Full-life-cycle carbon emission measurement and calculation and emission reduction optimization method for aluminum industrial products

Abstract

The invention relates to the technical field of carbon management, and particularly provides an aluminum industrial product-oriented full-life-cycle carbon emission calculation and emission reduction optimization method, which comprises the following steps of: acquiring carbon emission activity data of each link and each region of an aluminum industrial product in a full life cycle; selecting a corresponding carbon footprint factor according to the source of the carbon emission activity data, the type of the corresponding greenhouse gas, the moment and the area; wherein the power carbon footprint factor in the carbon footprint factors is obtained by inputting the carbon emission activity data into a carbon footprint factor calculation model and outputting through the carbon footprint factor calculation model; combining the carbon emission activity data with the corresponding carbon footprint factors to obtain the total carbon emission amount of each link; and carrying out emission reduction optimization on a carbon emission hotspot link. According to the full-life-cycle carbon emission calculation and emission reduction optimization method for the aluminum industrial products, the timeliness and regional pertinence of carbon emission calculation can be improved, and the dynamic emission reduction requirement is met.

Description

Technical Field

[0001] This invention relates to the field of carbon management technology, and in particular to a method for calculating and optimizing carbon emissions throughout the entire life cycle of aluminum industrial products. Background Technology

[0002] The aluminum industry is a major sector of energy consumption and carbon dioxide emissions. Calculating carbon emissions and optimizing emission reductions throughout the life cycle of aluminum products can effectively reduce emissions of greenhouse gases such as carbon dioxide, mitigate the impact of the aluminum industry on climate change, and improve the overall efficiency of resource utilization.

[0003] Traditional methods for calculating carbon emissions from aluminum industry products typically involve directly selecting the electricity carbon footprint factor and then combining the power generation of different types of power sources with the electricity carbon footprint factor to obtain the electricity carbon emissions. This method has the following drawbacks:

[0004] (1) Poor timeliness: The selected electricity carbon footprint factor is a fixed value, which is difficult to reflect the dynamic changes of the carbon footprint factor at different times;

[0005] (2) The regional specificity is poor. The selected electricity carbon footprint factor is the national average, which may differ from the actual carbon footprint factor of the region by several times.

[0006] (3) It cannot adapt to dynamic emission reduction requirements.

[0007] There is currently no effective solution to the problems of poor timeliness and insufficient regional applicability in carbon emission measurement methods in related technologies. Summary of the Invention

[0008] The method for calculating and optimizing carbon emissions throughout the entire life cycle of aluminum industrial products provided by the embodiments of this invention at least solves the problems of poor timeliness and insufficient regional specificity in carbon emission calculation.

[0009] To achieve the above objectives, the present invention provides the following technical solution.

[0010] The first aspect of this invention provides a method for calculating and optimizing carbon emissions throughout the entire life cycle of aluminum industrial products, comprising the following steps: collecting carbon emission activity data of aluminum industrial products at various stages and in various regions throughout their entire life cycle; wherein the stages include: raw material mining, smelting and processing, transportation and logistics, and recycling; selecting corresponding carbon footprint factors based on the source of the carbon emission activity data, the type of corresponding greenhouse gas, the time period, and the region; wherein the carbon footprint factors include: an electricity carbon footprint factor; the electricity carbon footprint factor is obtained by inputting the carbon emission activity data into a carbon footprint factor calculation model and outputting the result through the carbon footprint factor calculation model; the carbon footprint factor calculation model is obtained by combining multiple time-sharing and regional calculation models; the time-sharing and regional calculation model is a time-varying regional calculation model constructed based on the total carbon emissions of each region, the total power generation of each region, the net flow of electricity between different regions, and the balance relationship between the electricity carbon footprint factors of each region; the time-sharing and regional calculation model is expressed as follows: ;in, This represents the total carbon emissions in the i-th region at time t. This represents the electricity carbon footprint factor of the j-th region at time t. Represents the electricity carbon footprint factor of the i-th region at time t, where j ≠ i; This represents the total power generation of the i-th region at time t. This represents the net inflow of electricity from region j to region i; combining the various time-sharing and regional calculation models, the resulting carbon footprint factor calculation model is expressed as follows: ;in, This represents the total carbon emissions in the first region at time t. This represents the electricity carbon footprint factor of the first region at time t; This represents the total power generation of the first region at time t. This represents the net inflow of electricity from region j to region 1; the initial values ​​of total carbon emissions, total power generation, and net inflow for each region are calculated and substituted into the carbon footprint factor calculation model. The carbon footprint factor for each region is calculated by simultaneously solving the various time-sharing and regional calculation models; the carbon emission activity data is combined with the corresponding carbon footprint factor to obtain the total carbon emissions for each stage; the total carbon emissions for each stage are evaluated, carbon emission hotspots are identified, and emission reduction optimization is performed on the carbon emission hotspots.

[0011] Preferably, carbon emission activity data of aluminum industrial products are collected at each stage and in each region throughout their entire life cycle, including: determining the regions for carbon emission calculation, the types of greenhouse gases generated by aluminum industrial products throughout their entire life cycle, and the stages they go through; collecting carbon emission activity data related to the emissions of the greenhouse gases at each stage and in each region; wherein the carbon emission activity data includes: primary energy carbon consumption data, electricity energy consumption data, steam energy consumption data, material input data, and waste treatment carbon consumption data.

[0012] Preferably, a corresponding carbon footprint factor is selected based on the source of the carbon emission activity data, the type of the corresponding greenhouse gas, the time of occurrence, and the region of occurrence; wherein, the carbon footprint factor includes: an electricity carbon footprint factor; the electricity carbon footprint factor is obtained by inputting the carbon emission activity data into a carbon footprint factor calculation model and outputting the carbon footprint factor calculation model, including: inputting the carbon emission activity data into a carbon footprint factor calculation model based on the carbon footprint factor calculation model, the time of occurrence, and the region of occurrence of the carbon emission activity data, and outputting the electricity carbon footprint factor in the carbon footprint factors corresponding to the current time and current region through the carbon footprint factor calculation model; and retrieving other carbon footprint factors from the corresponding carbon footprint factors from a comprehensive database based on the source of the carbon emission activity data and the type of corresponding greenhouse gas; wherein, the other carbon footprint factors include: primary energy carbon footprint factor, steam carbon footprint factor, material carbon footprint factor, and waste treatment carbon footprint factor.

[0013] Preferably, before inputting the carbon emission activity data into the carbon footprint factor calculation model, the method includes: obtaining the total power generation and net inflow of electricity in each region; wherein, the total power generation is the sum of the power generation of each type of power source; the net inflow of electricity is the difference between the electricity flowing into and out of each region from other regions; combining the total power generation, net inflow of electricity and the corresponding electricity carbon footprint factor to obtain the total carbon emissions and net inflow of carbon emissions in each region; constructing various time-sharing and regional calculation models based on the balance relationship between the total carbon emissions, net inflow of carbon emissions, total power generation, net inflow of electricity and electricity carbon footprint factor in each region at each time point; and combining the various time-sharing and regional calculation models to obtain the carbon footprint factor calculation model.

[0014] Preferably, before retrieving other carbon footprint factors from the corresponding carbon footprint factors in the database based on the source of the carbon emission activity data and the type of greenhouse gas, the method further includes: collecting historical carbon emission activity data and carbon footprint factors in industry standards at various stages of aluminum industry production; constructing a primary energy carbon footprint factor database, a steam carbon footprint factor database, a material carbon footprint factor database, and a waste treatment carbon footprint factor database; and combining the primary energy carbon footprint factor database, the steam carbon footprint factor database, the material carbon footprint factor database, and the waste treatment carbon footprint factor database to obtain a comprehensive database.

[0015] Preferably, based on the source of the carbon emission activity data and the corresponding type of greenhouse gas, the remaining carbon footprint factors in the corresponding carbon footprint factors are retrieved from the comprehensive database, including: retrieving the corresponding primary energy carbon footprint factors from the primary energy carbon footprint factor database; retrieving the corresponding steam carbon footprint factors from the steam carbon footprint factor database; retrieving the corresponding material carbon footprint factors from the material carbon footprint factor database; and retrieving the corresponding waste treatment carbon footprint factors from the waste treatment carbon footprint factor database.

[0016] Preferably, the carbon emission activity data is combined with the corresponding carbon footprint factors to obtain the total carbon emissions of each stage, including: combining the electricity consumption data, primary energy carbon consumption data, steam energy consumption data, material input data, and waste treatment carbon consumption data from the carbon emission activity data of each stage with the corresponding carbon footprint factors to obtain the carbon emissions of electricity, primary energy, steam consumption, material consumption, and waste treatment for each stage; and adding the carbon emissions of electricity, primary energy, steam consumption, material consumption, and waste treatment for each stage to obtain the total carbon emissions of each stage.

[0017] Preferably, assessing the total carbon emissions of each stage and identifying carbon emission hotspots includes: inputting the total carbon emissions of each stage into a machine learning model, and identifying carbon emission hotspots through the machine learning model; wherein the machine learning model includes: a clustering algorithm model, a regression algorithm model, or a classification algorithm model.

[0018] Preferably, the emission reduction optimization of the carbon emission hotspot includes: adjusting the power generation of the carbon emission hotspot based on the power footprint factor of each region; increasing the power generation of different types of energy in the region and time corresponding to the lowest power footprint factor; and gradually reducing the power generation in the corresponding region and time according to the power footprint factor from low to high, while ensuring that the total power generation remains unchanged, thereby optimizing the emission reduction of the carbon emission hotspot.

[0019] On the other hand, this invention provides a system for calculating and optimizing carbon emissions throughout the entire life cycle of aluminum industrial products, including: a data acquisition module for collecting carbon emission activity data of aluminum industrial products at various stages and in various regions throughout their entire life cycle; wherein the stages include: raw material mining, smelting and processing, transportation and logistics, and recycling; and a model building module for constructing time-sharing and zone-based calculation models for each region based on the balance relationship between the total carbon emissions of each region, the total power generation of each region, the net flow of electricity between different regions, and the electricity carbon footprint factor of each region, and then combining the various zone-based calculation models. A carbon footprint factor calculation model is obtained; a carbon emission measurement module is used to select the corresponding carbon footprint factor based on the source of the carbon emission activity data, the type of greenhouse gas, the time and region, and combine the carbon emission activity data with the corresponding carbon footprint factor to obtain the total carbon emissions of each link; wherein, the electricity carbon footprint factor in the carbon footprint factor is obtained by inputting the carbon emission activity data into the carbon footprint factor calculation model and outputting it through the carbon footprint factor calculation model; an emission reduction optimization module is used to evaluate the total carbon emissions of each link, identify carbon emission hotspots, and optimize emission reduction for the carbon emission hotspots.

[0020] On the other hand, the present invention provides an electronic device, including: a processor and a memory storing a program, characterized in that the program includes instructions, which, when executed by the processor, cause the processor to perform the method for calculating and optimizing carbon emissions throughout the entire life cycle of aluminum industry products.

[0021] Compared with the prior art, the above-mentioned technical solution of the present invention has the following beneficial effects: The method for calculating and optimizing carbon emissions throughout the entire life cycle of aluminum industrial products provided by the embodiments of the present invention summarizes and analyzes the carbon emissions of aluminum industrial products throughout their entire life cycle, and selects the raw material mining, smelting and processing, transportation and logistics, and recycling stages with high carbon emissions as the carbon emission calculation stages throughout the entire life cycle. The constructed time-sharing and regional calculation model considers the net inflow of electricity between different regions and the changes in the electricity carbon footprint factor at different times, making the carbon emission accounting of each region more closely reflect the regional characteristics and adapt to changes in time. By combining the time-sharing and regional calculation models of each region, a carbon footprint factor calculation model is constructed. This allows for the calculation of the electricity carbon footprint factor of each region even when the carbon footprint factor of some regions is unknown, without the need for recursion, resulting in higher calculation efficiency. The accuracy, scientificity, and real-time performance of the regional carbon emissions calculated based on this electricity carbon footprint factor are all improved. On this basis, further emission reduction optimization can meet the needs of dynamic emission reduction, thereby further improving the emission reduction effect. Attached Figure Description

[0022] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention, and those skilled in the art can obtain other embodiments based on these drawings without creative effort.

[0023] Figure 1 This is a flowchart illustrating the method for calculating and optimizing carbon emissions throughout the entire life cycle of aluminum industrial products, as described in an embodiment of the present invention.

[0024] Figure 2 This invention provides a schematic diagram of the power generation and current flow of electricity in various regions of the method for calculating and optimizing carbon emissions throughout the entire life cycle of aluminum industrial products, based on an embodiment of the invention.

[0025] Figure 3 This is a schematic diagram of the structure of the electronic device created by this invention. Detailed Implementation

[0026] Embodiments of the present invention will now be described in more detail with reference to the accompanying drawings. While some embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0027] To address the issues of poor timeliness and insufficient regional specificity in carbon emission measurement, one embodiment of this invention provides a method for carbon emission measurement and reduction optimization throughout the entire life cycle of aluminum industrial products, including the following steps.

[0028] Step S1: Collect carbon emission activity data for aluminum industrial products at each stage of their entire life cycle; these stages include: raw material mining, smelting and processing, transportation and logistics, and recycling.

[0029] Step S2: Based on the source of carbon emission activity data, the type of corresponding greenhouse gas, the time period, and the region, select the corresponding carbon footprint factor. The carbon footprint factor includes: an electricity carbon footprint factor. The electricity carbon footprint factor is obtained by inputting carbon emission activity data into the carbon footprint factor calculation model and outputting the model. The carbon footprint factor calculation model is obtained by combining multiple time-sharing and regional calculation models. The time-sharing and regional calculation model is constructed based on the time-varying changes in each region, the total carbon emissions of each region, the total power generation of each region, the net flow of electricity between different regions, and the balance relationship between the electricity carbon footprint factors of each region. The time-sharing and regional calculation model is expressed as follows:

[0030] (1).

[0031] in, This represents the total carbon emissions in the i-th region at time t. This represents the electricity carbon footprint factor of the j-th region at time t. Represents the electricity carbon footprint factor of the i-th region at time t, where j ≠ i; This represents the total power generation of the i-th region at time t. This represents the net inflow of electricity from region j to region i.

[0032] The carbon footprint factor calculation model obtained by combining the various time-sharing and partitioned calculation models is expressed as follows.

[0033] (2).

[0034] in, This represents the total carbon emissions in the first region at time t. This represents the electricity carbon footprint factor of the first region at time t; This represents the total power generation of the first region at time t. This represents the net inflow of electricity from region j to region j.

[0035] Calculate the initial values of the total carbon emissions of each region, the total power generation of each region, and the net inflow, and substitute them into the carbon footprint factor calculation model. Solve by联立各个分时分区计算模型求解, and calculate the electricity carbon footprint factors of each region.

[0036] Step S3: Combine the carbon emission activity data with the corresponding carbon footprint factors to obtain the total carbon emissions of each link.

[0037] Step S4: Evaluate the total carbon emissions of each link, identify the carbon emission hot spots, and optimize the emission reduction of the carbon emission hot spots.

[0038] Among them, the raw material extraction link includes: the bauxite mining link. The carbon emission sources in this link mainly come from the fuel consumption (diesel, natural gas, etc.) and transportation links (trucks, railways, etc.) during bauxite mining.

[0039] The smelting and processing link is the key process for extracting aluminum from bauxite. The common smelting method is the Hall-Héroult electrolysis method, which obtains metallic aluminum by electrolyzing bauxite. This process consumes a large amount of electricity, especially the electrolysis process.

[0040] The transportation and logistics link includes: raw material transportation and aluminum product transportation. Raw material transportation: The transportation of bauxite from the mining site to the smelter is usually carried out by road, railway or waterway. Bauxite usually needs to be transported over long distances, so the carbon emissions during transportation may be very significant. Aluminum product transportation: Finished aluminum or semi-finished aluminum (such as aluminum ingots) also need to be transported to the end market or customers, including by air, sea, railway, etc.

[0041] The recycling link includes collecting waste aluminum, cleaning, melting, etc. Aluminum is a very easy-to-recycle metal, and the energy consumption during its recycling process is much lower than that of raw material smelting. Although the recycling process consumes some electricity and energy, compared with primary smelting, the carbon emissions of recycled aluminum are greatly reduced. The main emissions during the recycling process come from electricity consumption (melting of recycled aluminum) and emissions from other auxiliary processes.

[0042] It should be noted that the expression "联立各个分时分区计算模型求解" in the original text seems to be an incorrect or unclear表述. I have translated it as literally as possible, but it may need to be further clarified in the original context for a more accurate translation.The present invention provides a method for calculating and optimizing carbon emissions throughout the entire lifecycle of aluminum industrial products. This method summarizes and analyzes the carbon emissions of aluminum industrial products throughout their lifecycle, selecting the raw material mining, smelting and processing, transportation and logistics, and recycling stages—all with high carbon emissions—as the calculation stages for the entire lifecycle. The constructed time-sharing and regional calculation model considers the net inflow of electricity between different regions and the changes in the electricity carbon footprint factor at different times, making the carbon emission accounting for each region more closely reflect regional characteristics and adaptable to changes over time. By combining the time-sharing and regional calculation models for each region, a carbon footprint factor calculation model is constructed. This model can solve for the electricity carbon footprint factor for each region even when the carbon footprint factor for some regions is unknown, without the need for recursion, resulting in higher computational efficiency. The accuracy, scientific validity, and real-time performance of the regional carbon emissions calculated based on this electricity carbon footprint factor are improved. Furthermore, emission reduction optimization can meet the needs of dynamic emission reduction, thereby further improving the emission reduction effect.

[0043] Furthermore, step S1 preferably includes the following steps.

[0044] Step S11: Determine the regions for carbon emission calculation, the types of greenhouse gases generated by aluminum industry products throughout their entire life cycle, and the various stages they go through.

[0045] Step S12: Collect carbon emission activity data related to greenhouse gas emissions at various stages and in various regions; wherein, carbon emission activity data includes: primary energy carbon consumption data, electricity energy consumption data, steam energy consumption data, material input data and waste treatment carbon consumption data.

[0046] Specifically, the various regions for carbon emission measurement are obtained by dividing the overall scope of the selected carbon emission measurement into multiple regions according to geographical space.

[0047] The main greenhouse gases generated throughout the entire life cycle of aluminum industrial products include carbon dioxide, methane, and nitrous oxide.

[0048] The collected primary energy consumption data mainly includes data on the consumption of primary energy sources such as natural gas, coal, gasoline, and diesel, which are typically present throughout the entire life cycle of aluminum industry products. Material input data mainly includes data on raw material inputs and auxiliary material inputs during the smelting, processing, and recycling stages. Waste treatment carbon consumption data mainly includes data on the consumption of chemical reagents during the recycling stage. Electricity consumption data and steam consumption data represent secondary energy consumption and are generally present throughout the entire life cycle of aluminum industry products.

[0049] The electricity consumption data is obtained through measurements using an electricity consumption sensor. Fuel consumption data for diesel, gasoline, and coal is calculated based on transportation distance, cargo weight, and fuel efficiency. Material input data is obtained through weighing records. Steam consumption data is calculated using a vortex flow meter and pressure / temperature compensation as the core technologies.

[0050] This invention creates an embodiment that statistically analyzes carbon emission activity data of aluminum industrial products throughout their entire life cycle by collecting data on carbon emission activities in the raw material mining, smelting and processing, transportation and logistics, and recycling stages. This avoids the incomplete carbon footprint map caused by the lack of collection of carbon emission activity data throughout the entire life cycle, which in turn affects the generation of subsequent emission reduction methods and the emission reduction effect.

[0051] Furthermore, step S2 preferably includes the following steps.

[0052] Step S21: Based on the carbon footprint factor calculation model, the time and region of the carbon emission activity data, input the carbon emission activity data into the carbon footprint factor calculation model, and output the electricity carbon footprint factor in the carbon footprint factor corresponding to the current time and current region through the carbon footprint factor calculation model.

[0053] Step S22: Based on the source of carbon emission activity data and the corresponding type of greenhouse gas, retrieve the remaining carbon footprint factors from the comprehensive database; wherein, the remaining carbon footprint factors include: primary energy carbon footprint factor, steam carbon footprint factor, material carbon footprint factor, and waste treatment carbon footprint factor.

[0054] This invention modifies the method of retrieving the electricity carbon footprint factor within the carbon footprint factor framework. Considering the significant regional and temporal influences on the electricity carbon footprint factor, a carbon footprint factor calculation model is constructed. This model calculates the time- and regionally differentiated electricity carbon footprint factor. Compared to related technologies, this significantly improves the scientific rigor, accuracy, and real-time performance of the electricity carbon footprint factor, facilitating the acquisition of more scientific and accurate carbon emission measurement data.

[0055] Furthermore, prior to step S21, the method of this embodiment of the invention further includes the following steps.

[0056] Step S021: Obtain the total power generation and net power inflow for each region; where the total power generation is the sum of the power generation of each type of power source; and the net power inflow is the difference between the power inflow and outflow from other regions for each region.

[0057] Step S022: Combine the total power generation, net inflow of electricity and the corresponding electricity carbon footprint factor to obtain the total carbon emissions and net inflow of carbon emissions for each region.

[0058] Step S023: Based on the balance relationship between total carbon emissions, net inflow carbon emissions, total power generation, net inflow electricity, and electricity carbon footprint factor in each region at each time point, construct calculation models for each time-sharing region.

[0059] Step S024: Combine the various time-sharing and partitioned calculation models to obtain the carbon footprint factor calculation model.

[0060] Specifically, in step S021, the power generation of each type of power source in each region is first collected, and then the power generation of each type of power source is added together to obtain the total power generation of each region, preferably expressed as the following expression.

[0061] (3).

[0062] Where i represents the i-th region and k represents the k-th power supply type; This represents the total power generation of the i-th region at time t; This represents the power generation of the k-th power source in the i-th region at time t.

[0063] Furthermore, the flow of electricity between different regions is obtained, and the net inflow of electricity to each region is obtained by subtracting the electricity flowing into each region from the electricity flowing out of the other regions.

[0064] See Figure 2 As shown in Table 1, G1-G5 represent five generating units, with power source types of coal-fired power generation, photovoltaic power generation, wind power generation, gas-fired power generation, and hydropower generation, respectively. Regions 1-3 are three measurement regions. The lines connecting G1, G2, and G3 to node #1 represent the power generation of G1, G2, and G3, respectively, which are 120MW, 40MW, and 60MW. The line connecting G4 to node #2 represents the power generation of G4, which is 19MW. The line connecting G5 to node #3 represents the power generation of G5, which is 20MW. Arrows pointing from node #1 to nodes #2 and #3 indicate that the power flowing from region 1 to region 2 is 24.95 MW, and the power flowing from region 1 to region 3 is 23.75 MW, respectively. The arrow pointing from node #2 to node #3 indicates that the power flowing from region 2 to region 3 is 9.65 MW.

[0065] Table 1. Comparison of Power Generation Type, Power Generation, and Electricity Carbon Footprint Factors

[0066]

[0067] See Table 2 for the net inflow of electricity to each region.

[0068] Table 2 Net Electricity Inflow to Each Region

[0069]

[0070] In step S022, the total power generation of each region is combined with the corresponding power carbon footprint factor to obtain the total carbon emissions of each region. The calculation formula is as follows.

[0071] (4).

[0072] in, This represents the total carbon emissions in the i-th region at time t. The power carbon footprint factor corresponding to the k-th power source type is shown in Table 3. This embodiment of the invention selects... The purpose is to calculate the initial value of the total carbon emissions for each region.

[0073] Table 3. Power Carbon Footprint Factors for Various Power Source Types Recommended by the Industry

[0074]

[0075] The total power generation of the first, second, and third regions is calculated to be 220MW, 19MW, and 20MW, respectively.

[0076] In step S023, the constructed time-sharing partitioned computing model is preferably expressed as formula (1).

[0077] In step S024, the various time-sharing and partitioned calculation models are combined to obtain the preferred expression formula (2) for the carbon footprint factor calculation model.

[0078] Substitute the initial values ​​of total carbon emissions, total power generation, and net inflow of each region calculated in step S022 into formula (2), and solve the time-of-use and zoning calculation models to calculate the time-of-use and zoning power carbon footprint factor of each region, as follows.

[0079] (5).

[0080] This invention considers the flow of electricity generation between different regions. By calculating the net inflow of electricity into each region, it can better reflect the actual electricity generation situation in each region. Furthermore, based on the balance relationship between the total carbon emissions of each region, the total electricity generation of each region, the net flow of electricity between different regions, and the electricity carbon footprint factor of each region, a time-sharing and regional calculation model is constructed. This model can solve for a more scientific, accurate, and time-sensitive electricity carbon footprint factor that reflects regional characteristics. The accuracy, scientific validity, and real-time performance of the regional carbon emissions calculated based on this electricity carbon footprint factor are all improved.

[0081] Prior to step S22, the method of this embodiment of the invention preferably includes the following steps.

[0082] Step S025: Collect historical carbon emission activity data and carbon footprint factors in industry standards for each stage of aluminum production, and construct a primary energy carbon footprint factor database, a steam carbon footprint factor database, a material carbon footprint factor database, and a waste treatment carbon footprint factor database.

[0083] Step S026: Combine the primary energy carbon footprint factor database, the steam carbon footprint factor database, the material carbon footprint factor database, and the waste treatment carbon footprint factor database to obtain a comprehensive database.

[0084] The embodiments of this invention construct a comprehensive database, which can quickly retrieve the corresponding carbon footprint factor from the comprehensive database based on the source of carbon emission activity data and the type of greenhouse gas, thereby improving the data management effect.

[0085] Furthermore, step S22 preferably includes the following steps.

[0086] Based on the source of carbon emission activity data and the corresponding greenhouse gas type, the corresponding primary energy carbon footprint factor is retrieved from the primary energy carbon footprint factor database; the corresponding steam carbon footprint factor is retrieved from the steam carbon footprint factor database; the corresponding material carbon footprint factor is retrieved from the material carbon footprint factor database; and the corresponding waste treatment carbon footprint factor is retrieved from the waste treatment carbon footprint factor database.

[0087] Specifically, the primary energy carbon footprint factor database includes the primary energy carbon footprint factors corresponding to natural gas, coal, gasoline, and diesel, preferably with a natural gas carbon footprint factor of 0.608. The coal footprint factor is 0.9440. The carbon footprint factor for 500 tons of gasoline or diesel is 633.96. The carbon footprint factor for 300 tons of gasoline or diesel is 650.86. .

[0088] The steam carbon footprint factor database includes: a carbon footprint factor of 3.768 for 10.0 MPa steam. The 5.0MPa steam pressure is 3.684. The 3.5MPa steam pressure is 0.6838. The 2.5MPa steam pressure is 0.6369. The 1.5MPa steam pressure is 0.3672. .

[0089] The materials carbon footprint factor database includes carbon footprint factors for raw materials and carbon footprint factors for auxiliary materials. The selection is based on the type of material. If the material is a primary energy source, carbon footprint factors from the primary energy carbon footprint factor database can be used; if the material is alumina, a carbon footprint factor of approximately 1.5-2.0 is used. Oxidation rate; if the material is an anode carbon block, the carbon footprint factor used is approximately 0.8-1.2. Anode carbon blocks; if the material is a fluoride salt, the carbon footprint factor used includes: cryolite has a carbon footprint factor of approximately 3.0-4.0. Aluminum fluoride is approximately 2.5-3.5. If the material is aluminum hydroxide, the carbon footprint factor used is 0.8-1.2. Aluminum hydroxide.

[0090] The waste disposal carbon footprint factor database includes: if the chemical agent used is sulfuric acid, the corresponding carbon footprint factor is 1.5-2.0. If it is hydrochloric acid, the corresponding carbon footprint factor is approximately 0.8-1.2. If it is sodium hydroxide, the corresponding carbon footprint factor is approximately 2.5-3.0. If it is a synthetic surfactant, the corresponding carbon footprint factor is approximately 4.0-6.0. For traditional chromates, the corresponding carbon footprint factor is approximately 8.0-10.0. If a chromium-free passivating agent (such as silane) is used, the corresponding carbon footprint factor is approximately 3.0-4.5. If ethylenediaminetetraacetic acid (EDTA) is used, the corresponding carbon footprint factor is approximately 5.0-6.5. If it is citric acid, the corresponding carbon footprint factor is approximately 1.5-2.5. .

[0091] Step S3 preferably includes the following steps.

[0092] Step S31: Combine the data on electricity consumption, primary energy carbon consumption, steam energy consumption, material input, and waste treatment carbon consumption from the carbon emission activity data of each stage with the corresponding carbon footprint factors to obtain the carbon emissions of electricity, primary energy, steam consumption, material consumption, and waste treatment for each stage.

[0093] Step S32: Add up the carbon emissions from electricity, primary energy, steam consumption, material consumption, and waste treatment at each stage to obtain the total carbon emissions for each stage.

[0094] Step S32 can be represented by the following expression.

[0095] (6).

[0096] in, This indicates the total carbon emissions at the corresponding stage; This represents the carbon emission activity data for the m-th stage; This represents the carbon footprint factor of the m-th stage.

[0097] This invention improves the method for calculating the carbon footprint factor of electricity. Based on the carbon footprint factor calculation model, time- and zone-specific carbon footprint factors are obtained. Combining these factors with corresponding electricity energy consumption data yields a more scientific and accurate estimate of electricity carbon emissions. Finally, adding the electricity carbon emissions to the carbon emissions from primary energy consumption, steam consumption, material consumption, and waste disposal yields the total carbon emissions for each stage.

[0098] Furthermore, step S4 in the embodiments of the present invention preferably includes the following steps.

[0099] Step S41: Input the total carbon emissions of each stage into the machine learning model, and identify carbon emission hotspots through the machine learning model; wherein, the machine learning model includes: clustering algorithm model, regression algorithm model or classification algorithm model.

[0100] Specifically, clustering algorithms can help identify patterns and trends in carbon emissions across different production stages by grouping similar data points to find potentially high-emission stages. Regression algorithms are used to establish relationships between greenhouse gas emissions and various variables in the production process (such as energy consumption, material usage, and equipment efficiency). Through regression analysis, it's possible to determine which variables have a significant impact on carbon emissions, thereby identifying high-emission stages. Classification algorithms can be used to categorize different production stages according to their carbon emissions, for example, classifying them into low-carbon and high-carbon categories. These algorithms can identify stages with high carbon emissions and provide guidance for developing emission reduction methods.

[0101] In addition, the present invention generates a visualized carbon footprint map based on the total carbon emissions of each stage, and after identifying carbon emission hotspots, marks the carbon emission hotspots in the visualized carbon footprint map.

[0102] Specifically, the visualized carbon footprint map uses the entire life cycle as the horizontal axis and carbon emissions as the vertical axis. It uses stacked bar charts to show the contribution of each stage and marks carbon emission hotspots with color intensity (red for high emissions) and arrows.

[0103] The total carbon emissions calculated in the embodiments of this invention are based on time- and region-specific electricity carbon footprint factors, making the carbon emissions dynamically change over time and region. Furthermore, by analyzing the total carbon emissions of each stage, current carbon emission hotspots can be identified in real time, enabling more precise emission reduction optimization at these hotspots and meeting the needs of dynamic emission reduction.

[0104] Furthermore, step S4 preferably includes the following steps.

[0105] Step S42: Adjust the power generation of carbon emission hotspots based on the power footprint factor of each region.

[0106] Step S43: Increase the power generation of different types of energy in the region and time corresponding to the lowest ranked power footprint factor.

[0107] Step S44: According to the order of power footprint factor from low to high, gradually reduce the power generation in the corresponding region and time, while ensuring that the total power generation remains unchanged, and optimize the emission reduction of carbon emission hotspots.

[0108] This invention, through the construction of an electronic carbon footprint factor calculation model, can calculate the electricity carbon footprint factor for various regions over future time periods. While keeping the total power generation constant, increasing power generation when the electricity carbon footprint factor is low and decreasing power generation when the factor is high can reduce the total carbon emissions across the entire process.

[0109] Furthermore, a high carbon footprint factor for electricity may be due to the large amount of electricity generated by thermal power sources and the small amount generated by renewable energy sources. Therefore, when the carbon footprint factor for electricity is high, the generation of renewable energy sources can be increased and the generation of thermal power sources can be reduced to improve emission reduction.

[0110] The embodiments of the present invention can achieve precise control of carbon emissions from aluminum products, reduce the carbon compliance costs of enterprises, and provide data support for carbon asset development.

[0111] Another embodiment of the present invention provides a system for calculating and optimizing carbon emissions throughout the entire life cycle of aluminum industrial products. This system is adaptable to the method for calculating and optimizing carbon emissions throughout the entire life cycle of aluminum industrial products provided in the above embodiments. The system includes: a data acquisition module, a model building module, a carbon emission calculation module, and an emission reduction optimization module.

[0112] The data acquisition module is used to collect carbon emission activity data of aluminum industrial products at all stages and in all regions throughout their entire life cycle; these stages include: raw material mining, smelting and processing, transportation and logistics, and recycling.

[0113] The model building module is used to construct time-sharing and partitioned calculation models for each region based on the total carbon emissions of each region, the total power generation of each region, the net flow of electricity between different regions, and the balance relationship between the electricity carbon footprint factors of each region. Then, the calculation models of each partition are combined to obtain the carbon footprint factor calculation model.

[0114] The carbon emission calculation module is used to select the corresponding carbon footprint factor based on the source of carbon emission activity data, the type of greenhouse gas, the time and region of the event, and combine the carbon emission activity data with the corresponding carbon footprint factor to obtain the total carbon emissions of each stage. Among them, the electricity carbon footprint factor is obtained by inputting the carbon emission activity data into the carbon footprint factor calculation model and outputting it through the carbon footprint factor calculation model.

[0115] The emission reduction optimization module is used to assess the total carbon emissions of each stage, identify carbon emission hotspots, and optimize emission reduction for these hotspots.

[0116] The system provided by this invention can collect carbon emission activity data of aluminum industry products throughout their entire life cycle. By constructing a time-sharing and regional calculation model that considers the net inflow of electricity between different regions and the changes in the electricity carbon footprint factor at different times, the carbon emission accounting for each region is more closely aligned with regional characteristics and can adapt to changes over time. When calculating carbon emissions, the corresponding time-sharing and regional calculation model can be input according to the region and time of calculation to obtain the corresponding electricity carbon footprint factor. The accuracy, scientific validity, and real-time performance of the regional carbon emission calculation based on this electricity carbon footprint factor are improved. An emission reduction optimization module is also provided to meet the needs of dynamic emission reduction, thereby further improving the emission reduction effect.

[0117] An embodiment of the present invention also provides an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores a computer program executable by the at least one processor, which, when executed by the at least one processor, causes the electronic device to perform the method of the embodiment of the present invention.

[0118] refer to Figure 3This is a structural block diagram of an electronic device that can serve as an embodiment of the present invention, representing a server or client, and is an example of a hardware device applicable to various aspects of the present invention. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0119] like Figure 3 As shown, the electronic device includes a computing unit 501, which can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) 502 or a computer program loaded from a storage unit 508 into a random access memory (RAM) 503. The RAM 503 may also store various programs and data required for the operation of the electronic device. The computing unit 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0120] Multiple components in the electronic device are connected to I / O interface 505, including: input unit 506, output unit 507, storage unit 508, and communication unit 509. Input unit 506 can be any type of device capable of inputting information into the electronic device. Input unit 506 can receive input digital or character information and generate key signal inputs related to user settings and / or function control of the electronic device. Output unit 507 can be any type of device capable of presenting information and may include, but is not limited to, a display, speaker, video / audio output terminal, vibrator, and / or printer. Storage unit 508 may include, but is not limited to, disks and optical discs. Communication unit 509 allows the electronic device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, and / or wireless communication transceivers, such as Bluetooth devices, WiFi devices, WiMax devices, cellular communication devices, and / or the like.

[0121] The computing unit 501 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, CPUs, graphics processing units (GPUs), various special-purpose artificial intelligence (AI) computing units, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. The computing unit 501 performs the various methods and processes described above. For example, in some embodiments, the method embodiments of the present invention can be implemented as computer programs tangibly contained in a machine-readable medium, such as storage unit 508. In some embodiments, part or all of the computer program can be loaded and / or installed on an electronic device via ROM 502 and / or communication unit 509. In some embodiments, the computing unit 501 can be configured to perform the methods described above by any other suitable means (e.g., by means of firmware).

[0122] Computer programs for implementing the methods of embodiments of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0123] In the context of embodiments of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable signal medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, or infrared systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0124] It should be noted that the term "comprising" and its variations used in the embodiments of this invention are open-ended, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The modifications of "one" and "a plurality" mentioned in the embodiments of this invention are illustrative and not restrictive, and those skilled in the art should understand that unless explicitly indicated otherwise in the context, they should be understood as "one or more".

[0125] The user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the embodiments of this invention are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0126] The steps described in the method embodiments provided by the present invention can be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of protection of the present invention is not limited in this respect.

[0127] The term "embodiment" in this specification refers to a specific feature, structure, or characteristic described in connection with an embodiment that may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily imply the same embodiment, nor does it imply independence or alternativeity from other embodiments. The various embodiments in this specification are described in a related manner, with reference to each other for similar or identical parts. In particular, for apparatus, device, and system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, and relevant details are referred to in the description of the method embodiments.

[0128] The above-described embodiments are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of protection. It should be noted that those skilled in the art can make various modifications and improvements without departing from the inventive concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the appended claims.

Claims

1. A method for calculating and optimizing carbon emissions throughout the entire life cycle of aluminum industrial products, characterized in that... Includes the following steps: Data on carbon emission activities of aluminum industrial products are collected at all stages and in all regions throughout their entire life cycle; these stages include: raw material mining, smelting and processing, transportation and logistics, and recycling. Based on the source of the carbon emission activity data, the type of corresponding greenhouse gas, the time of occurrence, and the region, a corresponding carbon footprint factor is selected. This carbon footprint factor includes an electricity carbon footprint factor. The electricity carbon footprint factor is obtained by inputting the carbon emission activity data into a carbon footprint factor calculation model and outputting the model. The carbon footprint factor calculation model is obtained by combining multiple time-sharing and regional calculation models. The time-sharing and regional calculation model is constructed based on the time-varying changes in the total carbon emissions of each region, the total power generation of each region, the net flow of electricity between different regions, and the balance relationship between the electricity carbon footprint factors of each region. The time-sharing and regional calculation model is expressed as follows: ； in, This represents the total carbon emissions in the i-th region at time t. This represents the electricity carbon footprint factor of the j-th region at time t. Represents the electricity carbon footprint factor of the i-th region at time t, where j ≠ i; This represents the total power generation of the i-th region at time t. This represents the net inflow of electricity from region j to region i. Combining the various time-sharing and partitioned calculation models, the resulting carbon footprint factor calculation model is expressed as follows: ； in, This represents the total carbon emissions in the first region at time t. This represents the electricity carbon footprint factor of the first region at time t; This represents the total power generation of the first region at time t. This represents the net inflow of electricity from region j to region j. Calculate the initial values ​​of total carbon emissions, total power generation, and net inflow for each region, and substitute them into the carbon footprint factor calculation model. Solve the time-sharing and zoning calculation models simultaneously to calculate the electricity carbon footprint factor for each region. By combining the carbon emission activity data with the corresponding carbon footprint factor, the total carbon emissions of each stage can be obtained. The total carbon emissions at each stage are assessed, carbon emission hotspots are identified, and emission reduction optimizations are carried out for these hotspots.

2. The method for calculating and optimizing carbon emissions throughout the entire life cycle of aluminum industrial products according to claim 1, characterized in that, Collect carbon emission data for aluminum industry products at all stages of their life cycle and in all regions, including: Identify the regions for carbon emission calculations, the types of greenhouse gases generated by aluminum industry products throughout their entire life cycle, and the various stages they go through. Collect carbon emission activity data related to the emission of greenhouse gases in each of the aforementioned stages and regions; wherein, the carbon emission activity data includes: primary energy carbon consumption data, electricity energy consumption data, steam energy consumption data, material input data, and waste treatment carbon consumption data.

3. The method for calculating and optimizing carbon emissions throughout the entire life cycle of aluminum industrial products according to claim 1, characterized in that, Based on the source of the carbon emission activity data, the type of corresponding greenhouse gas, the time period, and the region, a corresponding carbon footprint factor is selected; wherein, the carbon footprint factor includes: an electricity carbon footprint factor; the electricity carbon footprint factor is obtained by inputting the carbon emission activity data into the carbon footprint factor calculation model and outputting the carbon footprint factor calculation model, including: Based on the carbon footprint factor calculation model, the time and region of the carbon emission activity data, the carbon emission activity data is input into the carbon footprint factor calculation model, and the carbon footprint factor of electricity is output from the carbon footprint factor corresponding to the current time and region. Based on the source of the carbon emission activity data and the corresponding greenhouse gas type, the remaining carbon footprint factors are retrieved from the comprehensive database; wherein, the remaining carbon footprint factors include: primary energy carbon footprint factor, steam carbon footprint factor, material carbon footprint factor, and waste treatment carbon footprint factor.

4. The method for calculating and optimizing carbon emissions throughout the entire life cycle of aluminum industrial products according to claim 3, characterized in that, Before inputting the carbon emission activity data into the carbon footprint factor calculation model, the method includes: The total power generation and net power inflow for each region are obtained; wherein, the total power generation is the sum of the power generation of each type of power source; and the net power inflow is the difference between the power inflow and outflow from other regions for each region. By combining the total power generation and net inflow of electricity with the corresponding electricity carbon footprint factor, the total carbon emissions and net inflow of carbon emissions for each region can be obtained. Based on the balance relationship between the total carbon emissions, net inflow carbon emissions, total power generation, net inflow electricity, and electricity carbon footprint factor of each region at each time point, a time-sharing and zone-specific calculation model is constructed. By combining the various time-sharing and partitioned calculation models, a carbon footprint factor calculation model is obtained.

5. The method for calculating and optimizing carbon emissions throughout the entire life cycle of aluminum industrial products according to claim 3, characterized in that, Before retrieving other carbon footprint factors from the database based on the source of the carbon emission activity data and the corresponding greenhouse gas type, the method further includes: Collect historical carbon emission activity data and carbon footprint factors in industry standards from all stages of aluminum production, and construct databases for primary energy carbon footprint factors, steam carbon footprint factors, materials carbon footprint factors, and waste treatment carbon footprint factors. The primary energy carbon footprint factor database, steam carbon footprint factor database, material carbon footprint factor database, and waste treatment carbon footprint factor database are combined to obtain a comprehensive database.

6. The method for calculating and optimizing carbon emissions throughout the entire life cycle of aluminum industrial products according to claim 5, characterized in that, Based on the source of the carbon emission activity data and the corresponding greenhouse gas types, retrieve the remaining carbon footprint factors from the comprehensive database, including: Based on the source of the carbon emission activity data and the corresponding greenhouse gas type, retrieve the corresponding primary energy carbon footprint factor from the primary energy carbon footprint factor database; Retrieve the corresponding steam carbon footprint factor from the steam carbon footprint factor database; Retrieve the corresponding material carbon footprint factor from the material carbon footprint factor database; Retrieve the corresponding waste treatment carbon footprint factor from the waste treatment carbon footprint factor database.

7. The method for calculating and optimizing carbon emissions throughout the entire life cycle of aluminum industrial products according to claim 1, characterized in that, By combining the aforementioned carbon emission activity data with the corresponding carbon footprint factors, the total carbon emissions at each stage are obtained, including: By combining the data on electricity consumption, primary energy carbon consumption, steam energy consumption, material input, and waste treatment carbon consumption from each stage of the carbon emission activities with the corresponding carbon footprint factors, we can obtain the carbon emissions from electricity, primary energy, steam consumption, material consumption, and waste treatment for each stage. The total carbon emissions for each stage are obtained by adding up the carbon emissions from electricity, primary energy, steam consumption, material consumption, and waste treatment.

8. The method for calculating and optimizing carbon emissions throughout the entire life cycle of aluminum industrial products according to claim 1, characterized in that, Assess the total carbon emissions at each stage and identify carbon emission hotspots, including: The total carbon emissions from each stage are input into a machine learning model, which identifies carbon emission hotspots. The machine learning model includes clustering, regression, or classification algorithms.

9. The method for calculating and optimizing carbon emissions throughout the entire life cycle of aluminum industrial products according to claim 8, characterized in that, Optimize emission reductions for the aforementioned carbon emission hotspots, including: The power generation of the carbon emission hotspots is adjusted based on the power footprint factor of each region. Increase the power generation of different types of energy in the region and time corresponding to the lowest ranked power footprint factor; According to the order of the power footprint factor from low to high, the power generation is gradually reduced in the corresponding regions and times, while ensuring that the total power generation remains unchanged, so as to optimize the emission reduction of the carbon emission hotspots.

10. A system for calculating and optimizing carbon emissions throughout the entire life cycle of aluminum industrial products, characterized in that: include: The data acquisition module is used to collect carbon emission activity data of aluminum industrial products at all stages and in all regions throughout their entire life cycle; wherein the stages include: raw material mining, smelting and processing, transportation and logistics, and recycling. The model building module is used to construct time-sharing and partitioned calculation models for each region based on the total carbon emissions of each region, the total power generation of each region, the net flow of electricity between different regions, and the balance relationship between the electricity carbon footprint factors of each region. Then, the various partitioned calculation models are combined to obtain the carbon footprint factor calculation model. The carbon emission calculation module is used to select the corresponding carbon footprint factor based on the source of the carbon emission activity data, the type of greenhouse gas, the time and region of the event, and combine the carbon emission activity data with the corresponding carbon footprint factor to obtain the total carbon emissions of each stage; wherein, the electricity carbon footprint factor among the carbon footprint factors is obtained by inputting the carbon emission activity data into the carbon footprint factor calculation model and outputting it through the carbon footprint factor calculation model. The emission reduction optimization module is used to assess the total carbon emissions of each stage, identify carbon emission hotspots, and optimize emission reduction for the carbon emission hotspots.

11. An electronic device, comprising: A processor and a memory storing a program, characterized in that the program includes instructions that, when executed by the processor, cause the processor to perform the method for calculating and optimizing carbon emissions throughout the entire life cycle of aluminum industry products according to any one of claims 1-9.

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