Biological waste combustion carbon emission prediction method and system based on digital twinning

Abstract

The application discloses a biological waste combustion carbon emission prediction method and system based on digital twinning, and belongs to the technical field of biomass combustion carbon emission metering. Fuel property parameters and real-time combustion condition parameters are obtained in sequence: the fuel property covers auxiliary indexes such as industrial analysis, element analysis and particle size; the condition parameters include the furnace weighted combustion temperature, the excess air coefficient of flue gas inversion and the high-temperature zone residence time corrected by temperature and pressure; the theoretical carbon emission is calculated through a pre-constructed digital twinning model; then, the property adjustment coefficient and the condition adjustment coefficient are generated respectively; and the prediction result is output after two-dimensional coupling correction of the theoretical value. The above method is realized by synchronously setting corresponding function modules, the systematic deviation of the ideal combustion assumption can be eliminated, the prediction accuracy is improved, the fuel and condition fluctuations are dynamically responded, and accurate data support is provided for carbon accounting and combustion optimization.

Description

Technical Field

[0001] This invention relates to the field of biomass combustion carbon emission measurement technology, and more specifically, to a method and system for predicting carbon emissions from biomass combustion based on digital twins. Background Technology

[0002] Driven by the "dual carbon" strategic goals, biomass energy, as an important component of renewable energy, has become a crucial technological pathway for the energy utilization of biomass fuels such as agricultural and forestry waste and industrial biological waste through incineration. Accurately quantifying the carbon emissions during the combustion process of biological waste is the core foundation for carbon accounting, carbon trading, emission control, and combustion process optimization, and is of significant practical importance for improving the cleanliness of biomass energy utilization and perfecting the carbon measurement system.

[0003] Current methods for predicting carbon emissions from bio-waste combustion still suffer from the following technical shortcomings in practical applications: First, traditional emission factor methods are based on the ideal assumption of complete fuel combustion and use static parameters for linear calculations. They fail to fully consider the significant heterogeneity of biowaste's inherent properties and its impact on actual carbon emissions, resulting in significant systematic biases. Biowaste origins are complex and its quality fluctuates greatly, with significant differences in properties such as moisture content, ash content, volatile matter, and fuel particle size. Increased moisture content leads to evaporation and absorption of a large amount of furnace heat, lowering the combustion zone temperature and exacerbating incomplete combustion, thus significantly increasing greenhouse gas equivalent emissions. Ash easily coats carbon particles during combustion, increasing the carbon content of ash residue and reducing the fuel's carbon oxidation rate. Volatile matter content and fuel particle size directly determine the fuel's ignition characteristics and burnout rate, affecting combustion completeness. Most existing methods rely solely on the average carbon content of the fuel as the core calculation basis, failing to quantify the impact of these fuel property differences on burnout rate and incomplete combustion, leading to significant discrepancies between the calculated results and actual emission levels. Secondly, existing prediction methods lack sufficient accuracy in characterizing combustion parameters and lack a multi-parameter coupling correction mechanism. Furnace combustion temperature, excess air coefficient, and residence time in the high-temperature zone are the core operating conditions that determine combustion completeness: temperatures below the reasonable range will lead to a significant decrease in the oxidation reaction rate and an increase in mechanical incomplete combustion losses; excessively high temperatures will easily cause ash melting and coking, which will coat unburned carbon particles and reduce the burnout rate; an excessively low excess air coefficient will cause insufficient oxygen supply, while an excessively high excess air coefficient will dilute the furnace temperature and increase flue gas heat loss, both of which will exacerbate the tendency for incomplete combustion; the residence time in the high-temperature zone directly determines the contact reaction time between combustible components and oxygen, directly affecting the degree of carbon oxidation, and cannot dynamically respond to the real-time impact of operating condition fluctuations on carbon emissions.

[0004] To address this, a method and system for predicting carbon emissions from the combustion of bio-waste based on digital twins have been developed. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a method and system for predicting carbon emissions from bio-waste combustion based on digital twins.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A digital twin-based method for predicting carbon emissions from biowaste combustion includes: Step 1: Obtain fuel property parameters, including industrial analysis indicators, elemental analysis indicators, and auxiliary indicators; Step 2: Obtain real-time combustion parameters, including furnace combustion temperature, excess air coefficient, and residence time in the high-temperature zone; Step 3: Pre-construct a digital twin model of the bio-waste combustion process. The digital twin model calculates the theoretical carbon emissions based on fuel property parameters. Based on the theoretical carbon emissions, it makes predictions by combining combustion condition parameters and outputs the predicted carbon emissions. Theoretical carbon emissions = theoretical carbon emissions × operating condition adjustment factor × attribute adjustment factor.

[0007] Specifically, industrial analysis indicators include moisture content, ash content, and volatile matter; Elemental analysis indicators include basic carbon content, oxygen content, and nitrogen content; Auxiliary indicators include fuel particle size.

[0008] Specifically, the logic for obtaining the combustion temperature in the furnace; Furnace combustion temperature ;in The temperature of each preset area point, The volume weight preset for the corresponding region.

[0009] Specifically, the logic for obtaining the excess air coefficient; Real-time acquisition of the volume fractions of carbon monoxide, carbon dioxide, and oxygen in the dry flue gas at the furnace outlet; Based on fuel element analysis, the theoretical air volume per unit mass of fuel is calculated according to the complete combustion reaction. The excess air coefficient Through formula Calculated; and These represent the volume fractions of oxygen and carbon monoxide. in The total integral of the triatomic gas in the dry flue gas; Theoretical maximum volume fraction of triatomic gases in dry flue gas; and These represent the volume fractions of carbon dioxide and sulfur dioxide in dry flue gas.

[0010] Specifically, the logic for obtaining the dwell time in the high-temperature zone; The effective volume of the pre-marked high-temperature zone in the furnace is extracted from the pre-constructed digital twin geometric model; the actual flue gas volumetric flow rate in the flue is measured using a flue gas flow meter. The corrected flue gas volumetric flow rate is obtained by correcting the actual flue gas volumetric flow rate with the temperature and pressure in the high-temperature zone. The residence time in the high-temperature zone is calculated by using the effective volume as the numerator and the corrected flue gas volumetric flow rate as the denominator.

[0011] Specifically, the logic for obtaining flue gas volume flow rate was modified; The temperature and pressure at each pre-marked point in the flue are obtained, and the arithmetic average is taken to output the flue temperature and flue pressure. Extract the temperature and pressure of the pre-marked high-temperature zone in the furnace as the high temperature and high pressure values; For flue gas temperature and pressure uniformity, a weighted fusion calculation is performed by combining the high temperature and high pressure values, and then a correction coefficient is converted using pre-edited mapping rules. The correction coefficient is then multiplied by the actual flue gas volume flow rate to obtain the corrected flue gas volume flow rate.

[0012] Specifically, the logic for obtaining theoretical carbon emissions; Based on feed rate and received basic carbon content The calculations yielded the theoretical carbon emissions; the formula is as follows: .

[0013] Specifically, the logic for obtaining the operating condition adjustment coefficient; Extract the furnace combustion temperature, excess air coefficient, and residence time in the high-temperature zone from the combustion operating parameters; Combustion temperature in the furnace Excess air coefficient Time spent in high-temperature zones After normalization, substitute into the formula The operating condition performance index is calculated. ; , as well as These are preset weighting coefficients, and their sum is one; Using pre-edited mapping rules, the operating condition adjustment coefficient is converted into an operating condition adjustment coefficient.

[0014] Specifically, the logic for obtaining attribute adjustment coefficients; Extracting volatile matter, moisture content, and particle size uniformity from fuel property parameters, focusing on moisture content... fuel particle size and volatile matter After normalization, the formula is used. Calculate the attribute performance index ; , as well as These are preset weighting coefficients, and their sum is one; By using pre-edited mapping rules, attribute performance indices are converted into attribute adjustment coefficients.

[0015] A digital twin-based system for predicting carbon emissions from bio-waste combustion includes: The fuel property acquisition module is used to acquire fuel property parameters of biowaste, including industrial analysis indicators, elemental analysis indicators, and auxiliary indicators. The combustion condition sensing module is used to acquire real-time combustion condition parameters in the furnace, including furnace combustion temperature, excess air coefficient, and residence time in the high-temperature zone. The digital twin calculation module pre-builds a digital twin model to calculate theoretical carbon emissions based on fuel attribute parameters, and then corrects the theoretical carbon emissions by combining combustion condition parameters to output predicted carbon emissions.

[0016] The technical effects and advantages of this invention are as follows: This method significantly improves the prediction accuracy of carbon emissions from biowaste combustion and eliminates the systematic bias of ideal models. Based on the theoretical carbon emissions calculated from fuel carbon content, it introduces a dual-dimensional correction mechanism of fuel properties and combustion conditions. On the one hand, it combines attribute parameters such as moisture content, volatile matter, and fuel particle size to quantify the impact of fuel characteristics on burnout rate and degree of incomplete combustion. On the other hand, it integrates real-time operating conditions such as furnace combustion temperature, excess air coefficient, and residence time in the high-temperature zone, and generates corresponding adjustment coefficients through normalized weighting and mapping rules to dynamically correct theoretical values. This greatly reduces the deviation between predicted values ​​and actual emissions, providing accurate data support for carbon accounting and emission control. It enables precise and quantitative characterization of key parameters in the combustion process, supporting operation optimization and maintenance decisions. Based on a digital twin geometric model, it calculates flue gas volume flow rate with temperature and pressure correction for residence time in the high-temperature zone, solving the problem of residence time calculation distortion caused by directly applying low-temperature measurement data from the flue. At the same time, it clarifies the optimal range and influence law of parameters such as combustion temperature and excess air coefficient, and can respond in real time to fluctuations in fuel quality and operating conditions, dynamically outputting carbon emission prediction results. Attached Figure Description

[0017] Figure 1 This is a flowchart of the method for predicting carbon emissions from bio-waste combustion based on digital twins, as described in this invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Example 1 like Figure 1 As shown, the method for predicting carbon emissions from bio-waste combustion based on digital twins is as follows: Step 1: Obtain fuel property parameters, including industrial analysis indicators, elemental analysis indicators, and auxiliary indicators; Fuel property parameters are obtained through sampling analysis or online near-infrared detection; Industrial analytical parameters include moisture content, ash content, and volatile matter; The drying loss method was adopted: the sample was placed in a forced-air drying oven at 105±2℃ and dried to constant weight. The moisture content was calculated as the percentage of the weight loss to the original sample mass. The ignition residue method was adopted: the dried sample was ignited in a muffle furnace at 550℃ to constant weight, and the percentage of the residue mass to the original sample mass was the air-dried ash content, which was then converted to the received basis. The air-isolated heating method was used: the sample was placed in a covered crucible and heated at 900±10℃ for 7 min. The percentage of the original mass after deducting water loss was the air-dried volatile matter.

[0020] Increased moisture content lowers furnace temperature and exacerbates incomplete combustion, leading to increased CO and other pollutants. Increased emissions; while increased ash content lowers the theoretical upper limit of carbon emissions, it also increases the carbon content of ash residue due to the encapsulation of carbon particles; high volatile matter content is conducive to complete combustion and increases the carbon oxidation rate; Elemental analysis indicators include basic carbon content, oxygen content, and nitrogen content; After the sample was burned at high temperature, the carbon was converted into The carbon content on the air-dried basis was quantitatively obtained using an infrared detector and converted to the received basis. Nitrogen content was determined using the Dumas combustion method or the Kjeldahl method, with the same conversion method as for carbon content. The oxygen content was obtained by subtraction method (the sulfur content of biomass is extremely low and can be ignored).

[0021] Carbon content directly determines the theory Production rate; oxygen content is beneficial for self-supplied oxygen to enhance combustion; nitrogen content directly affects... The generation of .

[0022] Auxiliary indicators include fuel particle size; The smaller the particle size and the larger the specific surface area, the higher the burnout rate.

[0023] Offline screening uses a standard sieve set and a vibrating sieve machine; online screening uses a laser particle size analyzer. The characteristic particle size is the particle size corresponding to the 50% cumulative distribution as the average particle size.

[0024] Step 2: Obtain real-time combustion parameters, including furnace combustion temperature, excess air coefficient, and residence time in the high-temperature zone; Specifically: The combustion temperature values ​​of each preset area point in the furnace are read using an infrared pyrometer, and the furnace combustion temperature is obtained by weighted averaging of each area. Furnace combustion temperature ;in The temperature of each preset area point, The volume weight preset for the corresponding region.

[0025] Real-time acquisition of the volume fractions of carbon monoxide, carbon dioxide, and oxygen in the dry flue gas at the furnace outlet; Based on fuel element analysis, the theoretical air volume per unit mass of fuel is calculated according to the complete combustion reaction. Theoretical air volume ; The excess air coefficient Through formula Calculated; and These represent the volume fractions of oxygen and carbon monoxide. in The total integral of the triatomic gas in the dry flue gas; The theoretical maximum volume fraction of triatomic gases in dry flue gas; that is, the highest volume fraction of triatomic gases that can be achieved in dry flue gas when complete combustion is achieved (without residual oxygen and without incomplete combustion) under the theoretical air volume. and These are the volume fractions of carbon dioxide and sulfur dioxide in dry flue gas. Extract the effective volume of the pre-marked high-temperature zone in the furnace from a pre-constructed digital twin geometric model; The actual flue gas volume flow rate in the flue is measured using a flue gas flow meter. Flue gas flow meters are usually installed after the economizer or dust collector, where the temperature may be around 150~200℃, while the temperature in the high-temperature zone of the furnace is 800~1100℃ or even higher. According to the ideal gas law, for the same mass of gas, the volume will double for every 273℃ increase in temperature. Directly taking the volumetric flow rate of the low-temperature flue as the volumetric flow rate in the high-temperature zone will seriously underestimate the actual volumetric flow rate in the high-temperature zone, resulting in a significantly larger calculated residence time.

[0026] The temperature and pressure at each pre-marked point in the flue are acquired simultaneously, and the arithmetic average of these values ​​is taken to output the flue temperature and flue pressure. The temperature in the high-temperature zone is much higher than that in the flue, so the flue gas expands in volume in the high-temperature zone. The actual volumetric flow rate through the high-temperature zone is much greater than the volumetric flow rate measured in the flue. Gas volume is inversely proportional to absolute pressure. If the high-temperature zone of the furnace is under positive pressure, the pressure will be higher than that at the flue gas measuring point, and the gas will be compressed, resulting in a smaller volumetric flow rate for the same mass flow rate.

[0027] Extract the temperature and pressure of the pre-marked high-temperature zone in the furnace as the high temperature and high pressure values; For flue gas temperature and pressure uniformity, after weighted fusion calculation of temperature and pressure height values, the correction coefficient is converted using pre-edited mapping rules; the correction coefficient is then multiplied by the actual flue gas volume flow rate to obtain the corrected flue gas volume flow rate. The calculation process is as follows: State Index ;in and These represent the high temperature value and the average temperature of the flue, respectively. and These represent the flue gas pressure equalization and pressure height values, respectively. and These are preset weighting coefficients; Use Kelvin for temperature and absolute pressure for pressure. The preset state index corresponds to each group of index intervals, and each group of index intervals corresponds to a set of correction coefficients, with the correction coefficient range limited to 1.12-1.57; Match the state index with the corresponding index range to determine the correction coefficient.

[0028] The residence time in the high-temperature zone is calculated by using the effective volume as the numerator and the corrected flue gas volumetric flow rate as the denominator.

[0029] Step 3: Pre-construct a digital twin model of the bio-waste combustion process. The digital twin model calculates the theoretical carbon emissions based on fuel property parameters. Based on the theoretical carbon emissions, it makes predictions by combining combustion condition parameters and outputs the predicted carbon emissions. Specifically: Theoretical carbon emissions, based on feed rate and received basic carbon content The calculations were performed to obtain the result. The formula is expressed as: theoretical carbon emissions ; 44 / 12 is The molar mass ratio of carbon.

[0030] Volatile matter, moisture content, and particle size uniformity are extracted from fuel property parameters, and after weighted fusion processing, the property performance index is output. Specifically: Increased moisture content will increase total carbon emissions in two ways; Incomplete combustion is exacerbated: Moisture evaporation absorbs a large amount of heat, lowering the furnace temperature and leading to increased CO and other combustion. The significant increase in incomplete combustion products, such as those from combustion gases, results in a much stronger greenhouse effect than the total greenhouse effect equivalent (CVE-1), leading to an increase in the overall CVE-1. Lowering the effective calorific value: Water itself does not provide heat, but instead consumes heat. To meet the same heat production demand, fuel consumption must increase, thereby increasing total carbon emissions.

[0031] Volatile matter is the component of biological waste that is easily gasified and burned; the higher its content, the better. It ignites more easily, burns more completely, increases the carbon oxidation rate, and converts more carbon in the fuel into carbon. Incomplete combustion products (CO, ... The generation of VOCs has been significantly reduced; Although Direct emissions may increase slightly, but the reduction in the equivalent of emissions from incomplete combustion products will have a more significant impact on the final total greenhouse gas emissions. The equivalent is decreasing; Therefore, when volatile matter increases, the predicted carbon emissions should be revised downward.

[0032] The smaller the fuel particle size, the more carbon emissions need to be reduced; Small fuel particle size means fine fuel particles: large specific surface area, fast combustion reaction rate, high burnout rate, which also reduces the carbon content of ash residue and incomplete combustion gas products, thus reducing the total carbon emission equivalent. Therefore, the carbon emission forecast should be reduced.

[0033] For moisture content fuel particle size and volatile matter After normalization, the formula is used. Calculate the attribute performance index ; , as well as These are preset weighting coefficients, and their sum is one.

[0034] The furnace combustion temperature, excess air coefficient, and residence time in the high-temperature zone are extracted from the combustion condition parameters, and after weighted fusion processing, the operating condition performance index is output. The specific calculation process is as follows: The longer the residence time, the more fully the combustible components come into contact with oxygen, the higher the burnout rate, and the predicted carbon emissions should be lowered. The temperature is too low (below 800℃); The combustion reaction rate decreases significantly, the carbon in the fuel cannot be fully oxidized, the carbon content of the ash increases, mechanical incomplete combustion loss increases, and carbon emissions are amplified. Excessive temperature (above 1300℃) may cause coking: ash melts to form coke blocks, which encapsulate unburned carbon particles, hindering the combustion reaction and reducing the burnout rate, thus amplifying the total carbon emissions. The optimal temperature range (800–1100℃) has the fastest carbon oxidation reaction rate and the highest burnout rate, and the total carbon emission equivalent should be reduced.

[0035] When the excess air coefficient is too low, oxygen supply is severely insufficient, fuel cannot be completely burned, and total carbon emissions are significantly amplified. If the coefficient is too high, a large amount of excess air enters the furnace, absorbs heat, and causes the combustion temperature to drop, which in turn exacerbates the tendency for incomplete combustion, requiring amplification of carbon emissions. The coefficient is in the optimal range (usually 1.3 to 1.5). With sufficient oxygen supply and stable furnace temperature, the carbon oxidation rate is the highest, the incomplete combustion products are the least, the flue gas loss is moderate, and the total carbon emission equivalent should be reduced. The furnace combustion temperature was normalized, and a segmented calculation formula was used. Low temperature range ; optimal interval The further away from the center, the slightly higher the degree of deterioration. ; This refers to the normalized furnace combustion temperature. Overheating range ; The excess air coefficient is normalized based on the set boundary conditions: Optimal value: 1.4, deterioration = 0; Optimal interval edges: 1.3, 1.5, with a deterioration of 0.15; The severe hypoxia / hypoxia boundary is defined as a deterioration level of 1. Calculate the normalized excess air coefficient ; The coefficient 15 is the calibration value. This represents the optimal value; it is a quadratic function fit, and the value is truncated to 1 if it exceeds the boundary.

[0036] Combustion temperature in the furnace Excess air coefficient Time spent in high-temperature zones After normalization, substitute into the formula The operating condition performance index is calculated. ; , as well as These are preset weighting coefficients, and their sum is one.

[0037] After correcting the theoretical carbon emissions using the operating condition adjustment coefficient and attribute adjustment coefficient converted from the operating condition performance index and attribute performance index, the predicted carbon emissions are output. That is, theoretical carbon emissions = theoretical carbon emissions × operating condition adjustment factor × attribute adjustment factor; That is, by pre-editing the working condition adjustment coefficient and the attribute adjustment coefficient, each set of coefficient intervals corresponds to a set of working condition adjustment coefficients; the working condition adjustment coefficient range is limited to 1-1.5; Each set of coefficient intervals corresponding to the attribute adjustment coefficient corresponds to a set of attribute adjustment coefficients, and the range of attribute adjustment coefficients is limited to 1-1.4; The larger the operating condition adjustment coefficient and attribute adjustment coefficient are, the higher the probability of matching 1.5 or 1.4, which requires amplification of the theoretical carbon emissions.

[0038] Example 2 Based on the digital twin-based method for predicting carbon emissions from bio-waste combustion provided in Embodiment 1 of this application, Embodiment 2 of this application proposes a digital twin-based system for predicting carbon emissions from bio-waste combustion. Embodiment 2 is merely a preferred embodiment of Embodiment 1, and its implementation will not affect the individual implementation of Embodiment 1.

[0039] Specifically, the method for predicting carbon emissions from bio-waste combustion based on digital twins provided in Embodiment 2 of this application includes: The fuel property acquisition module is used to acquire fuel property parameters of biowaste, including industrial analysis indicators, elemental analysis indicators, and auxiliary indicators. The combustion condition sensing module is used to acquire real-time combustion condition parameters in the furnace, including furnace combustion temperature, excess air coefficient, and residence time in the high-temperature zone. The digital twin computing module pre-builds a digital twin model to calculate theoretical carbon emissions based on fuel attribute parameters, and then corrects the theoretical carbon emissions by combining combustion condition parameters to output predicted carbon emissions.

[0040] The above formulas are all dimensionless calculations. Dimensionless calculations can be performed using various methods such as standardization, which will not be elaborated here. The formulas are derived from software simulations based on a large amount of collected data, and the preset parameters in the formulas can be set by those skilled in the art according to the actual situation.

[0041] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, ATA hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium can be a solid-state ATA hard disk.

[0042] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0043] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. 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 implementation should not be considered beyond the scope of this application.

[0044] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0045] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0046] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0047] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable ATA hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0048] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included 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 predicting carbon emissions from bio-waste combustion based on digital twins, characterized in that, include: Step 1: Obtain fuel property parameters, including industrial analysis indicators, elemental analysis indicators, and auxiliary indicators; Step 2: Obtain real-time combustion parameters, including furnace combustion temperature, excess air coefficient, and residence time in the high-temperature zone; Step 3: Pre-construct a digital twin model of the bio-waste combustion process. The digital twin model calculates the theoretical carbon emissions based on fuel property parameters. Based on the theoretical carbon emissions, it makes predictions by combining combustion condition parameters and outputs the predicted carbon emissions. Theoretical carbon emissions = theoretical carbon emissions × operating condition adjustment factor × attribute adjustment factor.

2. The method for predicting carbon emissions from bio-waste combustion based on digital twins according to claim 1, characterized in that: Industrial analytical parameters include moisture content, ash content, and volatile matter; Elemental analysis indicators include basic carbon content, oxygen content, and nitrogen content; Auxiliary indicators include fuel particle size.

3. The method for predicting carbon emissions from bio-waste combustion based on digital twins according to claim 2, characterized in that: Logic for obtaining furnace combustion temperature; Furnace combustion temperature ;in The temperature of each preset area point, The volume weight preset for the corresponding region.

4. The method for predicting carbon emissions from bio-waste combustion based on digital twins according to claim 3, characterized in that: Logic for obtaining excess air coefficient; Real-time acquisition of the volume fractions of carbon monoxide, carbon dioxide, and oxygen in the dry flue gas at the furnace outlet; Based on fuel element analysis, the theoretical air volume per unit mass of fuel is calculated according to the complete combustion reaction. The excess air coefficient Through formula Calculated; and These represent the volume fractions of oxygen and carbon monoxide. in The total integral of the triatomic gas in the dry flue gas; Theoretical maximum volume fraction of triatomic gases in dry flue gas; and These represent the volume fractions of carbon dioxide and sulfur dioxide in dry flue gas.

5. The method for predicting carbon emissions from bio-waste combustion based on digital twins according to claim 4, characterized in that: Logic for obtaining the dwell time in the high-temperature zone; The effective volume of the pre-marked high-temperature zone in the furnace is extracted from the pre-constructed digital twin geometric model; the actual flue gas volumetric flow rate in the flue is measured using a flue gas flow meter. The corrected flue gas volume flow rate is obtained by correcting the actual flue gas volume flow rate in combination with the temperature and pressure in the high-temperature zone. The residence time in the high-temperature zone is calculated by using the effective volume as the numerator and the corrected flue gas volumetric flow rate as the denominator.

6. The method for predicting carbon emissions from bio-waste combustion based on digital twins according to claim 5, characterized in that: Corrected the logic for obtaining flue gas volume flow rate; The temperature and pressure at each pre-marked point in the flue are obtained, and the arithmetic average is taken to output the flue temperature and flue pressure. Extract the temperature and pressure of the pre-marked high-temperature zone in the furnace as the high temperature and high pressure values; For flue temperature and flue pressure uniformity, after weighted fusion calculation of temperature and pressure height values, the conversion of correction coefficients is performed using pre-edited mapping rules; The corrected flue gas volumetric flow rate is obtained by multiplying the correction factor by the actual flue gas volumetric flow rate.

7. The method for predicting carbon emissions from bio-waste combustion based on digital twins according to claim 6, characterized in that: The logic behind obtaining theoretical carbon emissions; Based on feed rate and received basic carbon content The calculations yielded the theoretical carbon emissions; the formula is as follows: .

8. The method for predicting carbon emissions from bio-waste combustion based on digital twins according to claim 7, characterized in that: Logic for obtaining operating condition adjustment coefficients; Extract the furnace combustion temperature, excess air coefficient, and residence time in the high-temperature zone from the combustion operating parameters; Combustion temperature in the furnace Excess air coefficient Time spent in high-temperature zones After normalization, substitute into the formula The operating condition performance index is calculated. ; , as well as These are preset weighting coefficients, and their sum is one; Using pre-edited mapping rules, the operating condition adjustment coefficient is converted into an operating condition adjustment coefficient.

9. The method for predicting carbon emissions from bio-waste combustion based on digital twins according to claim 7, characterized in that: Logic for obtaining attribute adjustment coefficients; Extracting volatile matter, moisture content, and particle size uniformity from fuel property parameters, focusing on moisture content... fuel particle size and volatile matter After normalization, the formula is used. Calculate the attribute performance index ; , as well as These are preset weighting coefficients, and their sum is one; By using pre-edited mapping rules, attribute performance indices are converted into attribute adjustment coefficients.

10. A digital twin-based system for predicting carbon emissions from bio-waste combustion, applied to any one of the digital twin-based methods for predicting carbon emissions from bio-waste combustion according to claims 1-9, characterized in that, include: The fuel property acquisition module is used to acquire fuel property parameters of biowaste, including industrial analysis indicators, elemental analysis indicators, and auxiliary indicators. The combustion condition sensing module is used to acquire real-time combustion condition parameters in the furnace, including furnace combustion temperature, excess air coefficient, and residence time in the high-temperature zone. The digital twin computing module pre-builds a digital twin model to calculate theoretical carbon emissions based on fuel attribute parameters, and then corrects the theoretical carbon emissions by combining combustion condition parameters to output predicted carbon emissions.

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