Life Cycle Assessment-Based Carbon Footprint Management Method and System for Steel Products

Abstract

This invention discloses a method and management system for managing the carbon footprint of steel products based on life cycle assessment, relating to the technology of energy conservation and carbon reduction for steel companies. Considering that steel companies implementing long processes using blast furnaces and converters rely on the conversion and flow relationships between material and energy flows in the manufacturing process of steel products when managing their carbon footprint, this invention constructs a model for calculating, predicting, and optimizing the carbon footprint of steel products at the manufacturing equipment level. Based on the characteristic diversity of manufacturing process paths for steel products, this invention realizes diverse carbon footprint management methods, including calculation, analysis, prediction, and optimization of carbon footprints, and constructs a carbon footprint management system based on these methods. This invention compensates for the shortcomings of conventional carbon footprint management methods, which analyze carbon footprints only by comparing results, and realizes accurate, interconnected, and systematic carbon footprint management for the steel product lifecycle.

Description

Technical Field

[0001] The present invention relates to the technical field of energy conservation and carbon reduction in steel enterprises, and relates to a carbon footprint management method and system for steel products based on life cycle assessment, and particularly relates to a carbon footprint management method and system for long-process steel products.

Background Art

[0002] Green and low-carbon development has become the most important point in the transformation, upgrading and development of the steel industry. Steel enterprises themselves need to establish a carbon footprint management system, deeply analyze and understand the composition and emission indicators of steel products, continuously optimize the manufacturing process and product composition, and strengthen their competitiveness in carbon reduction.

[0003] Currently, carbon footprint management systems at the steel industry level lack accuracy and often do not reflect the actual production conditions of steel companies. This makes it difficult for companies to effectively make specific adjustments and controls based on carbon emission data during the carbon footprint management process, thus hindering the maximization of carbon footprint reduction. Specifically, conventional lifecycle carbon footprint calculation models simply linearly connect upstream and downstream processes via material flow, studying only crude steel products, or merely allocating inputs and outputs from each process after the steelmaking stage based on the quality of different types of steel products. In actual manufacturing sites, multiple manufacturing facilities with the same function operate in parallel, and different types of steel products have different specific manufacturing routes between facilities, resulting in variations in their carbon footprints. Furthermore, different sources of specific substances or energy input to the same facility result in different direct and indirect carbon emissions. Within this context, carbon emissions from self-produced energy are determined by the energy conversion relationships between facilities within the energy system. Process-based modeling cannot fully reflect the specific energy conversion relationships within a steel company's own energy system, nor can it fully reflect the impact of product manufacturing on the carbon footprint through specific production routes between manufacturing facilities.

[0004] Furthermore, conventional carbon footprint management systems lack systematicity and do not incorporate all of the effective carbon footprint management methods. Current carbon footprint management systems are limited to comparative analysis of the carbon footprint of companies and products, and this comparative analysis provides a basis for decision-making in multiple aspects, such as company allocation, product composition, and energy and raw material composition. For example, by comparing various carbon emission data between different types of companies, between different companies of the same type, and between different time periods within the same company, differences can be detected and the potential for carbon reduction can be analyzed. Specifically, steel companies mainly compare various products, carbon emission boundaries, carbon emission contribution rates, and carbon emissions before and after the implementation of carbon reduction measures by the company. However, the manufacturing process of steel companies involves many types of energy and materials, and the manufacturing equipment and production routes are complex and subject to frequent changes, so when steel products are manufactured via different production routes, the carbon footprint results will also differ. Therefore, comparative analyses of carbon footprints based solely on the manufacturing results of steel products lack effective carbon footprint management methods for the manufacturing process. This makes it difficult to effectively achieve systematic management and adjustment control of the carbon footprint of the steel product lifecycle from the perspective of the manufacturing process at each stage of the lifecycle. [Overview of the Initiative] [Problems that the invention aims to solve]

[0005] This invention has been made in view of the above technical problems, and aims to provide a carbon footprint management method and system for steel products based on life cycle assessment, and to overcome the shortcomings of conventional carbon footprint calculation models and carbon footprint management methods based on processes, which include the calculation of carbon footprint results subdivided into different product types by post-allocation methods, the lack of carbon footprint calculation models for different manufacturing routes composed of specific conversion relationships between different types of energy in a company's energy system and corresponding input / output relationships between each manufacturing facility, and the lack of a single management method, which includes a lack of management methods such as evaluation, analysis, prediction, and optimization of carbon footprints related to manufacturing routes. [Means for solving the problem]

[0006] To achieve the above objectives, the present invention employs the following technical means. A carbon footprint management method for steel products based on a life cycle assessment, according to one embodiment of the present invention, We collect information on the company's steel products and the material flow, energy flow, and flow paths of each unit process in the steel product manufacturing process. We introduce a background database of the material and energy flow lifecycles that are exchanged with external parties in the manufacturing process, and a database of carbon emission factors for material and energy flow throughout the lifecycle process. The collected material flow and energy flow data for each unit process in the manufacturing process are verified using metal balance and carbon balance methods, the background database and carbon emission factor database are updated, and data corresponding to different manufacturing routes, different steel types, and different material and energy flows are classified and stored. Determine the carbon footprint calculation function unit, manufacturing route, and calculation system boundaries for different types of steel products, select data allocation principles, and construct a lifecycle carbon footprint model. This includes managing the carbon footprint of the steel products based on the lifecycle carbon footprint model.

[0007] Furthermore, the lifecycle carbon footprint model includes a lifecycle carbon footprint calculation model, a lifecycle carbon footprint prediction model, and / or a lifecycle carbon footprint optimization model. Building a lifecycle carbon footprint model is Based on the request to calculate the carbon footprint of steel products, the calculation scope and allocation principles are determined, appropriate functional units are selected according to the properties, quantity, and application of the steel products, and a life cycle carbon footprint calculation model for steel products is constructed based on the relationships between intermediate products in each unit process of the steel product manufacturing process and the conversion relationships between material flow and energy flow within each unit process. Based on a life cycle carbon footprint calculation model, the conversion relationship and efficiency of material flow and energy flow in the past manufacturing routes and unit processes of the steel type to be predicted are obtained, appropriate route information and unit process information are selected and analyzed to create a life cycle carbon footprint prediction model for information and data training, and / or Based on a lifecycle carbon footprint calculation model for steel products, the conversion relationship and efficiency of material flow and energy flow in each unit process, as well as the quantity and correspondence of intermediate flows between each unit process, are obtained. Based on these correspondence relationships, conversion relationships, and efficiencies, a target function and constraints are constructed, and the carbon footprint of the product is minimized under the assumption that the manufacturing process conditions are not changed.

[0008] Furthermore, building a lifecycle carbon footprint calculation model for steel products is possible. Based on the conversion relationships between material and energy flows within each unit process, and the flow relationships between different unit processes of intermediate products, a steel product manufacturing process model was constructed. Based on the steel product manufacturing process model, and using the material flow and energy flow related to the manufacturing process included in the aforementioned background database, a steel product lifecycle process model was constructed. Based on steel product manufacturing process models and steel product lifecycle process models, this includes using carbon emission factors for material and energy flows related to the manufacturing process to convert material and energy flows in the process into carbon flows, and constructing a model for calculating the lifecycle carbon footprint of steel products. Building a lifecycle carbon footprint prediction model is Based on a lifecycle carbon footprint calculation model for steel products, all historical manufacturing routes included in the carbon footprint calculation results for the steel product of the target steel type are obtained, and appropriate route information is selected and analyzed. Based on the conversion relationship and efficiency of material flow and energy flow included in the unit process and path information, and the correspondence between the intermediate flow of each unit process and its upstream and downstream connections, if the input energy, type of raw material, input quantity, input unit process, and manufacturing path between unit processes are known, the output of material flow and energy flow of the unit process and the quantity and correspondence between the intermediate flow between each unit process in the entire manufacturing process can be obtained. Based on the output of material flow and energy flow in each unit process, and the quantity and correspondence of intermediate flows between each unit process in the entire manufacturing process, the conversion relationship and conversion efficiency of material flow and energy flow in each unit process are obtained. This includes analyzing and selecting appropriate routes and route information based on the conversion relationship and efficiency of material flow and energy flow in each unit process, and the correspondence between intermediate flows in each unit process and their upstream and downstream connections, using the time-proximity past manufacturing routes of the steel type to be predicted, and utilizing the basic information of each unit process, to create a lifecycle carbon footprint prediction model for information and data training. Building a lifecycle carbon footprint optimization model is Based on a life cycle carbon footprint calculation model, target functions and constraints are set according to the conversion relationship and conversion efficiency of material flow and energy flow in each unit process, and the quantity and correspondence of intermediate flows between each unit process. By optimizing the manufacturing route without changing the manufacturing process conditions, the carbon footprint of the product can be minimized. Based on the lifecycle carbon footprint calculation model, the process includes obtaining historical manufacturing route information, manufacturing data, and carbon footprint result data for a steel product to be optimized, and combining these with a target function and constraints to form a lifecycle carbon footprint optimization model for the steel product.

[0009] Furthermore, managing the carbon footprint of steel products based on a life cycle carbon footprint model is important. Based on a lifecycle carbon footprint calculation model for steel products, material flow and energy flow data, background data, and carbon emission coefficient data for corresponding unit processes are introduced. Based on the relevant data for steel products and the quality of the relevant data, the carbon footprint result data and uncertainty intervals for the results of steel products are calculated. Based on the carbon footprint result data for steel products, the differences in carbon footprints of different products and the differences in the contribution of each factor to the carbon footprint result are analyzed. Based on the lifecycle carbon footprint calculation model for steel products, the corresponding changes in the carbon footprint when a certain factor changes within the range of actual manufacturing values ​​are analyzed, thereby obtaining the sensitivity of the carbon footprint to that factor, and classifying and storing the calculation and analysis results. Based on the same carbon footprint calculation model, carbon footprint data for steel products is used to compare and analyze carbon footprint results across different steel products within a company, between subdivided steel grades belonging to the same major steel product category, and within different calculation time ranges for the same steel grade. Based on carbon footprint calculation models with different calculation boundaries, this system compares and analyzes carbon footprint results for steel products within different calculation boundary ranges for the same steel type, helping companies understand their actual carbon footprint situation. Based on carbon footprint data for steel products, the contribution of steel product carbon footprint data to the process is classified and calculated from three aspects: direct emissions in the manufacturing process, indirect emissions from the consumption of external electricity and thermal energy, and emissions in the supply chain. The composition ratio and potential contribution impact of carbon footprint data at each stage of steel products are then shown. Based on carbon footprint data for steel products, this includes classifying and calculating the contribution of steel product carbon footprint data to the manufacturing process from three aspects: each manufacturing process, the equipment used in each manufacturing process, and the inputs and outputs of each piece of equipment. It also includes showing the composition ratio and potential contribution impact of carbon footprint data at each stage of steel production.

[0010] Furthermore, managing the carbon footprint of the steel products based on a life cycle carbon footprint model is possible. Given that the material and energy flows input in the manufacturing route and unit processes of steel products are known, a life cycle carbon footprint prediction model includes predicting the location and amount of energy and raw material inputs or the carbon footprint of the steel product after its manufacture in the product manufacturing process.

[0011] Furthermore, predicting the carbon footprint after the manufacture of steel products is possible. Based on a life cycle carbon footprint prediction model for steel products, the model calculates the life cycle carbon footprint of steel products at the time of energy and raw material input or during the product manufacturing process, thereby enabling prediction of the carbon footprint of steel products after manufacturing. Given that the input energy, raw material type, input quantity, and input unit process are known, and the final steel product is not manufactured through the manufacturing process, a life cycle carbon footprint prediction model can be used to predict the input and output of material and energy flows in each unit process of the steel product, as well as the intermediate flows between each unit process of the entire manufacturing process and their corresponding relationships. When the path at a certain stage in the steel manufacturing process changes, the system adjusts the unit process at that stage, as well as each downstream unit process related to that unit process, in a timely manner, based on a lifecycle carbon footprint prediction model for steel products. This enables real-time updated carbon footprint predictions when the path in the manufacturing process changes. By combining the input and output data of each unit process with the corresponding relationships selected from the carbon footprint prediction model, a carbon footprint prediction result is obtained. The accuracy of the prediction result is analyzed by comparing it with the carbon footprint prediction result calculated based on data collected at each unit process after the manufacture of similar steel products. Based on the relevant data of the steel product and the quality of the relevant data, the carbon footprint prediction result data and the uncertainty interval of the result are calculated for the steel product. This includes comparing and analyzing the carbon footprint calculation results and uncertainty intervals calculated based on data from each unit process after manufacturing of similar steel products manufactured using the same manufacturing route, and analyzing the accuracy of the prediction results within the uncertainty interval by comparing the results within the uncertainty interval.

[0012] Furthermore, managing the carbon footprint of steel products based on a life cycle carbon footprint model is important. When steel products are not yet manufactured or are in the manufacturing process, this includes minimizing the carbon footprint of the product by adjusting the lifecycle carbon footprint of similar steel products based on a lifecycle carbon footprint optimization model.

[0013] Furthermore, coordinating the supply chain for similar steel products is Based on the optimized manufacturing path in the lifecycle carbon footprint optimization model, the carbon footprint optimization results are obtained from the input and output data of each unit process, and the carbon footprint optimization result data and the uncertainty interval of the result are calculated based on the relevant data and the quality of the relevant data for the steel product. When it becomes possible to optimize and adjust the path at a certain stage in the steel manufacturing process, based on a lifecycle carbon footprint optimization model, it is possible to optimize the path at the unit process within the optimizationable stage and at each downstream unit process related to that unit process. This enables real-time optimization of the carbon footprint when the path in the manufacturing process changes, and by comparing the original manufacturing path with the adjusted manufacturing path, the carbon footprint reduction amount is obtained from the changes in the manufacturing path and carbon footprint results of the steel product, and the possibility of optimizing the carbon footprint of the steel product is analyzed. Once the product manufacturing process is complete, the carbon footprint results after optimization of the product path are compared with the actual results based on the lifecycle carbon footprint optimization model to analyze whether the product's manufacturing path is a low-carbon manufacturing path. If there is room for optimization, the possibilities for further optimization of the manufacturing path are analyzed. This includes comparing and analyzing the carbon footprint calculation results and uncertainty intervals of similar steel products manufactured using the original manufacturing route, based on the results data and uncertainty intervals of the carbon footprint optimization, and analyzing the carbon footprint reduction effect of the optimization results within the uncertainty interval range based on the comparison of the results.

[0014] Another embodiment of the present invention, a carbon footprint management system for steel products based on life cycle assessment, A data collection module that collects information on a company's steel products and material flow, energy flow, and flow paths in each process of the steel product manufacturing process, introduces a background database of material and energy flow lifecycles that are exchanged with external parties in the manufacturing process, and introduces a database of carbon emission factors for material and energy flow in the lifecycle process. By means of the metal balance and carbon balance methods, verify the data of the material flow and energy flow of each unit process in the collected manufacturing process, update the background database and carbon emission factor database, and classify and store the data corresponding to different manufacturing routes, different steel grades, different material flows and energy flows. A data management module, A carbon footprint calculation function unit for the carbon footprint of different types of steel products, a model construction module for determining the manufacturing route and calculation system boundary, selecting a data distribution principle, and constructing a life cycle carbon footprint model, Based on the life cycle carbon footprint model constructed by the model construction module, it includes a carbon footprint analysis module for managing the carbon footprint of steel products.

[0015] Furthermore, the carbon footprint analysis module A carbon footprint calculation and analysis module for calculating and comparing the carbon footprint of products based on the above carbon footprint calculation model, Based on the above life cycle carbon footprint prediction model, predict the carbon footprint of steel products produced during the input of energy and raw materials or during the manufacturing process, and analyze the prediction results. A carbon footprint prediction and analysis module, Based on the above life cycle carbon footprint optimization model, adjust the specific manufacturing route of steel products to optimize the carbon footprint result of the products, and analyze the optimization result. A carbon footprint optimization and analysis module.

Advantages of the Invention

[0016] Compared with the prior art, the present invention has the following features and advantages. This invention enables carbon footprint management of steel products at the manufacturing equipment level for various types of steel products in steel companies implementing long processes such as blast furnaces and converters, based on life cycle assessment methods, material flow, energy flow conversion and flow relationships, and the interconnected process units. Considering the characteristics of the steel manufacturing process, such as the conversion relationships of material and energy flows within unit processes, the flow relationships between different unit processes, and the diverse and complex origins of raw materials input to unit processes and the destinations of output products and by-products, a carbon footprint calculation and optimization model for steel products has been invented. If the manufacturing route of a steel product and the material and energy flows input to unit processes are known, the carbon footprint after steel product manufacturing can be predicted at the raw material input stage or during the manufacturing process. If the original manufacturing route of a steel product is known, the carbon footprint of a product can be reduced by adjusting and optimizing the route for the same type of steel product. Various carbon footprint management methods, such as calculation, analysis, prediction, and optimization of carbon footprints, can be realized. The carbon footprint management method of the present invention allows for accurate evaluation of the impact of changes in the manufacturing process on the carbon footprint of steel products throughout their lifecycle. This overcomes the shortcomings of conventional management methods, which only analyze carbon footprint results, and enables accurate, interconnected, and systematic carbon footprint management of steel product lifecycles. [Brief explanation of the drawing]

[0017] To more clearly explain the embodiments of the present invention or the technical means in the prior art, the following drawings related to the embodiments or the prior art will be briefly introduced. However, the following drawings represent only some embodiments of the present invention, and it goes without saying that those skilled in the art can obtain other drawings based on these drawings without any creative work.

[0018] [Figure 1] This is a flowchart of a carbon footprint management method for steel products based on life cycle assessment according to an embodiment of the present invention. [Figure 2]This is a block diagram of the configuration of a carbon footprint management system for steel products based on life cycle assessment according to an embodiment of the present invention. [Modes for carrying out the invention]

[0019] Life cycle assessment can comprehensively evaluate the carbon footprint of steel products and their manufacturing processes throughout their entire lifecycle, identify key influencing factors, and explore energy-saving and carbon reduction measures for the entire process, enabling companies to manage their carbon footprint. Based on life cycle assessment of steel products, this invention refers to the characteristics of material flow and energy flow conversion and dynamics throughout the steel product lifecycle and describes a method and management system for managing the carbon footprint of steel products.

[0020] To further clarify the technical means of the present invention, the technical means in the embodiments of the present invention will be described clearly and completely below with reference to the drawings of the embodiments, and it goes without saying that the embodiments described are not all embodiments but only a selection of embodiments of the present invention. Any other embodiments that a person skilled in the art could obtain without creative work based on the embodiments of the present invention are all included within the scope of the present invention.

[0021] As shown in Figure 1, an embodiment of the present invention provides a method for managing the carbon footprint of steel products based on life cycle assessment, and includes the following S1 to S4.

[0022] S1. Collect information on the company's steel products and the material flow, energy flow, and flow paths of each unit process in the steel product manufacturing process. Introduce a background database of the lifecycle of material and energy flows that are exchanged with external parties in the manufacturing process, and introduce a database of carbon emission factors for material and energy flows in the lifecycle process.

[0023] S2. Using metal balance and carbon balance methods, the collected material flow and energy flow data for each unit process in the manufacturing process are verified, the background database and carbon emission factor database are updated, and data corresponding to different manufacturing routes, different steel types, and different material and energy flows are classified and stored.

[0024] S3 defines the carbon footprint calculation function unit for different types of steel products, the manufacturing route and calculation system boundaries, selects data allocation principles, and constructs a lifecycle carbon footprint model. Here, the lifecycle carbon footprint model includes a lifecycle carbon footprint calculation model, a lifecycle carbon footprint prediction model, and / or a lifecycle carbon footprint optimization model. Building a life cycle carbon footprint model is Based on the request to calculate the carbon footprint of steel products, the calculation scope and allocation principles are determined, appropriate functional units are selected according to the properties, quantity, and application of the steel products, and a life cycle carbon footprint calculation model for steel products is constructed based on the relationships between intermediate products in each unit process of the steel product manufacturing process and the conversion relationships between material flow and energy flow within each unit process. Based on the carbon footprint calculation model, the relationship between material flow and energy flow conversion and efficiency in the past manufacturing routes and unit processes of the steel type to be predicted are obtained, appropriate route information and unit process information are selected and analyzed to create a lifecycle carbon footprint prediction model for information and data training, and / or Based on a lifecycle carbon footprint calculation model for steel products, the conversion relationship and efficiency of material flow and energy flow in each unit process, as well as the quantity and correspondence of intermediate flows between each unit process, are obtained. Based on these correspondence relationships, conversion relationships, and efficiencies, a target function and constraints are constructed, and the carbon footprint of the product is minimized under the assumption that the manufacturing process conditions are not changed.

[0025] S4, based on the Life Cycle Carbon Footprint Model, will manage the carbon footprint of steel products. Specifically, this will include the following three management methods: (1) Based on the life cycle carbon footprint calculation model for steel products, data on material flow and energy flow for the corresponding unit processes, background data, and carbon emission coefficient data are introduced. Based on the relevant data for steel products and the quality of the relevant data, the carbon footprint result data and uncertainty intervals for the results of steel products are calculated. Based on the carbon footprint result data for steel products, the differences in carbon footprints of different products and the differences in the contribution of each factor to the carbon footprint result are analyzed. Based on the life cycle carbon footprint calculation model for steel products, the corresponding changes in the carbon footprint when a certain factor changes within the range of actual manufacturing values ​​are analyzed, thereby obtaining the sensitivity of the carbon footprint to that factor, and classifying and storing the calculation and analysis results. Based on the same carbon footprint calculation model, carbon footprint data for steel products is used to compare and analyze carbon footprint results across different steel products within a company, between subdivided steel grades belonging to the same major steel product category, and within different calculation time ranges for the same steel grade. Based on carbon footprint calculation models with different calculation boundaries, this system compares and analyzes carbon footprint data for steel products within different calculation boundary ranges for the same steel type, helping companies understand their actual carbon footprint situation. Based on carbon footprint data for steel products, the contribution of steel product carbon footprint data to the process is classified and calculated from three aspects: direct emissions in the manufacturing process, indirect emissions from the consumption of electricity and thermal energy from external sources, and emissions in the supply chain. The composition ratio of carbon footprint data at each stage of steel products and their potential contributions are shown. Based on carbon footprint data for steel products, the contribution of steel product carbon footprint data to the manufacturing process is classified and calculated from three aspects: each manufacturing process, the equipment used in each manufacturing process, and the inputs and outputs of each piece of equipment. The composition ratio and potential contribution impact of carbon footprint data at each stage of steel production are then shown.

[0026] (2) When the material flow and energy flow input in the manufacturing route and unit process of steel products are known, a life cycle carbon footprint prediction model is used to predict the carbon footprint of steel products after manufacturing, either at the time of energy and raw material input or during the product manufacturing process.

[0027] (3) If the steel product is not yet manufactured or is in the manufacturing process, the carbon footprint of the product will be minimized by adjusting the lifecycle carbon footprint optimization model for similar steel products.

[0028] In the above embodiment, carbon footprint management of steel products at the manufacturing equipment level is realized for each type of steel product in a steel company that implements a long blast furnace-converter process, based on a life cycle assessment method, the relationship between material flow / energy flow conversion and flow, and the connected process units. The carbon footprint management method of the present invention makes it possible to accurately evaluate the impact of changes in the manufacturing route on the life cycle carbon footprint of steel products, compensating for the shortcomings of conventional management methods that only analyze the carbon footprint results, and realizing accurate, interconnected, and systematic carbon footprint management of the steel product life cycle.

[0029] As shown in Figure 2, an embodiment of the present invention provides a carbon footprint management system for steel products based on life cycle assessment, and includes the following modules.

[0030] Data Acquisition Module M1: Collects information on a company's steel products and material flow, energy flow, and flow paths in unit processes of the steel product manufacturing process. It introduces and updates a background database of the lifecycle of material and energy flows that are exchanged with external parties in the manufacturing process. It also introduces and updates a database of carbon emission factors for material and energy flows in the cradle-to-gate lifecycle process within the company. It collects relevant information on input data, including information related to data quality such as data acquisition time, data source, and data calculation type.

[0031] Data Management Module M2: Using metal balance and carbon balance methods, it verifies the material flow and energy flow data for unit processes collected by Data Collection Module M1, updates the background database and carbon emission factor database, selects and filters unit process data according to selection principles, and classifies and stores data corresponding to different steel types and different material and energy flows.

[0032] Model building module M3: Defines the carbon footprint calculation function unit and calculation system boundaries for different types of steel products, selects data allocation principles, and builds models for calculating, predicting, and optimizing lifecycle carbon footprints.

[0033] The carbon footprint analysis module M4: Based on the lifecycle carbon footprint calculation model, it obtains carbon footprint results for different types of steel products, analyzes and stores the carbon footprint results, and, if the manufacturing route and the material and energy flows input in each unit process of the steel product are known, it predicts the carbon footprint after the steel product is manufactured using the lifecycle carbon footprint prediction model. If the original manufacturing route of the steel product is known, it reduces the carbon footprint of the product by adjusting the route of the same type of steel product based on past input and output data of the unit process, using the lifecycle carbon footprint optimization model.

[0034] The data collection module M1 includes the steel product information collection module M1.1, the manufacturing route determination module M1.2, the background database introduction module M1.3, and the carbon emission factor database introduction module M1.4. The steel product information collection module M1.1 subdivides all steel products manufactured within a company into different product categories based on information such as the product's application, shape, processing steps, and performance, and collects basic information including the product's name, carbon content, and processing steps.

[0035] The unit process is defined as the smallest granularity for collecting on-site data, specifically the manufacturing processes of each piece of equipment included in the company's main processes and energy systems. Material flow includes raw materials (iron ore, iron powder, alloys, etc.) and auxiliary raw materials (limestone, dolomite, refractory materials, etc.) input into the unit process, as well as products and by-products output from the unit process (molten iron, molten steel, scrap steel, etc.). Energy flow includes energy (washing carbon, anthracite, coke, etc.) input into the unit process, energy media (electricity, coke oven gas, industrial water, etc.), and energy and energy media output from the unit process.

[0036] Within a company, all steel products manufactured are classified in detail based on the manufacturing process (e.g., cold-rolled products / hot-rolled products), processing process (e.g., batch annealing / continuous annealing), shape (e.g., round bars / square bars), size (e.g., hot-rolled thin strips / medium-thick plates), properties (e.g., plasticity / hardness), and application (e.g., automotive materials / construction materials).

[0037] The manufacturing path determination module M1.2 determines the time granularity of information collection, determines the basic information and major physical quantities of material flow and energy flow input and output in unit processes in the manufacturing process of a certain type of steel product, collects basic information for all unit processes in the manufacturing process, determines the flow relationships of material flow and energy flow in all unit processes and between unit processes included in the company's manufacturing process, and determines the flow paths of material flow and energy flow between unit processes in the manufacturing process of a certain type of steel product.

[0038] Based on the process flow and metallurgical mechanism in the manufacturing process, the conversion relationship between input raw materials and output products within each unit process is determined by the input of raw materials and the output of products within each unit process.

[0039] Based on the process flow and metallurgical mechanism in the manufacturing process, we trace back all unit processes through which the intermediate flow in the manufacturing process of iron and steel products flows. Here, the intermediate flow is the product or by-product of the upstream unit process that flows into the downstream unit process as raw material or auxiliary raw material, and is usually the main product of the outgoing unit process. For example, molten iron is the intermediate flow from the blast furnace to the converter, and molten steel is the intermediate flow from the converter to the smelting equipment.

[0040] Depending on the type of steel product, the unit manufacturing process is used as the collection granularity, and all material and energy flows input and output in the manufacturing process of each type of steel product are collected.

[0041] Depending on the type of steel product, the unit manufacturing process is used as the collection granularity to collect and recognize all carbon-containing material flow and energy flow inputs and outputs in the manufacturing process of each type of steel product.

[0042] The time granularity collected includes one or more units such as year, month, day, team, hour, furnace number, and real-time. Basic information on the material and energy flows input and output of a unit process includes the type, properties, units of measurement, and constraints of the material and energy flows. The main physical quantities of the material and energy flows input and output of a unit process are consumption, generation, recovery, emission, external sales, vapor pressure, and carbon content. Basic information on all unit processes collected in the manufacturing process includes the equipment status of each unit process (stable operation, fluctuations, inspection, shutdown, etc.), the raw material status of the unit process (processing, transport, stagnation, etc.), and the furnace number of the steelmaking, refining, and continuous casting equipment.

[0043] Based on the type of steel product and the inputs and outputs of material and energy flows between each unit process in the manufacturing process, the relationship between intermediate products and the conversion relationships of material and energy flows within each unit process are determined for a given type of steel product.

[0044] The enterprise manufacturing process includes a manufacturing system and an energy system, with each unit process being a piece of equipment within the manufacturing system and the energy system. The unit processes within the manufacturing system include one or more of the following: coke ovens, dry coke extinguishing equipment, sintering machines, annular coolers, roasting equipment, cooling equipment, blast furnaces, hot blast furnaces, converters, ladles, refining furnaces, continuous casting machines, heating furnaces, roughing mills, finishing mills, annealing furnaces, and cold rolling mills. The unit processes within the energy system include one or more of the following: gas generators, combustion chambers, waste heat boilers, coal boilers, gas boilers, generator sets, oxygen generators, compressors, blowers, pressurizers, and water treatment equipment.

[0045] The material and energy flows between unit processes include material flows from the manufacturing system such as sintered ore, pelletized ore, molten iron, molten steel, steel billets, hot-rolled coils, scrap iron, and steel slag, and energy flows such as coke and coke powder; energy flows from the energy system such as fresh water, clean circulating water, dirty circulating water, electricity, low-pressure steam, high-pressure steam, oxygen gas, nitrogen gas, argon gas, coke oven gas, blast furnace gas, and converter gas; and energy flows between the manufacturing system and the energy system such as fresh water, clean circulating water, dirty circulating water, electricity, low-pressure steam, high-pressure steam, oxygen gas, nitrogen gas, argon gas, coke oven gas, blast furnace gas, and converter gas.

[0046] The flow paths of material and energy flows between unit processes include, from which unit processes the raw materials, auxiliary raw materials, and energy of a given unit process flow out, the types and quantities of these flows out from each of these upstream unit processes, and all unit processes into which the product and by-product flows of that unit process flow, as well as the types and corresponding quantities of these flows into each of these unit processes.

[0047] Of course, in other embodiments, each unit process includes other equipment (such as lime kilns and slag powdering equipment), types of energy (such as natural gas and diesel), and other systems (such as waste treatment systems). In actual use, the systems, equipment, and types of energy will be determined according to actual needs and are not limited thereto.

[0048] The background database implementation module M1.3 specifically implements background databases and data-related information for material and energy flows that are exchanged with external parties during the manufacturing process, based on different types of steel products.

[0049] Here, material flows with exchange relationships with external firms include raw materials and auxiliary raw materials purchased by the firm from external firms, and by-products sold by the firm to other firms. Energy flows with exchange relationships with external firms include energy and energy media purchased by the firm from external firms, and energy and energy media sold by the firm to other firms.

[0050] Here, the background database includes all inputs and outputs for each energy extraction and processing process, as well as all inputs and outputs for the transportation process.

[0051] Here, a complete background database related to all external raw materials entered by a steel company includes all inputs and outputs of the raw material extraction, processing, and manufacturing processes, and all inputs and outputs of the transportation process. A complete background database related to the manufacturing process of alternative industrial products for external sales by-products includes the energy and raw material extraction and processing required for the manufacturing process of the alternative industrial products, and all inputs and outputs of the manufacturing process of these industrial products.

[0052] Here, a complete background database corresponding to all external energy input by steel companies includes all inputs and outputs of energy extraction, processing, and manufacturing processes, and all inputs and outputs of transportation processes. A complete background database related to the manufacturing processes of industrial products that use externally sold by-energy as an alternative includes the extraction and processing of energy required for the manufacturing processes of industrial products, and all inputs and outputs of the manufacturing processes of industrial products.

[0053] Here, the background database includes life cycle inventory coefficients for energy and raw materials provided by the company's suppliers, transportation process inventory coefficients for energy and raw materials from suppliers to the company gate provided by the transportation department, and inventory coefficients for energy and raw materials in each implemented life cycle background database.

[0054] Specifically, the materials provided by a company's suppliers include data from upstream product life cycle assessment reports provided by upstream suppliers, which meet standard requirements and have been independently verified by a third party.

[0055] Specifically, life cycle background data includes publicly available life cycle assessment data showing average domestic production levels, and technical data on the production of the same type of energy and raw materials overseas.

[0056] If the background database data needs updating, the system detects whether data has been updated in each database. If an update is detected, it replaces the updated data with the original database data to complete the update.

[0057] Specifically, relevant information for background data includes detailed origins of the data, such as provision from upstream suppliers, extraction from National Bureau of Statistics databases, and acquisition from the CLCD database. Regarding the statistical year of the data, the closer the statistical year of the background data is to the calculation year, the more accurate the calculation results will be. Regarding the type of data calculation, this includes on-site measurements, calculations based on formulas, and estimations based on actual results. Regarding the regional representativeness of the data, that is, the data is divided at the product level, company level, industry level, regional level, and country level, specifically referring to a particular product, a particular company, a particular industry, a particular region, or a particular country. If some information is missing, the reason must be clearly stated.

[0058] Specifically, the system constructs a data evaluation index system based on relevant database information included in the background database, updates the priority of carbon emission factor selection in real time in conjunction with the construction of the data evaluation index system and updates to the background database, and determines data quality based on the index system to support the selection of background data suitable for managing the carbon footprint of corporate products.

[0059] Within this framework, the data evaluation indicator system specifically considers indicators such as data source (field data, literature, background databases, etc.), data calculation type (measurement, calculation, average, estimation, etc.), data year (real-time, within the last year, within the last five years), and data collection area (within a company, relevant ministry, country) to conduct a comprehensive evaluation of the quantity and determine the overall quality of data in each background database.

[0060] The carbon emission factor database introduction module M1.4 specifically introduces a carbon emission factor database and information on carbon emission factor data for material flow and energy flow in the "cradle-to-gate" lifecycle process, depending on the type of steel product.

[0061] Here, the "cradle-to-gate" lifecycle process refers to the process from the extraction of resources and energy to production and manufacturing, and before sales and shipment to external parties. Specifically, it includes the extraction and processing of upstream energy and raw materials for steel products, the transportation of upstream raw materials, and the production process of steel products. Here, the carbon emission factor refers to the carbon released into the system or atmosphere through the input of carbon-containing substances and the use of carbon-containing energy in the "cradle-to-gate" lifecycle process, as well as the carbon fixed by carbon-containing products and by-products that contain carbon in their components.

[0062] The direct carbon emission factor database for all external carbon-containing energy, raw materials, products, and by-products input and output in the "cradle-to-gate" lifecycle process of steel products includes the direct carbon emission factor of the fuel combustion process, the carbon emission factor calculated based on the carbon content of carbon-containing solvents, carbon-containing alloys, carbon extenders, etc., contained in the input materials, and the carbon emission offset factor calculated based on the carbon content of carbon-containing products and carbon-containing by-products in the process.

[0063] Specifically, if the composition or carbon content of the fuel used by a company is available, the carbon emission factor of the fuel is calculated using the formula for the direct carbon emission factor of fuel combustion. If the carbon content of carbon-containing materials, carbon-containing products, and by-products used by a company is available, the amount of carbon dioxide produced when a unit material is completely decomposed is calculated using elemental balance, and the carbon emission factor is determined accordingly. For parts where this information is unavailable, carbon emission factors provided by the National Development and Reform Commission (NDRC) guidelines or IPCC guidelines integrated into the module can be used.

[0064] Some information regarding carbon emission factors should be stored in detail in the database management module, specifically including the detailed source of the carbon emission factors, such as calculations by formulas or guidelines from the National Development and Reform Commission. Regarding the statistical year of the data, the closer the statistical year of the carbon emission factors and the calculation year are, the more accurate the calculation results will be. Regarding the calculation type of the data, this should include calculations by formulas, estimations based on actual results, and the adoption of national averages. Regarding the regional representativeness of the data, that is, the data should be divided into product level, enterprise level, industry level, regional level, and national level, specifically referring to specific types of products, enterprises with specific names, specific types of industries, specific regions, or specific countries. If some information is missing, the reason must be clearly stated.

[0065] The carbon emission coefficient database, in particular, integrates multiple different databases. The integration process requires clearly defining the data source, data type, and data collection time for each database. When the carbon emission coefficient database needs updating, it's necessary to check if there are updates in each database. If there are, the original data in the database is replaced with the updated data to complete the update.

[0066] Specifically, the system will construct a data evaluation index system based on information about the data in the carbon emission factor database, evaluate data quality based on this index system, set priorities for selecting carbon emission factors based on the data index system, and update these priorities in real time in accordance with updates to the carbon emission factor database, thereby supporting the selection of appropriate carbon emission factor data for managing the carbon footprint of corporate products.

[0067] Here, the data evaluation index system determines the overall quality of data in each carbon emission coefficient database by comprehensively considering indicators such as data source (field data, literature, background databases, etc.), data type (measured, calculated, averaged, estimated, etc.), data year (real-time, within the last year, within the last five years), and data collection area (within a company, relevant ministry, national).

[0068] The data management module M2 includes a data validation and correction module M2.1, a data selection module M2.2, and a data classification and storage module M2.3.

[0069] The data verification and correction module M2.1 verifies problems present in the input and output data of each unit process. If a problem is found, it feeds the problem data back to the data collection module M1 for recollection and updates the corrected data. It also verifies whether the input and output data of each unit process satisfies the metal balance and carbon balance. It feeds any problems present in the data back to the data collection module M1 for recollection and updates the corrected data, and updates the background database and carbon emission factor database.

[0070] Problems with the input and output data for each unit process include whether each unit process is representative, i.e., whether the manufacturing data statistics are based on the data collection scope of the unit process, and whether the field data is complete, i.e., whether there is a lack of data on material flow and energy flow or important physical quantity information for some of the unit processes. Ensuring the consistency of the field data includes unifying the naming of identical material flow and energy flow types related to all unit processes, and matching the data names of identical material flow and energy flow types in the background database with those in the carbon emission coefficient database.

[0071] To determine whether the input and output data of each unit process satisfy the metal balance and carbon balance, the metal balance refers to the balance of iron flow, that is, whether the iron content in the iron-containing raw materials input to the unit process, the iron-containing products output, by-products, and residues is balanced, or balanced within an acceptable margin of error. The carbon balance refers to whether the carbon content in the input energy, raw materials, and auxiliary raw materials is balanced, or balanced within an acceptable margin of error, in the output CO2, carbon-containing products, by-products, and solid waste.

[0072] The updated data will include updating the background database and carbon emission factor database based on the data collection time requirements for the background database and carbon emission factor database. The update method will involve feeding back data from earlier years to M1 based on the data year information for the background database and carbon emission factor database within the data evaluation index system.

[0073] The data selection module M2.2 identifies and analyzes data for each unit process in the manufacturing process of steel products, based on the data selection principles for input and output data of steel products, and selects the input and output data that meets the requirements. Here, the data selection principles refer to the principle of reducing the degree of complexity of the data for each unit process by the proportion of the input and output of raw materials and energy related to each unit process of steel products to the total amount of this type.

[0074] Selecting input and output data that meet the requirements according to the selection principle involves accurately positioning, identifying, and analyzing raw materials, auxiliary raw materials, energy input data, products, by-products, and energy output of the manufacturing unit processes for steel products. This involves scientifically processing input and output data related to the manufacturing unit processes of steel products based on the selection principle created by the ratio of raw material and energy inputs and outputs, thereby simplifying complex material compositions and complex output unit processes using the selection principle specified in the normative standard.

[0075] The data classification and storage module M2.3 matches the collected material flow and energy flow data for each unit process with the introduced background database and carbon emission factor database, based on information from different steel grades, to form a complete dataset of that type of steel product, classify it, and store it in this module.

[0076] Information on different steel grades includes key physical quantities of material and energy flows at the input and output of unit processes in the manufacturing process of a certain type of steel product, the flow paths of material and energy flows between unit processes in the manufacturing process of a certain type of steel product, the material and energy flows with external exchange relationships input at the manufacturing unit processes of the same type of steel product, a life cycle background database of by-products and energy products sold to external companies, the relevant carbon emission factors of all inputs and outputs in the "cradle-to-gate" life cycle process of the same type of steel product, and the results of an overall data quality assessment of each data to form a life cycle carbon footprint dataset of the same type of steel product.

[0077] Based on the lifecycle carbon footprint dataset of the company's steel products, these datasets are classified by type, such as steel product type, data collection time granularity, data collection scope, and data quality, and stored in module M2.3.

[0078] The model building module M3 includes the carbon footprint calculation model module M3.1, the carbon footprint prediction model module M3.2, and the carbon footprint optimization model module M3.3.

[0079] The carbon footprint calculation model module M3.1 determines the calculation scope and allocation principles based on the requirements for calculating the carbon footprint of steel products, selects appropriate functional units based on the properties, quantity, and application of steel products, and constructs a carbon footprint calculation model for steel products based on the relationships between intermediate products between each unit process in the steel product manufacturing process and the conversion relationships between material flow and energy flow within each unit process.

[0080] The requirement for calculating the carbon footprint of steel products includes clarifying, before calculating, the purpose of using the carbon footprint results for steel products, the reasons for calculating and managing them, how the results will be used, and who the results will be shared with. This will determine the classification method and degree of subdivision of steel products for which the carbon footprint will be calculated, what type of carbon footprint analysis method will be adopted, and whether specific analysis and evaluation of the carbon footprint results are necessary.

[0081] Regarding the possibility of selecting the actual calculation scope, when a certain type of steel product is selected for carbon footprint calculation based on the requirements for calculating the carbon footprint of steel products, the selectable range of the actual carbon footprint calculation boundary is the "gate-to-gate" life cycle process and the "cradle-to-gate" life cycle process. The "gate-to-gate" life cycle process includes in-house energy production (including coke processes, power systems, oxygen systems, water systems, gas systems, and steam systems) and the manufacturing stages of steel products (including sintering processes, pelletizing processes, ironmaking processes, steelmaking processes, hot rolling processes, and cold rolling processes). The "cradle-to-gate" life cycle process includes the extraction, production and transportation stages of raw materials and energy, and the manufacturing stages of steel products. Specifically, it includes the extraction and production of raw materials, the extraction and production of auxiliary materials, the extraction and production of energy, the recovery and processing of scrap metal, transportation (transportation of raw materials, energy, auxiliary raw materials, and scrap metal), in-house energy production, and in-house steel product manufacturing.

[0082] A functional unit refers to the total carbon footprint calculation result, expressed as a cumulative amount, for the lifecycle process of manufacturing a "specified quantity of a certain type of steel product."

[0083] Selecting an appropriate functional unit based on the properties, quantity, and application of steel products specifically involves selecting comparable functional units to compare different types of steel products obtained by further subdividing a broad category of steel products. For example, depending on the properties, quantity, and application of the steel products, a 1 kg mass of steel product is typically selected as a functional unit, and on this basis, the carbon footprint results of different steel products are compared.

[0084] The reason for establishing allocation principles is that, specifically, when two or more products are manufactured simultaneously in a single process, and raw material and energy inputs are not collected separately, it is possible that multiple raw materials may be input in a single process, resulting in only one output, or that multiple raw materials may be input in a single process, resulting in the production of multiple products. In these cases, since the necessary input and output data for the unit process cannot be obtained directly, it is necessary to recombine and allocate the data from these unit processes to form a new unit process.

[0085] As a distribution principle, data must be distributed to a unit process if it contains multiple products. Here, a unit process containing multiple products includes similar-function unit processes and multi-function unit processes. A similar-function unit process refers to a unit process that outputs multiple products that are similar in nature and function, while a multi-function unit process refers to a unit process that outputs multiple products that are significantly different in nature and function.

[0086] Based on similar functional unit processes, the allocation principle first identifies the existence of similar functional unit processes and other unit processes related to these unit processes, divides the unit processes, allocates the material flow and energy flow of the unit processes to different products based on the proportion of mass, energy, and volume of the unit process products, and for the divided unit processes, verifies whether the sum of the inputs and outputs of the unit processes before allocation and after allocation are equal.

[0087] Based on multifunctional unit processes, the allocation principle includes including certain unit processes that produce by-products within the system boundary, according to the actual use of the by-products.

[0088] Including certain unit processes that produce by-products in accordance with their actual uses within the system boundary means that, specifically, if the by-products are sold to an external company and used as a substitute for a certain product, the total carbon footprint of the manufacturing process throughout its lifecycle is reduced based on the actual use of that substitute product. Therefore, including certain unit processes that produce by-products within the system boundary is equivalent to offsetting the carbon footprint of that portion of the product production. For example, if blast furnace slag is sold externally and used as cement clinker, the carbon footprint benefit obtained from recycling by the external company is the amount of carbon footprint generated in the production process of the substitute cement clinker.

[0089] Based on each unit process, the conversion relationship between material flow and energy flow within each unit process, and the flow relationship of intermediate products between different unit processes, a "gate-to-gate" manufacturing process model for steel products is constructed.

[0090] Based on the manufacturing process model of steel products, a "cradle-to-gate" lifecycle process model for steel products is constructed using a lifecycle background database of material and energy flows related to the manufacturing process, which is included in the background database.

[0091] Based on the manufacturing process model for steel products and the "cradle-to-gate" lifecycle process model for steel products, a carbon footprint calculation model for steel products is constructed by converting the material and energy flows in the process into carbon flows, based on the carbon emission factors of the material and energy flows related to the manufacturing process.

[0092] The carbon footprint prediction model M3.2 is based on a carbon footprint calculation model for steel products and constructs a carbon footprint prediction model for steel products based on the historical manufacturing routes of a specific type of steel product.

[0093] Based on the M3.1 carbon footprint calculation model module, the historical manufacturing route of a given steel product is obtained from the carbon footprint calculation results, and appropriate route information is analyzed and selected, which is based on input material flow, energy flow data, and route information flowing through unit processes.

[0094] Based on the conversion relationship and efficiency of mass flow and energy flow in unit processes and path information, and the correspondence between the intermediate flow of each unit process and its upstream and downstream, if the input energy, raw material type, input quantity, input unit process and manufacturing path between unit processes are known, the output of mass flow and energy flow of a unit process and the amount and correspondence of intermediate flow between each unit process in the entire manufacturing process can be obtained.

[0095] Based on the output of material flow and energy flow in each unit process, and the amount and correspondence of intermediate flows between each unit process in the entire manufacturing process, the conversion relationship and conversion efficiency of material flow and energy flow in each unit process are obtained.

[0096] Based on the conversion relationship and efficiency of material flow and energy flow in each unit process, and the correspondence between the intermediate flow in each unit process and its upstream and downstream areas, an appropriate route and route information are analyzed and selected using the temporally close past manufacturing routes of the steel type to be predicted and the basic information of each unit process to create a carbon footprint prediction model for information and data training.

[0097] The carbon footprint optimization model M3.3 builds a carbon footprint optimization model for steel products by setting target functions and constraints based on the lifecycle carbon footprint calculation model for steel products.

[0098] Based on a carbon footprint calculation model for each unit process, the conversion relationship and conversion efficiency between material flow and energy flow in each unit process, as well as the amount and correspondence of intermediate flows between each unit process, are used to introduce a target function and constraints for the carbon footprint results.

[0099] In constructing a carbon footprint optimization model, the minimum carbon footprint target function of a product depends on the design variables, and each design variable can be expressed as a function correlated with the other design variables; this is called the target function.

[0100] In constructing a carbon footprint optimization model, the selection of each design variable is not arbitrary. For example, even in the selection of a unit process, there are various constraints, such as the quantity of the unit process and the distribution of intermediate flows between unit processes. There is a certain selection interval and range, which are called constraints.

[0101] Based on a carbon footprint calculation model, historical manufacturing route information, manufacturing data, and carbon footprint result data for a certain type of steel product to be optimized are obtained, and a carbon footprint prediction model is constructed based on a target function and constraints.

[0102] The carbon footprint analysis module M4 includes the carbon footprint calculation analysis module M4.1, the carbon footprint prediction analysis module M4.2, and the carbon footprint optimization analysis module M4.3.

[0103] The carbon footprint calculation and analysis module M4.1 calculates the carbon footprint result data and uncertainty intervals for steel products based on the lifecycle carbon footprint calculation model for steel products, according to the correlation data and correlation data quality of steel products. Based on the carbon footprint result data for steel products, it analyzes the differences in carbon footprints of different products and the differences in the contribution of each factor to the carbon footprint result. Based on the lifecycle carbon footprint calculation model for steel products, it analyzes the corresponding change in the carbon footprint when a certain factor changes within the range of actual manufacturing values, thereby obtaining the sensitivity of the carbon footprint to that factor, and classifies and stores the calculation and analysis results in this module.

[0104] The reason for calculating the uncertainty interval for the carbon footprint results of steel products is that there are problems with data inaccuracies in lifecycle carbon footprint management. Here, the cause of data inaccuracies is that some data in the background database originates from the lifecycle processes of similar types of products, and the data collection year, data collection region, and data coverage cannot fully represent the actual data in the industrial chain. Furthermore, the carbon emissions of a company's unit process cannot be measured directly, and these carbon emissions are calculated by combining carbon emission factors from the carbon emission factor database with the input and output of the unit process using a formula, so there is a certain degree of uncertainty in the calculation process using the carbon emission formula itself. In addition, the types of substances and energies in the carbon emission factor database of each source cannot cover the entire lifecycle process of steel manufacturing, and when selecting, there are differences in the data collection year, data collection region, and data coverage of carbon emission factors from different sources, so it is necessary to analyze the uncertainty of the carbon footprint results of steel products.

[0105] The uncertainty interval of the result is determined by obtaining a fixed range of values ​​for the data in the model based on the overall evaluation of each data in the data evaluation index system, inputting the data in the form of a fixed range of values ​​into the carbon footprint calculation model for steel products, and converting the uncertainty of each input and output data into the uncertainty of the final carbon footprint value of the steel product.

[0106] Based on carbon footprint calculation models, this system compares and analyzes carbon footprint data for steel products within a company, between subdivided steel grades belonging to the same major steel product category, and within different calculation time ranges for the same steel grade. It also compares and analyzes carbon footprint data for steel products within different calculation boundary ranges for the same steel grade based on carbon footprint calculation models with different calculation boundaries, helping companies understand their actual carbon footprint situation.

[0107] Based on carbon footprint data for steel products, the contribution of steel product carbon footprint data to the process is classified and calculated from three aspects: direct emissions in the manufacturing process, indirect emissions from the consumption of external electricity and thermal energy, and emissions in the supply chain. The composition ratio and potential contribution impact of carbon footprint data at each stage of steel product production are then shown.

[0108] Based on carbon footprint data for steel products, the contribution of steel product carbon footprint data to the manufacturing process is classified and calculated from three aspects: each manufacturing process, the equipment used in each manufacturing process, and the inputs and outputs of each piece of equipment. The composition ratio and potential contribution impact of carbon footprint data at each stage of the steel product process are then shown.

[0109] Sensitivity to certain factors in the carbon footprint is determined by evaluating the differences in the impact of changes in various factors throughout the lifecycle of industrial products on the carbon footprint, based on the carbon footprint calculation results of steel products, taking into account each factor and each combination of influencing factors that contribute to the carbon footprint. This is done using a sensitivity analysis evaluation method, comparing the sensitivity of steel products to changes in different influencing factors, ranking the sensitivities, selecting the influencing factors with high sensitivity, and analyzing their causes.

[0110] Combined influencing factors involve combining some influencing factors from different aspects and dimensions of carbon footprints, based on carbon footprint data for steel products, through their transformation, inclusion, and influence relationships, to form new types of influencing factors. These new types of influencing factors can reflect certain proportional relationships, technical parameters, and recycling methods in the manufacturing process of steel products, and the carbon footprint of steel products also has a certain sensitivity to changes in it.

[0111] The sensitivity analysis evaluation method involves inputting the range of change for each assumed factor and each combination of influencing factors into the model to obtain the range of the product's carbon footprint result. When the influencing factors change within the same range, the sensitivity result is shown as the amount or percentage change in the carbon footprint result. Here, the assumed data change range is the range in which the data actually input into the model changes within a certain percentage range according to the actual production conditions based on that data.

[0112] Classifying and storing the results of calculation and analysis includes storing carbon footprint results, factor analysis results, sensitivity analysis results, and uncertainty analysis results in modules according to data acquisition time granularity, system boundary range, steel product type, etc.

[0113] The carbon footprint prediction analysis module M4.2 predicts the carbon footprint of steel products after their manufacture, based on a carbon footprint prediction model for steel products, either at the time of energy and raw material input or during the product manufacturing process.

[0114] Predicting the carbon footprint of steel products after their manufacture, either at the energy and raw material input stage or during the manufacturing process, is important because the manufacturing process can change due to updates in product orders, meaning the manufacturing path can change at any stage of the process. Therefore, when using a known path or when the manufacturing path changes, carbon footprint-related information in the process flow for manufacturing that type of steel product can be provided as a reference for the possible carbon footprint results when a company manufactures that type of steel product. Furthermore, it can provide decision targets, reference criteria, and alternatives for steel product manufacturing decisions, ensuring that carbon footprint predictions for steel products can be achieved for either the original or adjusted manufacturing path, and furthermore, the predictions provide a basis for achieving rational allocation and effective use of resources.

[0115] Assuming the input energy, raw material type, input quantity, and input unit process are known, and the final steel product has not yet been manufactured through the manufacturing process, input data for energy and raw materials, background data, and carbon emission coefficient data for a certain type of steel product to be predicted are introduced. Based on a carbon footprint prediction model, prediction results are obtained for the input and output of material flow and energy flow at each unit process of the steel product, as well as the intermediate flow and corresponding relationships between each unit process in the entire manufacturing process.

[0116] When the path at a certain stage in the steel manufacturing process changes, the system adjusts the unit process at that stage, as well as each downstream unit process related to that unit process, in a timely manner, based on a carbon footprint prediction model for steel products. This enables real-time updated carbon footprint predictions when the path in the manufacturing process changes.

[0117] The carbon footprint prediction results are obtained by combining the input and output data of each unit process with the corresponding relationships selected from the carbon footprint prediction model. The accuracy of the prediction results is then analyzed by comparing them with the carbon footprint prediction results calculated based on data collected at each unit process after the manufacture of similar steel products. Based on the relevant data for the steel product and the quality of that data, the carbon footprint prediction results and uncertainty intervals for the results are calculated.

[0118] The carbon footprint prediction results and uncertainty intervals are compared and analyzed with the carbon footprint calculation results and uncertainty intervals calculated based on data from each unit process after manufacturing of the same type of steel product manufactured using the same manufacturing route. The accuracy of the prediction results is analyzed by comparing the results, and the accuracy of the prediction results within the uncertainty interval is analyzed by comparing the results within the uncertainty interval range.

[0119] The carbon footprint optimization analysis module M4.3 optimizes the carbon footprint of a steel product by obtaining the results of adjusting the manufacturing route of that type of steel product based on a carbon footprint optimization model for steel products, and comparing it with the carbon footprint of a similar product manufactured using the original manufacturing route.

[0120] The purpose of the carbon footprint optimization analysis for steel products is as follows: The manufacturing process for steel products involves many pieces of equipment with the same function, and the carbon footprint of steel products produced by different combinations of this equipment varies. The appropriateness of the manufacturing route selection significantly impacts energy efficiency, manufacturing speed, and the carbon footprint of the product. While selecting a highly efficient and rational route can achieve energy savings and carbon reduction in the steel product manufacturing process, the manufacturing process involves many unit processes and complex manufacturing routes, making it difficult to analyze each route individually. Therefore, by constructing a carbon footprint optimization model for steel products, it is possible to directly solve the problem and explore the optimal manufacturing route.

[0121] The objectives for obtaining the results of adjusting the manufacturing route of this type of steel product and optimizing the carbon footprint of the product are as follows: In the steel product manufacturing process, the scheduling of the product manufacturing process is determined by updating product orders, meaning that the route in the product manufacturing process can be adjusted at any time at each stage of the manufacturing process. When the type of product order is relatively simple and manufacturing can be adjusted within a certain range between equipment with the same function, optimizing the carbon footprint of steel product manufacturing at the time of energy and raw material input or during the manufacturing process serves as a reference for companies to select an appropriate manufacturing route, based on information regarding the production scheduling of this type of steel product when the type and quantity of energy and raw material input are known. Furthermore, it provides decision targets, reference criteria, and alternatives for steel product manufacturing decisions, which are advantageous in selecting an appropriate manufacturing route during manufacturing and ensure the achievement of carbon footprint optimization targets. In addition, the route adjustment results and optimization results serve as a basis for achieving rational resource allocation and reduction of the carbon footprint of steel products.

[0122] Based on relevant data and data quality for steel products, background data and carbon emission factor data are introduced to calculate the carbon footprint optimization results and uncertainty intervals for steel products.

[0123] When optimizing the path at a certain stage of the steel manufacturing process, a carbon footprint optimization model is used to optimize the path of each unit process that can be optimized and adjusted, as well as the path of each downstream unit process linked to that unit process. This enables real-time carbon footprint optimization when the path changes in the manufacturing process. Furthermore, by comparing the original manufacturing path with the adjusted manufacturing path, the carbon reduction amount is obtained from the changes in the manufacturing path and carbon footprint results of the steel product, and the possibility of optimizing the carbon footprint of the steel product is analyzed.

[0124] Once the product manufacturing process is complete, the carbon footprint results after optimization of the product route are compared with the actual results based on the carbon footprint optimization model to analyze whether the product's manufacturing route is a low-carbon manufacturing route. If there is room for optimization, the potential for further optimization of the manufacturing route is analyzed.

[0125] The results data and uncertainty intervals of the carbon footprint optimization will be compared and analyzed with the carbon footprint calculation results and uncertainty intervals of similar steel products manufactured using the original manufacturing process. By comparing the results with the results within the uncertainty interval, the carbon footprint reduction effect based on the results and the carbon footprint reduction effect based on the optimization results within the uncertainty interval will be analyzed.

[0126] In embodiments of the present invention, a carbon footprint management system and multiple carbon footprint management methods, including calculation, analysis, prediction, and optimization of carbon footprints, enable accurate evaluation of the impact of changes in the manufacturing process on the lifecycle carbon footprint of steel products at different stages: before, during, and after manufacturing. This compensates for the shortcomings of conventional management systems, which only analyze carbon footprint results.

[0127] It should be understood that the technical content disclosed in the embodiments of the present invention can be realized in other forms. The embodiments of the apparatus described above are merely illustrative, and for example, the division of the units described above may be based on logical functions, and in practice, other division methods may be adopted, for example, multiple units or modules may be combined, or integrated into another system, or some features may be omitted or not implemented. Furthermore, the coupling, direct coupling, or communication connection between the units shown or described above may be indirect coupling or communication connection via interfaces, units, or modules, and may be in electrical or other forms.

[0128] The units described as separation means above may or may not be physically separated. The components shown as units may or may not be physical units. That is, they may be placed in one location or distributed among multiple units. If necessary, some or all of the units can be selected to achieve the objective of this embodiment.

[0129] Furthermore, the functional units in each embodiment of the present invention may be integrated into a single processing unit, each unit may exist separately physically, or two or more units may be integrated into a single unit. The integrated units described above may be implemented as hardware or as software functional units.

[0130] The above-mentioned integrated unit may be implemented in the form of a software function unit and, if sold or used as an independent product, may be stored in a computer-readable storage medium. Based on this understanding, the substance of the technical aspects of the present invention, or the part that contributes to the prior art, or all or part of the technical aspects thereof, may be embodied in the form of a software product, which includes a plurality of instructions and is stored in a storage medium, causing a computer (such as a personal computer, server, or network equipment) to execute all or part of the steps of the methods described in each embodiment of the present invention. The storage mediums mentioned above include various media capable of storing program code, such as USB memory, ROM (read-only memory), RAM (random access memory), removable hard disk, magnetic disk, or optical disk.

[0131] Finally, the following should be explained: The above embodiments are merely for illustrating, and not limiting, the technical means of the present invention. Although the present invention has been described in detail with reference to the embodiments described above, it is possible to modify the technical means described in the embodiments described above, or to make equivalent substitutions to some or all of their technical features, and it will be understood by those skilled in the art that such modifications or substitutions do not cause the essence of the corresponding technical means to deviate from the scope of the technical means of the embodiments of the present invention.

[0132] (Note) (Note 1) We collect information on the company's steel products and the material flow, energy flow, and flow paths of each unit process in the manufacturing process of steel products. We introduce a background database of the lifecycle of material and energy flows that are exchanged with external parties in the manufacturing process, and we introduce a database of carbon emission factors for material and energy flows in the lifecycle process. The collected material flow and energy flow data for each unit process in the manufacturing process are verified using metal balance and carbon balance methods, the background database and carbon emission factor database are updated, and data corresponding to different manufacturing routes, different steel types, and different material and energy flows are classified and stored. Determine the carbon footprint calculation function unit, manufacturing route, and calculation system boundaries for different types of steel products, select data allocation principles, and construct a lifecycle carbon footprint model. A method for managing the carbon footprint of steel products based on a life cycle assessment, characterized by including managing the carbon footprint of the steel products based on the life cycle carbon footprint model.

[0133] (Note 2) The lifecycle carbon footprint model includes a lifecycle carbon footprint calculation model, a lifecycle carbon footprint prediction model, and / or a lifecycle carbon footprint optimization model. The construction of the aforementioned lifecycle carbon footprint model is Based on the request to calculate the carbon footprint of steel products, the calculation scope and allocation principles are determined, appropriate functional units are selected according to the properties, quantity, and application of the steel products, and a life cycle carbon footprint calculation model for steel products is constructed based on the relationships between intermediate products in each unit process of the steel product manufacturing process and the conversion relationships between material flow and energy flow within each unit process. Based on the life cycle carbon footprint calculation model, the conversion relationship and efficiency of material flow and energy flow in the past manufacturing routes and unit processes of the steel type to be predicted are obtained, appropriate route information and unit process information are selected and analyzed to form a life cycle carbon footprint prediction model for information and data training, and / or A method for managing the carbon footprint of a steel product based on a life cycle assessment as described in Appendix 1, characterized by obtaining the conversion relationship and efficiency of material flow and energy flow in a unit process, the quantity and correspondence relationship of intermediate flow between each unit process, and constructing a target function and constraints based on the correspondence relationship, conversion relationship and efficiency, and minimizing the carbon footprint of the product on the premise that the manufacturing process conditions are not changed.

[0134] (Note 3) The construction of a life cycle carbon footprint calculation model for the aforementioned steel products is Based on the conversion relationships between material and energy flows within each unit process, and the flow relationships between different unit processes of intermediate products, a steel product manufacturing process model was constructed. Based on the aforementioned steel product manufacturing process model, and using the material and energy flows related to the manufacturing process included in the aforementioned background database, a steel product lifecycle process model is constructed. Based on the aforementioned steel product manufacturing process model and steel product lifecycle process model, the process is converted into a carbon flow using the carbon emission factors of material and energy flows related to the manufacturing process, and a model for calculating the lifecycle carbon footprint of steel products is constructed. The construction of the aforementioned lifecycle carbon footprint prediction model is Based on the lifecycle carbon footprint calculation model for the aforementioned steel products, all past manufacturing routes included in the carbon footprint calculation results for the steel product of the steel type to be predicted are obtained, and appropriate route information is selected and analyzed. Based on the conversion relationship and efficiency of material flow and energy flow included in the unit process and path information, and the intermediate flow of each unit process and its upstream and downstream correspondence, if the input energy, type of raw material, input quantity, input unit process, and manufacturing path between unit processes are known, the output of material flow and energy flow of the unit process and the quantity and correspondence of intermediate flow between each unit process in the entire manufacturing process can be obtained. Based on the output of material flow and energy flow in each unit process, and the quantity and correspondence of intermediate flows between each unit process in the entire manufacturing process, the conversion relationship and conversion efficiency of material flow and energy flow in each unit process are obtained. This includes analyzing and selecting appropriate routes and route information based on the conversion relationship and efficiency of material flow and energy flow in each unit process, and the correspondence between intermediate flows and their upstream and downstream processes in each unit process, and following the temporally close past manufacturing routes of the steel type to be predicted, while utilizing the basic information of each unit process, to create a lifecycle carbon footprint prediction model for information and data training. The construction of the aforementioned lifecycle carbon footprint optimization model is Based on the lifecycle carbon footprint calculation model, target functions and constraints are set according to the conversion relationship and conversion efficiency of material flow and energy flow in each unit process, and the quantity and correspondence of intermediate flows between each unit process. By optimizing the manufacturing route without changing the manufacturing process conditions, the carbon footprint of the product can be minimized. A method for managing the carbon footprint of a steel product based on a life cycle assessment, as described in Appendix 2, characterized by comprising obtaining past manufacturing route information, manufacturing data, and carbon footprint result data for a steel product to be optimized based on the life cycle carbon footprint calculation model, and forming a life cycle carbon footprint optimization model for the steel product by combining these with the target function and constraints.

[0135] (Note 4) Based on the lifecycle carbon footprint model, managing the carbon footprint of the steel products is: Based on the steel product lifecycle carbon footprint calculation model, material flow and energy flow data, background data, and carbon emission coefficient data for the corresponding unit processes are introduced. Based on the relevant data for steel products and the quality of the relevant data, the carbon footprint result data and uncertainty intervals for the results of steel products are calculated. Based on the carbon footprint result data for steel products, the differences in carbon footprints of different products and the differences in the contribution of each factor to the carbon footprint result are analyzed. Based on the steel product lifecycle carbon footprint calculation model, the corresponding changes in the carbon footprint when a certain factor changes within the range of actual manufacturing values ​​are analyzed, thereby obtaining the sensitivity of the carbon footprint to that factor, and classifying and storing the calculation and analysis results. Based on the same carbon footprint calculation model, the carbon footprint data for the aforementioned steel products is used to compare and analyze carbon footprint results between different steel products within a company, between subdivided steel grades belonging to the same major steel product category, and within different calculation time ranges for the same steel grade. Based on carbon footprint calculation models with different calculation boundaries, the carbon footprint data for the steel products mentioned above allows for comparative analysis of carbon footprint results within different calculation boundary ranges for the same steel type, helping companies understand their actual carbon footprint situation. Based on the carbon footprint data of the aforementioned steel products, the contribution of the steel product carbon footprint data to the process is classified and calculated from three aspects: direct emissions in the manufacturing process, indirect emissions from the consumption of electricity and thermal energy from external sources, and emissions in the supply chain. The composition ratio of carbon footprint data at each stage of the steel product and the potential contribution impact are shown. A method for managing the carbon footprint of steel products based on a life cycle assessment as described in Appendix 2 or 3, characterized by including, based on the carbon footprint result data of the steel products, classifying and calculating the contribution of the steel product carbon footprint data to the process from three aspects: each manufacturing process, the equipment used in each manufacturing process, and the input / output of each piece of equipment, and showing the composition ratio and potential contribution impact of the carbon footprint data at each stage of the steel product.

[0136] (Note 5) Managing the carbon footprint of the steel products based on the life cycle carbon footprint model is: A method for managing the carbon footprint of steel products based on a life cycle assessment as described in Appendix 2 or 3, characterized in that, given that the material flow and energy flow input in the manufacturing route and unit processes of steel products are known, the life cycle carbon footprint prediction model predicts the input location and amount of energy and raw materials or the carbon footprint of the steel product after manufacturing in the product manufacturing process.

[0137] (Note 6) Predicting the carbon footprint after manufacturing steel products is Based on the life cycle carbon footprint prediction model for steel products, the life cycle carbon footprint calculation model for steel products is used at the time of energy and raw material input or during the product manufacturing process to predict the carbon footprint of steel products after manufacturing. Given that the input energy, raw material type, input quantity, and input unit process are known, and the final steel product has not yet been manufactured through the manufacturing process, the life cycle carbon footprint prediction model can be used to obtain prediction results for the input and output of material and energy flows in each unit process of the steel product, as well as the intermediate flows between each unit process of the entire manufacturing process and their corresponding relationships. When the path at a certain stage in the steel manufacturing process changes, the system adjusts the unit process containing that stage and each downstream unit process related to that unit process in a timely manner, based on the lifecycle carbon footprint prediction model of the steel product, thereby realizing a real-time updated carbon footprint prediction when the path in the manufacturing process changes. By combining the input and output data of each unit process with the corresponding relationships selected from the lifecycle carbon footprint prediction model, a carbon footprint prediction result is obtained. The accuracy of the prediction result is analyzed by comparing it with the carbon footprint prediction result calculated based on data collected at each unit process after the manufacture of similar steel products. Based on the relevant data of the steel product and the quality of the relevant data, the carbon footprint prediction result data and the uncertainty interval of the result for the steel product are calculated. A method for managing the carbon footprint of steel products based on a life cycle assessment as described in Appendix 5, characterized by including comparing and analyzing the calculated carbon footprint results and uncertainty intervals of the results calculated based on data from each unit process after manufacturing of the same type of steel product manufactured by the same manufacturing route, and analyzing the accuracy of the predicted results within the uncertainty interval by comparing the results within the uncertainty interval.

[0138] (Note 7) Managing the carbon footprint of the steel products based on the life cycle carbon footprint model is: A method for managing the carbon footprint of steel products based on a life cycle assessment, as described in Appendix 2 or 3, characterized in that, when the steel products are not yet manufactured or are in the manufacturing process, the carbon footprint of the products is minimized by adjusting the paths of similar steel products based on the life cycle carbon footprint optimization model.

[0139] (Note 8) Adjusting the route for the aforementioned similar steel products is Based on the optimized manufacturing path in the lifecycle carbon footprint optimization model, the carbon footprint optimization results are obtained from the input and output data of each unit process. Based on the relevant data and quality of the steel products, the carbon footprint optimization result data and the uncertainty interval of the results for the steel products are calculated. When it becomes possible to optimize and adjust the path at a certain stage in the steel manufacturing process, based on the lifecycle carbon footprint optimization model, path optimization is achieved at the unit process within the stage where optimization and adjustment are possible, and at each downstream unit process related to that unit process. Real-time optimization of the carbon footprint when the path in the manufacturing process changes is achieved, and the amount of carbon footprint reduction is obtained by comparing the original manufacturing path with the adjusted manufacturing path and the carbon footprint results of the steel product, and the possibility of optimizing the carbon footprint of the steel product is analyzed. Once the product manufacturing process is complete, the carbon footprint results after optimization of the product path are compared with the actual results based on the lifecycle carbon footprint optimization model to analyze whether the product's manufacturing path is a low-carbon manufacturing path. If there is room for optimization, the possibilities for optimizing the manufacturing path are analyzed. A method for managing the carbon footprint of steel products based on a life cycle assessment as described in Appendix 7, characterized by including comparing and analyzing the carbon footprint calculation results and uncertainty intervals of similar steel products manufactured by the original manufacturing route, based on the result data and uncertainty intervals of the carbon footprint optimization, and analyzing the carbon footprint reduction effect of the optimization results within the uncertainty interval range based on the comparison of the results.

[0140] (Note 9) A data collection module that collects information on a company's steel products and material flow, energy flow, and flow paths in each process of the steel product manufacturing process, introduces a background database of material and energy flow lifecycles that are exchanged with external parties in the manufacturing process, and introduces a database of carbon emission factors for material and energy flow in the lifecycle process. A data management module that verifies the material flow and energy flow data for each unit process in the manufacturing process collected using metal balance and carbon balance methods, updates the background database and the carbon emission factor database, and classifies and stores data corresponding to different manufacturing routes, different steel types, and different material and energy flows, A carbon footprint calculation function unit for different types of steel products, a model building module that determines the manufacturing route and calculation system boundaries, selects data allocation principles, and constructs a lifecycle carbon footprint model, A carbon footprint management system for steel products based on a life cycle assessment, characterized by including a carbon footprint analysis module that manages the carbon footprint of the steel product based on a life cycle carbon footprint model constructed by the model construction module.

[0141] (Note 10) The carbon footprint analysis module described above is Based on the lifecycle carbon footprint calculation model described in Appendix 2, a carbon footprint calculation and analysis module is provided to perform a comparative analysis of the carbon footprint of a product, Based on the lifecycle carbon footprint prediction model described in Appendix 2, a carbon footprint prediction analysis module predicts the carbon footprint of steel products manufactured at the time of energy and raw material input or during the manufacturing process, and analyzes the prediction results. A carbon footprint management system for steel products based on the life cycle assessment described in Appendix 9, characterized by including a carbon footprint optimization analysis module that optimizes the carbon footprint results of steel products by adjusting the specific manufacturing routes of steel products based on the life cycle carbon footprint optimization model described in Appendix 2, and analyzes the optimization results.

Claims

1. We collect information on the company's steel products and the material flow, energy flow, and flow paths of each unit process in the manufacturing process of steel products. We introduce a background database of the lifecycle of material and energy flows that are exchanged with external parties in the manufacturing process, and we introduce a database of carbon emission factors for material and energy flows in the lifecycle process. The collected material flow and energy flow data for each unit process in the manufacturing process are verified using metal balance and carbon balance methods, the background database and carbon emission factor database are updated, and data corresponding to different manufacturing routes, different steel types, and different material and energy flows are classified and stored. Determine the carbon footprint calculation function unit, manufacturing route, and calculation system boundaries for different types of steel products, select data allocation principles, and construct a lifecycle carbon footprint model. A method for managing the carbon footprint of steel products based on a life cycle assessment, characterized by including managing the carbon footprint of the steel products based on the life cycle carbon footprint model.

2. The lifecycle carbon footprint model includes a lifecycle carbon footprint calculation model, a lifecycle carbon footprint prediction model, and / or a lifecycle carbon footprint optimization model. The construction of the aforementioned lifecycle carbon footprint model is Based on the request to calculate the carbon footprint of steel products, the calculation scope and allocation principles are determined, appropriate functional units are selected according to the properties, quantity, and application of the steel products, and a life cycle carbon footprint calculation model for steel products is constructed based on the relationships between intermediate products in each unit process of the steel product manufacturing process and the conversion relationships between material flow and energy flow within each unit process. Based on the life cycle carbon footprint calculation model, the conversion relationship and efficiency of material flow and energy flow in the past manufacturing routes and unit processes of the steel type to be predicted are obtained, appropriate route information and unit process information are selected and analyzed to form a life cycle carbon footprint prediction model for information and data training, and / or A method for managing the carbon footprint of a steel product based on a life cycle assessment, characterized in that, based on the life cycle carbon footprint calculation model for the steel product, the conversion relationship and efficiency of material flow and energy flow in a unit process, the quantity and correspondence relationship of intermediate flow between each unit process, and based on the correspondence relationship, conversion relationship and efficiency, a target function and constraints are constructed, and the carbon footprint of the product is minimized on the premise that the manufacturing process conditions are not changed.

3. The construction of a life cycle carbon footprint calculation model for the aforementioned steel products is Based on the conversion relationships between material and energy flows within each unit process, and the flow relationships between different unit processes of intermediate products, a steel product manufacturing process model was constructed. Based on the aforementioned steel product manufacturing process model, and using the material and energy flows related to the manufacturing process included in the aforementioned background database, a steel product lifecycle process model is constructed. Based on the aforementioned steel product manufacturing process model and steel product lifecycle process model, the process is converted into a carbon flow using the carbon emission factors of material and energy flows related to the manufacturing process, and a model for calculating the lifecycle carbon footprint of steel products is constructed. The construction of the aforementioned lifecycle carbon footprint prediction model is Based on the lifecycle carbon footprint calculation model for the aforementioned steel products, all past manufacturing routes included in the carbon footprint calculation results for the steel product of the steel type to be predicted are obtained, and appropriate route information is selected and analyzed. Based on the conversion relationship and efficiency of material flow and energy flow included in the unit process and path information, and the intermediate flow of each unit process and its upstream and downstream correspondence, if the input energy, type of raw material, input quantity, input unit process, and manufacturing path between unit processes are known, the output of material flow and energy flow of the unit process and the quantity and correspondence of intermediate flow between each unit process in the entire manufacturing process can be obtained. Based on the output of material flow and energy flow in each unit process, and the quantity and correspondence of intermediate flows between each unit process in the entire manufacturing process, the conversion relationship and conversion efficiency of material flow and energy flow in each unit process are obtained. This includes analyzing and selecting appropriate routes and route information based on the conversion relationship and efficiency of material flow and energy flow in each unit process, and the correspondence between intermediate flows and their upstream and downstream processes in each unit process, and following the temporally close past manufacturing routes of the steel type to be predicted, while utilizing the basic information of each unit process, to create a lifecycle carbon footprint prediction model for information and data training. The construction of the aforementioned lifecycle carbon footprint optimization model is Based on the lifecycle carbon footprint calculation model, target functions and constraints are set according to the conversion relationship and conversion efficiency of material flow and energy flow in each unit process, and the quantity and correspondence of intermediate flows between each unit process. By optimizing the manufacturing route without changing the manufacturing process conditions, the carbon footprint of the product can be minimized. A method for managing the carbon footprint of a steel product based on a life cycle assessment, as described in claim 2, characterized in that it includes obtaining past manufacturing route information, manufacturing data, and carbon footprint result data for a steel product to be optimized based on the life cycle carbon footprint calculation model, and forming a life cycle carbon footprint optimization model for the steel product by combining these with the target function and constraints.

4. Based on the lifecycle carbon footprint model, managing the carbon footprint of the steel products is: Based on the steel product lifecycle carbon footprint calculation model, material flow and energy flow data, background data, and carbon emission coefficient data for the corresponding unit processes are introduced. Based on the relevant data for steel products and the quality of the relevant data, the carbon footprint result data and uncertainty intervals for the results of steel products are calculated. Based on the carbon footprint result data for steel products, the differences in carbon footprints of different products and the differences in the contribution of each factor to the carbon footprint result are analyzed. Based on the steel product lifecycle carbon footprint calculation model, the corresponding changes in the carbon footprint when a certain factor changes within the range of actual manufacturing values ​​are analyzed, thereby obtaining the sensitivity of the carbon footprint to that factor, and classifying and storing the calculation and analysis results. Based on the same carbon footprint calculation model, the carbon footprint data for the aforementioned steel products is used to compare and analyze carbon footprint results between different steel products within a company, between subdivided steel grades belonging to the same major steel product category, and within different calculation time ranges for the same steel grade. Based on carbon footprint calculation models with different calculation boundaries, the carbon footprint data for the steel products mentioned above allows for comparative analysis of carbon footprint results within different calculation boundary ranges for the same steel type, helping companies understand their actual carbon footprint situation. Based on the carbon footprint data of the aforementioned steel products, the contribution of the steel product carbon footprint data to the process is classified and calculated from three aspects: direct emissions in the manufacturing process, indirect emissions from the consumption of electricity and thermal energy from external sources, and emissions in the supply chain. The composition ratio of carbon footprint data at each stage of the steel product and the potential contribution impact are shown. A method for managing the carbon footprint of steel products based on a life cycle assessment according to claim 2 or 3, characterized in that, based on the carbon footprint result data of the steel products, the contribution of the steel product carbon footprint data to the process is classified and calculated from three aspects: each manufacturing process, the equipment used in each manufacturing process, and the input / output of each piece of equipment, and the composition ratio of the carbon footprint data at each stage of the steel product is shown.

5. Managing the carbon footprint of the steel products based on the life cycle carbon footprint model is: A method for managing the carbon footprint of a steel product based on a life cycle assessment, characterized in that, given that the material flow and energy flow input in the manufacturing route and unit process of the steel product are known, the life cycle carbon footprint prediction model is used to predict the input location and amount of energy and raw materials or the carbon footprint of the steel product after manufacturing in the product manufacturing process.

6. Predicting the carbon footprint after manufacturing steel products is Based on the life cycle carbon footprint prediction model for steel products, the life cycle carbon footprint calculation model for steel products is used at the time of energy and raw material input or during the product manufacturing process to predict the carbon footprint of steel products after manufacturing. Given that the input energy, raw material type, input quantity, and input unit process are known, and the final steel product has not yet been manufactured through the manufacturing process, the life cycle carbon footprint prediction model can be used to obtain prediction results for the input and output of material and energy flows in each unit process of the steel product, as well as the intermediate flows between each unit process of the entire manufacturing process and their corresponding relationships. When the path at a certain stage in the steel manufacturing process changes, the system adjusts the unit process containing that stage and each downstream unit process related to that unit process in a timely manner, based on the lifecycle carbon footprint prediction model of the steel product, thereby realizing a real-time updated carbon footprint prediction when the path in the manufacturing process changes. By combining the input and output data of each unit process with the corresponding relationships selected from the lifecycle carbon footprint prediction model, a carbon footprint prediction result is obtained. The accuracy of the prediction result is analyzed by comparing it with the carbon footprint prediction result calculated based on data collected at each unit process after the manufacture of similar steel products. Based on the relevant data of the steel product and the quality of the relevant data, the carbon footprint prediction result data and the uncertainty interval of the result for the steel product are calculated. A method for managing the carbon footprint of steel products based on a life cycle assessment, as described in claim 5, characterized in that it includes comparing and analyzing the calculated carbon footprint results and uncertainty intervals of the results calculated based on data from each unit process after manufacturing of the same type of steel product manufactured by the same manufacturing route, and analyzing the accuracy of the predicted results within the uncertainty interval by comparing the results within the uncertainty interval.

7. Managing the carbon footprint of the steel products based on the life cycle carbon footprint model is: A method for managing the carbon footprint of steel products based on a life cycle assessment according to claim 2 or 3, characterized in that, when steel products are not yet manufactured or are in the manufacturing process, the carbon footprint of the product is minimized by adjusting the path of similar steel products based on the life cycle carbon footprint optimization model.

8. Adjusting the route for the aforementioned similar steel products is Based on the optimized manufacturing path in the lifecycle carbon footprint optimization model, the carbon footprint optimization results are obtained from the input and output data of each unit process. Based on the relevant data and quality of the steel products, the carbon footprint optimization result data and the uncertainty interval of the results for the steel products are calculated. When it becomes possible to optimize and adjust the path at a certain stage in the steel manufacturing process, based on the lifecycle carbon footprint optimization model, path optimization is achieved at the unit process within the stage where optimization and adjustment are possible, and at each downstream unit process related to that unit process. Real-time optimization of the carbon footprint when the path in the manufacturing process changes is achieved, and the amount of carbon footprint reduction is obtained by comparing the original manufacturing path with the adjusted manufacturing path and the carbon footprint results of the steel product, and the possibility of optimizing the carbon footprint of the steel product is analyzed. Once the product manufacturing process is complete, the carbon footprint results after optimization of the product path are compared with the actual results based on the lifecycle carbon footprint optimization model to analyze whether the product's manufacturing path is a low-carbon manufacturing path. If there is room for optimization, the possibilities for optimizing the manufacturing path are analyzed. A method for managing the carbon footprint of a steel product based on a life cycle assessment, characterized in that it includes comparing and analyzing the carbon footprint calculation results and uncertainty intervals of a similar steel product manufactured by the original manufacturing route, based on the result data and uncertainty intervals of the carbon footprint optimization, and analyzing the carbon footprint reduction effect of the optimization results within the uncertainty interval range based on the comparison of the results.

9. A data collection module that collects information on a company's steel products and material flow, energy flow, and flow paths in each process of the steel product manufacturing process, introduces a background database of material and energy flow lifecycles that are exchanged with external parties in the manufacturing process, and introduces a database of carbon emission factors for material and energy flow in the lifecycle process. A data management module that verifies the material flow and energy flow data for each unit process in the manufacturing process collected using metal balance and carbon balance methods, updates the background database and the carbon emission factor database, and classifies and stores data corresponding to different manufacturing routes, different steel types, and different material and energy flows, A carbon footprint calculation function unit for different types of steel products, a model building module that determines the manufacturing route and calculation system boundaries, selects data allocation principles, and constructs a lifecycle carbon footprint model, A carbon footprint management system for steel products based on a life cycle assessment, characterized by including a carbon footprint analysis module that manages the carbon footprint of the steel product based on a life cycle carbon footprint model constructed by the model construction module.

10. The carbon footprint analysis module described above is A carbon footprint calculation and analysis module that performs a comparative analysis of the carbon footprint of a product based on the lifecycle carbon footprint calculation model described in claim 2, A carbon footprint prediction analysis module that predicts the carbon footprint of steel products manufactured at the time of energy and raw material input or during the manufacturing process, based on the lifecycle carbon footprint prediction model described in claim 2, and analyzes the prediction results, A carbon footprint management system for steel products based on a life cycle assessment, as described in claim 9, comprising: a carbon footprint optimization analysis module that optimizes the carbon footprint results of steel products by adjusting the specific manufacturing route of the steel products based on the life cycle carbon footprint optimization model described in claim 2, and analyzes the optimization results.

Images