Carbon emission factor accounting method and system in raw material combustion process of enterprise

Abstract

The invention discloses a carbon emission factor accounting method and system in an enterprise raw material combustion process, and the method comprises the steps: building a coordinate system according to the geographic position of an enterprise, determining the optimal monitoring point of the CO2 mass concentration of the enterprise, obtaining the actual value of the CO2 mass concentration at the optimal monitoring point through detection, and taking the minimum error between the actual value of the CO2 mass concentration of the optimal monitoring point and a simulation value as a target. The carbon emission intensity of the emission source is inverted, and the actual carbon emission factor of each combustion raw material is calculated based on the inverted carbon emission intensity of the emission source. The optimal monitoring point is reasonably selected near the industrial production emission source of an enterprise, direct and effective monitoring and data analysis of carbon emission are carried out, a carbon monitoring system capable of updating carbon emission factors in real time is provided for the enterprise, the accuracy of carbon emission monitoring is improved, and the carbon emission level of the enterprise can be truly reflected.

Description

Technical Field

[0001] The present invention belongs to the field of carbon dioxide concentration monitoring, and specifically relates to a method and system for calculating carbon emission factors in the process of raw material combustion in an enterprise. Background Art

[0002] In the industrial production of enterprises, the emission factor method is the most commonly used carbon emission accounting method at home and abroad. This method calculates carbon emissions by collecting activity data of a certain process and the corresponding carbon emission factors generated during the activity process. The carbon emission source inventory is the data support for the emission factor method. At present, most studies use the IPCC default emission factors to calculate carbon emissions from carbon emission sources, but the default value of this default emission factor is an energy consumption emission factor at the global or national average level, ignoring the characteristic differences in emission factors between enterprises and between various links of enterprises, resulting in the fact that the carbon emission accounting results may not accurately reflect the actual carbon emission level of the enterprise. Summary of the invention

[0003] The purpose of the present invention is to provide a method and system for calculating the carbon emission factor in the process of raw material combustion in an enterprise in view of the above-mentioned problems existing in the prior art.

[0004] To achieve the above objectives, the technical solution of the present invention is as follows:

[0005] In the first aspect, the present invention proposes a method for calculating carbon emission factors in the process of raw material combustion in an enterprise, comprising:

[0006] S1. Determine the optimal monitoring point for the enterprise's CO2 mass concentration;

[0007] S2, detect and obtain the actual value of CO2 mass concentration at the optimal monitoring point;

[0008] S3, with the goal of minimizing the error between the actual value and the simulated value of the CO2 mass concentration at the optimal monitoring point, invert the carbon emission intensity of the emission source;

[0009] S4. Based on the inverted carbon emission intensity of the emission sources, calculate the actual carbon emission factors in the combustion process of each raw material.

[0010] The S2 includes:

[0011] S21, absorbing the gas at the optimal monitoring point into a CO2 gas absorption pool through a pump suction processing controller, wherein the CO2 gas absorption pool is a closed multi-ventilation chamber;

[0012] S22, emitting a single-path dual-wavelength infrared beam from a determined incident point to a CO2 gas absorption cell, and after being reflected and absorbed in the gas absorption cell, emitting from the incident point and transmitting to a detector;

[0013] The incident point is calculated using the following formula:

[0014]

[0015] In the above formula, n is the number of reflections of the light source, d is n is the reflection point P n Distance from the optical axis, θ n is the reflected light S n The angle between the incident light S0 and the optical axis, l is the distance between the two concave mirrors in the air chamber, d0 is the distance from the incident point P0 to the optical axis, and θ0 is the angle between the incident light S0 and the optical axis;

[0016] S23. The detector divides the received infrared light beam into signal light and reference light, and converts the two optical signals into voltage signals. The converted voltage signal is calculated using the following formula:

[0017] U(λ1,l)=η(λ1)I0(λ1)k(λ1)exp[-α(λ1)cl];

[0018] U(λ2, l) = η(λ2) I 0(λ2)k(λ2);

[0019]

[0020] k(λ1)=exp[m(λ1)l+n(λ1)l+p(λ1)l];

[0021] k(λ2)=exp[m(λ2)l+n(λ2)l+p(λ2)l];

[0022] In the above formula, U(λ1, l) and U(λ2, l) are the voltage values ​​after the signal light is converted into a voltage signal and the voltage values ​​after the reference light is converted into a voltage signal, respectively; η(λ1) and η(λ2) are the photoelectric conversion coefficients of the signal light and the reference light, respectively; I0(λ1) and I0(λ2) are the initial light intensities of the signal light and the reference light, respectively; k(λ1) and k(λ2) are the intermediate variables for the calculation of the signal light and the reference light, respectively; α(λ1) is the absorption coefficient of the gas; c is the volume fraction of CO2 gas in the gas chamber; l is the length of the absorption cell; I0 ( λ) is the initial light intensity, is the electro-optical conversion coefficient, i is the driving current of the infrared beam, m(λ1) and m(λ2) are the Rayleigh scattering coefficients of the signal light and the reference light, n(λ1) and n(λ2) are the Mie scattering coefficients of the signal light and the reference light, p(λ1) and p(λ2) are the absorption coefficients of water molecules of the signal light and the reference light, respectively;

[0023] S24, sampling and calculating the converted voltage signal, and using the following formula to calculate the volume fraction of CO2 gas in the absorption cell:

[0024]

[0025] In the above formula, c is the volume fraction of CO2 gas in the absorption cell, λ1 and λ2 are the central wavelengths of the signal light and the reference light, respectively, α(λ1) is the absorption coefficient of the gas, l is the length of the absorption cell, η(λ1) and η(λ2) are the photoelectric conversion coefficients of the signal light and the reference light, respectively. are the electro-optical conversion coefficients of the signal light and the reference light, respectively; k((λ1) and k(λ2) are the intermediate variables of the signal light calculation and the reference light calculation, respectively; U(λ1, l) and U(λ2, l) are the voltage values ​​after the signal light is converted into a voltage signal and the reference light is converted into a voltage signal, respectively;

[0026] S25. Use the following formula to convert the volume fraction of CO2 gas into mass concentration to obtain the actual value of CO2 mass concentration at the optimal monitoring point:

[0027]

[0028] In the above formula, is the actual CO2 mass concentration at the ith monitoring point, MO2 is the molar mass of CO2, P is the atmospheric pressure, R is the ideal gas constant, and T is the absolute humidity.

[0029] The S3 includes:

[0030] S31. Use the following model to calculate the simulated value of CO2 mass concentration at the optimal monitoring point:

[0031]

[0032] In the above formula, C(x, y, z) is the CO2 gas mass concentration at the wind direction (x, y, z), Q is the emission intensity of the enterprise emission source, σ y , σ z are the dimensionless gas diffusion coefficients in the y and z directions, μ is the wind speed, and H is the effective source height of the emission point;

[0033] S32. Construct a function that aims to minimize the error between the actual value and the simulated value of the CO2 mass concentration at the optimal monitoring point:

[0034]

[0035]

[0036] In the above formula, is the actual CO2 mass concentration at the ith monitoring point, is the simulated value of CO2 mass concentration at the corresponding position of the i-th monitoring point;

[0037] S33. Based on the particle swarm optimization algorithm, the objective function is iteratively optimized. When the preset maximum number of iterations is reached, the algorithm terminates and the optimal monitoring point whose simulation value is closest to the actual value is obtained. The coordinates of this optimal monitoring point are substituted into the CO2 mass concentration simulation value calculation model of S31 to obtain the optimal carbon emission intensity of the emission source.

[0038] The S1 includes:

[0039] S11. Pre-select multiple enterprise carbon emission factor monitoring points based on the spatiotemporal distribution of CO2 emissions from enterprise emission sources;

[0040] S12. Based on the pre-selected multiple enterprise carbon emission factor monitoring points, the cluster center of the C-means cluster analysis method is initialized to P0, the number of iterations is l, the fuzzy weighted index is m, the number of cluster categories is C, and the threshold is ε;

[0041] S13. Set the objective function of the C-means cluster analysis method to:

[0042]

[0043] In the above formula, U is the membership matrix, v is the cluster center of the fuzzy group, n is the total number of pre-selected enterprise carbon emission factor monitoring points, c is the number of cluster categories, u is ik is the degree to which the kth monitoring point belongs to the i-th cluster center, d ik is the distance norm between the kth monitoring point and the i-th cluster center, m∈[1,∞) is the fuzzy weighted index;

[0044] S14, membership in C-means cluster analysis Under the constraints of , the Lagrangian algorithm is used to construct a new objective function and minimize it:

[0045]

[0046] In the above formula, λ is the Lagrange multiplier of the constraint condition;

[0047] S15. Deriving all input parameters of the new objective function, the necessary condition for minimizing the new objective function is obtained as follows:

[0048]

[0049]

[0050] In the above formula, x kis the data of the kth monitoring point;

[0051] S16, update the membership and cluster center according to the necessary conditions to minimize the new objective function. Or when the number of iterations reaches the maximum, the algorithm stops and outputs the optimal monitoring point of the enterprise's carbon emission factor. Otherwise, the number of iterations is l=l+1, and the membership degree and cluster center are updated again until the algorithm stopping condition is met and the optimal monitoring point of the enterprise's carbon emission factor is selected.

[0052] In S4, the carbon emission factor in the actual combustion process of each raw material is calculated using the following formula:

[0053]

[0054] In the above formula, EF i is the carbon emission factor of the i-th raw material fuel, E i is the carbon emission intensity of the i-th raw material fuel, A i is the consumption of the i-th raw material fuel.

[0055] In a second aspect, the present invention proposes a carbon emission factor accounting system in the process of raw material combustion in an enterprise, comprising an optimal monitoring point determination module, an actual value detection module, an inversion module, and a carbon emission factor accounting module;

[0056] The optimal monitoring point determination module is used to determine the optimal monitoring point of the enterprise's CO2 mass concentration;

[0057] The actual value detection module is used to detect and obtain the actual value of CO2 mass concentration at the optimal monitoring point;

[0058] The inversion module is used to invert the carbon emission intensity of the emission source with the goal of minimizing the error between the actual value and the simulated value of the CO2 mass concentration at the optimal monitoring point;

[0059] The carbon emission factor calculation module is used to calculate the carbon emission factor in the actual combustion process of each raw material based on the inverted carbon emission intensity of the emission source.

[0060] The actual value detection module includes a gas absorption unit, a light absorption unit, a signal conversion unit, a sampling calculation unit, and a mass concentration calculation unit;

[0061] The gas absorption unit is used to absorb the gas at the optimal monitoring point into the CO2 gas absorption pool through a pump suction processing controller, and the CO2 gas absorption pool is a closed multi-ventilation chamber;

[0062] The light absorption unit is used to emit a single-path dual-wavelength infrared light beam from a determined incident point to the CO2 gas absorption cell, and after being reflected and absorbed in the gas absorption cell, it is emitted from the incident point and transmitted to the detector;

[0063] The incident point is calculated using the following formula:

[0064]

[0065] In the above formula, n is the number of reflections of the light source, d is n is the reflection point P n Distance from the optical axis, θ n is the reflected light S n The angle between the incident light S0 and the optical axis, l is the distance between the two concave mirrors in the air chamber, d0 is the distance from the incident point P0 to the optical axis, and θ0 is the angle between the incident light S0 and the optical axis;

[0066] The signal conversion unit is used for the detector to divide the received infrared light beam into signal light and reference light, and convert the two optical signals into voltage signals. The converted voltage signal is calculated using the following formula:

[0067] U(λ1,l)=η(λ1)I0(λ1)k(λ1)exp[-α(λ1)cl];

[0068] U(λ2,l)=η(λ2)I0(λ2)k(λ2);

[0069]

[0070] k(λ1)=exp[m(λ1)l+n(λ1)l+p(λ1)l];

[0071] k(λ2)=exp[m(λ2)l+n(λ2)l+p(λ2)l];

[0072] In the above formula, U(λ1, l) and U(λ2, l) are the voltage values ​​after the signal light is converted into a voltage signal and the voltage values ​​after the reference light is converted into a voltage signal, respectively; η(λ1) and η(λ2) are the photoelectric conversion coefficients of the signal light and the reference light, respectively; I0(λ1) and I0(λ2) are the initial light intensities of the signal light and the reference light, respectively; k(λ1) and k(λ2) are the intermediate variables for signal light calculation and the reference light calculation, respectively; α(λ1) is the absorption coefficient of the gas; c is the volume fraction of CO2 gas in the gas chamber; l is the length of the absorption cell; I0(λ) is the initial light intensity; is the electro-optical conversion coefficient, i is the driving current of the infrared beam, m(λ1) and m(λ2) are the Rayleigh scattering coefficients of the signal light and the reference light, n(λ1) and n(λ2) are the Mie scattering coefficients of the signal light and the reference light, p(λ1) and p(λ2) are the absorption coefficients of water molecules of the signal light and the reference light, respectively;

[0073] The sampling and calculation unit is used to sample and calculate the converted voltage signal, and calculate the volume fraction of CO2 gas in the absorption cell using the following formula:

[0074]

[0075] In the above formula, c is the volume fraction of CO2 gas in the absorption cell, λ1 and λ2 are the central wavelengths of the signal light and the reference light, respectively, α(λ1) is the absorption coefficient of the gas, l is the length of the absorption cell, η(λ1) and η(λ2) are the photoelectric conversion coefficients of the signal light and the reference light, respectively. are the electro-optical conversion coefficients of the signal light and the reference light, respectively; k(λ1) and k(λ2) are the intermediate variables of the signal light calculation and the reference light calculation, respectively; U(λ1, l) and U(λ2, l) are the voltage values ​​after the signal light is converted into a voltage signal and the reference light is converted into a voltage signal, respectively;

[0076] The mass concentration calculation unit is used to convert the volume fraction of CO2 gas into mass concentration using the following formula to obtain the actual value of CO2 mass concentration at the optimal monitoring point:

[0077]

[0078] In the above formula, is the actual CO2 mass concentration at the i-th monitoring point, M CO2 is the molar mass of CO2, P is the atmospheric pressure, R is the ideal gas constant, and T is the absolute humidity.

[0079] The inversion module includes a concentration simulation value calculation unit, an objective function construction unit, and an iteration unit;

[0080] The concentration simulation value calculation unit is used to calculate the CO2 mass concentration simulation value of the optimal monitoring point using the following model:

[0081]

[0082] In the above formula, C(x, y, z) is the CO2 gas mass concentration at the wind direction (x, y, z), Q is the emission intensity of the enterprise emission source, σ y , σ z are the dimensionless gas diffusion coefficients in the y and z directions, μ is the wind speed, and H is the effective source height of the emission point;

[0083] The objective function construction unit is used to construct a function with the goal of minimizing the error between the actual value and the simulated value of the CO2 mass concentration at the optimal monitoring point:

[0084]

[0085]

[0086] In the above formula, is the actual CO2 mass concentration at the ith monitoring point, is the simulated value of CO2 mass concentration at the corresponding position of the i-th monitoring point;

[0087] The iteration unit is used to iteratively optimize the objective function based on the particle swarm optimization algorithm. When the preset maximum number of iteration steps is reached, the algorithm terminates and the optimal monitoring point whose simulated value is closest to the actual value is obtained. The coordinates of this optimal monitoring point are substituted into the calculation model of the CO2 mass concentration simulation value to obtain the optimal carbon emission intensity of the emission source.

[0088] The optimal monitoring point determination module includes a monitoring point preselection unit, an algorithm initialization unit, an objective function setting unit, an objective function updating unit, a necessary condition determination unit, and a judgment unit;

[0089] The monitoring point preselection unit is used to preselect multiple enterprise carbon emission factor monitoring points according to the spatiotemporal distribution of CO2 emitted by the enterprise emission source;

[0090] The algorithm initialization unit is used to initialize the cluster center of the C-means cluster analysis method to P0, the number of iterations to l, the fuzzy weighted index to m, the number of cluster categories to C, and the threshold to ε based on the pre-selected multiple enterprise carbon emission factor monitoring points;

[0091] The objective function setting unit is used to set the objective function of the C-means cluster analysis method as:

[0092]

[0093] In the above formula, U is the membership matrix, P is the cluster center of the fuzzy group, n is the total number of pre-selected enterprise carbon emission factor monitoring points, c is the number of cluster categories, u ik is the degree to which the kth monitoring point belongs to the i-th cluster center, d ik is the distance norm between the kth monitoring point and the i-th cluster center, m∈[1,∞) is the fuzzy weighted index;

[0094] The objective function updating unit is used to update the membership degree of the C-means clustering analysis method Under the constraints of , the Lagrangian algorithm is used to construct a new objective function and minimize it:

[0095]

[0096] In the above formula, λ is the Lagrange multiplier of the constraint condition;

[0097] The necessary condition determination unit is used to derive all input parameters of the new objective function, and obtain the necessary condition for minimizing the new objective function as follows:

[0098]

[0099]

[0100] In the above formula, x k is the data of the kth monitoring point;

[0101] The judgment unit is used to update the membership degree and cluster center according to the necessary conditions for minimizing the new objective function. Or when the number of iterations reaches the maximum, the algorithm stops and outputs the optimal monitoring point of the enterprise's carbon emission factor. Otherwise, the number of iterations is l=l+1, and the membership degree and cluster center are updated again until the algorithm stopping condition is met and the optimal monitoring point of the enterprise's carbon emission factor is selected.

[0102] In the carbon emission factor calculation module, the following formula is used to calculate the carbon emission factor in the actual combustion process of each raw material:

[0103]

[0104] In the above formula, EF i is the carbon emission factor of the i-th raw material fuel, E i is the carbon emission intensity of the i-th raw material fuel, A i is the consumption of the i-th raw material fuel.

[0105] Compared with the prior art, the present invention has the following beneficial effects:

[0106] The present invention proposes a method and system for calculating carbon emission factors in the process of raw material combustion in an enterprise. First, the optimal monitoring point of the CO2 mass concentration of the enterprise is determined, and the actual value of the CO2 mass concentration is detected at the optimal monitoring point. Then, the carbon emission intensity of the emission source is inverted with the goal of minimizing the error between the actual value of the CO2 mass concentration at the optimal monitoring point and the simulated value. Finally, based on the inverted carbon emission intensity of the emission source, the carbon emission factor of each actual combustion raw material is calculated, and the default value of the carbon emission factor is replaced with it for comparison and correction. The method reasonably selects the optimal monitoring point near the industrial production emission source of the enterprise, carries out direct and effective monitoring and data analysis of the carbon emission factor, and provides the enterprise with a carbon monitoring system that can update the carbon emission factor in real time, which not only improves the accuracy of carbon emission monitoring, but also truly reflects the carbon emission level of the enterprise, and provides a scientific basis for the environmental protection management and carbon reduction and emission measures of the enterprise, helping the enterprise to better comply with environmental laws and regulations and standards, and promote the sustainable development of the enterprise. BRIEF DESCRIPTION OF THE DRAWINGS

[0107] Figure 1 The figure is an overall flow chart of the method of the present invention.

[0108] Figure 2 The figure is a structural diagram of the system of the invention. DETAILED DESCRIPTION

[0109] The present invention is further described in detail below in conjunction with specific implementations and drawings.

[0110] The present invention proposes a method and system for calculating carbon emission factors in the process of raw material combustion in an enterprise. A coordinate system is established according to the geographical location of the enterprise, and the coordinate information of the CO2 emission source and the emission intensity of the emission source are marked. The optimal CO2 monitoring point is selected according to the spatiotemporal distribution of the diffusion of CO2 emitted by the emission source of the enterprise. The actual value of the CO2 mass concentration is detected, and the error between the actual value of the CO2 mass concentration at the optimal monitoring point and the simulated value is minimized as the goal. The carbon emission intensity of the emission source is inverted, and the carbon emission factor of each actual combustion raw material is calculated, so as to provide the enterprise with more accurate carbon emission data and accurately reflect the actual carbon emission level of the enterprise.

[0111] Embodiment 1:

[0112] This embodiment takes a certain enterprise as the research object. The raw material of this enterprise in the calcination stage is mainly coal. The carbon emission factor generated by this enterprise in the process of raw material combustion is calculated.

[0113] like Figure 1 As shown in the figure, a method for calculating the carbon emission factor in the process of raw material combustion in an enterprise is carried out in the following steps:

[0114] 1. Establish a coordinate system based on the company's geographical location, mark the coordinates of its CO2 emission source as (0.36m, 2.71m), the height as 50m, and the actual emission intensity of the emission source as 4000 (kg / h).

[0115] 2. Based on the coordinate information and emission intensity of the enterprise's CO2 emission sources, simulate the spatiotemporal distribution of CO2 emissions from the enterprise's emission sources, and pre-select multiple CO2 monitoring points based on their spatiotemporal distribution;

[0116] 3. The C-means cluster analysis method is used to select the optimal monitoring point from the pre-selected multiple monitoring points. The coordinates of the five optimal monitoring points are (730.13m, -1678.79m), (-493.7m, -2247.34m), (970.14m, -4168.03m), (1694.62m, -3508.62m), (1910.88m, -4568.11m), which provide more reliable data support for CO2 inversion;

[0117] The C-means cluster analysis method includes:

[0118] Initialize the cluster center of the C-means cluster analysis method to P0 = 1, the number of iterations to l = 100, the fuzzy weighted index to m = 1, the number of cluster categories to C = 5, and the threshold to ε = 0.1;

[0119] The objective function of the C-means cluster analysis method is set as:

[0120]

[0121] In the above formula, U is the membership matrix, P is the cluster center of the fuzzy group, n is the total number of pre-selected CO2 monitoring points, c is the number of cluster categories, and u is ik is the degree to which the kth monitoring point belongs to the i-th cluster center, d ik is the distance norm between the kth monitoring point and the i-th cluster center, m∈[1,∞) is the fuzzy weighted index;

[0122] Membership in C-means cluster analysis Under the constraints of , the Lagrangian algorithm is used to construct a new objective function and minimize it:

[0123]

[0124] In the above formula, λ is the Lagrange multiplier of the constraint condition;

[0125] By taking the derivative of all input parameters of the new objective function, we can obtain the necessary condition for minimizing the new objective function:

[0126]

[0127]

[0128] In the above formula, x k is the data of the kth monitoring point;

[0129] Update the membership and cluster center according to the necessary conditions to minimize the new objective function. Or when the number of iterations reaches the maximum, the algorithm stops and outputs the optimal monitoring point of the enterprise's carbon emission factor. Otherwise, the number of iterations is l=l+1, and the membership degree and cluster center are updated again until the algorithm stopping condition is met and the optimal monitoring point of the enterprise's carbon emission factor is selected.

[0130] 4. Monitor carbon emissions at the optimal monitoring point of CO2 and determine the actual value of CO2 mass concentration at the optimal monitoring point;

[0131] Carbon emission monitoring includes wind speed and direction collection, enterprise production raw material measurement, and CO2 concentration detection;

[0132] Wind speed and direction acquisition includes wind speed sensor, wind direction sensor and signal processing module;

[0133] The wind speed sensor is based on the airflow sensing effect. When the airflow passes through the sensor, it will generate gas dynamic pressure to form a feedback signal, and transmit the feedback signal through the signal processing module. The wind direction sensor includes multiple wind vanes. Based on the azimuth sensing of the wind direction, when the airflow passes through the wind direction sensor, the force generated by the airflow on the wind vane will cause the wind vane to rotate, thereby indicating the direction of the airflow;

[0134] Industrial production raw material measurement includes floor scales, dynamic weighing scales and sensor modules, which are used for weighing and measuring raw materials in the production process;

[0135] CO2 concentration detection includes:

[0136] The gas at the optimal monitoring point is absorbed into the CO2 gas absorption cell by a pumping process controller. The CO2 gas absorption cell is a closed multi-ventilation chamber, and a single-path dual-wavelength infrared light source is used to emit a single-path dual-wavelength infrared light beam from a determined incident point to the CO2 gas absorption cell. After being reflected and absorbed in the gas absorption cell, the infrared light beam is emitted from the incident point and transmitted to the detector;

[0137] The incident point is calculated using the following formula:

[0138]

[0139] In the above formula, n is the number of reflections of the light source, d is n is the reflection point P n Distance from the optical axis, θ nis the reflected light S n The angle between the incident light S0 and the optical axis, l is the distance between the two concave mirrors in the air chamber, d0 is the distance from the incident point P0 to the optical axis, and θ0 is the angle between the incident light S0 and the optical axis;

[0140] The detector divides the received infrared beam into signal light and reference light, and converts the two optical signals into voltage signals. The converted voltage signal is calculated using the following formula:

[0141] U(λ1,l)=η(λ1)I0(λ1)k(λ1)exp[-α(λ1)cl];

[0142] U(λ2,l)=η(λ2)I0(λ2)k(λ2);

[0143]

[0144] k(λ1)=exp[m(λ1)l+n(λ1)l+p(λ1)l];

[0145] k(λ2)=exp[m(λ2)l+n(λ2)l+p(λ2)l];

[0146] In the above formula, U(λ1, l) and U(λ2, l) are the voltage values ​​after the signal light is converted into a voltage signal and the voltage values ​​after the reference light is converted into a voltage signal, respectively; λ(λ1) and λ(λ2) are the photoelectric conversion coefficients of the signal light and the reference light, respectively; I0(λ1) and I0(λ2) are the initial light intensities of the signal light and the reference light, respectively; k(λ1) and k(λ2) are the intermediate variables for signal light calculation and the reference light calculation, respectively; α(λ1) is the absorption coefficient of the gas; c is the volume fraction of CO2 gas in the gas chamber; l is the length of the absorption cell; I0(λ) is the initial light intensity; is the electro-optical conversion coefficient, i is the driving current of the infrared beam, m(λ1) and m(λ2) are the Rayleigh scattering coefficients of the signal light and the reference light, n(λ1) and n(λ2) are the Mie scattering coefficients of the signal light and the reference light, p(λ1) and p(λ2) are the absorption coefficients of water molecules of the signal light and the reference light, respectively;

[0147] The converted voltage signal is sampled and calculated, and the volume fraction of CO2 gas in the absorption cell is calculated using the following formula:

[0148]

[0149] In the above formula, c is the volume fraction of CO2 gas in the absorption cell, expressed in ppm, λ1 and λ2 are the central wavelengths of the signal light and the reference light, respectively, α(λ1) is the absorption coefficient of the gas, l is the length of the absorption cell, η(λ1) and λ(λ2) are the photoelectric conversion coefficients of the signal light and the reference light, respectively. are the electro-optical conversion coefficients of the signal light and the reference light, respectively; k(λ1) and k(λ2) are the intermediate variables of the signal light calculation and the reference light calculation, respectively; U(λ1, l) and U(λ2, l) are the voltage values ​​after the signal light is converted into a voltage signal and the reference light is converted into a voltage signal, respectively;

[0150] The volume fraction of CO2 gas is converted into mass concentration using the following formula to obtain the actual value of CO2 mass concentration at the optimal monitoring point:

[0151]

[0152] In the above formula, is the actual CO2 mass concentration at the ith monitoring point, in mg / m 3 , M CO2 is the molar mass of CO2, which is 44 g / mol, P is the atmospheric pressure, in Pa, R is the ideal gas constant, which is 8.314 J / (mol·K), and T is the absolute humidity, in K.

[0153] 5. Take the relevant data of wind speed and direction collection, enterprise production raw material measurement, and CO2 concentration detection as input (including the actual value of CO2 mass concentration, wind speed, wind direction, and the consumption of electrodes, scrap steel, and ferromanganese alloy involved in calcination during the production process), and minimize the error between the actual value of CO2 mass concentration and the simulated value at the optimal monitoring point to invert the carbon emission intensity of the emission source;

[0154] The following model is used to calculate the simulated value of CO2 mass concentration at the optimal monitoring point:

[0155]

[0156] In the above formula, C(x, y, z) is the CO2 gas mass concentration (mg / m3) at the wind direction (x, y, z), Q is the emission intensity of the enterprise emission source (kg / h), σ y , σ z are the dimensionless gas diffusion coefficients in the y and z directions, respectively, and their calculation models are shown in Table 1 , where μ is the wind speed (m / step S), and H is the effective source height of the emission point (m);

[0157] Table 1 Gas diffusion coefficient σ y , σ z Calculation model table

[0158] Atmospheric stability <![CDATA[σ y ]]> <![CDATA[σ z ]]> A <![CDATA[0.22x / (1+0.0001x) 0.5 ]]> 0.2x B <![CDATA[0.16x / (1+0.0001x) 0.5 ]]> 0.12x C <![CDATA[0.11x / (1+0.0001x) 0.5 ]]> <![CDATA[0.08x / (1+0.0002x) 0.5 ]]> D <![CDATA[0.08x / (1+0.0001x) 0.5 ]]> <![CDATA[0.06x / (1+0.0015x) 0.5 ]]> E <![CDATA[0.06x / (1+0.0001x) 0.5 ]]> <![CDATA[0.03x / (1+0.0003x) 0.5 ]]> F <![CDATA[0.04x / (1+0.0001x) 0.5 ]]> <![CDATA[0.016x / (1+0.0003x) 0.5 ]]> ;

[0159] Construct a function that aims to minimize the error between the actual value and the simulated value of CO2 mass concentration at the optimal monitoring point:

[0160]

[0161]

[0162] In the above formula, is the actual CO2 mass concentration at the ith monitoring point, is the simulated value of CO2 mass concentration at the corresponding position of the i-th monitoring point;

[0163] Based on the particle swarm optimization algorithm, the objective function is iteratively optimized. When the preset maximum number of iterations is reached, the algorithm terminates and the optimal monitoring point with the closest simulation value to the actual value is obtained. The coordinates of this optimal monitoring point are substituted into the calculation model of the CO2 mass concentration simulation value to obtain the optimal carbon emission intensity of the emission source.

[0164] The particle swarm optimization algorithm is:

[0165] Assume a d-dimensional space, initialize a population of N = 1000 particles, and set its variables as follows: the position of the i-th particle is represented by the vector x i =[x i1 , x i2 , …, x id ], and the flight speed is represented by v i =[v i1 , v i2 , …, v id ] indicates that the optimal position searched by the i-th particle itself is represented by p i =[p i1 , p i2 ,…,p id ] indicates that the optimal position currently searched by the entire group is represented by p g Indicates that the velocity and position of the particle are updated using the following formula:

[0166] v i (n+1)=v i (n)+c1r1(p i -x i (n))+c2r2(p g -x i (n));

[0167] x i (n+1)=x i (n)+vi (n+1);

[0168] In the above formula, n is the number of iterations, that is, the number of flight steps of the particle n = 500, c1 and c2 are learning factors greater than 0 (also called acceleration coefficients), c1 = c2 = 2, which is used to adjust the maximum step length of the particle flying towards the most optimal position it has found and the optimal position found by the entire population, and r1 and r2 are random numbers between [0, 1].

[0169] 6. Based on the inverted carbon emission intensity of the emission source, the carbon emission factor of each raw material combustion process is calculated to provide enterprises with more accurate carbon emission data. The staff can view the obtained carbon emission data, different raw material consumption and the emission factor of each raw material combustion in real time through the user interface, which is convenient for enterprises to view, obtain and operate data in real time;

[0170] The carbon emission factor in the actual combustion process of each raw material is calculated using the following formula:

[0171]

[0172] In the above formula, EF i is the carbon emission factor of the i-th raw material fuel, E i is the carbon emission intensity of the i-th raw material fuel (kg), A i is the consumption of the ith raw material fuel (kg);

[0173] The enterprise carbon emission factor monitoring points and emission source related parameters and inversion results are shown in Table 2;

[0174] Table 2 Monitoring point and emission source related parameters and inversion results

[0175]

[0176] The accuracy of the inversion model algorithm module in the carbon monitoring system was verified, and the verification result showed an extremely low error value of only 0.73%. Therefore, the present invention can be applied in enterprises to achieve more accurate calculation of the carbon emission factors in the actual combustion process of each raw material.

[0177] Embodiment 2:

[0178] This example takes a steel enterprise in Shiyan City, Wuhan City as the research object. The raw materials of this enterprise in the calcination stage mainly include electrodes, scrap steel, and ferromanganese alloy;

[0179] According to the specific steps of the method for calculating the carbon emission factor in the raw material combustion process described in Example 1, the raw material consumption in the calcination stage of the industrial production process of the steel enterprise and the carbon emission factors calculated by the IPCC default method and this scheme are shown in Table 3;

[0180] Table 3 Raw material consumption and emission factors

[0181]

[0182] It can be seen from Table 3 that this real-time monitoring and data analysis not only improves the accuracy of carbon emission monitoring, but also provides a scientific basis for the company's environmental management and carbon emission reduction measures, helping companies to better respond to environmental regulations and standards and promote sustainable development.

[0183] Embodiment 3:

[0184] like Figure 2 As shown, a carbon emission factor accounting system in the process of raw material combustion in an enterprise includes an optimal monitoring point determination module, an actual value detection module, an inversion module, and a carbon emission factor accounting module;

[0185] The optimal monitoring point determination module is used to determine the optimal monitoring point of the enterprise's CO2 mass concentration;

[0186] The actual value detection module is used to detect and obtain the actual value of CO2 mass concentration at the optimal monitoring point;

[0187] The inversion module is used to invert the carbon emission intensity of the emission source with the goal of minimizing the error between the actual value and the simulated value of the CO2 mass concentration at the optimal monitoring point;

[0188] The carbon emission factor calculation module is used to calculate the carbon emission factor in the actual combustion process of each raw material based on the inverted carbon emission intensity of the emission source.

[0189] The optimal monitoring point determination module includes a monitoring point preselection unit, an algorithm initialization unit, an objective function setting unit, an objective function updating unit, a necessary condition determination unit, and a judgment unit;

[0190] The monitoring point preselection unit is used to preselect multiple enterprise carbon emission factor monitoring points according to the spatiotemporal distribution of CO2 emitted by the enterprise emission source;

[0191] The algorithm initialization unit is used to initialize the cluster center of the C-means cluster analysis method to P0, the number of iterations to l, the fuzzy weighted index to m, the number of cluster categories to C, and the threshold to ε based on the pre-selected multiple enterprise carbon emission factor monitoring points;

[0192] The objective function setting unit is used to set the objective function of the C-means cluster analysis method as:

[0193]

[0194] In the above formula, U is the membership matrix, v is the cluster center of the fuzzy group, n is the total number of pre-selected enterprise carbon emission factor monitoring points, c is the number of cluster categories, u is ik is the degree to which the kth monitoring point belongs to the i-th cluster center, d ik is the distance norm between the kth monitoring point and the i-th cluster center, m∈[1,∞) is the fuzzy weighted index;

[0195] The objective function updating unit is used to update the membership degree of the C-means clustering analysis method Under the constraints of , the Lagrangian algorithm is used to construct a new objective function and minimize it:

[0196]

[0197] In the above formula, λ is the Lagrange multiplier of the constraint condition;

[0198] The necessary condition determination unit is used to derive all input parameters of the new objective function, and obtain the necessary condition for minimizing the new objective function as follows:

[0199]

[0200]

[0201] In the above formula, x k is the data of the kth monitoring point;

[0202] The judgment unit is used to update the membership degree and cluster center according to the necessary conditions for minimizing the new objective function. Or when the number of iterations reaches the maximum, the algorithm stops and outputs the optimal monitoring point of the enterprise's carbon emission factor. Otherwise, the number of iterations is l=l+1, and the membership degree and cluster center are updated again until the algorithm stopping condition is met and the optimal monitoring point of the enterprise's carbon emission factor is selected.

[0203] The actual value detection module includes a gas absorption unit, a light absorption unit, a signal conversion unit, a sampling calculation unit, and a mass concentration calculation unit;

[0204] The gas absorption unit is used to absorb the gas at the optimal monitoring point into the CO2 gas absorption pool through a pump suction processing controller, and the CO2 gas absorption pool is a closed multi-ventilation chamber;

[0205] The light absorption unit is used to emit a single-path dual-wavelength infrared light beam from a determined incident point to the CO2 gas absorption cell, and after being reflected and absorbed in the gas absorption cell, it is emitted from the incident point and transmitted to the detector;

[0206] The incident point is calculated using the following formula:

[0207]

[0208] In the above formula, n is the number of reflections of the light source, d is n is the reflection point P n Distance from the optical axis, θ n is the reflected light S n The angle between the incident light S0 and the optical axis, l is the distance between the two concave mirrors in the air chamber, d0 is the distance from the incident point P0 to the optical axis, and θ0 is the angle between the incident light S0 and the optical axis;

[0209] The signal conversion unit is used for the detector to divide the received infrared light beam into signal light and reference light, and convert the two optical signals into voltage signals. The converted voltage signal is calculated using the following formula:

[0210] U(λ1,l)=η(λ1)I0(λ1)k(λ1)exp[-α(λ1)cl];

[0211] U(λ2,l)=η(λ2)I0(λ2)k(λ2);

[0212]

[0213] k(λ1)=exp[m(λ1)l+n(λ1)l+p(λ1)l];

[0214] k(λ2)=exp[m(λ2)l+n(λ2)l+p(λ2)l];

[0215] In the above formula, U(λ1, l) and U(λ2, l) are the voltage values ​​after the signal light is converted into a voltage signal and the voltage values ​​after the reference light is converted into a voltage signal, respectively; η(λ1) and η(λ2) are the photoelectric conversion coefficients of the signal light and the reference light, respectively; I0(λ1) and I0(λ2) are the initial light intensities of the signal light and the reference light, respectively; k(λ1) and k(λ2) are the intermediate variables for signal light calculation and the reference light calculation, respectively; α(λ1) is the absorption coefficient of the gas; c is the volume fraction of CO2 gas in the gas chamber; l is the length of the absorption cell; I0(λ) is the initial light intensity; is the electro-optical conversion coefficient, i is the driving current of the infrared beam, m(λ1) and m(λ2) are the Rayleigh scattering coefficients of the signal light and the reference light, n(λ1) and n(λ2) are the Mie scattering coefficients of the signal light and the reference light, p(λ1) and p(λ2) are the absorption coefficients of water molecules of the signal light and the reference light, respectively;

[0216] The sampling and calculation unit is used to sample and calculate the converted voltage signal, and calculate the volume fraction of CO2 gas in the absorption cell using the following formula:

[0217]

[0218] In the above formula, c is the volume fraction of CO2 gas in the absorption cell, λ1 and λ2 are the central wavelengths of the signal light and the reference light, respectively, α(λ1) is the absorption coefficient of the gas, l is the length of the absorption cell, η(λ1) and η(λ2) are the photoelectric conversion coefficients of the signal light and the reference light, respectively. are the electro-optical conversion coefficients of the signal light and the reference light, respectively; k(λ1) and k(λ2) are the intermediate variables of the signal light calculation and the reference light calculation, respectively; U(λ1, l) and U(λ2, l) are the voltage values ​​after the signal light is converted into a voltage signal and the reference light is converted into a voltage signal, respectively;

[0219] The mass concentration calculation unit is used to convert the volume fraction of CO2 gas into mass concentration using the following formula to obtain the actual value of CO2 mass concentration at the optimal monitoring point:

[0220]

[0221] In the above formula, is the actual CO2 mass concentration at the ith monitoring point, in mg / m 3 , M CO2 is the molar mass of CO2, which is 44 g / mol, P is the atmospheric pressure, in Pa, R is the ideal gas constant, which is 8.314 J / (mol·K), and T is the absolute humidity, in K.

[0222] The inversion module includes a concentration simulation value calculation unit, an objective function construction unit, and an iteration unit;

[0223] The concentration simulation value calculation unit is used to calculate the CO2 mass concentration simulation value of the optimal monitoring point using the following model:

[0224]

[0225] In the above formula, C(x, y, z) is the CO2 gas mass concentration (mg / m3) at the wind direction (x, y, z), Q is the emission intensity of the enterprise emission source (kg / h), σ y , σ z are the dimensionless gas diffusion coefficients in the y and z directions, respectively, μ is the wind speed (m / step S), and H is the effective source height of the emission point (m);

[0226] The objective function construction unit is used to construct a function with the goal of minimizing the error between the actual value and the simulated value of the CO2 mass concentration at the optimal monitoring point:

[0227]

[0228]

[0229] In the above formula, is the actual CO2 mass concentration at the ith monitoring point, is the simulated value of CO2 mass concentration at the corresponding position of the i-th monitoring point;

[0230] The iteration unit is used to iteratively optimize the objective function based on the particle swarm optimization algorithm. When the preset maximum number of iteration steps is reached, the algorithm is terminated to obtain the optimal monitoring point where the simulation value is closest to the actual value, and the coordinates of the optimal monitoring point are brought into the calculation model of the CO2 mass concentration simulation value, thereby obtaining the optimal carbon emission intensity of the emission source;

[0231] The particle swarm optimization algorithm is:

[0232] Assume a d-dimensional space, initialize a population of N particles, and set its variables as follows: the position of the i-th particle is represented by the vector x i =[x i1 , x i2 , …, x id ], and the flight speed is represented by v i =[v i1 , v i2 , …, v id ] indicates that the optimal position searched by the i-th particle itself is represented by p i =[p i1 , p i2 ,…,p id ] indicates that the optimal position currently searched by the entire group is represented by p g Indicates that the velocity and position of the particle are updated using the following formula:

[0233] v i (n+1)=v i (n)+c1r1(p i -x i (n))+c2r2(P g -x i (n));

[0234] x i (n+1)=x i (n)+v i (n+1);

[0235] In the above formula, n is the number of iterations, that is, the number of flight steps of the particle, c1 and c2 are learning factors greater than 0, which are used to adjust the maximum step length of the particle flying towards the optimal position it has found and the optimal position found by the entire population, and r1 and r2 are random numbers between [0, 1].

[0236] In the carbon emission factor calculation module, the following formula is used to calculate the carbon emission factor in the actual combustion process of each raw material:

[0237]

[0238] In the above formula, EF i is the carbon emission factor of the i-th raw material fuel, E i is the carbon emission intensity of the i-th raw material fuel (kg), A i is the consumption of the ith raw material fuel (kg).

Claims

1. A method for calculating carbon emission factors in the process of raw material combustion in an enterprise, characterized in that: The method comprises: S1. Determine the optimal monitoring point for the enterprise's CO2 mass concentration; S2, detect and obtain the actual value of CO2 mass concentration at the optimal monitoring point; S3, with the goal of minimizing the error between the actual value and the simulated value of the CO2 mass concentration at the optimal monitoring point, invert the carbon emission intensity of the emission source; S4. Based on the inverted carbon emission intensity of the emission sources, calculate the actual carbon emission factors in the combustion process of each raw material.

2. According to claim 1, a method for calculating carbon emission factors in the process of raw material combustion in an enterprise is characterized in that: The S2 includes: S21, absorbing the gas at the optimal monitoring point into a CO2 gas absorption pool through a pump suction processing controller, wherein the CO2 gas absorption pool is a closed multi-ventilation chamber; S22, emitting a single-path dual-wavelength infrared beam from a determined incident point to a CO2 gas absorption cell, and after being reflected and absorbed in the gas absorption cell, emitting from the incident point and transmitting to a detector; The incident point is calculated using the following formula: In the above formula, n is the number of reflections of the light source, d is n is the reflection point P n Distance from the optical axis, θ n is the reflected light S n The angle between the incident light S0 and the optical axis, l is the distance between the two concave mirrors in the air chamber, d0 is the distance from the incident point P0 to the optical axis, and θ0 is the angle between the incident light S0 and the optical axis; S23. The detector divides the received infrared light beam into signal light and reference light, and converts the two optical signals into voltage signals. The converted voltage signal is calculated using the following formula: U(λ1,l)=η(λ1)I0(λ1)k(λ1)exp[-α(λ1)cl]; U(λ2,l)=η(λ2)I0(λ2)k(λ2); k(λ1)=exp[m(λ1)l+n(λ1)l+p(λ1)l]; k(λ2)=exp[m(λ2)ln(λ2)l+p(λ2)l]; In the above formula, U(λ1, l) and U(λ2, l) are the voltage values ​​after the signal light is converted into a voltage signal and the voltage values ​​after the reference light is converted into a voltage signal, respectively; η(λ1) and η(λ2) are the photoelectric conversion coefficients of the signal light and the reference light, respectively; I0(λ1) and I0(λ2) are the initial light intensities of the signal light and the reference light, respectively; k(λ1) and k(λ2) are the intermediate variables for signal light calculation and the reference light calculation, respectively; α(λ1) is the absorption coefficient of the gas; c is the volume fraction of CO2 gas in the gas chamber; l is the length of the absorption cell; I0(λ) is the initial light intensity; is the electro-optical conversion coefficient, i is the driving current of the infrared beam, m(λ1) and m(λ2) are the Rayleigh scattering coefficients of the signal light and the reference light, n(λ1) and nn(λ2) are the Mie scattering coefficients of the signal light and the reference light, p(λ1) and p(λ2) are the absorption coefficients of water molecules of the signal light and the reference light, respectively; S24, sampling and calculating the converted voltage signal, and using the following formula to calculate the volume fraction of CO2 gas in the absorption cell: In the above formula, c is the volume fraction of CO2 gas in the absorption cell, λ1 and λ2 are the central wavelengths of the signal light and the reference light, respectively, α(λ1) is the absorption coefficient of the gas, l is the length of the absorption cell, η(λ1) and η(λ2) are the photoelectric conversion coefficients of the signal light and the reference light, respectively. are the electro-optical conversion coefficients of the signal light and the reference light, respectively; k(λ1) and k(λ2) are the intermediate variables of the signal light calculation and the reference light calculation, respectively; U(λ1, l) and U(λ2, l) are the voltage values ​​after the signal light is converted into a voltage signal and the reference light is converted into a voltage signal, respectively; S25. Use the following formula to convert the volume fraction of CO2 gas into mass concentration to obtain the actual value of CO2 mass concentration at the optimal monitoring point: In the above formula, is the actual CO2 mass concentration at the i-th monitoring point, M CO2 is the molar mass of CO2, P is the atmospheric pressure, R is the ideal gas constant, and T is the absolute humidity.

3. The method for calculating carbon emission factors in the process of raw material combustion in an enterprise according to claim 1 is characterized in that: The S3 includes: S31. Use the following model to calculate the simulated value of CO2 mass concentration at the optimal monitoring point: In the above formula, C(x, y, z) is the CO2 gas mass concentration at the wind direction (x, y, z), Q is the emission intensity of the enterprise emission source, σ y , σ z are the dimensionless gas diffusion coefficients in the y and z directions, μ is the wind speed, and H is the effective source height of the emission point; S32. Construct a function that aims to minimize the error between the actual value and the simulated value of the CO2 mass concentration at the optimal monitoring point: In the above formula, is the actual CO2 mass concentration at the ith monitoring point, is the simulated value of CO2 mass concentration at the corresponding position of the i-th monitoring point; S33. Based on the particle swarm optimization algorithm, the objective function is iteratively optimized. When the preset maximum number of iterations is reached, the algorithm terminates and the optimal monitoring point whose simulation value is closest to the actual value is obtained. The coordinates of this optimal monitoring point are substituted into the CO2 mass concentration simulation value calculation model of S31 to obtain the optimal carbon emission intensity of the emission source.

4. The method for calculating carbon emission factors in the process of raw material combustion in an enterprise according to claim 1 is characterized in that: The S1 includes: S11. Pre-select multiple enterprise carbon emission factor monitoring points based on the spatiotemporal distribution of CO2 emissions from enterprise emission sources; S12. Based on the pre-selected multiple enterprise carbon emission factor monitoring points, the cluster center of the C-means cluster analysis method is initialized to P0, the number of iterations is l, the fuzzy weighted index is m, the number of cluster categories is C, and the threshold is ε; S13. Set the objective function of the C-means cluster analysis method to: In the above formula, U is the membership matrix, P is the cluster center of the fuzzy group, n is the total number of pre-selected enterprise carbon emission factor monitoring points, c is the number of cluster categories, u ik is the degree to which the kth monitoring point belongs to the i-th cluster center, d ik is the distance norm between the kth monitoring point and the i-th cluster center, m∈[1,∞) is the fuzzy weighted index; S14, membership in C-means cluster analysis Under the constraints of , the Lagrangian algorithm is used to construct a new objective function and minimize it: In the above formula, λ is the Lagrange multiplier of the constraint condition; S15. Deriving all input parameters of the new objective function, the necessary condition for minimizing the new objective function is obtained as follows: In the above formula, xk is the data of the kth monitoring point; S16, update the membership and cluster center according to the necessary conditions to minimize the new objective function, and when max|P is satisfied i (l+1) -P i (l) |<ε or the number of iterations reaches the maximum, the algorithm stops and outputs the optimal monitoring point of the enterprise's carbon emission factor. Otherwise, the number of iterations is l=l+1, and the membership degree and cluster center are updated again until the algorithm stopping condition is met and the optimal monitoring point of the enterprise's carbon emission factor is selected.

5. The method for calculating carbon emission factors in the process of raw material combustion in an enterprise according to claim 1 is characterized in that: In S4, the carbon emission factor in the actual combustion process of each raw material is calculated using the following formula: In the above formula, EF i is the carbon emission factor of the i-th raw material fuel, E i is the carbon emission intensity of the i-th raw material fuel, A i is the consumption of the i-th raw material fuel.

6. A carbon emission factor calculation system in the process of raw material combustion in an enterprise, characterized in that: The system includes an optimal monitoring point determination module, an actual value detection module, an inversion module, and a carbon emission factor calculation module; The optimal monitoring point determination module is used to determine the optimal monitoring point of the enterprise's CO2 mass concentration; The actual value detection module is used to detect and obtain the actual value of CO2 mass concentration at the optimal monitoring point; The inversion module is used to invert the carbon emission intensity of the emission source with the goal of minimizing the error between the actual value and the simulated value of the CO2 mass concentration at the optimal monitoring point; The carbon emission factor calculation module is used to calculate the carbon emission factor in the actual combustion process of each raw material based on the inverted carbon emission intensity of the emission source.

7. The carbon emission factor calculation system in the process of raw material combustion of an enterprise according to claim 6 is characterized in that: The actual value detection module includes a gas absorption unit, a light absorption unit, a signal conversion unit, a sampling calculation unit, and a mass concentration calculation unit; The gas absorption unit is used to absorb the gas at the optimal monitoring point into the CO2 gas absorption pool through a pump suction processing controller, and the CO2 gas absorption pool is a closed multi-ventilation chamber; The light absorption unit is used to emit a single-path dual-wavelength infrared light beam from a determined incident point to the CO2 gas absorption cell, and after being reflected and absorbed in the gas absorption cell, it is emitted from the incident point and transmitted to the detector; The incident point is calculated using the following formula: In the above formula, n is the number of reflections of the light source, d is n is the reflection point P n Distance from the optical axis, θ n is the reflected light S n The angle between the incident light S0 and the optical axis, l is the distance between the two concave mirrors in the air chamber, d0 is the distance from the incident point P0 to the optical axis, and θ0 is the angle between the incident light S0 and the optical axis; The signal conversion unit is used for the detector to divide the received infrared light beam into signal light and reference light, and convert the two optical signals into voltage signals. The converted voltage signal is calculated using the following formula: U(λ1,l)=η(λ1)I0(λ1)k(λ1)exp[-α(λ1)cl]; U(λ2,l)=η(λ2)I0(λ2)k(λ2); k(λ1)=exp[m(λ1)l+n(λ1)l+p(λ1)l]; k(λ2)=exp[m(λ2)l+n(λ2)l+p(λ2)l]; In the above formula, U(λ1, l) and U(λ2, l) are the voltage values ​​after the signal light is converted into a voltage signal and the voltage values ​​after the reference light is converted into a voltage signal, respectively; η(λ1) and η(λ2) are the photoelectric conversion coefficients of the signal light and the reference light, respectively; I0(λ1) and I0(λ2) are the initial light intensities of the signal light and the reference light, respectively; k(λ1) and k(λ2) are the intermediate variables for signal light calculation and the reference light calculation, respectively; α(λ1) is the absorption coefficient of the gas; c is the volume fraction of CO2 gas in the gas chamber; l is the length of the absorption cell; I0(λ) is the initial light intensity; is the electro-optical conversion coefficient, i is the driving current of the infrared beam, m(λ1) and m(λ2) are the Rayleigh scattering coefficients of the signal light and the reference light, n(λ1) and n(λ2) are the Mie scattering coefficients of the signal light and the reference light, p(λ1) and p(λ2) are the absorption coefficients of water molecules of the signal light and the reference light, respectively; The sampling and calculation unit is used to sample and calculate the converted voltage signal, and calculate the volume fraction of CO2 gas in the absorption cell using the following formula: In the above formula, c is the volume fraction of CO2 gas in the absorption cell, λ1 and λ2 are the central wavelengths of the signal light and the reference light, respectively, α(λ1) is the absorption coefficient of the gas, l is the length of the absorption cell, η(λ1) and η(λ2) are the photoelectric conversion coefficients of the signal light and the reference light, respectively. are the electro-optical conversion coefficients of the signal light and the reference light, respectively; k(λ1) and k(λ2) are the intermediate variables of the signal light calculation and the reference light calculation, respectively; U(λ1, l) and U(λ2, l) are the voltage values ​​after the signal light is converted into a voltage signal and the reference light is converted into a voltage signal, respectively; The mass concentration calculation unit is used to convert the volume fraction of CO2 gas into mass concentration using the following formula to obtain the actual value of CO2 mass concentration at the optimal monitoring point: In the above formula, is the actual CO2 mass concentration at the i-th monitoring point, M CO2 is the molar mass of CO2, P is the atmospheric pressure, R is the ideal gas constant, and T is the absolute humidity.

8. The carbon emission factor calculation system in the process of raw material combustion of an enterprise according to claim 6 is characterized in that: The inversion module includes a concentration simulation value calculation unit, an objective function construction unit, and an iteration unit; The concentration simulation value calculation unit is used to calculate the CO2 mass concentration simulation value of the optimal monitoring point using the following model: In the above formula, C(x, y, z) is the CO2 gas mass concentration at the wind direction (x, y, z), Q is the emission intensity of the enterprise emission source, σ y , σ z are the dimensionless gas diffusion coefficients in the y and z directions, μ is the wind speed, and H is the effective source height of the emission point; The objective function construction unit is used to construct a function with the goal of minimizing the error between the actual value and the simulated value of the CO2 mass concentration at the optimal monitoring point: In the above formula, is the actual CO2 mass concentration at the ith monitoring point, is the simulated value of CO2 mass concentration at the corresponding position of the i-th monitoring point; The iteration unit is used to iteratively optimize the objective function based on the particle swarm optimization algorithm. When the preset maximum number of iteration steps is reached, the algorithm terminates and the optimal monitoring point whose simulated value is closest to the actual value is obtained. The coordinates of this optimal monitoring point are substituted into the calculation model of the CO2 mass concentration simulation value to obtain the optimal carbon emission intensity of the emission source.

9. The carbon emission factor calculation system in the process of raw material combustion of an enterprise according to claim 6 is characterized in that: The optimal monitoring point determination module includes a monitoring point preselection unit, an algorithm initialization unit, an objective function setting unit, an objective function updating unit, a necessary condition determination unit, and a judgment unit; The monitoring point preselection unit is used to preselect multiple enterprise carbon emission factor monitoring points according to the spatiotemporal distribution of CO2 emitted by the enterprise emission source; The algorithm initialization unit is used to initialize the cluster center of the C-means cluster analysis method to P0, the number of iterations to l, the fuzzy weighted index to m, the number of cluster categories to C, and the threshold to ε based on the pre-selected multiple enterprise carbon emission factor monitoring points; The objective function setting unit is used to set the objective function of the C-means cluster analysis method as: In the above formula, U is the membership matrix, P is the cluster center of the fuzzy group, n is the total number of pre-selected enterprise carbon emission factor monitoring points, c is the number of cluster categories, u ik is the degree to which the kth monitoring point belongs to the i-th cluster center, d ik is the distance norm between the kth monitoring point and the i-th cluster center, m∈[1,∞) is the fuzzy weighted index; The objective function updating unit is used to update the membership degree of the C-means clustering analysis method Under the constraints of , the Lagrangian algorithm is used to construct a new objective function and minimize it: In the above formula, λ is the Lagrange multiplier of the constraint condition; The necessary condition determination unit is used to derive all input parameters of the new objective function, and obtain the necessary condition for minimizing the new objective function as follows: In the above formula, xk is the data of the kth monitoring point; The judgment unit is used to update the membership degree and cluster center according to the necessary conditions for minimizing the new objective function. i (l+1) -P i (l) |<ε or the number of iterations reaches the maximum, the algorithm stops and outputs the optimal monitoring point of the enterprise's carbon emission factor. Otherwise, the number of iterations is l=l+1, and the membership degree and cluster center are updated again until the algorithm stopping condition is met and the optimal monitoring point of the enterprise's carbon emission factor is selected.

10. The carbon emission factor calculation system in the process of raw material combustion of an enterprise according to claim 6 is characterized in that: In the carbon emission factor calculation module, the following formula is used to calculate the carbon emission factor in the actual combustion process of each raw material: In the above formula, EF i is the carbon emission factor of the i-th raw material fuel, E i is the carbon emission intensity of the i-th raw material fuel, A i is the consumption of the i-th raw material fuel.

Images