Method and system for accounting carbon emission factor of layered double hydroxide

Abstract

This invention provides a method and system for calculating the carbon emission factor of layered double hydroxides, relating to the field of carbon emission accounting technology. The method includes: acquiring boundary data and carbon emission source data of the accounting object; determining the accounting boundary and carbon emission source level based on the boundary data and carbon emission source data; constructing a carbon emission accounting formula system; acquiring basic parameters and characteristic parameters in laboratory or industrial scenarios; substituting the basic parameters and characteristic parameters into the carbon emission accounting formula system to calculate the corresponding carbon emissions and carbon emission factor per unit product; obtaining sensitivity analysis results through multi-dimensional sensitivity analysis based on the corresponding emissions and carbon emission factor per unit product; identifying core optimization parameters based on the sensitivity analysis results; adjusting the preparation process parameters and the value standards of each coefficient in the carbon emission accounting formula system based on the core optimization parameters, updating the accounting results, and establishing a two-way feedback mechanism between accounting and process optimization.

Description

Technical Field

[0001] This invention relates to the field of carbon emission accounting technology, and in particular to a method and system for calculating the carbon emission factor of layered double hydroxides. Background Technology

[0002] Layered hydrogen hydroxides (LDHs) are a class of green functional materials with a unique layered crystal structure and highly efficient ion exchange performance. Leveraging their selective adsorption capacity for chloride ions and their slow-release properties of rust-inhibiting anions, they exhibit irreplaceable advantages in improving the durability of reinforced concrete structures. They also have broad application prospects in environmental protection fields such as pollutant adsorption and catalytic degradation. Against the backdrop of the deepening global dual-carbon strategy, the low-carbon transformation of the building materials industry has become a key link in achieving emission reduction targets. Quantifying the carbon emission factor of LDHs is a core prerequisite for assessing their environmental friendliness and supporting the selection and large-scale application of low-carbon materials in engineering projects.

[0003] Currently, existing carbon emission accounting methods mainly target bulk building materials with simple compositions and mature processes, such as cement and aggregates. Their core logic involves simple summation of raw material consumption, direct calculation of energy consumption, and universally applicable emission factors. The main method involves statistically analyzing raw material and energy consumption, multiplying them by the corresponding carbon emission factors, and then summing them to obtain the total carbon emissions. This approach is suitable for traditional building materials with fixed compositions and standardized production processes, and the accounting process is simple and convenient.

[0004] However, existing methods for LDH preparation rely on a bivariate raw material system of cationic and anionic metal salts. The mixing of these two raw materials has a synergistic effect on carbon emissions, which cannot be quantified by a single summation logic, leading to inaccurate calculations of raw material carbon emissions. At the same time, LDH production exhibits significant heterogeneity across different scenarios. The energy consumption characteristics of small-batch, intermittent production at the gram level in the laboratory differ greatly from those of continuous production at the ton level in the industrial sector. Existing methods apply a uniform accounting standard, which fails to distinguish between these scenario differences, resulting in significant discrepancies between the calculated results and actual values. Summary of the Invention

[0005] To address the technical challenges of existing methods for LDH preparation relying on a bivariate raw material system of cationic and anionic metal salts, where the mixing of these two raw materials exhibits a synergistic effect on carbon emissions that cannot be quantified by a simple summation logic, leading to distorted raw material carbon emission accounting; and to further address the significant heterogeneity in LDH production scenarios, where the energy consumption characteristics of small-batch, intermittent production at the gram level in the laboratory differ greatly from those of continuous, ton-scale production in the industrial sector, existing methods, by applying a uniform accounting standard, fail to differentiate between these scenario differences, resulting in significant discrepancies between the calculated results and actual values.

[0006] The technical solution provided by this invention is as follows: The first aspect of this invention provides a method for calculating the carbon emission factor of layered double hydroxides, comprising: S1: Obtain boundary data and carbon emission source data of the accounting object; S2: Determine the accounting boundary and carbon emission source level based on boundary data and carbon emission source data; S3: Construct a carbon emission accounting formula system based on the accounting boundary and carbon emission source level; S4: Obtain basic and characteristic parameters in laboratory or industrial settings; S5: Substitute the basic parameters and characteristic parameters into the carbon emission accounting formula system to calculate the corresponding carbon emissions and carbon emission factor per unit product. S6: Based on the corresponding emissions and carbon emission factors per unit product, the sensitivity analysis results are obtained through multi-dimensional sensitivity analysis; S7: Identify the core optimization parameters based on the sensitivity analysis results; S8: Based on the core optimization parameters, adjust the values ​​of each coefficient in the preparation process parameters and carbon emission accounting formula system, update the accounting results, and establish a two-way feedback mechanism between accounting and process optimization.

[0007] A second aspect of the present invention provides a system for calculating the carbon emission factor of layered double hydroxides, comprising: processor; The memory stores computer-readable instructions that, when executed by a processor, implement the method for calculating the layered double hydroxide carbon emission factor as described in the first aspect.

[0008] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for calculating the layered double hydroxide carbon emission factor as described in the first aspect.

[0009] The beneficial effects of the technical solution provided by this invention include: In this embodiment of the invention, by constructing a proprietary formula system that includes a bivariate raw material coupling coefficient, a scenario-based energy consumption correction factor, and a production scale energy consumption allocation coefficient, the synergistic carbon emission effect of cations and anions in the preparation of layered double hydroxides is accurately quantified. This effectively solves the problem of raw material carbon emission accounting distortion caused by the single summation logic of existing methods. At the same time, by differentiating parameter values ​​for laboratory and industrial scenarios and introducing scenario-based energy consumption correction factors and production scale energy consumption allocation coefficients, the differences in energy consumption characteristics under different production scales are accurately adapted, avoiding the result deviation caused by applying a uniform accounting standard. This significantly improves the accuracy and engineering reference value of the accounting results, providing a reliable quantitative tool for optimizing the low-carbon preparation process of layered double hydroxides and assessing carbon emissions from large-scale production. Attached Figure Description

[0010] Figure 1 A schematic flowchart illustrating a method for calculating the carbon emission factor of layered double hydroxides provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of a layered double hydroxide carbon emission factor calculation system provided in an embodiment of the present invention. Detailed Implementation

[0011] Reference manual attached Figure 1 The diagram shows a flowchart illustrating a method for calculating the carbon emission factor of layered double hydroxides provided in an embodiment of the present invention.

[0012] This invention provides a method for calculating the carbon emission factor of layered double hydroxides, which may include the following steps: S1: Obtain boundary data and carbon emission source data of the accounting object.

[0013] S2: Determine the accounting boundary and carbon emission source level based on boundary data and carbon emission source data.

[0014] In one possible implementation, S2 specifically includes sub-steps S201 and S202: S201: Based on boundary data, and combining the principles of life cycle assessment with the preparation characteristics of layered double hydroxides, the accounting boundary is defined.

[0015] Life Cycle Assessment (LCA) is a method for assessing the environmental impact of a product, process, or service throughout its entire life cycle. Its core principle is "cradle to grave" or "cradle to gate," meaning a systematic analysis of the entire process from raw material acquisition, production and processing, transportation and distribution, use and maintenance, to disposal.

[0016] Among them, the preparation characteristics of layered double hydroxides (LDHs) refer to the unique process features of this type of material that distinguish it from conventional single-component materials during synthesis.

[0017] Specifically, based on the cradle-to-gate principle of life cycle assessment (LCA) and combined with the characteristics of LDH preparation, a three-level accounting boundary is defined: the raw material layer boundary, the transportation layer boundary, and the preparation layer boundary. The raw material layer boundary covers the entire life cycle of cationic metal salts, anionic metal salts, alkali sources, and solvents; the transportation layer boundary covers the entire transportation process of raw materials and excipients from the production site to the preparation site; and the preparation layer boundary covers all process stages, including solution preparation, synthesis reaction, and post-processing, excluding post-product transportation, engineering applications, and waste disposal.

[0018] S202: Based on carbon emission source data, carbon emission sources are classified according to their contribution ratio and generation stage to determine the carbon emission source level.

[0019] Optionally, the accounting boundaries specifically include: the raw material layer boundary, the transportation layer boundary, and the preparation layer boundary.

[0020] The carbon emission source levels specifically include: core carbon emission sources, minor carbon emission sources, and trace carbon emission sources.

[0021] Specifically, carbon emission sources are divided into three levels according to their contribution ratio and generation stage: core carbon emission sources, secondary carbon emission sources, and trace carbon emission sources. Core carbon emission sources are those that account for ≥80% of the total emission, secondary carbon emission sources are those that account for 10% to 80% of the total emission, and trace carbon emission sources are those that account for <10% of the total emission.

[0022] In this embodiment of the invention, by applying the cradle-to-gate principle of life cycle assessment and combining it with the preparation characteristics of layered hydrogen hydride (LDHs), a three-tiered accounting boundary is defined, comprising the raw material layer, transportation layer, and preparation layer. Irrelevant stages after product delivery are excluded, ensuring the accounting scope accurately focuses on the entire LDH preparation process and avoiding accounting biases caused by ambiguity in the boundaries. Simultaneously, by categorizing carbon emission sources into core, minor, and trace amounts based on their contribution percentage and clearly defining their respective percentage thresholds, a hierarchical and refined management of carbon emission sources is achieved. This ensures that core emission sources are prioritized for accounting while reducing accounting complexity through the combined estimation of trace emission sources. Thus, while maintaining accounting accuracy, the method's engineering operability and scenario adaptability are significantly improved.

[0023] In one possible implementation, steps S2A and S2B are included after S2 and before S3: S2A: Each carbon emission source level is labeled in a segmented manner, and a carbon emission source correspondence table is established.

[0024] S2B: Based on the carbon emission source correspondence table, the accounting object is limited to layered double hydroxides for corrosion inhibition of reinforced concrete, and the range of suitable cationic metal salt raw materials and anionic metal salt raw materials are defined.

[0025] Among them, layered double hydroxides for corrosion inhibition of reinforced concrete refer to a class of functional materials specifically designed to inhibit the corrosion of steel bars in reinforced concrete structures.

[0026] Specifically, the three levels of carbon emission sources are labeled in a step-by-step manner, clarifying the corresponding preparation steps, equipment types, and raw material categories for each emission source, and establishing a correspondence table of LDH carbon emission sources-steps-equipment / raw materials. Furthermore, the accounting object is limited to LDHs used for corrosion inhibition in reinforced concrete, defining the range of suitable cationic and anionic metal salt raw materials, and excluding carbon emission accounting for incompatible raw materials.

[0027] Specifically, the LDHs used for corrosion inhibition in reinforced concrete include at least one of CaAl-LDH, ZnAl-LDH, MgAl-LDH, CaFeAl-LDH, and ZnFeAl-LDH. The cationic metal salts include at least one of calcium chloride, calcium nitrate, zinc chloride, zinc nitrate, magnesium chloride, and magnesium nitrate. The anionic metal salts include at least one of aluminum chloride, aluminum nitrate, ferric chloride, and ferric nitrate. The alkali source includes at least one of sodium hydroxide and potassium hydroxide. The solvent is ultrapure water or industrial deionized water.

[0028] In this embodiment of the invention, by labeling each carbon emission source level in a step-by-step manner and establishing a carbon emission source correspondence table, a precise mapping between emission sources and preparation steps, equipment types, and raw material categories is achieved, providing a clear data index foundation for subsequent refined accounting. Simultaneously, by limiting the accounting object to layered double hydroxides for corrosion inhibition of reinforced concrete and defining the suitable range of cationic and anionic metal salt raw materials, the carbon emission interference from incompatible raw materials is eliminated. This gives the accounting method a clear scope of application and unified raw material boundaries, effectively avoiding the incomparability of results caused by the generalization of the accounting object and the confusion of raw material types, significantly improving the standardization of the accounting method and its operability in engineering applications.

[0029] S3: Construct a carbon emission accounting formula system based on the accounting boundary and carbon emission source level.

[0030] Optionally, the carbon emission accounting formula system specifically includes: a dual-variable raw material coupled carbon emission formula, a scenario-based energy consumption correction and production scale energy consumption allocation coupled formula, a refined total carbon emission accounting formula, and a final carbon emission factor formula.

[0031] Specifically, a creative formula system is constructed, consisting of a dual-variable raw material coupled carbon emission formula for LDHs, a scenario-based energy consumption correction and production scale energy consumption allocation coupled formula, a refined accounting formula for total carbon emissions of LDHs, and a final formula for LDHs carbon emission factors.

[0032] Specifically, the carbon emission formula for LDHs with dual-variable feedstock coupling is as follows: ; in, This indicates carbon emissions coupled during the raw material production stage. This represents the bivariate feedstock coupling coefficient, ranging from 0.85 to 0.98. For chlorinated feedstock combinations, the coefficient is 0.85 to 0.90, and for nitric acid feedstock combinations, it is 0.95 to 0.98. Indicates the first i Consumption of anionic metal salts, in kg. Indicates the first i Carbon emission factor of anionic metal salts, unit: kgCO2e / kg. Indicates the first i Consumption of a type of cationic metal salt, in kg. Represented as the first j Carbon emission factor of a type of cationic metal salt, unit: kgCO2e / kg. and These represent the consumption of alkali source and solvent, respectively, in kg. and These represent the carbon emission factors of the alkali source and solvent, respectively, in kgCO2e / kg.

[0033] Furthermore, the specific coupling formula for scenario-based energy consumption correction and production scale energy consumption allocation is as follows: ; in, This indicates carbon emissions during the preparation and processing stage. This represents the scenario-based energy consumption correction factor, with a value of 1.2~1.5 for laboratory scenarios and 0.6~0.8 for industrial scenarios. This represents the energy consumption allocation coefficient based on production scale, with a value of 20-30 for gram-level products, 5-10 for kilogram-level products, and 0.8-1.0 for ton-level products. This indicates the basic energy consumption and carbon emissions during the solution preparation stage, expressed in kgCO2e. This indicates the basic energy consumption and carbon emissions during the post-synthesis processing stage, expressed in kgCO2e.

[0034] Specifically, the energy consumption allocation coefficient based on production scale Calibration was achieved through a production-energy consumption fitting experiment. The unit product energy consumption was measured under different production volumes, and a production-energy consumption curve was obtained through fitting. The reciprocal of the curve's slope is... The actual value. At gram-level production. The value of increases as output decreases, and at ton-level output... The value of tends to be stable.

[0035] Furthermore, the detailed formula for calculating the total carbon emissions of LDHs is as follows: ; in, This indicates the total carbon emissions for a single batch. This indicates the basic carbon emissions from the transportation of raw materials and auxiliary materials, expressed in kgCO2e. This represents the transportation load factor correction item. For transportation load factors ≥80%, the value is 0.9~0.95; for <80%, the value is 1.0~1.1. This indicates carbon emissions from trace carbon sources, expressed in kgCO2e, and is calculated as 1% to 3% of those from core carbon sources.

[0036] Specifically, the final formula for the LDHs carbon emission factor is as follows: ; in, This indicates the carbon emission factor per unit product, expressed in kgCO2e / kg. This indicates the actual output of a single batch of LDHs, in kg. This represents the process correction factor; 1.0 is used for coprecipitation methods, and 1.5~2.0 is used for hydrothermal methods.

[0037] It should be noted that the preparation and processing stages employ either co-precipitation or hydrothermal methods. Co-precipitation methods include low supersaturation and high supersaturation methods. Scenario-specific energy consumption correction factors are provided for different processes. With process correction factor Differential labeling, specifically: Low supersaturation coprecipitation method: laboratory =1.2~1.3, Industrial =0.6~0.7, =1.0.

[0038] High supersaturation coprecipitation method: laboratory =1.3~1.4, Industrial =0.7~0.8, =1.0.

[0039] Hydrothermal method: Laboratory =1.4~1.5, Industrial =0.75~0.8, =1.5~2.0, take 2.0 for reaction time ≥12h, and take 1.5 for reaction time <12h.

[0040] Furthermore, a three-dimensional parameter calibration standard was developed for mainstream LDH preparation processes, production scenarios, and raw material combinations, clarifying the scenario-specific value ranges of each coefficient and basic parameter in the formula. An LDH accounting parameter database was also established, incorporating parameter values ​​for different raw material combinations, production scenarios, and preparation processes; the database supports dynamic updates.

[0041] In this embodiment of the invention, by constructing a proprietary formula system including a bivariate raw material coupling coefficient, a scenario-based energy consumption correction factor, a production scale energy consumption allocation coefficient, and a process correction coefficient, the synergistic carbon emission effect of cation and anion metal salts during LDHs preparation is accurately quantified, solving the accounting distortion problem caused by the existing single accumulation logic. Simultaneously, differentiated calibration is performed in laboratory and industrial scenarios. , The values ​​were determined and combined with different preparation processes (coprecipitation method, hydrothermal method) to... By implementing tiered settings, precise adaptation to energy consumption characteristics across multiple scenarios and processes is achieved. Furthermore, by establishing three-dimensional parameter calibration standards and creating a dynamically updated database of accounting parameters, the standardization and traceability of coefficient values ​​in the formula system are ensured, significantly improving the scientific rigor, repeatability, and engineering applicability of the accounting method.

[0042] S4: Obtain basic and characteristic parameters in laboratory or industrial settings.

[0043] Specifically, the parameters are collected and calculated according to laboratory and industrial scenarios, including basic parameters and characteristic parameters. The basic parameters include raw material consumption, equipment rated power, running time, transportation distance, transportation mode, and actual output. The characteristic parameters are the actual values ​​of the bivariate raw material coupling coefficient, scenario-based energy consumption correction factor, and production scale energy consumption allocation coefficient.

[0044] Furthermore, parameter acquisition adopts a scenario-based precise acquisition method: in laboratory scenarios, start-up and shutdown parameters of single batches of equipment in intermittent production are collected and included in the energy consumption of equipment start-up and shutdown. In industrial scenarios, steady-state operation parameters of continuous production are collected and included in the waste heat recovery efficiency correction item. When the waste heat recovery efficiency is ≥50%, energy consumption and carbon emissions are reduced by 10%~20%.

[0045] S5: Substitute the basic parameters and characteristic parameters into the carbon emission accounting formula system to calculate the corresponding carbon emissions and carbon emission factors per unit product.

[0046] In one possible implementation, S5 specifically includes sub-steps S501 to S506: S501: Substitute the raw material parameters in the basic parameters and the bivariate raw material coupling coefficient in the characteristic parameters into the bivariate raw material coupling carbon emission formula to calculate the coupled carbon emissions during the raw material production stage.

[0047] It should be noted that the bivariate raw material coupling coefficient is calibrated through a cation-anion combination test. After mixing the raw materials according to the molar ratio, the ratio of actual carbon emissions to theoretical cumulative carbon emissions is measured, which is the actual value of the bivariate raw material coupling coefficient.

[0048] S502: Substitute the transportation parameters from the basic parameters into the basic formula for transportation carbon emissions, and combine it with the transportation load rate correction term to calculate the corrected transportation carbon emissions.

[0049] Among them, the transport load rate correction term refers to the correction coefficient used to correct the change in carbon emission intensity per unit of cargo caused by different vehicle loading efficiencies during actual transport.

[0050] S503: Calculate the basic energy consumption and carbon emissions for each of the equipment parameters in the basic parameters, based on solution preparation, synthesis reaction, and post-processing steps.

[0051] Specifically, the basic energy consumption and carbon emissions during the solution preparation stage Basic energy consumption and carbon emissions in the post-synthesis processing stage The formula for calculating energy consumption based on actual equipment energy consumption is: ; in, P This indicates the rated power of the equipment, in kW. t Indicates runtime, in hours (h). Indicates equipment load rate, in percentage. The energy carbon emission factor is represented as 0.5366 kgCO2e / kWh for grid electricity and 0.19 kgCO2e / kg for industrial steam.

[0052] S504: By substituting the basic energy consumption carbon emissions into the coupled formula of scenario-based energy consumption correction and production scale energy consumption allocation, the carbon emissions of the preparation and processing stage can be calculated.

[0053] S505: Based on the carbon emissions coupled during the raw material production stage, the carbon emissions corrected during transportation, and the carbon emissions during the preparation and processing stage, combined with the carbon emissions from trace carbon emission sources, the total carbon emissions for a single batch are calculated using a refined total carbon emission accounting formula.

[0054] Among them, trace carbon emission sources refer to the correction items that combine and estimate carbon emission sources that have a low individual contribution ratio and are difficult to measure or calculate individually when calculating carbon emissions from the entire process of preparing layered hydrogen hydroxides (LDHs).

[0055] Specifically, the total carbon emissions of LDHs are calculated by integrating carbon emissions from the three stages of raw materials, transportation, and preparation, and taking into account carbon emissions from trace carbon emission sources, to calculate the total carbon emissions for a single batch.

[0056] S506: Based on the total carbon emissions of a single batch and the actual output in the basic parameters, combined with the process correction coefficient, the carbon emission factor per unit product is calculated using the final formula for the carbon emission factor.

[0057] Among them, the process correction coefficient refers to the correction factor used to correct the change in carbon emission intensity per unit product caused by the difference in technical routes between different preparation processes.

[0058] For example, taking LDHs used for corrosion inhibition in reinforced concrete as the accounting object, the energy carbon emission factor is taken as 0.5366 kg CO2e / kWh for grid electricity. The transportation carbon emission factor is taken as road transportation (load > 30t). Railway transportation For laboratory scenarios, the equipment load rate is set at 70%~75%, while for industrial scenarios it is set at 90%.

[0059] Specifically, the preliminary step is: calibration of the core coefficients of the formula. This includes the coupling coefficients of the bivariate raw materials. Calibration: Taking CaAl-LDH (calcium chloride + aluminum chloride) as an example, 1 kg of calcium chloride and 0.5 kg of aluminum chloride were weighed in a molar ratio of 2:1. The carbon emissions of each raw material were measured to be 0.32 kg CO2e and 0.445 kg CO2e, respectively, with a theoretical cumulative carbon emission of 0.765 kg CO2e. After mixing, the actual carbon emission was measured to be 0.70 kg CO2e, and the calculated results were... =0.70 / 0.765≈0.91.

[0060] Specifically, scenario-based energy consumption correction factor Calibration: Taking the low supersaturation coprecipitation method as an example, the unit energy consumption of laboratory batch production was determined to be 0.5 kWh / g, and the unit energy consumption of industrial continuous production (waste heat recovery efficiency 55%) was 0.2 kWh / g. Calculations were then performed. =0.2 / 0.5=0.4, which is corrected to 0.65 based on waste heat recovery.

[0061] Specifically, the energy consumption allocation coefficient based on production scale Calibration: Taking CaAl-LDH as an example, the unit product energy consumption was measured to be 0.5 kWh / g and 0.0005 kWh / g at yields of 10g and 1t, respectively. After fitting the yield-energy consumption curve, the gram-level (10g) was calibrated. =25, tons =1.0.

[0062] For example, the carbon emission calculation for the laboratory preparation of CaAl-LDH using a low-supersaturation coprecipitation method is as follows: Parameters collected: Raw materials: 42.48g calcium chloride (EF=0.32kgCO2e / kg), 22.5g aluminum chloride (EF=0.89kgCO2e / kg), 16g sodium hydroxide (EF=1.59kgCO2e / kg), 0.6kg ultrapure water (EF=0.00033kgCO2e / kg). Transportation parameters: Road transportation, distance 100km, transportation load rate 70%. Equipment parameters: Magnetic stirrer 40W×1h, centrifuge 500W×0.5h, vacuum drying oven 400W×24h, load rate 70%. Characteristic coefficient: =0.91, =1.25, =25, =1.0. Production: .

[0063] Among them, the low supersaturation coprecipitation method refers to a commonly used coprecipitation process route in the preparation of layered double hydroxides (LDHs). Under low supersaturation conditions, a mixed solution containing cationic metal salts (such as calcium chloride and aluminum chloride) and anionic metal salts is mixed with an alkaline source solution (such as sodium hydroxide) by slow dropwise addition. At the same time, the dropwise addition rate and the pH value of the reaction system are strictly controlled, so that the metal ions gradually precipitate and self-assemble to form a layered structure under relatively mild conditions.

[0064] Furthermore, calculate the carbon emissions coupled with raw materials: ; Specifically, calculate the carbon emissions from the transportation base: ; in, This indicates the carbon emissions from transportation.

[0065] Furthermore, the energy consumption and carbon emissions for basic preparation are calculated: ; Specifically, calculate the carbon emissions from the preparation and processing: ; Furthermore, calculate trace carbon emission sources: ; Specifically, calculate total carbon emissions: ; Further, calculate the carbon emission factor: ; It should be noted that the carbon emission factor is higher in laboratory settings, mainly due to the significant energy consumption amortization effect at gram-level production. =25), with carbon emissions from preparation and processing being the core source of carbon emissions, accounting for over 98%. The introduction of a bivariate raw material coupling coefficient accurately quantifies the synergistic carbon emission effect of calcium and aluminum metal salts, avoiding the calculation error of single accumulation, and making the carbon emission accounting at the raw material stage more realistic.

[0066] For example, the carbon emission calculation for the industrial high-supersaturation co-precipitation method for preparing CaAl-LDH is as follows: Parameters collected: Calcium chloride 2.655t (EF=0.32kgCO2e / kg), aluminum chloride 1.406t (EF=0.89kgCO2e / kg), sodium hydroxide 1.0t (EF=1.59kgCO2e / kg), industrial deionized water 60t (EF=0.00021kgCO2e / kg). Transportation parameters: Calcium chloride and aluminum chloride are transported by rail for 300km, sodium hydroxide by road for 150km, with a transportation load rate of 85%. Equipment parameters: Continuous mixer 10kW×2h, plate and frame filter press 5kW×1h, spray drying tower 50kW×1h, air jet mill 3kW×0.5h, load rate 90%, waste heat recovery efficiency 55%, energy consumption and carbon emission reduction 15%. Characteristic coefficient: .Yield: .

[0067] Among them, the high supersaturation coprecipitation method refers to a coprecipitation process route suitable for industrial-scale production in the preparation of layered double hydroxides (LDHs). Under high supersaturation conditions, a mixed solution containing cationic and anionic metal salts is mixed with an alkaline source solution by rapid mixing or high-speed dropwise addition, so that the metal ions rapidly precipitate and assemble into a layered structure in a short time.

[0068] Specifically, the calculation of carbon emissions coupled with raw materials: ; Specifically, calculate the carbon emissions from the transportation base: ; Furthermore, the carbon emissions from the basic energy consumption for preparation (after correction for waste heat recovery) are calculated: ; Furthermore, the carbon emissions from the preparation and processing are calculated: ; Specifically, calculate trace carbon emission sources: ; Furthermore, calculate the total carbon emissions: ; Specifically, calculate the carbon emission factor: ; It should be noted that the carbon emission factor in industrial settings is much lower than that in laboratory settings, reflecting the energy consumption sharing advantage of large-scale production. =0.9) and the carbon reduction effect of waste heat recovery. Raw material production is the core carbon emission source, accounting for over 98%, while transportation carbon emissions account for a very small percentage, making it a secondary carbon emission source. The introduction of scenario-based energy consumption correction factors accurately adapts to the energy efficiency characteristics of equipment in continuous industrial production, avoiding the distortion of results caused by calculations based on laboratory parameters.

[0069] For example, the carbon emission calculation for the laboratory hydrothermal preparation of MgAl-LDH is as follows: Parameter collection: Raw materials: 38.1g magnesium chloride (EF=0.29kgCO2e / kg), 24.1g aluminum chloride (EF=0.92kgCO2e / kg), 24g sodium hydroxide (EF=1.59kgCO2e / kg), 0.8kg ultrapure water (EF=0.00033kgCO2e / kg). Transportation parameters: Road transportation, distance 100km, transportation load rate 70%. Equipment parameters: Magnetic stirrer 40W×1h, hydrothermal autoclave 1000W×24h, centrifuge 500W×0.5h, vacuum drying oven 400W×24h, load rate 75%. Characteristic coefficient: (Reaction time: 24 hours). Yield: .

[0070] Specifically, the calculation of carbon emissions coupled with raw materials: ; Specifically, calculate the carbon emissions from the transportation base: ; Furthermore, the energy consumption and carbon emissions for basic preparation are calculated: ; Specifically, calculate the carbon emissions from the preparation and processing: ; Furthermore, calculate trace carbon emission sources: ; Specifically, calculate total carbon emissions: ; Further, calculate the carbon emission factor: ; It should be noted that the carbon emission factor of the hydrothermal method is significantly higher than that of the co-precipitation method. This is mainly due to the high energy consumption caused by the prolonged high-temperature operation of the hydrothermal reactor, with carbon emissions from the preparation and processing being the core source of carbon emissions. The introduction of the process correction coefficient ε enables a standardized comparison of carbon emission factors under different preparation processes, eliminates the accounting bias caused by process differences, and provides a quantitative basis for the low-carbon selection of LDHs preparation processes.

[0071] In this embodiment of the invention, by substituting basic and characteristic parameters into the carbon emission accounting formula system, a refined accounting of the entire chain is achieved, encompassing raw material coupled carbon emissions, transportation-corrected carbon emissions, basic energy consumption carbon emissions at each stage, carbon emissions during preparation and processing, total carbon emissions per batch, and the carbon emission factor per unit product. The actual values ​​of the bivariate raw material coupling coefficient are calibrated through cation-anion combination experiments, ensuring accurate quantification of the raw material synergistic effect. The impact of loading efficiency on transportation carbon emissions is corrected by introducing a transportation load rate correction term. Accurate calculation of energy consumption at each stage—solution preparation, synthesis reaction, and post-processing—is achieved by incorporating the equipment's rated power, operating time, load rate, and energy carbon emission factor into the actual equipment energy consumption calculation formula. Simultaneously, by merging and estimating carbon emissions from trace carbon emission sources, the complexity of data acquisition and calculation is effectively reduced while ensuring accounting accuracy, making the overall accounting method both scientific and engineering-operable.

[0072] S6: Based on the corresponding emissions and carbon emission factors per unit product, the sensitivity analysis results are obtained through multi-dimensional sensitivity analysis.

[0073] Among them, multidimensional sensitivity analysis refers to an analytical method that systematically evaluates the impact of changes in parameters of each dimension on the target output result (i.e., carbon emission factor per unit product) from multiple independent variable dimensions.

[0074] In one possible implementation, S6 specifically includes sub-steps S601 and S602: S601: Based on the corresponding emissions and carbon emission factor per unit product, the impact rate of changes in each single parameter on the carbon emission factor per unit product is calculated by changing a single parameter in the dimensions of raw material combination, process parameters, production scale, transportation scheme and energy type through the control variable method.

[0075] Specifically, the controlled variable method was used to conduct a five-dimensional sensitivity analysis of raw material combination, process parameters, production scale, transportation plan, and energy type. The impact rate of changes in each parameter on the carbon emission factor per unit product was calculated, and parameters were classified as high, medium, and low impact parameters.

[0076] Among them, the controlled variable method refers to a factor analysis method commonly used in scientific research. When studying the influence of multiple factors on a certain outcome variable, only one factor is changed each time, while all other factors remain unchanged. This allows for the independent observation of the effect of the change of that factor on the outcome variable, avoiding the confounding effect of difficulty in distinguishing the degree of contribution when multiple factors change simultaneously.

[0077] S602: Compare the impact rate with the preset classification threshold to obtain the sensitivity analysis results.

[0078] Specifically, the impact rate classification standard is as follows: an impact rate of ≥20% is a high impact parameter, 5%~20% is a medium impact parameter, and <5% is a low impact parameter. Only high and medium impact parameters are optimized.

[0079] In this embodiment of the invention, through multi-dimensional sensitivity analysis, the impact rate of changes in each individual parameter on the carbon emission factor per unit product is calculated from five dimensions—raw material combination, process parameters, production scale, transportation scheme, and energy type—using the controlled variable method. The impact rate is then compared with a preset grading threshold, achieving a scientific grading of the degree of impact on the carbon emission factor. This method can accurately identify high-impact and medium-impact parameters, providing a quantitative basis for the subsequent selection of core optimization parameters. Simultaneously, it excludes low-impact parameters to focus on optimizing resources. This effectively solves the technical problem of existing methods that can only provide qualitative process suggestions but cannot quantitatively identify core parameters, enabling the calculation results to directly guide the precise low-carbon optimization of LDHs preparation processes.

[0080] S7: Identify the core optimization parameters based on the sensitivity analysis results.

[0081] Specifically, based on the results of sensitivity analysis, the core optimization parameters that significantly affect the carbon emission factors of LDHs are identified, and a correspondence table of core influencing parameters of LDHs, preparation steps, and optimization directions is established.

[0082] For example, multi-dimensional sensitivity analysis and process optimization parameter identification: Based on the preparation of CaAl-LDH using the industrial high-supersaturation co-precipitation method, a five-dimensional sensitivity analysis was conducted on raw material combination, process parameters, production scale, transportation scheme, and energy type. Controlling for changes in a single parameter while keeping the others constant, the impact rate of each parameter on the carbon emission factor was calculated. The results are as follows: Raw material combination: Replacing calcium chloride + aluminum chloride with calcium nitrate + aluminum nitrate increased the carbon emission factor to 1324.20 kgCO2e / kg, with an impact rate of 45.0%, classifying it as a high-impact parameter. Process parameters: Increasing the equipment load rate from 90% to 95% reduced the carbon emission factor to 849.30 kgCO2e / kg, with an impact rate of 7.0%, classifying it as a medium-impact parameter. Transportation scheme: Replacing road transportation with rail transportation reduced the carbon emission factor to 895.04 kgCO2e / kg, with an impact rate of 2.0%, classifying it as a low-impact parameter. Production scale: Increasing the production scale from 1 t to 2 t reduced the carbon emission factor to 890.05 kgCO2e / kg, with an impact rate of 2.5%, classifying it as a low-impact parameter. Energy type: The grid electricity is replaced with green electricity (EF=0), the carbon emissions in the production process are eliminated, the carbon emission factor is reduced to 892.27 kgCO2e / kg, the impact rate is 2.3%, which is a low impact parameter.

[0083] Furthermore, raw material combination and equipment load rate were identified as core optimization parameters. A table corresponding to core influencing parameters, preparation stages, and optimization directions was established. Specifically, the table shows that: the core optimization parameter is raw material combination, the corresponding preparation stage is raw material selection, and the specific optimization direction is to prioritize the use of chloride-type cationic or anionic metal salts to replace nitrate-type metal salts. The core optimization parameter is equipment load rate, the corresponding preparation stage is processing, and the specific optimization direction is to increase the equipment load rate to over 90% in industrial production, preferably operating at 95% of rated load.

[0084] It should be noted that by adjusting the process parameters according to the optimization direction, changing the raw material combination to a fully chlorinated type, and increasing the equipment load rate to 95%, and substituting these parameters into the formula system for updating the calculation, the optimized carbon emission factor was reduced to 540.12 kgCO2e / kg, achieving a significant carbon reduction. Simultaneously, the optimized parameters were calibrated into the formula coefficients, updating the LDHs calculation parameter database, completing the two-way feedback between the calculation results and process optimization, and providing parameter standards for the subsequent industrial-scale low-carbon preparation of LDHs.

[0085] In this embodiment of the invention, by identifying core optimization parameters based on sensitivity analysis results and establishing the correspondence between core influencing parameters and preparation steps and optimization directions, a closed-loop connection from quantitative analysis to precise optimization is achieved. Taking the preparation of CaAl-LDH by the industrial high-supersaturation co-precipitation method as an example, the raw material combination and equipment load rate are identified as core optimization parameters through five-dimensional sensitivity analysis. The corresponding raw material selection, preparation and processing steps, and specific optimization directions are clarified. Based on this, after adjusting the raw material combination to a fully chlorinated form and increasing the equipment load rate, the carbon emission factor is significantly reduced. At the same time, by calibrating the optimized parameters into the formula coefficients and updating the accounting parameter database, a two-way feedback between the accounting results and process optimization is completed, making the accounting model highly compatible with actual production. This provides quantifiable and replicable parameter standards and process optimization paths for the industrial-scale low-carbon preparation of LDHs.

[0086] S8: Based on the core optimization parameters, adjust the values ​​of each coefficient in the preparation process parameters and carbon emission accounting formula system, update the accounting results, and establish a two-way feedback mechanism between accounting and process optimization.

[0087] In one possible implementation, S8 specifically includes sub-steps S801 to S803: S801: Based on the core optimization parameters, determine the corresponding preparation steps and optimization directions.

[0088] S802: Based on the preparation process and optimization direction, adjust the preparation process parameters and the value standards of each coefficient in the carbon emission accounting formula system, substitute the adjusted preparation process parameters and the value standards of each coefficient in the carbon emission accounting formula system into the carbon emission accounting formula system, and update the accounting results.

[0089] S803: Feed back the updated accounting results to the carbon emission accounting formula system and establish a two-way feedback mechanism between accounting and process optimization.

[0090] Specifically, based on the optimization direction of the core optimization parameters, the value standards of the preparation process parameters and the coefficients in the formula are adjusted, and the optimized actual parameters are substituted into the formula system to update the calculation results, thus constructing a two-way feedback mechanism between the calculation results and the optimization of process parameters.

[0091] In this embodiment of the invention, the corresponding preparation steps and optimization directions are determined based on core optimization parameters. Accordingly, the preparation process parameters and the value standards of each coefficient in the carbon emission accounting formula system are adjusted. The optimized parameters are then re-substituted into the formula system to update the accounting results, achieving a complete optimization closed loop from parameter identification to process adjustment and accounting verification. By feeding the updated accounting results back to the formula system, a two-way feedback mechanism between accounting and process optimization is established. This allows the accounting model to continuously absorb optimization experience from actual production, constantly iterating and updating the coefficient value standards and parameter database. This ensures that the accounting method always keeps pace with the optimal process, providing a dynamic and self-optimizing quantitative tool for the continuous improvement of LDHs low-carbon preparation.

[0092] The beneficial effects of the technical solution provided by this invention include: In this embodiment of the invention, by constructing a proprietary formula system that includes a bivariate raw material coupling coefficient, a scenario-based energy consumption correction factor, and a production scale energy consumption allocation coefficient, the synergistic carbon emission effect of cations and anions in the preparation of layered double hydroxides is accurately quantified. This effectively solves the problem of raw material carbon emission accounting distortion caused by the single summation logic of existing methods. At the same time, by differentiating parameter values ​​for laboratory and industrial scenarios and introducing scenario-based energy consumption correction factors and production scale energy consumption allocation coefficients, the differences in energy consumption characteristics under different production scales are accurately adapted, avoiding the result deviation caused by applying a uniform accounting standard. This significantly improves the accuracy and engineering reference value of the accounting results, providing a reliable quantitative tool for optimizing the low-carbon preparation process of layered double hydroxides and assessing carbon emissions from large-scale production.

[0093] Reference manual attached Figure 2 The diagram shows a schematic of the structure of a layered double hydroxide carbon emission factor calculation system provided in an embodiment of the present invention.

[0094] The present invention also provides a system 20 for calculating the carbon emission factor of layered double hydroxides, applied to the above-mentioned method for calculating the carbon emission factor of layered double hydroxides, comprising: Processor 201.

[0095] The memory 202 stores computer-readable instructions, which, when executed by the processor 201, implement the method for calculating the layered double hydroxide carbon emission factor as described in the method embodiment.

[0096] The layered double hydroxide carbon emission factor calculation system 20 provided by the present invention can execute the above-mentioned layered double hydroxide carbon emission factor calculation method and achieve the same or similar technical effects. To avoid repetition, the present invention will not elaborate further.

[0097] This invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method for calculating the carbon emission factor of layered double hydroxides as described in the method embodiments.

[0098] The present invention provides a computer-readable storage medium that can implement the steps and effects of the method for calculating the carbon emission factor of layered double hydroxides in the above-described method embodiments. To avoid repetition, the present invention will not repeat the details.

[0099] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. The scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for accounting for carbon emission factors of layered double hydroxides, characterized by, include: S1: Obtain boundary data and carbon emission source data of the accounting object; S2: Based on the boundary data and the carbon emission source data, determine the accounting boundary and carbon emission source level; S3: Construct a carbon emission accounting formula system based on the accounting boundary and the carbon emission source level; S4: Obtain basic and characteristic parameters in laboratory or industrial settings; S5: Substitute the basic parameters and the characteristic parameters into the carbon emission accounting formula system to calculate the corresponding carbon emissions and carbon emission factor per unit product. S6: Based on the corresponding emissions and the carbon emission factor per unit product, the sensitivity analysis results are obtained through multi-dimensional sensitivity analysis; S7: Based on the sensitivity analysis results, identify the core optimization parameters; S8: Based on the core optimization parameters, adjust the preparation process parameters and the value standards of each coefficient in the carbon emission accounting formula system, update the accounting results, and establish a two-way feedback mechanism between accounting and process optimization.

2. The method for calculating the carbon emission factor of layered double hydroxides according to claim 1, characterized in that, S2 specifically includes: S201: Based on the boundary data, and combining the life cycle assessment principle with the preparation characteristics of layered double hydroxides, the accounting boundary is defined; S202: Based on the carbon emission source data, classify the carbon emission sources according to their contribution ratio and generation stage to determine the carbon emission source level.

3. The method for calculating the carbon emission factor of layered double hydroxides according to claim 2, characterized in that, The accounting boundaries specifically include: the raw material layer boundary, the transportation layer boundary, and the preparation layer boundary; The carbon emission source levels specifically include: core carbon emission sources, secondary carbon emission sources, and trace carbon emission sources.

4. The method for calculating the carbon emission factor of layered double hydroxides according to claim 1, characterized in that, The process includes the following steps after S2 and before S3: S2A: Each carbon emission source level is labeled in a segmented manner, and a carbon emission source correspondence table is established; S2B: Based on the carbon emission source correspondence table, the accounting object is limited to layered double hydroxides for corrosion inhibition of reinforced concrete, and the range of suitable cationic metal salt raw materials and anionic metal salt raw materials are defined.

5. The method for calculating the carbon emission factor of layered double hydroxides according to claim 1, characterized in that, The carbon emission accounting formula system specifically includes: a dual-variable raw material coupled carbon emission formula, a scenario-based energy consumption correction and production scale energy consumption allocation coupled formula, a refined total carbon emission accounting formula, and a final carbon emission factor formula.

6. The method for calculating the carbon emission factor of layered double hydroxides according to claim 5, characterized in that, S5 specifically includes: S501: Substitute the raw material parameters in the basic parameters and the bivariate raw material coupling coefficient in the characteristic parameters into the bivariate raw material coupling carbon emission formula to calculate the coupled carbon emissions during the raw material production stage. S502: Substitute the transportation parameters in the basic parameters into the basic formula for transportation carbon emissions, and combine it with the transportation load rate correction term to calculate the transportation corrected carbon emissions; S503: Calculate the basic energy consumption and carbon emissions for the equipment parameters in the basic parameters according to the solution preparation, synthesis reaction and post-processing steps respectively; S504: Substitute the basic energy consumption carbon emissions into the coupled formula of scenario-based energy consumption correction and production scale energy consumption allocation to calculate the carbon emissions during the preparation and processing stage; S505: Based on the carbon emissions coupled during the raw material production stage, the carbon emissions corrected during transportation, and the carbon emissions during the preparation and processing stage, combined with the carbon emissions from trace carbon emission sources, the total carbon emissions for a single batch are calculated using the refined total carbon emission accounting formula. S506: Based on the total carbon emissions of a single batch and the actual output in the basic parameters, combined with the process correction coefficient, the carbon emission factor per unit product is calculated using the final formula of the carbon emission factor.

7. The method for calculating the carbon emission factor of layered double hydroxides according to claim 1, characterized in that, S6 specifically includes: S601: Based on the corresponding emissions and the carbon emission factor per unit product, the influence rate of each single parameter change on the carbon emission factor per unit product is calculated by changing a single parameter in the dimensions of raw material combination, process parameters, production scale, transportation scheme and energy type through the control variable method. S602: Compare the influence rate with the preset classification threshold to obtain the sensitivity analysis results.

8. The method for calculating the carbon emission factor of layered double hydroxides according to claim 1, characterized in that, S8 specifically includes: S801: Based on the core optimization parameters, determine the corresponding preparation steps and optimization directions; S802: Based on the preparation process and the optimization direction, adjust the preparation process parameters and the coefficient value standards in the carbon emission accounting formula system, substitute the adjusted preparation process parameters and the coefficient value standards in the carbon emission accounting formula system into the carbon emission accounting formula system, and update the accounting result; S803: Feed the updated accounting results back to the carbon emission accounting formula system to establish a two-way feedback mechanism between the accounting and process optimization.

9. A system for calculating the carbon emission factor of layered double hydroxides, characterized in that, include: processor; A memory storing computer-readable instructions, which, when executed by the processor, implement the method for calculating the layered double hydroxide carbon emission factor as described in any one of claims 1 to 8.

10. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a processor, implement the steps of the method for calculating the carbon emission factor of layered double hydroxides as described in any one of claims 1 to 8.

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