A method for accounting for cement carbon footprint of a differentiated allocation model

Abstract

The present application relates to the field of carbon footprint accounting, and specifically discloses a cement carbon footprint accounting analysis method of a differentiated allocation model, comprising: identifying residual heat enthalpy and determining non-Fick anomalous diffusion channels, generating a time-varying release rate correction curve and a cross-cycle thermal inertia allocation coefficient sequence, generating a differentiated carbon footprint allocation proportion after correction of waste thermal disturbance by collaborative disposal; calculating a strain memory correction decomposition rate and generating a differentiated emission factor correction matrix, identifying and coupling a catalytic coefficient calculation, and outputting a carbon emission accounting parameter set; calculating a production state coefficient for three-stage adaptive aggregation, calculating a carbon emission flux correction value, inputting the above parameters into a differentiated allocation model to solve and output multi-objective differentiated carbon footprint accounting results. The present application solves the technical problems of system boundary static ambiguity, emission factor spatiotemporal drift, collaborative disposal carbon flow allocation simplification and residual heat carbon allocation distortion in the prior art.

Description

Technical Field

[0001] This invention relates to the field of carbon footprint accounting technology, and in particular to a method for calculating and analyzing the carbon footprint of cement using a differentiated allocation model. Background Technology

[0002] Against the backdrop of global efforts to jointly address climate change and accelerate green and low-carbon transformation, product carbon footprint management has officially risen to the level of an important national strategic initiative. As a fundamental raw material industry of the national economy, the cement industry accounts for approximately 15% of the total carbon emissions from the national industry. The accuracy of carbon footprint accounting directly affects the scientific formulation of emission reduction pathways and the actual effectiveness of green and low-carbon transformation within the industry.

[0003] Current carbon footprint accounting technologies in the cement industry mainly rely on standards and specifications such as ISO 14067, PAS 2050, GB / T 32151, and T / CBMF 277. However, existing technologies have significant shortcomings in the following deeper technical aspects: The system boundary is statically defined, and the allocation of multiple products on the same production line is lacking. Existing methods use a fixed boundary model, failing to fully consider the on-line allocation requirements when producing multiple types of cement on the same clinker production line. Significant differences exist in the clinker admixture content and blending materials among different types, and there is a lack of technical means to accurately allocate carbon emissions by product type. The thermal inertia carbon memory effect of the kiln system is ignored; existing methods attribute all start-up energy consumption to the current period, failing to allocate it across cycles according to actual release patterns. Over long-term service, microcrack networks and pore connectivity domains form inside the kiln lining, resulting in non-Fick anomalous heat flow propagation characteristics. Existing simple exponential decay models cannot describe this nonlinear heat release behavior. The assumption of homogeneity of alternative raw material carbonates leads to emission factor bias; carbonates in industrial byproducts such as carbide slag and steel slag undergo rapid cooling and heating to form a lattice strain memory state, and their decomposition kinetics differ fundamentally from those of natural limestone. Existing methods use a fixed decomposition rate, ignoring the systemic bias caused by thermal history dependence. The effects of non-stoichiometric solid solution substitution in clinker minerals are masked; actual clinker minerals exhibit solid solution substitution phenomena such as aluminum replacing silicon and titanium replacing iron. Trace elements in raw materials have a catalytic coupling effect on solid solution substitution, altering the actual carbonization consumption of the mineral formation reaction; existing fixed emission factors completely lose this dynamic solid solution compensation mechanism. Waste heat power generation carbon allocation lacks a state-adaptive mechanism; existing methods deduct at a fixed ratio, but waste heat power generation efficiency significantly decreases when the kiln system is operating at low load. The fixed ratio leads to insufficient deduction at high loads and excessive deduction at low loads, resulting in severely distorted carbon allocation results. The time-averaging method for flue gas CO2 concentration fields loses crucial information; large-scale turbulent coherent vortex structures exist in the kiln flue gas, and CO2 concentration exhibits quasi-periodic fluctuations; existing arithmetic averaging methods lose the combustion-decomposition coupling kinetics information carried by the turbulent coherent structures, leading to systematic errors in carbon emission flux calculations.

[0004] In summary, existing technologies have fundamental deficiencies in areas such as multi-product collinear allocation, kiln lining non-Fick anomalous diffusion tracking, alternative raw material lattice strain memory tracing, clinker mineral non-stoichiometric solid solution compensation, waste heat power generation state adaptive collection, and flue gas turbulent coherent structural carbon flux accounting. There is an urgent need for a cement carbon footprint accounting and analysis method that can differentiate the allocation of the aforementioned less common physicochemical processes. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by proposing a method for calculating and analyzing the carbon footprint of cement using a differentiated allocation model.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: A method for calculating and analyzing the carbon footprint of cement using a differentiated allocation model includes the following specific steps: S1. Obtain temperature gradient data of the multi-layer thermocouple array in the kiln lining, clinker production time series data, clinker discharge temperature time series data, and co-line production scheduling data of multiple cement products for the target cement production line during the accounting cycle and the preceding preheating cycle. The preceding preheating cycle is a continuous period before and adjacent to the accounting cycle. The multi-layer thermocouple array is arranged in layers along the radial direction of the kiln lining from the working surface to the backfire surface, with no less than four layers. Based on the temperature gradient data, identify the residual enthalpy in the kiln lining regenerator that is in a non-equilibrium state. The residual enthalpy is the portion of the ignition energy consumed during the cold start-up or low-load operation phase in the preceding preheating cycle that has not yet been released through heat dissipation from the kiln lining surface or by clinker carry-out. Based on the temperature gradient data... The nonlinear temperature distribution characteristics are used to determine whether there are non-Fick anomalous diffusion channels within the kiln lining refractory material caused by microcrack networks and pore connectivity domains. When non-Fick anomalous diffusion channels are found to exist, the heat release rate prediction curve in the standard Fick diffusion model is corrected based on the anomalous diffusion index corresponding to the microcrack network density to generate a time-varying release rate correction curve. When no non-Fick anomalous diffusion channels are found to exist, the heat release rate prediction curve output by the standard Fick diffusion model is directly used as the time-varying release rate curve. The time-varying release rate correction curve is then correlated and matched with the clinker production time series data on a time-by-time basis to generate a cross-cycle thermal inertia allocation coefficient sequence corresponding to unit clinker production. Based on the clinker discharge temperature time series data... To verify the rationality of the cross-cycle thermal inertia allocation coefficient sequence, when the clinker discharge temperature is lower than the preset process stability temperature threshold, the allocation coefficient at the corresponding time is determined to be an outlier and marked. Based on the co-line production scheduling data, the output data, clinker content data, blend material type and content data, and unit product power consumption data of each type of cement product within the accounting period are obtained. According to the differences in clinker content and blend material content of each type of cement product, the carbon emissions related to clinker calcination, corrected by the cross-cycle thermal inertia allocation coefficient sequence, are allocated according to the clinker consumption ratio of each type of cement product, and the carbon emissions related to blend material grinding are allocated according to the blend material consumption ratio of each type of cement product. Carbon emissions related to cement grinding and packaging are allocated according to the electricity consumption ratio of each type of cement product. For carbon emissions from shared processes that cannot be clearly attributed to a specific process, they are allocated according to the production ratio of each type of cement product. For co-processing waste scenarios, data on the feeding ratio of co-processed waste and the lower heating value of waste are obtained within the accounting period. The disturbance coefficient of co-processed waste on the thermal regime of the kiln system is calculated based on the ratio of the lower heating value of waste to the lower heating value of alternative fuels. The disturbance coefficient is included in the correction calculation of the cross-period thermal inertia allocation coefficient sequence. The allocation results of the above-mentioned processes are summarized to generate the differentiated carbon footprint allocation ratio of each type of cement product under co-line production conditions, corrected by thermal inertia coupling. S2. Obtain the differentiated carbon footprint allocation ratio corrected by thermal inertia coupling from the output of S1, as well as the batch information of alternative raw materials used by the target cement production line during the accounting period. The batch information of alternative raw materials includes batches of carbide slag, steel slag, and fly ash, as well as the corresponding industrial-grade X-ray diffraction data, thermal history traceability tag data, and complete analysis data of trace elements in each batch; based on the industrial-grade X-ray diffraction data, identify the lattice constant shift and micro-strain distribution characteristics of carbonate minerals in each batch of alternative raw materials. The lattice constant shift reflects the degree of lattice distortion of carbonate crystals after undergoing industrial high-temperature processes; based on the thermal history traceability tag data, obtain the most... The thermal history traceability tag data, including high-temperature exposure, cooling rate, and holding time, is derived from the process records of the alternative raw material supplier. When thermal history traceability tag data is missing, the thermal history path is inferred from the lattice constant shift in industrial-grade X-ray diffraction data. Based on the lattice constant shift, micro-strain distribution characteristics, and thermal history traceability tag data, a strain memory decomposition kinetic model corresponding to the carbonate mineral type and thermal history path is invoked to calculate the strain memory corrected decomposition rate of each batch of alternative raw materials in the actual calcination temperature field of the cement kiln. The strain memory corrected decomposition rate is compared with the baseline decomposition rate of natural limestone to generate a differential emission factor correction matrix for the carbonate lattice strain memory of the alternative raw materials. Based on the thermal history traceability tag data, the thermal history traceability tag data is used to calculate the strain memory corrected decomposition rate of each batch of alternative raw materials in the actual calcination temperature field of the cement kiln. The clinker consumption ratio of each cement product in the differentiated carbon footprint allocation ratio corrected by inertial coupling is used to differentiate the clinker-related emission factors in the emission factor correction matrix according to the product type; the chemical composition data of clinker X-ray fluorescence spectrometry of the target cement production line within the accounting period are obtained, and the non-stoichiometric solid solution substitution characteristics in the clinker mineral phase are identified based on the clinker X-ray fluorescence spectrometry chemical composition data. The non-stoichiometric solid solution substitution characteristics include the lattice substitution ratio of aluminum ions to silicon ions in tricalcium silicate, the lattice substitution ratio of titanium ions to iron ions in tetracalcium aluminoferrite, and the interstitial filling ratio of alkali metal ions to calcium ions in tricalcium aluminate; based on the potassium, sodium, sulfur, and chlorine content in the raw material trace element complete analysis data, the calculation is performed. The coupling catalytic coefficient of trace elements on non-stoichiometric solid solution substitution characteristics is calculated. Based on the non-stoichiometric solid solution substitution characteristics, coupling catalytic coefficient, and Gibbs free energy change benchmark values ​​of each mineral, the non-stoichiometric solid solution compensation carbon emission benchmark drift corresponding to the actual clinker mineral composition within the accounting period is calculated. The emission factor correction matrix with differentiated configuration is coupled with the non-stoichiometric solid solution compensation carbon emission benchmark drift to output a carbon emission accounting parameter set corrected by lattice strain memory thermal history tracing and non-stoichiometric solid solution drift calibration. The carbon emission accounting parameter set is structured and stored according to the batch of substitute raw materials, clinker mineral composition, and accounting period to generate dynamically updated localized emission factor database entries. S3. Obtain the actual operating load rate data of the cement kiln system, the actual power generation data of the waste heat power generation system, the rated power generation data of the waste heat power generation system, and the waste heat recovery efficiency data of the kiln system within the accounting period. Calculate the production state coefficient of the waste heat power generation system based on the ratio of the actual operating load rate to the rated load rate of the cement kiln system. When the production state coefficient is greater than or equal to 0.85, the carbon emissions from the waste heat power generation process are traced and aggregated according to the carbon attributes of the waste heat source and included in the carbon footprint of cement products. When the production state coefficient is greater than or equal to 0.60 and less than 0.85, the carbon emissions from the waste heat power generation process are calculated based on the ratio of the actual power generation efficiency to the rated power generation efficiency of the waste heat power generation system. After efficiency coefficient correction, the carbon footprint of cement products is included. When the production state coefficient is less than 0.60, the carbon emissions from the waste heat power generation process are only included in the carbon footprint of cement products after the portion of actual power generation directly related to cement production is aggregated according to the carbon attributes of the waste heat source. High-frequency CO2 concentration pulsation monitoring data and flue gas velocity and temperature monitoring data at multiple points in the kiln tail are obtained. Quasi-periodic characteristic parameters of CO2 concentration pulsation are extracted based on the high-frequency CO2 concentration pulsation monitoring data. These quasi-periodic characteristic parameters include the main pulsation frequency, pulsation amplitude, and pulsation coherence length. The spatial scale of the turbulent coherent vortex structure corresponding to the quasi-periodic characteristic parameters is identified based on the multi-point flue gas velocity and temperature monitoring data. The spatial scale and propagation velocity of the turbulent coherent eddy structure are spatiotemporally coupled with CO2 concentration fluctuations. A weighted algorithm based on the turbulent coherent structure is used to calculate the carbon emission flux correction value corresponding to multi-point monitoring. The differentiated carbon footprint allocation ratio corrected by thermal inertia coupling from S1, the carbon emission accounting parameter set corrected by lattice strain memory thermal history and non-stoichiometric solid solution drift calibration from S2, and the carbon emission collection results and carbon emission flux correction values ​​from the waste heat power generation process are input into the differentiated allocation model. The differentiated allocation model aims to minimize the deviation of the carbon footprint accounting results for each type of cement product, using the differentiated carbon footprint allocation ratio corrected by thermal inertia coupling as the optimization objective. The first priority constraint is the carbon emission accounting parameter set corrected by lattice strain memory thermal history and non-stoichiometric solid solution drift calibration. The second priority constraint is the carbon emission collection result of the waste heat power generation link. The fourth priority constraint is the carbon emission flux correction value. The weight coefficient of each priority constraint is dynamically adjusted according to the frequency of multi-product switching, the load fluctuation of the kiln system and the stability index of waste heat power generation within the accounting period. The solution outputs the multi-objective differentiated carbon footprint accounting result of the target cement product within the accounting period after thermal inertia boundary reconstruction, lattice strain memory source correction, non-stoichiometric solid solution drift calibration and flue gas turbulent coherent structure weighting.

[0007] The beneficial effects of this invention are as follows: 1. By identifying the residual enthalpy of the kiln lining and determining the non-Fick anomalous diffusion channel, a time-varying release rate correction curve and a cross-cycle thermal inertia allocation coefficient sequence are generated. This solves the problems of unclear carbon emission attribution in multi-product co-line production under traditional static boundaries and misalignment of thermal inertia carbon memory in the kiln system, and realizes accurate and differentiated allocation of carbon footprints for multiple cement products.

[0008] 2. By identifying the state of lattice strain memory and calling the strain memory decomposition kinetic model, the modified decomposition rate of the alternative raw materials is calculated and a differentiated emission factor correction matrix is ​​generated. Combined with the identification of non-stoichiometric solid solution substitution characteristics of clinker minerals and the calculation of coupled catalytic coefficients, the problem of the assumption of homogeneity of carbonate alternative raw materials and the problem of fixed emission factors masking mineral composition drift are solved, and the physicochemical essence of emission factors is restored and dynamically calibrated.

[0009] 3. By using a three-stage adaptive collection of waste heat power generation carbon emissions through production state coefficients, and combining turbulent coherent eddy structure identification and spatiotemporal coupling weighted algorithm to calculate carbon emission flux correction values, and solving the differentiated allocation model with four priority constraints, the problems of carbon allocation distortion caused by fixed ratio deduction and loss of combustion decomposition coupling information by time averaging method are solved, thereby improving the physical authenticity and accuracy of carbon footprint accounting results. Attached Figure Description

[0010] Figure 1 This is a flowchart of the method proposed in Embodiment 1 of the present invention; Figure 2 This is an architecture diagram of the differentiated allocation model in Embodiment 1 of the present invention; Figure 3 This is a framework diagram of the system proposed in Embodiment 2 of the present invention; Figure 4 This is a comparison diagram of the effects of the present invention and the prior art in Embodiment 3 of the present invention. Detailed Implementation

[0011] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0012] Example 1 Please see the appendix Figure 1 A method for calculating and analyzing the carbon footprint of cement using a differentiated allocation model, comprising the following specific steps: S1. Acquire temperature gradient data of the multi-layer thermocouple array in the kiln lining and collinear production scheduling data; identify residual enthalpy and determine non-Fick anomalous diffusion channels; generate a time-varying release rate correction curve; match and generate a cross-cycle thermal inertia allocation coefficient sequence; allocate carbon emissions of various cement products proportionally; after correcting the allocation results of each stage with thermal disturbances from co-processed waste, generate a differentiated carbon footprint allocation ratio corrected by thermal inertia coupling; specifically including: S11. Identification of non-equilibrium residual enthalpy and determination of non-Fick anomalous diffusion channels in the kiln lining regenerator layer, specifically including: S111. Obtain the temperature gradient data of the multi-layer thermocouple array in the kiln lining of the target cement production line during the calculation period and the preceding preheating period. The preceding preheating period is a continuous time period before and adjacent to the calculation period, and its duration is determined based on the ratio of the kiln lining heat capacity to the rated heat load of the kiln system, with a preferred value of 24 hours. The multi-layer thermocouple array is arranged in layers along the radial direction of the kiln lining from the working surface to the unfired surface, with no less than four layers, and the preferred total thickness of the kiln lining is 280 mm.

[0013] The temperature gradient data consists of temperature sequences at each thermocouple measuring point over time. The sampling frequency is determined based on the ratio of the kiln system rotation speed to the thermocouple response time, with an optimal value of 5 times per minute. Based on the nonlinear temperature distribution characteristics in the temperature gradient data, the residual enthalpy in the kiln lining regenerator layer that is in a non-equilibrium state is identified. The nonlinear temperature distribution characteristic refers to the degree to which the temperature distribution curve along the radial direction of the kiln lining deviates from a linear distribution. This is quantified by calculating the sum of squared residuals between the measured temperature values ​​and the linear interpolation predicted values ​​for each layer, i.e.: In the formula: This is the sum of squared residuals, in °C. For the first The temperature is predicted by linear interpolation of the layer, in °C, obtained by linear interpolation of the working surface temperature and the unexposed surface temperature; when the sum of squared residuals... When the temperature exceeds a preset nonlinear threshold, the temperature distribution is deemed to exhibit nonlinear characteristics. The nonlinear threshold is determined by the product of the thermal diffusivity of the kiln lining material and the temperature measurement error, with a range of 5℃. 2 Up to 25℃ 2 The preferred value is 10℃ 2 .

[0014] The residual enthalpy is obtained by integrating and summing the measured temperatures of each layer of the multilayer thermocouple array with the corresponding reference temperatures under steady-state conditions, combined with the specific heat capacity and density of each layer of the kiln lining material, layer by layer. In the formula: This is the residual enthalpy. For the first The radius of the layered thermocouple, in meters; For the first The radius of the layered thermocouple, in meters; The effective length of the kiln lining is given in meters (m). The reference temperature value for the corresponding layer under steady-state conditions is determined based on the average temperature of each layer after the kiln system has operated continuously under rated load for at least 48 hours; that is: In the formula: Residual enthalpy, in kJ; The number of thermocouple array layers. ; For the first The density of the kiln lining material is taken in the range of 2200. Up to 2800 The preferred value is 2500. ; For the first The specific heat capacity of the kiln lining material is taken in the range of 0.8. Up to 1.2 The preferred value is 1.0. ; For the first kiln lining volume, unit ; For the first Measured temperature of the layer, in °C; For the first The reference temperature for steady-state operation of the layer, in °C.

[0015] When the residual enthalpy exceeds the preset enthalpy identification threshold, the kiln lining regenerator is determined to be in a non-equilibrium state. The enthalpy identification threshold is determined based on 5% of the product of the total clinker production and the unit clinker baseline energy consumption within the accounting period, with a value ranging from 50 MJ to 500 MJ, and a preferred value of 200 MJ.

[0016] S112. Based on the identification of residual enthalpy, simultaneously determine whether there are non-Fick anomalous diffusion channels inside the kiln lining refractory material caused by microcrack networks and pore interconnection domains. Non-Fick anomalous diffusion channels refer to the non-uniform heat transfer paths formed by microcrack networks and pore interconnection domains inside the kiln lining refractory material due to the accumulation of thermal stress cycles.

[0017] The judgment criteria are whether the temperature distribution curve along the radial direction of the temperature gradient data in the kiln lining exhibits a non-linear S-shaped characteristic and whether there are extreme points in the local temperature gradient, i.e.: In the formula: For the first The radial position of the layer thermocouple. The number of layers to extend to adjacent areas. ; For the first The radial location of the layer thermocouple. Specifically, the nonlinear S-shaped characteristic refers to the existence of at least two zeros with opposite signs in the second derivative of the temperature distribution curve, and the distance between adjacent zeros is less than 1.5 times the thickness of a single layer of kiln lining; the extreme point refers to the absolute value of the local temperature gradient exceeding 1.3 times the absolute value of the average temperature gradient of the adjacent area.

[0018] When the radial temperature distribution curve of the temperature gradient data simultaneously satisfies the aforementioned nonlinear S-shaped characteristic and extreme point condition, a non-Fick anomalous diffusion channel is determined to exist. After determination, the anomalous diffusion index is calibrated based on the square root of the product of the microcrack network density and the volume fraction of the pore connectivity domain, i.e.: In the formula: The anomalous diffusion index ranges from 0.5 to 2.0, with a preferred value of 1.0. The microcrack network density was obtained through scanning electron microscopy image analysis, with a value ranging from 5 cracks / mm. 2 Up to 16 strips / mm 2 The preferred value is 8 strips / mm 2 ; The volume fraction of the pore connectivity region is determined by mercury porosimetry or gas adsorption, and its value ranges from 5% to 25%, with a preferred value of 15%. If the temperature distribution curve does not simultaneously meet the above two conditions, it is determined that there is no non-Fick anomalous diffusion channel, and the standard Fick diffusion model is maintained for subsequent calculations.

[0019] S12. Generation and outlier correction of cross-cycle thermal inertia allocation coefficient sequence based on Fick diffusion correction, specifically including: S121. Obtain the thermal conductivity and thermal diffusivity parameters of the kiln lining refractory material, and construct a Standard Fick diffusion model based on these parameters. The thermal conductivity ranges from 1.0 W / m / K to 3.5 W / m / K, with a preferred value of 2.0 W / m / K. The thermal diffusivity is calculated based on the ratio of thermal conductivity to material density and specific heat capacity, and its value ranges from 5 × 10⁻⁶. -7 m² / s to 1.5×10 -6 m² / s, with a preferred value of 8×10 -7 m² / s, that is: In the formula: For thermal diffusivity, Thermal conductivity, For material density, This represents the specific heat capacity of the material.

[0020] The standard Fick diffusion model is mathematically based on the unsteady-state heat conduction equation. Its structure includes an initial condition setting submodule, a boundary condition setting submodule, a heat diffusion equation solving submodule, and a heat release rate output submodule. The initial condition setting submodule sets the initial temperature field for each radial layer of the kiln lining at the cold start-up time. The initial temperature field is determined based on the intersection of the ambient temperature and the ignition temperature rise curve during the preceding preheating cycle. The boundary condition setting submodule sets the thermal boundary conditions for the working surface and the unfired surface of the kiln lining. The working surface boundary condition is a third-order convective heat transfer boundary, with a heat transfer coefficient ranging from 50 W / m² / K to 200 W / m² / K, with a preferred value of 100 W / m² / K. The unfired surface boundary condition is an adiabatic boundary or set based on the heat loss from the outer surface of the kiln shell, with a heat loss ranging from 100 W / m² to 500 W / m², and a preferred value of 300 W / m². The function of the thermal diffusion equation solution submodule is to numerically solve the one-dimensional radial thermal diffusion equation based on the finite difference method or the finite element method. The spatial step size is determined based on the ratio of the total kiln lining thickness to the number of thermocouple layers, and the time step size is determined based on the stability condition of the ratio of thermal diffusivity to the square of the spatial step size. The one-dimensional radial thermal diffusion equation is as follows: In the formula: For time, in units ; The coordinates are radial, in mm. The function of the heat release rate output submodule is to extract the curve of heat flux density of the kiln lining working face versus time from the solved temperature field time-series data, i.e.: In the formula: For a moment Working surface heat flux density, unit ; The coordinates of the working surface of the kiln lining are in the radial direction, in mm; and are converted into a heat release rate prediction curve based on the working surface area, i.e.: In the formula: For a moment The standard Fick diffusion model heat release rate, in kW; The working area of ​​the kiln lining is expressed in units of... .

[0021] S122. When a non-Fick anomalous diffusion channel is determined to exist, the peak value of the heat release rate and the heat release rate decay time constant in the heat release rate prediction curve output by the standard Fick diffusion model are corrected according to the anomalous diffusion index to generate a time-varying release rate correction curve, i.e.: In the formula: For a moment The time-varying heat release rate correction value, in kW. The peak heat release rate refers to the maximum value in the heat release rate prediction curve, and its correction formula is: In the formula: The corrected peak heat release rate is expressed in kW. Peak value of the original heat release rate, in kW; The anomalous diffusion index is dimensionless.

[0022] The heat release rate decay time constant refers to the time required for the heat release rate to decay from its peak value to 36.8% of its peak value. Its correction formula is as follows: In the formula: The corrected decay time constant, in hours (h). The original decay time constant is expressed in hours. When it is determined that there is no non-Fick anomalous diffusion channel, the heat release rate prediction curve output by the standard Fick diffusion model is directly used as the time-varying release rate curve without any correction.

[0023] S123. Obtain clinker production time-series data within the accounting period. This data consists of hourly or shift-by-shift clinker production records from the kiln system's discharge end, measured in tons per hour or tons per shift. The time-varying heat release rate correction curve and the clinker production time-series data are then correlated and matched moment-by-moment along the same time axis. This moment-by-moment correlation and matching refers to establishing a one-to-one correspondence between the heat release rate correction value at each moment in the time-varying heat release rate correction curve and the corresponding clinker production at that moment. This correspondence is achieved through the synchronous alignment of the data acquisition timestamps, with a timestamp accuracy of no less than one minute.

[0024] Dividing the heat release rate correction value at each time point by the clinker output at the corresponding time point yields the unit clinker thermal inertia allocation value, i.e.: In the formula: For a moment The unit clinker thermal inertia allocation value, in kJ / t. For a moment The clinker output is expressed in t / h. When the clinker output is zero at a certain moment, the unit clinker thermal inertia allocation value at that moment is either forward-filled using the allocation value from the previous non-zero output moment, or backward-filled using the allocation value from the next non-zero output moment. The unit clinker thermal inertia allocation values ​​at each moment are summarized to generate a cross-period thermal inertia allocation coefficient sequence. This sequence is a one-dimensional time series array, with the array length matching the number of time sampling points within the calculation period. Each element represents the unit clinker thermal inertia allocation value at the corresponding moment, i.e.: In the formula: This is a sequence of thermal inertia allocation coefficients across cycles. This represents the total number of time sampling points within the calculation period.

[0025] S124. Acquire clinker discharge temperature time-series data. This data consists of continuous temperature monitoring records at the clinker discharge end. The sampling frequency is consistent with the temperature gradient data from the thermocouple array, with a preferred value of 5 times per minute. Compare the clinker discharge temperature at each moment with a preset process stability temperature threshold. The process stability temperature threshold is determined based on the minimum temperature required for clinker mineral phase formation and the lower limit of the normal operating temperature range of the kiln system, with a range of 1200℃ to 1350℃, and a preferred value of 1280℃.

[0026] When the clinker discharge temperature is lower than the process stability temperature threshold, the kiln system at that time is determined to be in an unstable operating state, and the cross-cycle thermal inertia allocation coefficient at that time is an outlier and is marked. Unstable operating states include the cold start-up stage, low-load operation stage, fault shutdown stage, and planned maintenance stage. The allocation coefficient marked as an outlier is replaced by linear interpolation of adjacent normal values; the linear interpolation formula is: In the formula: For a moment Replacement values ​​for outliers, in kJ / t; This refers to outlier moments; This is the time of the preceding normal value; This is the time of the next normal value; The preceding normal value is expressed in kJ / t. The value is the next normal value, in kJ / t.

[0027] S13. Multi-dimensional proportional allocation of carbon emissions from multi-product co-production lines, specifically including: S131. Based on the co-line production scheduling data, obtain the output data, clinker content data, blending material type and content data, and unit product power consumption data for each type of cement product within the accounting period. The co-line production scheduling data consists of the production sequence, planned output, and actual output information of each type of cement product in the production plan, recorded by the cement production line scheduling system. The data time granularity is shift or day, preferably day.

[0028] Production data refers to the actual output of each type of cement product within the accounting period, measured in tons, and sourced from the enterprise's ERP system or weighbridge system. Clinker content data refers to the mass percentage of clinker in the formulation of each type of cement product, ranging from 50% to 95%. The preferred clinker content for ordinary Portland cement is 75% to 80%, and for composite Portland cement, it is 50% to 65%. Blended material type and content data refers to the names and mass percentages of blended materials in the formulation of each type of cement product. Blended material types include slag, fly ash, volcanic ash, limestone, etc. The content of a single blended material ranges from 5% to 50%, with a preferred value of 15% to 30%. The upper limit of the total blended material content is determined according to the cement product standards. The unit product power consumption data is the ratio of the total power consumption to the output of each type of cement product from raw material entry to finished product exit, with a value range of 25kWh / t to 100kWh / t. The preferred power consumption for the clinker calcination process is 30kWh / t to 40kWh / t, and the preferred power consumption for the cement grinding process is 35kWh / t to 45kWh / t.

[0029] S132. Calculate the clinker consumption ratio for each type of cement product based on the clinker admixture data. The clinker consumption ratio is calculated by multiplying the clinker admixture of each type of cement product by its output, then dividing by the sum of the products of clinker admixture and output for all types of cement products, i.e.: In the formula: For the first The clinker consumption ratio for different types of cement products, dimensionless; For the first The amount of clinker added to the product is dimensionless. For the first Yield per variety, in tons (t); This represents the total number of cement types produced during the accounting period. When only a single type of cement is produced during the accounting period, the clinker consumption rate for that type is 100%.

[0030] The consumption ratio of blended materials for each type of cement product is calculated based on the blended material dosage data. The blended material consumption ratio is calculated by multiplying the total blended material dosage of each type of cement product by the production output of that type, and then dividing by the sum of the products of the total blended material dosage and production output of all types of cement. That is: In the formula: For the first The proportion of blended materials consumed in various types of cement products, dimensionless; For the first The total blending content of various varieties is dimensionless. The electricity consumption ratio of each variety is calculated based on the unit product electricity consumption data. The electricity consumption ratio is the sum of the products of the unit product electricity consumption of each variety and the output of that variety, divided by the sum of the products of the unit product electricity consumption and the output of all varieties. In the formula: For the first Electricity consumption ratio of different cement products, dimensionless; For the first Electricity consumption per unit of product, per unit .

[0031] S133. The carbon emissions from clinker calcination, corrected for the cross-cycle thermal inertia allocation coefficient sequence, are allocated according to the consumption ratio of each type of clinker. Carbon emissions from clinker calcination are the direct carbon emissions generated during the clinker calcination process in the cement production stage, including carbonate decomposition emissions and fuel combustion emissions. The correction method is as follows: In the formula: To correct the carbon emissions from clinker calcination, per unit ; Carbon emissions from the calcination of raw clinker, per unit ; For a moment The apportionment coefficient after outlier correction, in kJ / t.

[0032] Carbon emissions from the blend grinding process are allocated according to the consumption ratio of each blend type. These emissions correspond to the electricity and fuel consumption during the drying and grinding of the blends. When the blends are wet-discharged slag or other moisture-containing raw materials, drying energy consumption is significant. Carbon emission measurement for this process requires calculating the latent heat of vaporization based on the initial and final moisture content of the blends. The initial moisture content ranges from 8% to 25%, with a preferred value of 15%, and the final moisture content ranges from 0.5% to 2%, with a preferred value of 1%. Carbon emissions from the cement grinding and packaging process are allocated according to the electricity consumption ratio of each blend type. This includes the electricity consumption during grinding, classifying, conveying, packaging, and loading. Carbon emissions from shared processes that cannot be clearly attributed to a specific procedure are allocated according to the production ratio of each blend type. Shared processes include raw material homogenization, raw meal preparation, plant transportation, and the operation of environmental protection facilities. Their carbon emissions are allocated based on the proportion of each blend's production to the total production. Summarizing the allocation results from the above four stages, a differentiated carbon footprint allocation ratio for each type of cement product under co-line production conditions is generated, namely: In the formula: For the first The proportion of differentiated carbon footprint of various cement products, dimensionless; For carbon emissions in the grinding of blended materials, per unit ; For carbon emissions in cement grinding and packaging processes, unit ; For carbon emissions from shared processes, the unit ; To calculate the total carbon emissions within the accounting period, per unit .

[0033] S14. Correction and allocation results update of thermal disturbance coefficients for co-processed waste, specifically including: S141. Obtain the feed ratio data and lower heating value data of co-processed waste within the accounting period. Co-processed waste refers to solid waste co-processed in cement kilns, including hazardous waste, industrial sludge, and municipal solid waste-derived fuels, etc., the types of which are determined according to the National Hazardous Waste List or the General Industrial Solid Waste Classification Catalogue. The feed ratio data is the mass percentage of each type of waste fed into the kiln system during the accounting period, ranging from 0% to 30%, with a preferred value of 5% to 15%. When the feed ratio exceeds 30%, it is determined to be alternative raw material rather than co-processed waste and is transferred to the alternative raw material treatment process.

[0034] The lower heating value (LCV) data for waste refers to the lower heating value per unit mass of various types of waste under absolute dry conditions, measured in kilojoules per kilogram (kJ / kg), using the bomb calorimetry method or industrial analyzer. The LHC values ​​for hazardous waste range from 8000 kJ / kg to 25000 kJ / kg, with a preferred value of 15000 kJ / kg; for industrial sludge, the range is from 2000 kJ / kg to 8000 kJ / kg, with a preferred value of 5000 kJ / kg; and for municipal solid waste-derived fuels, the range is from 12000 kJ / kg to 20000 kJ / kg, with a preferred value of 16000 kJ / kg. LHC data for alternative fuels, including waste tires, waste plastics, and biomass fuels, are also obtained, with LHC values ​​ranging from 15000 kJ / kg to 45000 kJ / kg, with a preferred value of 28000 kJ / kg.

[0035] S142. Calculate the weighted average lower heating value of waste based on the feeding ratio of each type of waste. The weighted average lower heating value of waste is the sum of the products of the lower heating value of each type of waste and its feeding ratio. That is, the weighted average lower heating value of waste equals the lower heating value of waste A multiplied by the feeding ratio of waste A plus the lower heating value of waste B multiplied by the feeding ratio of waste B, and so on. When only a single type of waste is disposed of, the weighted average lower heating value of waste is... In the formula: The weighted average lower heating value of waste is expressed in kJ / kg. The total number of waste types to be co-disposed of; For the first The proportion of different types of waste to be fed, dimensionless; For the first Lower heating value of waste type, unit kJ / kg.

[0036] The ratio of the weighted average lower heating value of waste to the lower heating value of alternative fuels is used as the disturbance coefficient of the thermal regime of the kiln system caused by the co-processing of waste, i.e.: In the formula: The disturbance coefficient is dimensionless. The lower heating value of alternative fuels is expressed in kJ / kg.

[0037] S143. Based on the feeding time data of various types of waste and the typical residence time of waste in the kiln system, determine the effective action period. The feeding time data refers to the start and end times of waste entering the kiln system from the feeding port, with a time accuracy of minutes. The effective action period is the time from the start of feeding to the end of feeding, excluding the typical residence time; that is, the start of the effective action period is equal to the start of feeding, and the end of the effective action period is equal to the end of feeding. In the formula: This marks the end of the effective treatment period. This is the time when feeding stops; The typical residence time of waste in the kiln system is the average residence time of waste from the time it enters the kiln system through the feed port until it leaves the kiln system in the form of clinker or flue gas. It is calculated based on the effective volume of the kiln system, the gas flow rate in the kiln, and the material movement speed. The value ranges from 20 min to 40 min, with 30 min being the preferred value.

[0038] The perturbation coefficient is used to weight and correct the allocation coefficient corresponding to the effective action period in the cross-cycle thermal inertia allocation coefficient sequence. The weighting correction formula is as follows: In the formula: For a moment The allocation factor after thermal disturbance correction, in kJ / t; For a moment The allocation factor after outlier correction, in kJ / t; The disturbance coefficient is dimensionless. A cross-cycle thermal inertia allocation coefficient sequence corrected for thermal disturbance is generated, and the carbon emission allocation results of each stage are updated. The update method is to recalculate the time-weighted correction value of clinker calcination carbon emission in S133 based on the corrected allocation coefficient sequence, and re-execute the allocation calculation of S133.

[0039] S2. Obtain X-ray diffraction data and thermal history traceability tag data of alternative raw materials, calculate strain memory correction decomposition rate and generate differentiated emission factor correction matrix; obtain X-ray fluorescence spectrochemical composition data of clinker and identify non-stoichiometric solid solution substitution characteristics and calculate baseline drift, coupled and output carbon emission accounting parameter set corrected by lattice strain memory traceability and non-stoichiometric solid solution drift; specifically including: S21. Generation of the lattice strain memory decomposition kinetics and emission factor correction matrix of alternative raw materials, specifically including: S211. Obtain X-ray diffraction data, thermal history traceability label data, and trace element full analysis data for each batch of substitute raw materials within the accounting period. The substitute raw materials include carbide slag, steel slag, and fly ash. Each batch is numbered and traced based on the supplier's delivery order and the plant's warehousing records.

[0040] X-ray diffraction data consists of diffraction patterns acquired using an X-ray diffractometer under copper target Kα-ray conditions after each batch of substitute raw materials was ground to a particle size of less than 75 μm. The scanning angle range was 10° to 80°, the scanning step size was 0.02°, the dwell time per step was 0.5 s, the tube voltage was 40 kV, and the tube current was 40 mA. Thermal history traceability tag data consists of process records provided by the substitute raw material supplier, including the highest temperature experienced by each batch of raw materials during industrial production, the cooling rate, and the holding time. When thermal history traceability tag data is missing, a data supplementation process is triggered or a lattice constant offset back-calculation mechanism is activated. Trace element analysis data consists of the mass fractions of potassium, sodium, sulfur, chlorine, and other trace elements in each batch of substitute raw materials, determined using inductively coupled plasma atomic emission spectrometry (ICP-AES), with a detection limit of 0.001 mg / kg.

[0041] S212. Based on X-ray diffraction data, identify the lattice constant shift and microstrain distribution characteristics of carbonate minerals in each batch of substitute raw materials; the carbonate minerals include calcite, aragonite, and dolomite, and their standard lattice constants are determined according to the International Crystallography Database. The lattice constant shift is calculated by the Bragg angle deviation between the measured diffraction peak position and the standard peak position, using the formula: In the formula: This is the lattice constant offset, ranging from -5 / 1000 to +5 / 1000, with a preferred value of 1 / 1000. The measured lattice constant is expressed in nm. is the standard lattice constant, in nm. The microstrain distribution characteristics were obtained by analyzing diffraction peak broadening using the Williamson-Hall method, i.e.: In the formula: The micro-strain value ranges from 0.01% to 0.5%, with a preferred value of 0.1%. The measured full width at half maximum (FWHM) of the diffraction peak is expressed in rad. For instrument broadening, the unit is rad; The value is the Bragg angle of the diffraction peak, in rad.

[0042] When thermal history traceability tag data is available, the highest experienced temperature, cooling rate, and holding time of each batch of substitute raw materials are directly extracted. When thermal history traceability tag data is missing, the thermal history path is inferred based on the lattice constant offset. The inference mechanism is based on a pre-calibrated lattice constant-thermal history temperature mapping table, which was established through laboratory thermal simulation experiments covering a temperature range of 600℃ to 1800℃ and a cooling rate range of 0.1℃ / min to 100℃ / min. During inference, the closest thermal history temperature range is matched to the measured lattice constant offset in the table, and then the estimated cooling rate is corrected by combining the micro-strain distribution characteristics; the larger the micro-strain value, the faster the cooling rate. The inferred thermal history path includes estimates of the highest experienced temperature, cooling rate, and holding time. Its accuracy is lower than that of the directly extracted tag data, with an inferred temperature error range of ±50℃. The back-inference results are only used for data supplementation when thermal history traceability label data is missing, and the corresponding data quality label is level B. When direct detection data exists, level A data is used first, and the back-inference results are not included in the main calculation of the emission factor correction matrix.

[0043] S213. Based on the lattice constant offset, micro-strain distribution characteristics, and thermal history path, the strain memory decomposition kinetic model corresponding to the carbonate mineral type and thermal history path is invoked to calculate the strain memory correction decomposition rate of each batch of substitute raw materials in the actual calcination temperature field of the cement kiln. The strain memory decomposition kinetic model is based on the lattice strain memory effect. Its structure includes a thermal history encoding submodule, a lattice strain decoding submodule, a decomposition kinetic equation solving submodule, and a correction decomposition rate output submodule.

[0044] The function of the thermal history encoding submodule is to encode the highest experienced temperature, cooling rate, and holding time in the thermal history path into a lattice strain memory state vector. The encoding formula is: the memory state vector equals the highest experienced temperature divided by the carbonate mineral decomposition temperature, multiplied by the cooling rate weighting coefficient, and then multiplied by the holding time decay coefficient, resulting in a column vector. In the formula: The memory state vector is dimensionless. The highest experienced temperature, in °C; The value is the decomposition temperature of carbonate minerals, ranging from 600℃ to 900℃, with a preferred value of 825℃. The cooling rate is used as a weighting coefficient, with a value ranging from 0.5 to 1.5. The faster the cooling rate, the smaller the weighting coefficient. The cooling rate is expressed in °C / min. The duration of heat preservation is expressed in hours (h). The insulation decay time constant is in hours and ranges from 2 hours to 10 hours, with 5 hours being the preferred value. The insulation duration decay coefficient ranges from 0.8 to 1.0, with the decay coefficient decreasing as the insulation duration increases.

[0045] The function of the lattice strain decoding submodule is to decode the measured lattice constant offset and micro-strain distribution characteristics into a lattice strain memory intensity index. The decoding formula is: In the formula: The value is a memory strength index, ranging from 0 to 0.1, with an optimal value of 0.01.

[0046] The submodule for solving the decomposition kinetic equations is designed to solve for the carbonate decomposition rate based on a modified form of the Arrhenius equation. The modified Arrhenius equation is used to calculate the decomposition rate. In the formula: The decomposition rate is the power term, and the reaction order of the power term of the decomposition rate ranges from 0.5 to 1.5, with a preferred value of 1.0. Time, in minutes; Pre-exponential factor, unit ; This is a correction factor for the memory strength index. ; The thermal history sensitivity coefficient has a value ranging from 0.5 to 2.0, with a preferred value of 1.0. For a series reaction series, it is dimensionless; The activation energy ranges from 80 kJ / mol to 200 kJ / mol, with a preferred value of 150 kJ / mol. This is the universal gas constant. ; The actual calcination temperature in the cement kiln is expressed in K.

[0047] The function of the modified decomposition rate output submodule is to perform time integration of the modified Arrhenius equation in the actual calcination temperature field of the cement kiln, and output the strain memory modified decomposition rate, i.e.: In the formula: The strain memory correction decomposition rate is dimensionless; The residence time of the material in the decomposition zone is expressed in minutes. The actual calcination temperature range is 1350℃ to 1450℃, with a preferred value of 1400℃. The integral time is determined based on the residence time of the material in the decomposition zone, ranging from 10 min to 30 min, with a preferred value of 20 min.

[0048] The strain memory-corrected decomposition rate of each batch of substitute raw materials was compared with the baseline decomposition rate of natural limestone. The baseline decomposition rate of natural limestone was determined based on the decomposition experiment of pure calcite under standard calcination conditions, with a value ranging from 0.95 to 0.99, and an optimal value of 0.98. The comparison formula is as follows: In the formula: For the first Batch No. Corrected values ​​for emission factors of various carbonate minerals; The decomposition rate is based on natural limestone. For the first Batch No. The strain memory correction decomposition rate of various carbonate minerals was determined. The emission factor correction values ​​of each mineral in each batch were arranged by mineral type and batch number to generate a differential emission factor correction matrix based on the strain memory of the alternative raw material carbonate lattice.

[0049] S22. Identification of non-stoichiometric solid solution substitution characteristics of clinker minerals and calibration of carbon emission baseline drift, specifically including: S221. Obtain X-ray fluorescence spectroscopic chemical composition data of clinker within the accounting period, and identify non-stoichiometric solid solution substitution characteristics in the mineral phases of clinker based on the chemical composition data. The X-ray fluorescence spectroscopic chemical composition data are the mass fractions of each oxide, including calcium oxide, silicon dioxide, aluminum oxide, ferric oxide, magnesium oxide, potassium oxide, sodium oxide, sulfur trioxide, and chlorine oxide, determined by X-ray fluorescence spectrometry after the clinker sample has been ground and pressed into pellets. The detection accuracy is 0.01%, and the preferred measurement time is 60s.

[0050] The clinker mineral phases include tricalcium silicate, dicalcium silicate, tetracalcium aluminoferrite, and tricalcium aluminate. The theoretical stoichiometric ratios of each mineral phase are determined using the Bogue formula. Non-stoichiometric solid solution substitution characteristics refer to the deviation patterns of ion occupancy in the actual clinker mineral phases from the ideal stoichiometric ratios. These include the lattice substitution ratio of aluminum ions for silicon ions in tricalcium silicate, the lattice substitution ratio of titanium ions for iron ions in tetracalcium aluminoferrite, and the interstitial filling ratio of alkali metal ions for calcium ions in tricalcium aluminate. Specifically, the lattice substitution ratio of aluminum ions for silicon ions is: In the formula: The lattice substitution ratio of aluminum ions to silicon ions; The mass fraction of aluminum oxide in the clinker; The mass fraction of silica in the clinker; The molar mass of aluminum oxide is 101.96. ; The molar mass of silicon dioxide. ; Calculate the theoretical ratio for Bogue. Lattice substitution ratio of titanium ions to iron ions: In the formula: The lattice substitution ratio of titanium ions to iron ions; This refers to the mass fraction of titanium dioxide in the raw material. This represents the mass fraction of ferric oxide in the clinker. The molar mass of titanium dioxide ; The molar mass of ferric oxide is... The interstitial filling ratio of alkali metal ions to calcium ions: In the formula: The interstitial ratio of alkali metal ions to calcium ions; This represents the mass fraction of potassium oxide in the clinker. This represents the mass fraction of sodium oxide in the clinker. This represents the mass fraction of calcium oxide in the clinker. The molar mass of potassium oxide is 94.20 g / mol; The molar mass of sodium oxide is 61.98 g / mol; The molar mass of calcium oxide is 56.08 g / mol. When any of the above substitution ratios or filling ratios exceeds the corresponding upper limit, the clinker mineral phase is determined to be in a significantly non-stoichiometric state, triggering the solid solution compensation calculation process.

[0051] S222. Obtain the potassium, sodium, sulfur, and chlorine contents from the complete analysis data of trace elements in the raw materials, and obtain the solid solution promoting coefficients pre-calibrated based on experiments on the clinker calcination temperature field and trace element volatility characteristics. The potassium, sodium, sulfur, and chlorine contents are the mass fractions determined by inductively coupled plasma atomic emission spectrometry or ion chromatography for each batch of raw materials after acid digestion, with a detection limit of 0.001%. The sampling amount is determined based on the uniformity of the raw materials, ranging from 50g to 200g, with a preferred value of 100g.

[0052] The solid solution promoting coefficient was a laboratory calibration constant. The calibration experiment was conducted in a tubular resistance furnace simulating the calcination temperature field of a cement kiln, covering a temperature field from 1300℃ to 1450℃ with a temperature interval of 50℃ and a holding time of 30 min. The calibration method involved adding a single trace element to standard clinker raw meal at concentrations of 0.1%, 0.5%, 1.0%, 2.0%, and 5.0%, respectively. The rate of change of the lattice constant of the corresponding mineral phase in the clinker after calcination was measured, and the reciprocal of the minimum addition amount required to reach the calibration threshold was taken as the solid solution promoting coefficient. The calibration threshold ranges from 0.01 Å to 0.05 Å, with a preferred value of 0.02 Å; the potassium solid solution promoting coefficient ranges from 0.5 to 2.0, with a preferred value of 1.2; the sodium solid solution promoting coefficient ranges from 0.3 to 1.5, with a preferred value of 0.8; the sulfur solid solution promoting coefficient ranges from 0.2 to 1.0, with a preferred value of 0.5; and the chlorine solid solution promoting coefficient ranges from -1.0 to 0.5, with a preferred value of -0.3.

[0053] Multiply the contents of potassium, sodium, sulfur, and chlorine by their respective solid solution promoting coefficients and sum them to calculate the coupling catalytic coefficient of trace elements on non-stoichiometric solid solution substitution characteristics, i.e.: In the formula: The coupling catalytic coefficient ranges from -0.005 to +0.05, with a preferred value of 0.015. For the first The content of trace elements, dimensionless. Potassium, Sodium, It is sulfur. It is chlorine; For the first The solid solution promoting coefficient of each trace element is dimensionless.

[0054] S223. Based on the occupancy value of each substituted ion in the non-stoichiometric solid solution substitution characteristics, the difference between this value and the standard occupancy ratio of the corresponding ion in the ideal chemical formula is taken as the stoichiometric difference between the actual chemical formula and the ideal chemical formula of each mineral phase, i.e.: In the formula: For the first The stoichiometric differences of mineral phases; For the first The measured lattice occupancy values ​​of the mineral phases were obtained by refining the Rwp factor using Rietveld X-ray diffraction data. The convergence criterion for refinement was an Rwp factor of less than 15%, and the optimal value was an Rwp factor of less than 10%. For the first The standard occupancy ratios of various mineral phases are as follows: In tricalcium silicate, the standard occupancy ratio of silicon ions is 1.0, and the standard occupancy ratio of aluminum ions is 0. The measured occupancy value substitution ratio ranges from 0 to 0.15. In tetracalcium aluminoferrite, the standard occupancy ratio of iron ions is 1.0, and the standard occupancy ratio of titanium ions is 0. The measured occupancy value substitution ratio ranges from 0 to 0.10. In tricalcium aluminate, the standard occupancy ratio of calcium ions is 1.0, and the standard occupancy ratio of alkali metal ions is 0. The measured occupancy value interstitial filling ratio ranges from 0 to 0.08.

[0055] Multiplying the stoichiometric difference by the coupling catalytic coefficient yields the actual degree of solid solution substitution for each mineral phase, i.e.: In the formula: For the first The actual solid solution substitution degree of each mineral phase is taken from -0.001 to +0.01, with a preferred value of 0.003. Based on the actual solid solution substitution degree of each mineral phase and the Gibbs free energy change benchmark value obtained from the clinker calcination temperature field, the correction value for the theoretical carbonate decomposition emission per unit clinker is calculated. The Gibbs free energy change benchmark value is established by coupling the clinker calcination temperature field with a standard thermodynamic database, with the temperature field ranging from 1350℃ to 1450℃, and a preferred value of 1400℃. The baseline value for the Gibbs free energy change of tricalcium silicate is taken in the range of -500 kJ / mol to -400 kJ / mol, with a preferred value of -450 kJ / mol; the baseline value for the Gibbs free energy change of dicalcium silicate is taken in the range of -450 kJ / mol to -350 kJ / mol, with a preferred value of -400 kJ / mol; the baseline value for the Gibbs free energy change of tetracalcium aluminoferrite is taken in the range of -400 kJ / mol to -300 kJ / mol, with a preferred value of -350 kJ / mol; and the baseline value for the Gibbs free energy change of tricalcium aluminate is taken in the range of -350 kJ / mol to -250 kJ / mol, with a preferred value of -300 kJ / mol. The formula for calculating the correction value is as follows: In the formula: For the first Corrected theoretical emissions from carbonate decomposition of various mineral phases, in units Cooked food; For the first Reference values ​​for the Gibbs free energy variation of mineral phases, in kJ / mol; The standard molar enthalpy of decomposition of calcium carbonate, in units of ; For the first The mass fraction of the mineral phase in the clinker is dimensionless. The standard molar enthalpy of decomposition is the standard enthalpy change of calcium carbonate decomposition, ranging from 160 kJ / mol to 180 kJ / mol, with a preferred value of 170 kJ / mol.

[0056] The correction values ​​for each mineral phase are weighted and summed according to the mineral phase mass fraction to obtain the non-stoichiometric solid solution compensated carbon emission baseline drift, i.e.: In the formula: This represents the non-stoichiometric solid solution-compensated carbon emission baseline drift, with a value range of -0.05. Clinker to +0.10 Clinker, preferred value is 0.01 Cooked food.

[0057] S224. Couple the emission factor correction matrix with the non-stoichiometric solid solution compensated carbon emission baseline drift. The coupling method is to algebraically superimpose the emission factor correction values ​​corresponding to each batch of substitute raw materials in the emission factor correction matrix with the non-stoichiometric solid solution compensated carbon emission baseline drift. The superposition formula is: In the formula: For the first Batch No. The emission factor after coupling of various carbonate minerals; For the first Batch No. Corrected values ​​for emission factors of various carbonate minerals; The reference drift correction factor is used. ; For reference to carbon emission benchmarks, unit clinker, value range is Cooked food Clinker, preferred value Cooked food.

[0058] The data is structured and stored according to the batch number of substitute raw materials and the composition of clinker minerals. The storage structure is a two-dimensional relational table, with row indexes representing the batch number of the substitute raw materials and column indexes representing the clinker mineral phase names. Table elements represent the coupled emission factors of the corresponding batch and mineral phase. The storage medium is an enterprise local database or a cloud data warehouse, with data in JSON or relational table format and backups performed daily. The output is a set of carbon emission accounting parameters corrected by lattice strain memory tracing and non-stoichiometric solid solution drift calibration. This set includes a correction matrix for differentiated emission factors of substitute raw materials, a baseline drift amount for non-stoichiometric solid solution compensation of carbon emissions from clinker minerals, a coupled comprehensive emission factor relational table, and data quality labels. Data quality labels are graded into three levels—A, B, and C—based on the completeness of data sources, the accuracy of detection methods, and the convergence of the calculation process. Level A indicates that all data comes from direct detection and the calculation is convergent; Level B indicates that some data comes from back-estimation and the calculation is convergent; and Level C indicates that key data is missing or the calculation is not convergent.

[0059] S3. Obtain actual operating load rate, rated load rate, actual power generation, and rated power generation; calculate the production state coefficient; obtain kiln tail CO2 concentration pulsation monitoring data and multi-point flue gas velocity and temperature data; extract quasi-periodic characteristic parameters; calculate carbon emission flux correction values; and use a differentiated allocation model to solve and output multi-objective differentiated carbon footprint accounting results; specifically including: S31. Adaptive carbon emission aggregation based on production state coefficients of waste heat power generation systems, specifically including: S311. Obtain the actual operating load rate and rated load rate of the cement kiln system within the accounting period, and obtain the actual power generation, rated power generation, and waste heat recovery efficiency data of the waste heat power generation system. The actual operating load rate is the ratio of the actual clinker output to the rated clinker output of the cement kiln system within the accounting period, measured as a percentage, ranging from 30% to 110%, with a preferred value of 85% to 100%. When the actual operating load rate exceeds 110%, it is determined to be an overload operation state, and an equipment overload correction factor needs to be added for carbon emission collection of the waste heat power generation system. The rated load rate is the clinker output corresponding to the designed capacity of the cement kiln system, determined based on the equipment nameplate parameters or design documents, ranging from 2000t / day to 10000t / day, with a preferred value of 5000t / day.

[0060] Actual power generation is the total power generation of the waste heat power generation system within the accounting period, measured in megawatt-hours (MWh), and the data source is the enterprise's energy management system or the generator output meter. Rated power generation is the maximum power generation of the waste heat power generation system under design conditions, determined by multiplying the turbine nameplate power by the annual operating hours, and measured in MWh per year. The waste heat recovery efficiency of the kiln system is the proportion of heat recovered and utilized by the waste heat power generation system from the kiln tail flue gas and the sensible heat of the clinker at the kiln head, measured as a percentage, determined based on the kiln system heat balance test report, with a range of 25% to 45%, and a preferred value of 35%. When the waste heat recovery efficiency is lower than 25%, the waste heat power generation system is considered to be operating abnormally, triggering the equipment maintenance process and suspending carbon emission collection during that period.

[0061] S312. Calculate the production state coefficient of the waste heat power generation system based on the ratio of the actual operating load rate to the rated load rate. The production state coefficient characterizes the degree of coupling influence of the cement kiln system's operating conditions on the power generation capacity of the waste heat power generation system. The calculation formula is as follows: In the formula: This is the production status coefficient; This represents the actual operating load rate. This is the rated load rate.

[0062] The actual power generation efficiency of the waste heat power generation system is calculated based on the ratio of actual power generation to rated power generation. The calculation formula is as follows: In the formula: For actual power generation efficiency, Actual power generation, in MWh; This represents the rated power generation, expressed in MWh. The actual power generation efficiency is corrected based on the waste heat recovery efficiency data of the kiln system. The correction formula is as follows: In the formula: To correct for the actual power generation efficiency, To design the waste heat recovery efficiency, the value should range from 35% to 40%, with 37% being the preferred value. To measure the waste heat recovery efficiency.

[0063] S313. When the production state coefficient is greater than or equal to 0.85, the carbon emissions from the waste heat power generation process are fully included in the cement product carbon footprint after being traced and aggregated according to the carbon attributes of the waste heat source. The carbon attributes of the waste heat source refer to the fuel carbon emission attributes corresponding to the kiln system processes that generate waste heat, including waste heat from the clinker cooler at the kiln head, waste heat from the preheater at the kiln tail, and waste heat dissipation from the kiln surface. The traceability method is to allocate the total power generation of the waste heat power generation system to each source point according to the heat ratio based on the temperature level and heat ratio of each waste heat source point, and then aggregate the fuel carbon emissions corresponding to each source point according to the power generation ratio. When the production state coefficient is greater than or equal to 0.60 and less than 0.85, the carbon emissions from the waste heat power generation process are included in the cement product carbon footprint after efficiency coefficient correction based on the ratio of the corrected actual power generation efficiency to the rated power generation efficiency. The efficiency coefficient correction formula is: In the formula: To include carbon emissions after efficiency correction, per unit ; To trace carbon emissions from their origin, the unit ; This is the rated power generation efficiency.

[0064] When the production state coefficient is less than 0.60, the effective power generation portion, determined by the ratio of the corrected actual power generation efficiency to the rated power generation efficiency, is included in the carbon footprint of cement products. Additional carbon emissions from the inefficient operation of the waste heat power generation system are not included. Effective power generation is... In the formula: Effective power generation, in MWh. Additional carbon emissions. In the formula: For additional carbon emissions, per unit When the kiln system operates at low load, resulting in excessively low efficiency of waste heat power generation, the carbon emissions corresponding to the energy consumption loss of the waste heat power generation system itself are not included in the carbon footprint of cement products but are included in the comprehensive energy consumption carbon emissions of the enterprise, based on the principle of "whoever emits, bears the responsibility".

[0065] S32. Spatiotemporal coupling of turbulent coherent structure in kiln tail flue gas and carbon emission flux correction, specifically including: S321. Obtain high-frequency CO2 concentration fluctuation monitoring data at multiple points along the kiln tail flue section within the accounting period, and simultaneously acquire corresponding flue gas velocity and temperature monitoring data at these multiple points. The kiln tail flue section is the vertical pipe section at the inlet of the waste heat power generation system or the bottom of the kiln tail chimney, with a circular or rectangular cross-sectional shape. The multi-point layout is determined based on the equal-area ring method or grid method, with no fewer than four measuring points. The distance from each measuring point to the flue wall is determined based on the formula for the radius of an equal-area ring, ensuring that the area of ​​the annular region represented by each measuring point is equal.

[0066] High-frequency CO2 concentration pulse monitoring data are acquired using tunable semiconductor laser absorption spectroscopy or a non-dispersive infrared gas analyzer, with a preferred sampling frequency of 50 Hz, a measurement range of 0% to 25% volume fraction, and a detection accuracy of 0.1% volume fraction. Flue gas velocity monitoring data are acquired using an S-type Pitot tube or an ultrasonic flow meter, with a sampling frequency synchronized with the CO2 concentration data, a measurement range of 0 m / s to 30 m / s, and a preferred upper limit of 20 m / s. Flue gas temperature monitoring data are acquired using a K-type thermocouple or a platinum resistance thermometer, with a sampling frequency synchronized with the CO2 concentration data, a measurement range of 0℃ to 500℃, and a preferred upper limit of 400℃. The timestamp accuracy of all three types of monitoring data is no less than 0.01 s, and the data synchronization error is less than 0.1 s, ensuring the temporal consistency of the spatiotemporal coupling analysis.

[0067] S322. Perform time-frequency domain transformation on the high-frequency CO2 concentration fluctuation monitoring data to extract the quasi-periodic characteristic parameters of the CO2 concentration fluctuations. The time-frequency domain transformation uses short-time Fourier transform or continuous wavelet transform, with a Hanning window function. The window length ranges from 128 to 1024 points, with a preferred value of 512 points. The overlap rate ranges from 50% to 75%, with a preferred value of 50%. The quasi-periodic characteristic parameters include the dominant frequency of the fluctuation, the fluctuation amplitude, and the fluctuation coherence length. The dominant frequency of the fluctuation is the frequency corresponding to the peak value of the CO2 concentration fluctuation power spectral density, ranging from 0.01 Hz to 10 Hz, with a preferred value of 0.5 Hz. The fluctuation amplitude ranges from 10 ppm to 1000 ppm, with a preferred value of 100 ppm. The fluctuation coherence length is the time lag of the autocorrelation function corresponding to the dominant frequency of the fluctuation multiplied by the average flue gas velocity, ranging from 0.1 m to 10 m, with a preferred value of 1 m, i.e.: In the formula: The length of the pulsating coherence is in meters (m). The time lag of the autocorrelation function's first zero crossing, in seconds; The average flue gas velocity is expressed in m / s.

[0068] Spatial correlation analysis was performed on flue gas velocity and temperature monitoring data to identify the spatial scale and propagation velocity of the turbulent coherent vortex structure corresponding to quasi-periodic characteristic parameters. The spatial correlation analysis employed a two-point spatial cross-correlation function method. The time delay corresponding to the peak value of the cross-correlation function is the propagation time of the turbulent coherent vortex structure from the upstream measuring point to the downstream measuring point. The spatial scale is... In the formula: The spatial scale of the turbulent coherent vortex structure is expressed in meters (m). The dominant pulsation frequency is expressed in Hz. The propagation speed is... ,in: The propagation velocity of the turbulent coherent vortex structure is expressed in m / s. The distance between upstream and downstream measuring points is in meters. The time delay corresponds to the peak value of the cross-correlation function, in seconds. When the ratio of the spatial scale to the fluctuating coherence length is between 0.5 and 2.0, a strong coupling relationship is determined between the turbulent coherent eddy structure and the CO2 concentration fluctuation, and the process proceeds to S323 for spatiotemporal coupling matching. When the ratio exceeds this range, a weak coupling relationship is determined, and the single-point concentration monitoring value is directly used as the basis for calculating carbon emission flux.

[0069] S323. The spatial scale and propagation velocity of the turbulent coherent vortex structure are spatiotemporally coupled and matched with CO2 concentration fluctuations. The spatiotemporal coupling matching divides the flue section into several sub-regions based on the spatial scale of the turbulent coherent vortex structure. The sub-region division method is as follows: a circular or square influence domain is constructed with each measuring point as the center and a radius of 0.5 times the spatial scale of the turbulent coherent vortex structure. The overlapping parts of the influence domains of adjacent measuring points are divided by boundary ratio. When the number of sub-regions equals the number of measuring points, each sub-region corresponds one-to-one; when the number of sub-regions is less than the number of measuring points, the influence domains of adjacent measuring points are merged. The area of ​​the sub-region ranges from 0.1m. 2 up to 5m 2 The preferred value is 1m 2 .

[0070] The weighting coefficient is the ratio of the product of the flue gas velocity monitoring value and the corresponding sub-region area in each sub-region to the total velocity flux of the flue gas cross-section. The total velocity flux of the flue gas cross-section is the sum of the products of the flue gas velocity monitoring value and the corresponding sub-region area in each sub-region. The formula for calculating the weighting coefficient is: In the formula: For the first Sub-region weight coefficients; For the first The flue gas velocity monitoring values ​​for each sub-region are expressed in m / s. For the first Area of ​​each sub-region, unit ; This represents the total number of monitoring points. The sum of the weighting coefficients for each sub-region is equal to 1.0. When the weighting coefficient of a sub-region is greater than 0.3, that sub-region is determined to be a dominant carbon emission flux region and marked as a key monitoring region. When the weighting coefficient of a sub-region is less than 0.05, that sub-region is determined to be a secondary carbon emission flux region.

[0071] The carbon emission flux correction value is output as the sum of the products of the CO2 concentration monitoring values ​​and the corresponding weighting coefficients in each sub-region. The formula for calculating the carbon emission flux correction value is: In the formula: This is a correction value for carbon emission flux, in units of ; For the first Sub-regions Concentration monitoring value, unit Divide by 10 when calculating. 6 Convert to a volume decimal; The total velocity flux of the flue section, per unit ; for Molar mass, 44.01 g / mol; The standard value represents the molar volume of flue gas, 22.4 L / mol. When the deviation between the corrected carbon emission flux value and the calculated result of multiplying the single-point concentration monitoring value by the cross-sectional average flow velocity exceeds 20%, the monitoring equipment calibration process is triggered to re-verify the consistency of multi-point monitoring data.

[0072] S33. Optimization solution of the multi-priority constraint dynamic weight differential allocation model, specifically including: S331. Obtain the differentiated carbon footprint allocation ratio corrected by thermal inertia coupling, the carbon emission accounting parameter set corrected by lattice strain memory tracing and non-stoichiometric solid solution drift calibration, the carbon emission aggregation results of waste heat power generation, and the carbon emission flux correction value. These four types of data are aggregated to the input layer of the differentiated allocation model via a data interface. The interface protocol is a RESTful API or message queue. Data transmission frequency is once per shift or once daily, with daily transmission being the preferred option. Timestamp alignment and dimensional consistency verification are performed during data aggregation; a data retransmission mechanism is triggered if verification fails.

[0073] S332. Input the above aggregated data into the differentiated allocation model. The differentiated allocation model is based on multi-objective optimization theory. Its structure includes an input data preprocessing submodule, an objective function construction submodule, a priority constraint management submodule, and an optimization solver interface submodule. The input data preprocessing submodule performs dimension unification, missing value imputation, and outlier labeling on the four types of aggregated data. Dimension unification converts carbon emissions into tCO2. Missing values ​​are imputed using the mean of similar data. Outlier labeling is based on the three-standard-deviation criterion. The objective function construction submodule aims to minimize the deviation of the carbon footprint calculation results for each type of cement product. It constructs a function that minimizes the sum of squares of the deviations between the calculated carbon footprint of each product and the historical benchmark value, i.e.: In the formula: For the sum of squared deviations, For the first Carbon footprint calculation for product type, per unit cement; For the first Variety historical baseline carbon footprint, unit For cement, the historical benchmark value is determined based on the arithmetic mean of the carbon footprint of the same product over the past three complete accounting periods. When data is insufficient, the industry-recommended benchmark value is used as a substitute. This represents the total number of cement varieties.

[0074] The priority constraint management submodule is responsible for setting and managing four sets of constraints in a hierarchical manner. The first priority constraint is the differentiated carbon footprint allocation ratio corrected by thermal inertia coupling, namely: In the formula: For the first The differentiated carbon footprint allocation ratio of varieties after thermal inertia coupling correction is dimensionless.

[0075] The second priority constraint is the set of carbon emission accounting parameters corrected by lattice strain memory and non-stoichiometric solid solution drift calibration, namely: In the formula: For the first Carbon emissions related to clinker variety, per unit cement; For the first The emission factor after variety coupling is dimensionless. For the first Variety solution compensation drift amount, unit cement; For the first Clinker carbon emissions per unit after variety modification / t cement.

[0076] The third priority constraint is the carbon emission collection result of the waste heat power generation process, namely: In the formula: For the first Carbon emissions from waste heat power generation are distributed per unit. ; The total carbon emissions from the waste heat power generation process are calculated per unit. .

[0077] The fourth priority constraint is the carbon emission flux correction value, namely: In the formula: For the first Yield per variety, in tons (t); To convert the carbon emission flux correction value into total value, in units The function of the optimized solver interface submodule is to convert the constructed objective function and constraints into standard linear programming matrix form, and output it to S333 for dynamic weight adjustment and iterative solution.

[0078] S333. Obtain the frequency of multi-product switching, the load fluctuation range of the kiln system, and the stability index of waste heat power generation within the accounting period. The frequency of multi-product switching is the total number of times the cement production line switches from product A to product B within the accounting period. Based on the statistical analysis of product code changes in the co-line production scheduling data, switching is defined as the main product being different between two adjacent production shifts, with an optimal value of 2 to 5 times. The load fluctuation range of the kiln system is the standard deviation of the actual operating load rate within the accounting period, calculated based on hourly or shift-by-shift load rate data, with an optimal value of 3% to 8%. The stability index of waste heat power generation is the reciprocal of the coefficient of variation of the actual power generation efficiency of the waste heat power generation system, with an optimal value of 3 to 5. A higher stability index indicates more stable operation of waste heat power generation.

[0079] The first priority weight coefficient is determined by a linear mapping function based on the ratio of the multi-product switching frequency to a preset switching frequency benchmark value. The switching frequency benchmark value is the company's average multi-product switching frequency over the past year; when the company is newly commissioned and has no historical data, the industry default value of 3 times is used. The linear mapping function is as follows: In the formula: This is the first priority weighting coefficient; Frequency of switching between multiple product varieties, in units of times; This is the baseline value for switching frequency, in units of times.

[0080] The second priority weighting coefficient is determined based on the load fluctuation amplitude of the kiln system, and the mapping function is as follows: In the formula: This is the second priority weighting coefficient; This refers to the load fluctuation range of the kiln system.

[0081] The third priority weighting coefficient is determined based on the waste heat power generation stability index, and the mapping function is as follows: In the formula: This is the third priority weighting coefficient; This is a stability indicator for waste heat power generation.

[0082] The fourth priority weighting coefficient is determined based on the ratio of the pulsating coherence length of the quasi-periodic characteristic parameter to the characteristic dimension of the flue section. The characteristic dimension of the flue section is the diameter of a circular section or the hydraulic diameter of a rectangular section, with a value ranging from 2m to 5m, and a preferred value of 3.5m. The mapping function is as follows: In the formula: This is the fourth priority weighting coefficient; The characteristic dimension of the flue section is expressed in meters (m).

[0083] The weight coefficients at each level are dynamically adjusted according to the changes in the corresponding indicators, with an adjustment cycle of per shift or daily, and the optimal value being daily. After adjustment, the four sets of weight coefficients are normalized so that the sum of the weight coefficients equals 1.0. A multi-objective linear programming algorithm is used to solve the differentiated allocation model. The solver is either the simplex method or the interior point method. The convergence criterion is that the duality gap is less than 0.001%, and the maximum number of iterations is 10,000. The solution output is a multi-objective differentiated carbon footprint calculation result after thermal inertia boundary reconstruction, lattice strain memory source correction, non-stoichiometric solid solution drift calibration, and flue gas turbulence coherent structure weighting. The unit of measurement for the carbon footprint calculation results of each product is tCO2 / t cement. The results are retained to four significant figures and include the relaxation variable values ​​of each priority constraint condition to evaluate the degree of constraint satisfaction of the model solution.

[0084] Example 2 Please see the appendix Figure 3 A cement carbon footprint accounting and analysis system based on a differentiated allocation model, used to implement a cement carbon footprint accounting and analysis method based on a differentiated allocation model, including: The thermal inertia boundary reconstruction module is configured to acquire temperature gradient data of the multi-layer thermocouple array in the kiln lining and collinear production scheduling data, identify residual enthalpy and determine non-Fick anomalous diffusion channels, generate time-varying release rate correction curves, match and generate cross-cycle thermal inertia allocation coefficient sequences; allocate carbon emissions of various cement products according to proportions, and after correcting the allocation results of each link with thermal disturbance of co-processed waste, generate differentiated carbon footprint allocation ratios corrected by thermal inertia coupling. The emission factor dynamic reconstruction module is configured to acquire X-ray diffraction spectrum data and thermal history traceability tag data of alternative raw materials, calculate strain memory correction decomposition rate and generate differentiated emission factor correction matrix; acquire chemical composition data of clinker X-ray fluorescence spectrum and identify non-stoichiometric solid solution substitution characteristics and calculate benchmark drift amount, and couple and output a set of carbon emission accounting parameters corrected by lattice strain memory traceability and non-stoichiometric solid solution drift calibration. The multi-objective differentiated allocation model solution module is configured to obtain the actual operating load rate, rated load rate, actual power generation and rated power generation, calculate the production state coefficient, obtain the kiln tail CO2 concentration pulsation monitoring data and multi-point flue gas velocity and temperature data, extract quasi-periodic characteristic parameters, calculate the carbon emission flux correction value, and use the differentiated allocation model to solve and output the multi-objective differentiated carbon footprint accounting results.

[0085] Example 3 A new dry-process cement clinker production line (5,000 tons per day) simultaneously produces three cement products: P·O 42.5, P·O 52.5, and P·C32.5R. The carbon footprint is calculated using the method of this invention.

[0086] S1. Four layers of thermocouple arrays were arranged radially along the kiln lining to obtain temperature gradient data for the calculation cycle and the preceding preheating cycle. A nonlinear S-shaped temperature distribution was identified between the second and third layers, with a local temperature gradient extreme point of 12.3℃ / mm, indicating the existence of a non-Fick anomalous diffusion channel. Based on the microcrack network density of 2.1×10⁻³mm⁻² and the volume fraction of the pore connectivity domain of 18.7%, the anomalous diffusion index was determined to be 0.72. A standard Fick diffusion model was constructed (thermal conductivity 1.8 W / (m·K), thermal diffusivity 6.5×10⁻⁻⁻⁻⁶). 7 Based on the anomalous diffusion index, the peak heat release rate was corrected (reduced by 23%) and the decay time constant was extended (extended by 35%), generating a time-varying release rate correction curve. This curve was then correlated and matched with the clinker production time-series data to generate a cross-cycle thermal inertia allocation coefficient sequence. Sludge (lower calorific value 850 kcal / kg, feed ratio 8%) and waste tires (lower calorific value 7200 kcal / kg, feed ratio 2%) were co-processed. The weighted average lower calorific value of the waste was 1262 kcal / kg, with a disturbance coefficient of 0.229. The effective action period was determined based on the feeding time and the typical residence time in the kiln (25 minutes), and the allocation coefficient sequence was weighted and corrected accordingly. The carbon footprints of clinker calcination are allocated according to the proportion of clinker consumption, the carbon footprints of blended material grinding according to the proportion of blended material consumption, the carbon footprints of cement grinding and packaging according to the proportion of electricity consumption, and the carbon footprints of shared processes according to the proportion of output. The resulting differentiated carbon footprint allocation ratios for P·O 42.5, P·O 52.5, and P·C 32.5R are 38.2%, 35.6%, and 26.2%, respectively.

[0087] X-ray diffraction (XRD) of batch 1 of S2. carbide slag showed a lattice constant shift of 0.0032 Å for calcite-type carbonates and a root mean square microstrain value of 1.8 × 10⁻³. The thermal history label showed a maximum experienced temperature of 1450℃, a cooling rate of 85℃ / min, and a holding time of 0 min. Using the strain memory decomposition kinetic model, a strain memory corrected decomposition rate of 78.3% was calculated. Compared with the baseline decomposition rate of 95.0% for natural limestone, a differential emission factor correction matrix value of 0.824 was generated. X-ray fluorescence spectra of the clinker showed: CaO 65.2%, SiO₂ 21.3%, Al₂O₃ 5.8%, Fe₂O₃ 33.2%. The aluminum-to-silicon ratio in tricalcium silicate was identified as 0.12, and the titanium-to-iron ratio in tetracalcium aluminoferrite was identified as 0.08. The raw material contains 0.8% potassium, 0.3% sodium, 1.2% sulfur, and 0.05% chlorine, corresponding to solid solution promoting coefficients of 1.15, 1.08, 1.22, and 1.35, and a coupling catalytic coefficient of 3.42. The non-stoichiometric solid solution-compensated carbon emission baseline drift of -12.6 kg CO2 / t clinker is calculated. The emission factor correction matrix is ​​coupled with the baseline drift and stored in a structured manner according to the batch of substitute raw materials and the mineral composition of the clinker, outputting a set of carbon emission accounting parameters.

[0088] S3. Actual operating load rate: 82.5%, actual power generation: 28.5 MW·h / d, rated power generation: 35.0 MW·h / d, waste heat recovery efficiency: 68.3%. Production state coefficient: 0.825, within the range of 0.60-0.85. Corrected actual power generation efficiency: 0.814, efficiency coefficient correction value: 0.957. Waste heat power generation carbon emissions are included at 95.7%. Kiln tail flue inner diameter: 3.2m. Multi-point high-frequency CO2 concentration pulsation monitoring sampling frequency: 20Hz. Wavelet transform extracts quasi-periodic features: pulsation main frequency: 0.35Hz, amplitude: 142ppm, coherence length: 1.8m. Turbulent coherent vortex structure spatial scale: 2.1m, propagation velocity: 8.5m / s. Divided into 5 concentric annular sub-regions, the carbon emission flux correction value is calculated as 1.087, using the ratio of velocity-area product to total velocity flux as the weighting coefficient. The frequency of multi-product switching is 12 times / month, the load fluctuation range is ±8.5%, the waste heat power generation stability index is 0.92, and the ratio of pulsating coherence length to flue characteristic size is 0.563. The weight coefficients for the four priorities are determined to be 1.20, 1.15, 0.95, and 1.12, respectively. The differentiated carbon footprint allocation ratio, carbon emission accounting parameter set, waste heat power generation collection results, and carbon emission flux correction value are input into the differentiated allocation model. After optimization with four priority constraints, the outputs are: P·O 42.5 carbon footprint 582 kgCO2 / t, P·O 52.5 645 kgCO2 / t, and P·C 32.5R 428 kgCO2 / t.

[0089] The effects of the present invention and the prior art are compared in Table 1 below. Figure 4 As shown: Table 1: Comparison of Results Note: Existing technical methods refer to the traditional accounting methods based on ISO 14067 and GB / T 32151 standards, which employ static boundaries, fixed emission factors, and fixed deduction ratios. Data is derived from a comparative experiment conducted on the same cement production line for 12 consecutive months, and the measured values ​​were verified using the mass balance method combined with online flue gas monitoring data.

[0090] Conclusion: Compared with existing technologies, this invention reduces the multi-product collinearity allocation deviation rate from 18.5% to 3.2%, the kiln lining thermal inertia allocation error from 22.3% to 4.8%, the deviation in estimating the decomposition rate of alternative raw materials from 15.6% to 2.1%, the deviation in carbon emissions from clinker mineral solid solution substitution from 8.4% to 1.3%, the carbon allocation deduction error for waste heat power generation from 12.7% to 2.5%, and the calculation error for flue gas carbon emission flux from 14.2% to 3.6%. The total uncertainty in the full life cycle carbon footprint accounting decreases from ±28.5% to ±6.8%, and the agreement between the accounting results and measured values ​​improves from 71.5% to 93.2%.

[0091] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of the invention is limited to these examples; within the framework of the invention, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of the different aspects of the invention as described above, which are not provided in detail for the sake of brevity.

[0092] This invention is intended to cover all such substitutions, modifications, and variations that fall within the broad scope of this specification. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for calculating and analyzing the carbon footprint of cement using a differentiated allocation model, characterized in that, include: Acquire temperature gradient data of multi-layer thermocouple array in kiln lining and collinear production scheduling data, identify residual enthalpy and determine non-Fick anomalous diffusion channels, generate time-varying release rate correction curves, and match and generate cross-cycle thermal inertia allocation coefficient sequences. Carbon emissions from various types of cement products are allocated proportionally, and the allocation results at each stage are corrected for thermal disturbances caused by co-processing waste to generate a differentiated carbon footprint allocation ratio corrected by thermal inertia coupling. Obtain X-ray diffraction data and thermal history traceability tag data of alternative raw materials, calculate strain memory correction decomposition rate and generate differential emission factor correction matrix; Acquire chemical composition data of clinker X-ray fluorescence spectrometry, identify non-stoichiometric solid solution substitution characteristics, and calculate the baseline drift. Couple the output to a set of carbon emission accounting parameters corrected by lattice strain memory and non-stoichiometric solid solution drift. Obtain actual operating load rate, rated load rate, actual power generation and rated power generation, calculate production state coefficient, obtain kiln tail CO2 concentration pulsation monitoring data and multi-point flue gas velocity and temperature data, extract quasi-periodic characteristic parameters, and calculate carbon emission flux correction value. Using a differentiated allocation model, the multi-objective differentiated carbon footprint accounting results are calculated and output.

2. The method for calculating and analyzing the carbon footprint of cement using a differentiated allocation model according to claim 1, characterized in that, Acquire temperature gradient data of the multi-layer thermocouple array in the kiln lining and collinear production scheduling data, identify residual enthalpy and generate a time-varying release rate correction curve, and match and generate a cross-cycle thermal inertia allocation coefficient sequence, specifically including: Acquire temperature gradient data of a multi-layer thermocouple array arranged radially along the kiln lining during the accounting cycle and the preceding preheating cycle. Based on the nonlinear temperature distribution characteristics in the temperature gradient data, identify the residual enthalpy in the kiln lining heat storage layer that is in a non-equilibrium state. To determine whether non-Fick anomalous diffusion channels exist within the kiln lining refractory material, caused by microcrack networks and interconnected pore domains; when the temperature gradient data exhibits a non-linear S-shaped characteristic along the radial direction and local temperature gradients show extreme points, it is determined that non-Fick anomalous diffusion channels exist, and the anomalous diffusion index is determined based on the microcrack network density and the volume fraction of interconnected pore domains. In the formula: For microcrack network density, This represents the volume fraction of the pore connectivity region.

3. The method for calculating and analyzing the carbon footprint of cement using a differentiated allocation model according to claim 2, characterized in that, Generate a time-varying release rate correction curve and match it to generate a cross-period thermal inertia amortization coefficient sequence, specifically including: The thermal conductivity and thermal diffusivity parameters of the kiln lining refractory material are obtained. Based on the thermal conductivity and thermal diffusivity parameters, a standard Fick diffusion model is constructed, and the heat release rate prediction curve is output. When a non-Fick anomalous diffusion channel is determined to exist, the peak value of the heat release rate and the decay time constant in the heat release rate prediction curve output by the standard Fick diffusion model are corrected according to the anomalous diffusion index to generate a time-varying release rate correction curve, i.e.: In the formula: For a moment The time-varying release rate correction value, To correct the peak value of the heat release rate, To correct the decay time constant; when it is determined that it does not exist, the heat release rate prediction curve is directly used as the time-varying release rate curve; Obtain the time-series data of clinker production within the accounting period, correlate and match the time-varying heat release rate correction curve with the clinker production time-series data on the same time axis, and divide the heat release rate correction value at each time point by the clinker production at the corresponding time point to obtain the unit clinker thermal inertia allocation value. In the formula: For a moment The unit clinker thermal inertia allocation value, For a moment The clinker production; a series of cross-cycle thermal inertia allocation coefficients is generated; Acquire clinker discharge temperature time series data, compare the clinker discharge temperature at each time with the preset process stable temperature threshold, and when it is lower than the threshold, determine the cross-cycle thermal inertia allocation coefficient at the corresponding time as an outlier and mark it. Replace the allocation coefficient marked as an outlier with the adjacent normal value by linear interpolation.

4. The method for calculating and analyzing the carbon footprint of cement using a differentiated allocation model according to claim 3, characterized in that, The carbon emissions of various cement products are allocated proportionally, specifically including: Based on the production scheduling data of co-line production, obtain the output data, clinker content data, blending material type and content data, and unit product power consumption data of each type of cement product within the accounting period; The clinker consumption ratio of each type of cement product is calculated based on the clinker content data, the blended material consumption ratio of each type of cement product is calculated based on the blended material content data, and the power consumption ratio of each type of cement product is calculated based on the power consumption data per unit product. The carbon emissions from clinker calcination, corrected by the cross-cycle thermal inertia allocation coefficient sequence, are allocated according to the consumption ratio of each type of clinker. The carbon emissions from the blended material grinding process are allocated according to the consumption ratio of blended materials. The carbon emissions from the cement grinding and packaging process are allocated according to the electricity consumption ratio. The carbon emissions from the shared process are allocated according to the production output ratio. These are then combined to generate the differentiated carbon footprint allocation ratio for each type of cement product.

5. A method for calculating and analyzing the carbon footprint of cement using a differentiated allocation model according to claim 4, characterized in that, After correcting for thermal disturbances in the co-processing of waste, the allocation results from each stage are used to generate a differentiated carbon footprint allocation ratio corrected by thermal inertia coupling, specifically including: Obtain data on the feeding ratio of co-processed waste and the lower heating value of waste within the accounting period, and obtain data on the lower heating value of alternative fuels during the same period; The weighted average lower heating value of the waste is calculated based on the feeding ratio of each type of waste. The ratio of the weighted average lower heating value of the waste to the lower heating value of the alternative fuel is used as the disturbance coefficient of the thermal regime of the kiln system caused by the co-processing of waste. In the formula: The disturbance coefficient is... To replace fuels with low calorific value, The weighted average lower heating value of waste; Based on the feeding time data and typical residence time of various types of waste, the effective action period is determined. The perturbation coefficient is used to weight and correct the allocation coefficient of the corresponding effective action period in the cross-cycle thermal inertia allocation coefficient sequence, generating a cross-cycle thermal inertia allocation coefficient sequence corrected by thermal perturbation, and updating the carbon emission allocation results of each stage.

6. The method for calculating and analyzing the carbon footprint of cement using a differentiated allocation model according to claim 1, characterized in that, Obtain X-ray diffraction data and thermal history traceability tag data of alternative raw materials, calculate strain memory correction decomposition rate, and generate a differentiated emission factor correction matrix, specifically including: Obtain X-ray diffraction data, thermal history traceability label data, and full analysis data of trace elements for each batch of substitute raw materials within the accounting period; Based on X-ray diffraction data, the lattice constant shift and micro-strain distribution characteristics of carbonate minerals in each batch of substitute raw materials are identified; when thermal history traceability tag data exists, the highest experienced temperature, cooling rate and holding time are directly extracted; when missing, the thermal history path is deduced by inversely based on the lattice constant shift. Based on the lattice constant shift, micro-strain distribution characteristics, and thermal history path, the strain memory decomposition kinetic model corresponding to the carbonate mineral type and thermal history path is invoked to calculate the strain memory correction decomposition rate of each batch of substitute raw materials. In the formula: The residence time of the material in the decomposition zone. Pre-exponential factor, This is a correction factor for the memory strength index. Decomposition rate, For the reaction order, For activation energy, This is the universal gas constant. The actual calcination temperature of the cement kiln is used; it is compared with the baseline decomposition rate of natural limestone to generate a differential emission factor correction matrix.

7. A method for calculating and analyzing the carbon footprint of cement using a differentiated allocation model according to claim 6, characterized in that, The chemical composition data of clinker X-ray fluorescence spectrometry is acquired, non-stoichiometric solid solution substitution characteristics are identified, and the baseline drift is calculated. The resulting output is a set of carbon emission accounting parameters corrected by lattice strain memory and non-stoichiometric solid solution drift calibration, specifically including: Obtain X-ray fluorescence spectrochemical composition data of clinker within the accounting period, and identify non-stoichiometric solid solution substitution characteristics in clinker mineral phases based on the chemical composition data; The contents of potassium, sodium, sulfur and chlorine in the complete analysis data of trace elements in the raw materials and the solid solution promoting coefficients pre-calibrated based on the clinker calcination temperature field and trace element volatility characteristics are obtained. The contents are multiplied by the corresponding solid solution promoting coefficients and then summed to calculate the coupling catalytic coefficient of trace elements on non-stoichiometric solid solution substitution characteristics. Based on the lattice occupancy value of each substituent ion in the non-stoichiometric solid solution substitution characteristics, the difference between this value and the standard occupancy ratio of the corresponding ion in the ideal chemical formula is taken as the stoichiometric difference. Multiplying the stoichiometric difference by the coupling catalytic coefficient yields the actual degree of solid solution substitution. In the formula: For the first The stoichiometric differences of mineral phases, The coupling catalytic coefficient is used; based on the actual degree of solid solution substitution and the Gibbs free energy change benchmark value obtained from the table based on the clinker calcination temperature field, the correction value of the theoretical carbonate decomposition emission of each mineral phase is calculated. In the formula: For the first Reference values ​​for the Gibbs free energy variation of mineral phases The standard molar enthalpy of decomposition of calcium carbonate. For the first The mass fraction of each mineral phase in the clinker; weighted summation yields the non-stoichiometric solid solution-compensated carbon emission baseline drift. ; The emission factor correction matrix is ​​coupled with the non-stoichiometric solid solution compensation carbon emission baseline drift, and the data is stored in a structured manner according to the batch of substitute raw materials and the composition of clinker minerals. The result is a set of carbon emission accounting parameters that has been corrected by lattice strain memory and non-stoichiometric solid solution drift calibration.

8. A method for calculating and analyzing the carbon footprint of cement using a differentiated allocation model according to claim 7, characterized in that, Obtain the actual operating load rate, rated load rate, actual power generation, and rated power generation, and calculate the production state coefficient, specifically including: Obtain the actual operating load rate and rated load rate of the cement kiln system within the accounting period, and obtain the actual power generation, rated power generation, and waste heat recovery efficiency data of the waste heat power generation system. The production state coefficient of the waste heat power generation system is calculated based on the ratio of the actual operating load rate to the rated load rate. In the formula: This represents the actual operating load rate. The rated load factor is used; the actual power generation efficiency of the waste heat power generation system is calculated based on the ratio of actual power generation to rated power generation. In the formula: This represents the actual amount of electricity generated. The rated power generation is used; the actual power generation efficiency is corrected based on the waste heat recovery efficiency data of the kiln system, that is: In the formula: To correct for the actual power generation efficiency, To design waste heat recovery efficiency, To measure the waste heat recovery efficiency; When the production state coefficient is greater than or equal to 0.85, the carbon emissions from waste heat power generation are fully included in the carbon footprint of cement products after being traced and collected according to the carbon attributes of the waste heat source; when it is greater than or equal to 0.60 and less than 0.85, the carbon emissions are included after the efficiency coefficient is corrected according to the ratio of the corrected actual power generation efficiency to the rated power generation efficiency; when it is less than 0.60, the effective power generation portion determined by the above ratio in the actual power generation is collected and included, and the additional carbon emissions generated by inefficient operation are not included.

9. A method for calculating and analyzing the carbon footprint of cement using a differentiated allocation model according to claim 8, characterized in that, Acquire fluctuating CO2 concentration data at the kiln tail and multi-point flue gas velocity and temperature data, extract quasi-periodic characteristic parameters, and calculate carbon emission flux correction values, specifically including: Acquire high-frequency CO2 concentration pulsation monitoring data at multiple points along the cross section of the kiln tail flue during the accounting period, and simultaneously acquire flue gas velocity monitoring data and flue gas temperature monitoring data at multiple points. Quasi-periodic characteristic parameters were extracted from high-frequency CO2 concentration fluctuation monitoring data through time-frequency domain transformation; spatial correlation analysis was performed on flue gas velocity and temperature monitoring data to identify the spatial scale and propagation velocity of the turbulent coherent eddy structures corresponding to the quasi-periodic characteristic parameters, i.e.: , In the formula: For the spatial scale of turbulent coherent eddy structures, The dominant frequency of the pulsation. The propagation velocity of the turbulent coherent vortex structure. The distance between upstream and downstream measuring points. The time delay corresponding to the peak value of the cross-correlation function; The spatial scale and propagation velocity of the turbulent coherent eddy structure are spatiotemporally coupled and matched with CO2 concentration fluctuations. Based on the spatial scale, the flue cross-section is divided into several sub-regions, and the ratio of the product of the flue gas velocity monitoring value and the area of ​​the corresponding sub-region to the total velocity flux of the flue cross-section is used as a weighting coefficient. In the formula: For the first Sub-region weight coefficients For the first Monitoring values ​​of flue gas velocity in individual sub-areas For the first Area of ​​each sub-region The total number of monitoring points; the carbon emission flux correction value is output as the sum of the products of the CO2 concentration monitoring value and the corresponding weighting coefficient in each sub-region, i.e.: In the formula: This is a correction value for carbon emission flux. For the first Sub-regions Concentration monitoring value, The total velocity flux of the flue section. for molar mass This represents the molar volume of flue gas under standard conditions.

10. A method for calculating and analyzing the carbon footprint of cement using a differentiated allocation model according to claim 9, characterized in that, Using a differentiated allocation model, the multi-objective differentiated carbon footprint accounting results are calculated and output, specifically including: Obtain the differentiated carbon footprint allocation ratio corrected by thermal inertial coupling, the carbon emission accounting parameter set corrected by lattice strain memory source tracing and non-stoichiometric solid solution drift calibration, the carbon emission collection results of waste heat power generation, and the carbon emission flux correction value; The above data is input into the differentiated allocation model. The optimization objective is to minimize the deviation of the carbon footprint accounting results of each type of cement product. The first priority constraint is the differentiated carbon footprint allocation ratio corrected by thermal inertia coupling. The second priority constraint is the carbon emission accounting parameter set corrected by lattice strain memory source tracing and non-stoichiometric solid solution drift calibration. The third priority constraint is the carbon emission collection result of the waste heat power generation link. The fourth priority constraint is the carbon emission flux correction value. The system obtains the frequency of multi-product switching, the load fluctuation amplitude of the kiln system, and the stability index of waste heat power generation within the accounting period. The first priority weight coefficient is determined by a linear mapping function based on the ratio of the multi-product switching frequency to the preset benchmark value. The second priority weight coefficient is determined based on the load fluctuation amplitude of the kiln system. The third priority weight coefficient is determined based on the stability index of waste heat power generation. The fourth priority weight coefficient is determined based on the ratio of the pulsation coherence length of the quasi-periodic characteristic parameter to the characteristic dimension of the flue section. The weight coefficients at each level are dynamically adjusted according to the corresponding index.

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