escape rate >10ppm: Excessive ammonia escape (commonly used for denitrification) (exceeding 10ppm) may react with acidic substances in the flue gas to form corrosive ammonium salts, damaging the boiler's heating surfaces. Ammonia itself is also toxic. Biomass moisture content >30%: Excessive moisture content in biomass fuel (exceeding 30%) will lead to a decrease in combustion temperature and incomplete combustion, potentially causing furnace coking, water carryover in the flue gas, and other problems, affecting stable equipment operation. Methanol incomplete vaporization rate >5%: When methanol is not fully vaporized (exceeding 5%), liquid methanol entering the furnace may cause localized low-temperature flameout or high-temperature deflagration, compromising combustion stability.

[0114] In this embodiment, safety risks can be mitigated by reducing the proportion of clean fuels used in combustion. For example, when the hydrogen volume concentration exceeds the limit, the proportion of hydrogen used in combustion can be reduced to lower the hydrogen concentration in the furnace. Similarly, when the biomass moisture content is too high, the amount of biomass used in combustion can be reduced to avoid coking or flameout caused by combustion deterioration.

[0115] As can be seen from the above, the embodiments of this application, by monitoring hydrogen volume concentration, ammonia escape, biomass moisture content, and methanol incomplete vaporization rate, can promptly detect potential safety hazards, providing comprehensive protection for the safe and stable operation of the boiler and improving the reliability of safety management.

[0116] In one embodiment of this application, please refer to Figure 2To address the limitations of existing technologies that rely on a single evaluation dimension and struggle to quantify the interactions between multiple fuels, this application employs four dimensions for final energy efficiency assessment: thermal efficiency, environmental friendliness, carbon emission reduction, and economic efficiency. Furthermore, it establishes a dynamically adjusted base for weighting coefficients. Finally, this application considers the interactions between fuels, employing orthogonal decoupling through three parameters: temperature difference, coking, and flame pulsation—the aforementioned interaction influence factors. Ultimately, a comprehensive energy efficiency index is determined, which represents the energy efficiency assessment value for a coal-fired boiler co-fired with multiple clean fuels.

[0117] Corresponding to the energy efficiency evaluation method for coal-fired boilers with multi-source clean fuel co-firing in the above embodiments, Figure 3 This is a structural block diagram of a coal-fired boiler energy efficiency evaluation system for the coupled combustion of multiple clean fuels, provided as an embodiment of this application. For ease of explanation, only the parts relevant to the embodiment of this application are shown. References Figure 3 The energy efficiency evaluation system 20 for coal-fired boilers with multi-source clean fuel coupling includes: parameter acquisition module 21, weight determination module 22 and energy efficiency evaluation module 23.

[0118] Among them, the parameter acquisition module 21 is used to acquire multiple energy efficiency evaluation parameters of the target boiler during the multi-source clean fuel coupling and co-firing process;

[0119] The weight determination module 22 is used to determine the weight coefficient of each energy efficiency assessment parameter based on preset rules. The preset rules include: setting a corresponding weight fluctuation impact factor for each energy efficiency assessment parameter and the correlation between the weight fluctuation impact factor and the weight coefficient of each energy efficiency assessment parameter.

[0120] The energy efficiency assessment module 23 is used to calculate the energy efficiency assessment value of a coal-fired boiler that uses multiple energy efficiency assessment parameters and weighting coefficients in combination with clean fuels from multiple sources.

[0121] In one embodiment of this application, the energy efficiency assessment parameters include at least one of the following: thermal efficiency, pollutant emission reduction rate, carbon emission reduction rate, and fuel cost ratio; the preset rules include at least one of the following:

[0122] The weighted fluctuation factors affecting thermal efficiency include: the load of the target boiler, and the load of the target boiler is negatively correlated with the weighting coefficient corresponding to thermal efficiency;

[0123] The weighting factors affecting the pollutant emission reduction rate include: the proportion of high-pollution clean fuels whose combustion pollutant emission rate exceeds the preset threshold among multi-source clean fuels; the proportion of high-pollution clean fuels is positively correlated with the weighting coefficient corresponding to the pollutant emission reduction rate.

[0124] The weighted fluctuation factors affecting the carbon emission reduction rate include: carbon price, and the weight coefficients of carbon price and carbon emission reduction rate are positively correlated.

[0125] The weighting factors affecting the fuel cost ratio fluctuation include: the price of each fuel, and the weighting coefficient of each fuel price is positively correlated with the corresponding fuel cost ratio.

[0126] In one embodiment of this application, the weight determination module 22 is specifically used to obtain the weight fluctuation impact factor corresponding to each energy efficiency assessment parameter;

[0127] The weighting coefficient for each energy efficiency assessment parameter is determined based on the correlation between the weighting fluctuation impact factor and each energy efficiency assessment parameter, specifically including at least one of the following:

[0128] Based on the negative correlation between the load of the target boiler and the weighting coefficients corresponding to the load and thermal efficiency of the target boiler, the weighting coefficient α of the thermal efficiency of the target boiler is determined.

[0129] Based on the positive correlation between the proportion of high-pollution clean fuels and the weighting coefficients corresponding to the pollutant emission reduction rate, the weighting coefficient β of the pollutant emission reduction rate is determined.

[0130] Based on the positive correlation between carbon price and the weighting coefficients corresponding to carbon emission reduction rate, the weighting coefficient γ of carbon emission reduction rate is determined.

[0131] Based on the positive correlation between the prices of each fuel and the weighting coefficients corresponding to the fuel cost ratio, the weighting coefficient δ of the fuel cost ratio is determined.

[0132] In one embodiment of this application, the energy efficiency evaluation system 20 for coal-fired boilers with multi-source clean fuel coupling and co-firing further includes: a standard value determination module, used to determine the standard value of the weight coefficient of each energy efficiency evaluation parameter. The standard value is the value of the weight coefficient of each energy efficiency evaluation parameter when the weight fluctuation influence factor corresponding to each energy efficiency evaluation parameter is within a preset range, wherein the standard value of α is 0.4, the standard value of β is 0.2, the standard value of γ is 0.3, and the standard value of δ is 0.1.

[0133] In one embodiment of this application, the weight determination module 22 is further used to determine whether the weight fluctuation impact factor corresponding to each energy efficiency assessment parameter is within a preset range;

[0134] In response to the fact that the weight fluctuation impact factor corresponding to the energy efficiency assessment parameter is not within the preset range, the weight coefficient corresponding to the energy efficiency assessment parameter is adjusted according to the preset step size based on the standard value and the correlation of each energy efficiency assessment parameter based on the weight fluctuation impact factor.

[0135] In one embodiment of this application, the coal-fired boiler energy efficiency evaluation system 20 for co-firing multiple clean fuels further includes: an interaction module, used to determine the interaction factor λ based on the following formula when there are more than two types of clean fuels co-firing;

[0136] λ=1+k1×(ΔT0–ΔT)-k2×(S-S0)+k3×(f / f0-1)

[0137] Wherein, ΔT0 is the cross-sectional temperature difference of a pure coal-fired furnace, ΔT is the measured cross-sectional temperature difference under co-firing conditions, S0 is the coking index of a pure coal-fired furnace, S is the actual coking index of the furnace, f0 is the reference flame pulsation frequency of pure coal-fired furnace, f is the measured main flame pulsation frequency, k1 is the temperature difference gain coefficient, k2 is the coking penalty coefficient, and k3 is the frequency gain coefficient.

[0138] The energy efficiency assessment value is updated based on the interaction factors.

[0139] In one embodiment of this application, the coal-fired boiler energy efficiency evaluation system 20 for co-firing multiple clean fuels further includes: a co-firing ratio adjustment module, used to determine whether the energy efficiency evaluation value is less than a first threshold; in response to the energy efficiency evaluation value being less than the first threshold, reducing the co-firing ratio of each clean fuel in the co-firing according to a set first priority order;

[0140] And / or, determine whether the energy efficiency assessment value is greater than or equal to the second threshold; in response to the energy efficiency assessment value being greater than or equal to the second threshold, increase the blending ratio of each clean fuel in the coupled co-firing according to the set second priority order.

[0141] In one embodiment of this application, the coal-fired boiler energy efficiency evaluation system 20 with multi-source clean fuel co-firing further includes: a safety constraint module, used to trigger a co-firing ratio downgrade command in response to detecting that the current combustion parameters meet preset safety constraint conditions, so as to execute the corresponding co-firing ratio downgrade operation.

[0142] In one embodiment of this application, the current combustion parameters satisfying preset safety constraints include at least one of the following:

[0143] Hydrogen volume concentration >1%, ammonia slip >10ppm, biomass moisture content >30%, and methanol incomplete vaporization rate >5%.

[0144] Corresponding to the energy efficiency evaluation method for coal-fired boilers with co-firing of multiple clean fuels in the above embodiments, one embodiment of this application may further include an energy efficiency evaluation device for coal-fired boilers with co-firing of multiple clean fuels. This energy efficiency evaluation device may include: an operation data and design document analysis module, a furnace temperature distribution monitoring module, a coking index analysis module, a flame pulsation frequency analysis module, and a pollutant emission concentration analysis module. The operation data and design document analysis module, the furnace temperature distribution monitoring module, the coking index analysis module, the flame pulsation frequency analysis module, and the pollutant emission concentration analysis module can all essentially be devices such as computers or servers.

[0145] The operational data and design document analysis module can acquire and analyze ΔT0, S0, and f0 from various data sources. Specifically, ΔT0 is determined by retrieving historical data from the power plant's DCS system and analyzing the 30-day moving average of the furnace cross-sectional temperature difference under pure coal-fired conditions; S0 is determined by taking the coking index of the corresponding coal type from the boiler manufacturer's design manual; and f0 is determined by calculating the average flame dominance frequency over the most recent 30 days under pure coal-fired conditions from the flame detection system database.

[0146] The furnace temperature distribution monitoring module is used to continuously collect temperature data based on at least 8 thermocouples, and to analyze the measured cross-sectional temperature difference under co-firing conditions. Multiple thermocouples are evenly distributed circumferentially at the burner outlet cross-section.

[0147] The coking index analysis module uses an acoustic temperature measurement array and an ultrasonic thickness gauge (placed on the high-temperature superheater tube wall) to obtain ash deposition rate data, and an online laser ash fusion analyzer to obtain the ash fusion characteristic coefficient. Ash deposition rate is a direct, real-time monitoring indicator of coking severity, directly reflecting the coking state and serving as a crucial basis for index assessment. The ash fusion characteristic coefficient is a fundamental physical property parameter for predicting coking tendency and risk; a preliminary coking index or risk level can be derived through comparison with the furnace temperature field or empirical formulas. In this embodiment, the furnace coking index can be determined based on the ash deposition rate and the ash fusion characteristic coefficient. A mapping relationship or calculation formula can be set based on experience in the field. Specifically, the ash deposition rate can be mapped to a deposition state coefficient, and the ash fusion characteristic coefficient can be mapped to a melting risk coefficient. The two are then weighted, with the deposition state coefficient having a weight of 0.55 and the melting risk coefficient having a weight of 0.45, emphasizing a balance between the current state and potential risk.

[0148] The flame pulsation frequency analysis module acquires flame images through a blue-violet light-enhanced CCD at the burner's observation port, extracts brightness fluctuation signals, and performs FFT spectrum analysis.

[0149] The pollutant emission concentration analysis module uses particulate matter / NOx concentration. xThe SO2 online monitoring system analyzes the pollutant emission concentrations under various operating conditions.

[0150] The energy efficiency assessment device for coal-fired boilers with multi-source clean fuel co-firing can also be connected to a server storing a digital twin platform, and train an energy efficiency prediction model based on historical data to obtain a trained energy efficiency prediction model. Fuel price fluctuations and carbon trading prices are then input into the trained energy efficiency prediction model to output the optimal fuel co-firing combination scheme.

[0151] Taking a 630MW supercritical coal-fired unit co-firing ammonia, hydrogen, methanol, and biomass as an example, the co-firing ratio is 20% ammonia + 5% hydrogen + 15% corn straw (25% moisture content) + 10% methanol. Based on historical data and design documents of the boiler under pure coal operation, ΔT0 is 50℃, S0 is 1.2, and f0 is 8Hz. Eight K-type thermocouples (accuracy ±2℃) are arranged circumferentially at the burner outlet. The measured ΔT is 68℃. Using an acoustic temperature array and an online laser ash fusion analyzer, the coking index S=1.55 is obtained. Using a blue-violet CCD, the flame pulsation frequency f is measured to be 9Hz. k1 is taken as 0.015, k2 as 0.03, and k3 as 0.25. From the above data, the interaction factor λ = 1 + 0.015 × (50 - 68) - 0.03 × (1.55 - 1.2) + 0.25 × (9 / 8 - 1) = 0.92. The weighting coefficients of the baseline value are used for calculation: α = 0.4, β = 0.2, γ = 0.3, δ = 0.1. Based on the interaction factor and each weighting coefficient, combined with the boiler thermal efficiency before and after co-firing, pollutant emission reduction rate, carbon emission reduction rate, and fuel cost ratio, the comprehensive energy efficiency index is calculated, which is the energy efficiency assessment value of a coal-fired boiler with multi-source clean fuel co-firing.

[0152] See Figure 4 , Figure 4 This is a schematic block diagram of an electronic device provided according to an embodiment of this application. Figure 4 The electronic device 300 in this embodiment may include one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The processors 301, input devices 302, output devices 303, and memories 304 communicate with each other via a communication bus 305. The memories 304 store computer programs, including program instructions. The processors 301 execute the program instructions stored in the memories 304. Specifically, the processors 301 are configured to invoke the program instructions to perform the functions of each module / unit in the above system embodiments, for example... Figure 3 The functions of the parameter acquisition module 21, weight determination module 22, and energy efficiency assessment module 23 are shown.

[0153] It should be understood that, in the embodiments of this application, the processor 301 may be a central processing unit (CPU), but it may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0154] Input device 302 may include a touchpad, a fingerprint sensor (for collecting the user's fingerprint information and fingerprint orientation information), a microphone, etc., and output device 303 may include a display (LCD, etc.), a speaker, etc.

[0155] The memory 304 may include read-only memory and random access memory, and provides instructions and data to the processor 301. A portion of the memory 304 may also include non-volatile random access memory. For example, the memory 304 may also store device type information.

[0156] In specific implementations, the processor 301, input device 302, and output device 303 described in the embodiments of this application can execute the implementation methods described in the energy efficiency evaluation method for coal-fired boilers with multi-source clean fuel coupling and co-firing provided in the embodiments of this application, or they can execute the implementation methods of the electronic devices described in the embodiments of this application, which will not be repeated here.

[0157] In another embodiment of this application, a computer-readable storage medium is provided. This computer-readable storage medium stores a computer program, which includes program instructions. When executed by a processor, the program instructions implement all or part of the processes in the methods described above. Alternatively, the computer program can instruct related hardware to complete the process. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include any entity or device capable of carrying computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0158] The computer-readable storage medium can be an internal storage unit of the electronic device in any of the foregoing embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., provided on the electronic device. Furthermore, the computer-readable storage medium can include both internal and external storage units of the electronic device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0159] Those skilled in the art will recognize that the modules / units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.

[0160] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the electronic devices and units described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0161] In the several embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of modules / units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules, units, or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces or modules / units, or it may be an electrical, mechanical, or other form of connection.

[0162] The modules / units described as separate components may or may not be physically separate. Similarly, the components shown as modules / units may or may not be physical modules / units; they may be located in one place or distributed across multiple network modules / units. Some or all of the modules / units can be selected to achieve the purpose of the embodiments of this application, depending on actual needs.

[0163] Furthermore, the functional modules / units in the various embodiments of this application can be integrated into one processing module / unit, or each module / unit can exist physically separately, or two or more modules / units can be integrated into one module / unit. The integrated modules / units described above can be implemented in hardware or in the form of software functional modules / units.

[0164] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for evaluating the energy efficiency of a coal-fired boiler using multi-source clean fuel co-firing, characterized in that, include: Obtain multiple energy efficiency evaluation parameters of the target boiler during the co-firing process of multiple clean fuels; The weight coefficient of each energy efficiency assessment parameter is determined based on preset rules, which include: setting a corresponding weight fluctuation impact factor for each energy efficiency assessment parameter and the correlation between the weight fluctuation impact factor and the weight coefficient corresponding to each energy efficiency assessment parameter; The energy efficiency assessment value of a coal-fired boiler with co-firing of multiple clean fuels is calculated based on multiple energy efficiency assessment parameters and weighting coefficients. When more than two types of clean fuels are co-fired, the method further includes: The interaction factor λ is determined based on the following formula; λ=1+k1×(ΔT0–ΔT)-k2×(S-S0)+k3×(f / f0-1) Wherein, ΔT0 is the cross-sectional temperature difference of a pure coal-fired furnace, ΔT is the measured cross-sectional temperature difference under co-firing conditions, S0 is the coking index of a pure coal-fired furnace, S is the actual coking index of the furnace, f0 is the reference flame pulsation frequency of pure coal-fired furnace, f is the measured main flame pulsation frequency, k1 is the temperature difference gain coefficient, k2 is the coking penalty coefficient, and k3 is the frequency gain coefficient. The energy efficiency assessment value is updated based on the aforementioned interactive influencing factors; When only two fuels are blended, the formula for λ is: ,in, Indicates the interaction factor. This indicates the total number of clean fuel types used in co-firing. , Indicates the first The blending ratio of various fuels, Indicates the first The blending ratio of various fuels, Indicates the first Type of fuel and the first The interaction coefficient of the two fuels is used to reflect the strength of their interaction. This indicates that the two fuels produce a synergistic effect when mixed. , indicating no interactive effect, when This indicates that the two fuels produce an antagonistic effect when mixed. Indicates the first Type of fuel and the first The difference between the actual thermal efficiency improvement and the theoretical thermal efficiency improvement of the mixed combustion of the two fuels. Indicates the baseline thermal efficiency. Indicates the first Type of fuel and the first The difference between the actual pollutant emissions and the theoretical pollutant emissions from the combustion of two fuel mixtures. This indicates the baseline pollutant emissions.

2. The method as described in claim 1, characterized in that, The energy efficiency assessment parameters include at least one of the following: thermal efficiency, pollutant emission reduction rate, carbon emission reduction rate, and fuel cost ratio; the preset rules include at least one of the following: The weighted fluctuation factors affecting thermal efficiency include: the load of the target boiler, and the load of the target boiler is negatively correlated with the weighting coefficient corresponding to the thermal efficiency; The weighting factors affecting the pollutant emission reduction rate include: the proportion of high-pollution clean fuels whose combustion pollutant emission rate exceeds the preset threshold among multi-source clean fuels; the proportion of high-pollution clean fuels is positively correlated with the weighting coefficient corresponding to the pollutant emission reduction rate. The weighted fluctuation factors affecting the carbon emission reduction rate include: carbon price, and the weight coefficients of carbon price and carbon emission reduction rate are positively correlated. The weighted fluctuation factors affecting the fuel cost ratio include: the price of each fuel, and the price of each fuel is positively correlated with the fuel cost ratio.

3. The method as described in claim 2, characterized in that, The weighting coefficients for each energy efficiency assessment parameter determined based on preset rules include: Obtain the weighted fluctuation impact factors corresponding to each energy efficiency assessment parameter; The weight coefficient of each energy efficiency assessment parameter is determined based on the correlation between the weight fluctuation impact factor and the weight coefficient corresponding to each energy efficiency assessment parameter, specifically including at least one of the following: Based on the negative correlation between the target boiler load and the weighting coefficient corresponding to the thermal efficiency, the weighting coefficient α of the thermal efficiency of the target boiler is determined. Based on the positive correlation between the proportion of high-pollution clean fuels and the weighting coefficients corresponding to the pollutant emission reduction rate, the weighting coefficient β of the pollutant emission reduction rate is determined. Based on the positive correlation between carbon price and the weighting coefficients corresponding to carbon emission reduction rate, the weighting coefficient γ of carbon emission reduction rate is determined. Based on the positive correlation between the prices of each fuel and the weighting coefficients corresponding to the fuel cost ratio, the weighting coefficient δ of the fuel cost ratio is determined.

4. The method as described in claim 3, characterized in that, The method further includes: The standard value of the weight coefficient of each energy efficiency assessment parameter is determined. The standard value is the value of the weight coefficient of each energy efficiency assessment parameter when the weight fluctuation influence factor corresponding to each energy efficiency assessment parameter is within a preset range. The standard value of α is 0.4, the standard value of β is 0.2, the standard value of γ is 0.3, and the standard value of δ is 0.

1.

5. The method as described in claim 4, characterized in that, The weighting coefficients for each energy efficiency assessment parameter determined based on preset rules include: Determine whether the weight fluctuation impact factor corresponding to each energy efficiency assessment parameter is within the preset range; In response to the fact that the weight fluctuation impact factor corresponding to the energy efficiency assessment parameter is not within the preset range, the weight coefficient corresponding to the energy efficiency assessment parameter is adjusted according to the preset step size based on the standard value and the correlation between the weight fluctuation impact factor and each energy efficiency assessment parameter.

6. The method as described in claim 1, characterized in that, The method further includes at least one of the following: Determine whether the energy efficiency assessment value is less than a first threshold; in response to the energy efficiency assessment value being less than the first threshold, reduce the blending ratio of each clean fuel in the coupled combustion according to the set first priority order; Determine whether the energy efficiency assessment value is greater than or equal to a second threshold; in response to the energy efficiency assessment value being greater than or equal to the second threshold, increase the blending ratio of each clean fuel in the coupled combustion according to a set second priority order.

7. The method as described in claim 1, characterized in that, The method further includes: In response to the detection that the current combustion parameters meet the preset safety constraints, a blending ratio downgrade command is triggered to execute the corresponding blending ratio downgrade operation.

8. The method as described in claim 7, characterized in that, The current combustion parameters satisfy at least one of the preset safety constraints: Hydrogen volume concentration >1%, ammonia slip >10ppm, biomass moisture content >30%, and methanol incomplete vaporization rate >5%.

9. An energy efficiency evaluation system for coal-fired boilers with multi-source clean fuel co-firing, characterized in that, include: The parameter acquisition module is used to acquire multiple energy efficiency evaluation parameters of the target boiler during the co-firing process of multiple clean fuels. The weight determination module is used to determine the weight coefficient of each energy efficiency assessment parameter based on preset rules. The preset rules include: setting a corresponding weight fluctuation impact factor for each energy efficiency assessment parameter and the correlation between the weight fluctuation impact factor and each energy efficiency assessment parameter. The energy efficiency assessment module is used to calculate the energy efficiency assessment value of a coal-fired boiler that uses multiple energy efficiency assessment parameters and weighting coefficients in combination with clean fuels from multiple sources. The interaction effect module is used to determine the interaction effect factor λ based on the following formula when there are more than two types of clean fuels being co-fired. λ=1+k1×(ΔT0–ΔT)-k2×(S-S0)+k3×(f / f0-1) Wherein, ΔT0 is the cross-sectional temperature difference of a pure coal-fired furnace, ΔT is the measured cross-sectional temperature difference under co-firing conditions, S0 is the coking index of a pure coal-fired furnace, S is the actual coking index of the furnace, f0 is the reference flame pulsation frequency of pure coal-fired furnace, f is the measured main flame pulsation frequency, k1 is the temperature difference gain coefficient, k2 is the coking penalty coefficient, and k3 is the frequency gain coefficient. The energy efficiency assessment value is updated based on the aforementioned interactive influencing factors; When only two fuels are blended, the formula for λ is: ,in, Indicates the interaction factor. This indicates the total number of clean fuel types used in co-firing. , Indicates the first The blending ratio of various fuels, Indicates the first The blending ratio of various fuels, Indicates the first Type of fuel and the first The interaction coefficient of the two fuels is used to reflect the strength of their interaction. This indicates that the two fuels produce a synergistic effect when mixed. , indicating no interactive effect, when This indicates that the two fuels produce an antagonistic effect when mixed. Indicates the first Type of fuel and the first The difference between the actual thermal efficiency improvement and the theoretical thermal efficiency improvement of the mixed combustion of the two fuels. Indicates the baseline thermal efficiency. Indicates the first Type of fuel and the first The difference between the actual pollutant emissions and the theoretical pollutant emissions from the combustion of two fuel mixtures. This indicates the baseline pollutant emissions.

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