A method and system for carbon footprint management of steel products based on life cycle assessment

Abstract

This invention discloses a carbon footprint management method and system for steel products based on life cycle assessment, relating to the field of energy conservation and carbon reduction technology in steel enterprises. Targeting steel enterprises characterized by a long blast furnace-converter process, this invention relies on the transformation and flow relationships of material and energy flows during steel product production to establish a carbon footprint calculation, prediction, and optimization model at the production equipment level. Considering the diverse paths in the steel product production process, it implements various carbon footprint management methods, including calculation, analysis, prediction, and optimization, and forms a carbon footprint management system based on these methods. This overcomes the shortcomings of existing carbon footprint management methods that only analyze carbon footprints through result comparison, achieving targeted, interconnected, and systematic life cycle carbon footprint management for steel products.

Description

Technical Field

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

[0002] Green and low-carbon development has become a top priority for the transformation and upgrading of the steel industry. Steel companies themselves should improve their carbon footprint management systems to deeply analyze and understand the structure of steel products and emission indicators, continuously optimize production processes and product structure, and enhance their carbon emission reduction competitiveness.

[0003] Existing carbon footprint management systems at the steel industry level are often not targeted enough and do not reflect the actual production conditions of steel enterprises. This makes it difficult for enterprises to effectively coordinate and control carbon emission data during the implementation of carbon footprint management, and to maximize the reduction of carbon footprint levels. Specifically, existing life cycle carbon footprint calculation models mainly use material flow to simply linearly connect upstream and downstream processes, focusing only on crude steel products or simply allocating the inputs and outputs of each process after ironmaking based on the quality of different types of steel products. However, in actual production, multiple production equipment with the same function often operate in parallel, and the specific production routes of different types of steel products differ between different equipment, resulting in variations in the carbon footprint of each product. Furthermore, the direct or indirect carbon emissions from different sources of certain substances or energy inputs into the same equipment also differ. Carbon emissions from self-produced energy are determined by the energy conversion relationships between various equipment in the energy system. Process-based models cannot fully reflect the specific conversion relationships of various types of energy in the steel enterprise's own energy system and the impact of the specific production paths of product production between various production equipment on the carbon footprint.

[0004] Furthermore, existing carbon footprint management systems lack systematicity and fail to incorporate all effective carbon footprint management methods. Current management methods primarily focus on comparative analysis of enterprise and product carbon footprints, providing multi-dimensional decision-making support for adjusting enterprise layout, product structure, energy and raw material structure, etc. For example, comparing various carbon emission data values ​​between different types of enterprises, different enterprises of the same type, and different time periods within the same enterprise to identify gaps and analyze emission reduction potential; for specific steel enterprises, this often involves comparisons of different product types, carbon emission boundaries, carbon emission contribution rates, and carbon emissions before and after implementing carbon reduction strategies. However, steel production involves diverse energy sources and complex, variable production equipment and paths. The carbon footprint results will differ depending on the production path. This results in a lack of effective carbon footprint management methods for production paths in the comparative analysis of steel product production results alone. It is difficult to effectively achieve systematic management and coordinated control of the carbon footprint throughout the steel product lifecycle from the perspective of the production path and all stages of the lifecycle. Summary of the Invention

[0005] In view of this, the purpose of this invention is to provide a carbon footprint management method and system for steel products based on life cycle assessment, so as to overcome the shortcomings of existing carbon footprint calculation models and carbon footprint management schemes based on processes, which calculate carbon footprint results detailed to different product types through ex-post allocation, and lack carbon footprint calculation models detailed to different production paths composed of specific conversion relationships between various types of energy in the enterprise's energy system and input-output correspondence between various production equipment. The management methods are also limited and lack management methods related to carbon footprint evaluation, analysis, prediction, and optimization related to production paths.

[0006] Therefore, the present invention provides the following technical solution:

[0007] This invention provides a method for managing the carbon footprint of steel products based on life cycle assessment, comprising:

[0008] Collect information on steel products from enterprises, as well as the material flow, energy flow, and flow paths of unit processes in the steel product production process; import the life cycle background database of material and energy flows that have exchange relationships with external entities during the production process; import the carbon emission factor database of material and energy flows in the life cycle process.

[0009] The material and energy flow data of each unit process in the production process are examined by metal balance and carbon balance methods; the background database and the carbon emission factor database are updated; and the data corresponding to different production paths, different steel grades, and different material and energy flows are classified and stored.

[0010] Determine the carbon footprint calculation function unit, production path, and calculation system boundary for different types of steel products; select data allocation principles; and establish a life cycle carbon footprint model.

[0011] Carbon footprint management of the steel products is carried out based on the life cycle carbon footprint model.

[0012] Furthermore, the life cycle carbon footprint model includes: a life cycle carbon footprint calculation model, a life cycle carbon footprint prediction model, and / or a life cycle carbon footprint optimization model;

[0013] Establish a life-cycle carbon footprint model, including:

[0014] Based on the requirements for calculating the carbon footprint of steel products, the calculation scope and allocation principles are determined. According to the nature, quantity and use of steel products, appropriate functional units are selected. Based on the connection between intermediate products in each unit process of steel product production and the conversion relationship of material flow and energy flow within the unit process, a life cycle carbon footprint calculation model for steel products is established.

[0015] Based on the carbon footprint calculation model, the historical production path and the conversion relationships and efficiencies of material and energy flows in unit processes of the steel grade to be predicted are obtained. Appropriate path information and unit process information are analyzed and selected as data for training the lifecycle carbon footprint prediction model; and / or,

[0016] Based on the carbon footprint calculation model of steel product life cycle, the conversion relationship and efficiency of material flow and energy flow in unit process, as well as the quantity and corresponding relationship of intermediate flow between each unit process, are obtained. Based on the corresponding relationship, conversion relationship and efficiency, objective function and constraints are constructed to minimize the carbon footprint of the product without changing the production process.

[0017] Furthermore, a lifecycle carbon footprint calculation model for steel products is established, including:

[0018] Based on the transformation relationships of each unit process and the material and energy flows within the unit process, as well as the flow relationships of intermediate products between different unit processes, a steel product production process model is established.

[0019] Based on the steel product production process model and combined with the material and energy flows related to the production process in the background database, a steel product life cycle process model is constructed.

[0020] Based on the steel product production process model and the steel product life cycle process model, the material flow and energy flow in the process are converted into carbon flow according to the carbon emission factors of the material flow and energy flow related to the production process, and a steel product life cycle carbon footprint calculation model is constructed.

[0021] Establish a life-cycle carbon footprint prediction model, including:

[0022] Based on the life cycle carbon footprint calculation model of steel products, all historical production paths in the calculation results of the carbon footprint of steel products of the steel type to be predicted are obtained, and appropriate path information is analyzed and selected.

[0023] Based on the conversion relationship and efficiency of material flow and energy flow in unit process and path information, as well as the intermediate flow of each unit process and its upstream and downstream correspondence, when the type and quantity of input energy and raw materials, the unit process to which they are input, and the production path between unit processes are known, the output of material flow and energy flow of unit processes and the quantity and correspondence of intermediate flow between each unit process in the entire production process can be obtained.

[0024] Based on the output of material and energy flows in unit processes and the quantity and correspondence of intermediate flows between various unit processes in the entire production process, the conversion relationship and conversion efficiency of material and energy flows in unit processes are derived.

[0025] Based on the conversion relationship and efficiency of material flow and energy flow in unit processes, as well as the intermediate flow of each unit process and its upstream and downstream correspondence, and according to the historical production path of the steel grade to be predicted in a similar time period, combined with the basic information of each unit process, we analyze and select appropriate paths and path information as a life cycle carbon footprint prediction model trained with information and data.

[0026] Establish a lifecycle carbon footprint optimization model, including:

[0027] Based on the life cycle carbon footprint calculation model, according to the conversion relationship and efficiency of material flow and energy flow in unit processes, and the quantity and correspondence of intermediate flows between each unit process, the objective function and constraints are set so that the product carbon footprint can be minimized by optimizing the production path without changing the production process.

[0028] Based on the life cycle carbon footprint calculation model, the historical production path information, production data and carbon footprint results of a certain steel product that needs to be optimized are obtained. Combined with the objective function and constraints, a carbon footprint optimization model for steel products is formed.

[0029] Furthermore, carbon footprint management of steel products based on the life cycle carbon footprint model includes:

[0030] Based on the life cycle carbon footprint calculation model of steel products, this paper imports material and energy flow data, background data, and carbon emission factor data of the corresponding unit processes. Based on relevant data and data quality of steel products, the paper calculates the carbon footprint results and uncertainty range of the steel products. Based on the carbon footprint results of 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 results. Based on the life cycle carbon footprint calculation model of steel products, it analyzes the corresponding changes in carbon footprint when a certain factor changes within the actual production range, thus obtaining the sensitivity of carbon footprint to that factor. The calculation and analysis results are then categorized and stored.

[0031] Based on the same carbon footprint calculation model, the carbon footprint results of steel products are compared and analyzed between different steel products of the enterprise, between sub-grades of the same category of steel products, and between different calculation time ranges of the same steel grade.

[0032] Based on carbon footprint calculation models with different calculation boundaries, this study compares and analyzes the carbon footprint results of the same steel grade within different calculation boundary ranges, based on the carbon footprint results of steel products, to help enterprises understand their actual carbon footprint situation.

[0033] Based on the carbon footprint data of steel products, this paper classifies and calculates the process contribution of the carbon footprint data of steel products from three aspects: direct emissions during the production process, indirect emissions from the consumption of externally input electricity and heat, and emissions in the supply chain. It also shows the composition ratio and potential contribution impact of the carbon footprint data of each link of steel products.

[0034] Based on the carbon footprint data of steel products, the process contribution of the carbon footprint data of steel products is classified and calculated from three dimensions: each production process, the equipment included in each process, and the input and output of each piece of equipment. This demonstrates the composition ratio and potential contribution impact of the carbon footprint data of each stage of steel product production.

[0035] Furthermore, carbon footprint management of steel products based on the life cycle carbon footprint model includes:

[0036] When the production path of steel products and the input material and energy flows in the unit process are known, the carbon footprint of steel products after production can be predicted by the life cycle carbon footprint prediction model at the location and quantity of energy and raw material inputs or during the product production process.

[0037] Furthermore, predicting the carbon footprint of steel products after production includes:

[0038] Based on the steel product lifecycle carbon footprint prediction model, the carbon footprint of steel products after production can be predicted during energy and raw material input or during the production process.

[0039] When the type, quantity, and unit process of the input energy and raw materials are known, but the final steel product has not yet been produced through the production process, the carbon footprint prediction model can be used to obtain the input and output of the material flow and energy flow of each unit process of the steel product, as well as the intermediate flow and corresponding relationship between each unit process in the entire production process.

[0040] When the path of a certain link in the steel production process changes, the unit process in which the link is located and the downstream unit processes connected to the unit process are adjusted in a timely manner based on the steel product carbon footprint prediction model, so as to realize the real-time updated carbon footprint prediction when the path changes in the production process.

[0041] The corresponding relationships selected in the carbon footprint prediction model are combined with the input and output data of each unit process to obtain the carbon footprint prediction results; the accuracy of the prediction results is compared and analyzed with the carbon footprint prediction results calculated based on the data collected from each unit process after the production of this type of steel product; based on the relevant data of steel products and the quality of the relevant data, the carbon footprint prediction results of steel products and the uncertainty range of the results are calculated.

[0042] Based on the carbon footprint prediction results and their uncertainty intervals, a comparative analysis is conducted on the carbon footprint calculation results and their uncertainty intervals calculated based on the data of each unit process after the production of the same type of steel product through the same production path. By comparing the results within the uncertainty intervals, the accuracy of the prediction results within the uncertainty intervals is analyzed.

[0043] Furthermore, carbon footprint management of steel products based on the life cycle carbon footprint model includes:

[0044] When steel products are not yet in production or are in the production process, the carbon footprint of the product can be minimized by adjusting the production path of this type of steel product based on the life cycle carbon footprint optimization model.

[0045] Furthermore, adjustments to the product roadmap for this type of steel product include:

[0046] Based on the optimized production path in the carbon footprint optimization model, and combined with the input and output data of each unit process, the carbon footprint optimization results are obtained; based on the relevant data of steel products and the relevant data quality, the carbon footprint optimization results data and the uncertainty range of the results of steel products are calculated.

[0047] When the path of a certain link in the steel production process can be optimized and adjusted during the production process, based on the carbon footprint optimization model, path optimization can be carried out in the unit process of the link that can be optimized and adjusted, as well as in the downstream unit processes connected to that unit process. This enables real-time carbon footprint optimization when the path changes during the production process. By comparing the changes in the steel product production path and the carbon footprint results after the original production path and the adjusted production path, the amount of carbon footprint reduction can be obtained, thereby analyzing the potential for optimizing the carbon footprint of steel products.

[0048] After the product manufacturing process is completed, based on the carbon footprint optimization model, the carbon footprint results of the product path optimization are compared with the actual results to analyze whether the product's production path is a low-carbon production path. If there is room for optimization, the optimization potential of the production path is analyzed.

[0049] Based on the carbon footprint optimization results and their uncertainty range, a comparative analysis was conducted with the carbon footprint calculation results and their uncertainty range based on the steel products produced through the original production path. The analysis aimed to assess the carbon footprint reduction effect of the optimization results within the uncertainty range based on the result comparison.

[0050] This invention also provides a carbon footprint management system for steel products based on life cycle assessment, comprising:

[0051] The data acquisition module is used to collect information on the company's steel products, as well as the material flow, energy flow, and flow path of the unit processes in the steel product production process; import the life cycle background database of material and energy flows that have exchange relationships with the outside world during the production process; and import the carbon emission factor database of material and energy flows in the life cycle process.

[0052] The data management module is used to verify the material and energy flow data of each unit process in the production process through metal balance and carbon balance methods; update the background database and carbon emission factor database; and classify and store the data corresponding to different production paths, different steel grades, and different material and energy flows.

[0053] The model building module is used to determine the carbon footprint calculation function unit, production path, and calculation system boundary for different types of steel products; select data allocation principles; and establish a life cycle carbon footprint model.

[0054] The carbon footprint analysis module is used to manage the carbon footprint of steel products based on the life cycle carbon footprint model built by the model building module.

[0055] Furthermore, the carbon footprint analysis module includes:

[0056] The carbon footprint calculation and analysis module is used to calculate and compare the carbon footprint of products based on the carbon footprint calculation model mentioned above.

[0057] The carbon footprint prediction and analysis module is used to predict the carbon footprint of steel products after production, based on the above carbon footprint prediction model, when energy and raw materials are input or during the production process, and to analyze the prediction results.

[0058] The carbon footprint optimization analysis module is used to optimize the carbon footprint of steel products by adjusting the specific production path of steel products based on the above carbon footprint optimization model, and to analyze the optimization results.

[0059] Compared with existing methods, the features and beneficial effects of the present invention are:

[0060] This invention targets various types of steel products in steel enterprises characterized by a blast furnace-converter long process. Based on life cycle assessment methods, it achieves carbon footprint management of steel products at the production equipment level by analyzing the transformation and flow relationships of material and energy flows and their connected process units. Combining the transformation relationships of material and energy flows within unit processes and their flow relationships between different unit processes, as well as the diverse and complex nature of the sources of raw materials and the destinations of output products and by-products in unit processes, a carbon footprint calculation and optimization model for steel products is invented. This model effectively predicts the carbon footprint of steel products during the raw material input stage or production process when the production path and the input material and energy flows in unit processes are known. It also effectively reduces the carbon footprint of steel products by adjusting and optimizing the original production path when the original production path is known. The invention implements multiple carbon footprint management methods, including carbon footprint calculation, analysis, prediction, and optimization. The adopted carbon footprint management method enables precise assessment of the impact of changes in the production path on the carbon footprint of steel products throughout their life cycle. This overcomes the shortcomings of existing management methods that only analyze carbon footprint results, and achieves targeted, interconnected, and systematic carbon footprint management of steel products throughout their life cycle. Attached Figure Description

[0061] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0062] Figure 1 This is a flowchart of a method for managing the carbon footprint of steel products based on life cycle assessment, as described in an embodiment of the present invention.

[0063] Figure 2 This is a structural block diagram of a steel product carbon footprint management system based on life cycle assessment, as described in an embodiment of the present invention. Detailed Implementation

[0064] Life cycle assessment can comprehensively evaluate the carbon footprint of steel products and production processes throughout their entire life cycle, identify key influencing factors, and find energy-saving and carbon-reduction measures for the entire process, enabling enterprise carbon footprint management. This invention, based on the life cycle assessment of steel products and considering the characteristics of material and energy flow transformation and flow during the steel product life cycle, proposes a method and system for managing the carbon footprint of steel products.

[0065] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0066] like Figure 1 As shown, this embodiment of the invention provides a method for managing the carbon footprint of steel products based on life cycle assessment, including:

[0067] S1. Collect information on the enterprise's steel products and the material flow, energy flow, and flow path of unit processes in the steel product production process; import the life cycle background database of material and energy flows that have exchange relationships with external entities during the production process; import the carbon emission factor database of material and energy flows in the life cycle process.

[0068] S2. Use metal balance and carbon balance methods to examine the material flow and energy flow data of each unit process in the production process; update the background database and carbon emission factor database; classify and store the corresponding data of different production paths, different steel grades, and different material and energy flows.

[0069] S3. Determine the carbon footprint calculation function unit, production path, and calculation system boundary for different types of steel products; select data allocation principles; and establish a life cycle carbon footprint model.

[0070] The life cycle carbon footprint model includes: life cycle carbon footprint calculation model, life cycle carbon footprint prediction model and / or life cycle carbon footprint optimization model;

[0071] Establish a life-cycle carbon footprint model, including:

[0072] Based on the requirements for calculating the carbon footprint of steel products, the calculation scope and allocation principles are determined. According to the nature, quantity and use of steel products, appropriate functional units are selected. Based on the connection between intermediate products in each unit process of steel product production and the conversion relationship of material flow and energy flow within the unit process, a life cycle carbon footprint calculation model for steel products is established.

[0073] Based on the carbon footprint calculation model, the historical production path and the conversion relationships and efficiencies of material and energy flows in unit processes of the steel grade to be predicted are obtained. Appropriate path information and unit process information are analyzed and selected as data for training the lifecycle carbon footprint prediction model; and / or,

[0074] Based on the carbon footprint calculation model of steel product life cycle, the conversion relationship and efficiency of material flow and energy flow in unit process, as well as the quantity and corresponding relationship of intermediate flow between each unit process, are obtained. Based on the corresponding relationship, conversion relationship and efficiency, objective function and constraints are constructed to minimize the carbon footprint of the product without changing the production process.

[0075] S4. Carbon footprint management of steel products based on life cycle carbon footprint model.

[0076] Specifically, it includes the following three management methods:

[0077] (1) Based on the carbon footprint calculation model of steel products life cycle, import the material flow and energy flow data, background data and carbon emission factor data of the corresponding unit processes; calculate the carbon footprint results and uncertainty range of steel products according to the relevant data and data quality of steel products; analyze the differences in carbon footprint of different products and the differences in the contribution of each factor to the carbon footprint results based on the carbon footprint results of steel products life cycle; analyze the corresponding changes in carbon footprint when a certain factor changes within the actual value range of production, thereby obtaining the sensitivity of carbon footprint to this factor; classify and store the calculation and analysis results.

[0078] Based on the same carbon footprint calculation model, the carbon footprint results of steel products are compared and analyzed between different steel products of the enterprise, between sub-grades of the same category of steel products, and between different calculation time ranges of the same steel grade.

[0079] Based on carbon footprint calculation models with different calculation boundaries, this study compares and analyzes the carbon footprint results of the same steel grade within different calculation boundary ranges, based on the carbon footprint results of steel products, to help enterprises understand their actual carbon footprint situation.

[0080] Based on the carbon footprint data of steel products, this paper classifies and calculates the process contribution of the carbon footprint data of steel products from three aspects: direct emissions during the production process, indirect emissions from the consumption of externally input electricity and heat, and emissions in the supply chain. It also shows the composition ratio and potential contribution impact of the carbon footprint data of each link of steel products.

[0081] Based on the carbon footprint data of steel products, the process contribution of the carbon footprint data of steel products is classified and calculated from three dimensions: each production process, the equipment included in each process, and the input and output of each piece of equipment. This demonstrates the composition ratio and potential contribution impact of the carbon footprint data of each stage of steel product production.

[0082] (2) When the production path of steel products and the input material flow and energy flow in the unit process are known, the carbon footprint of steel products after production can be predicted by the life cycle carbon footprint prediction model during the input of energy and raw materials or during the production process.

[0083] (3) When steel products have not yet been produced or are in the production process, the carbon footprint of the product is minimized by adjusting the path of this type of steel product based on the life cycle carbon footprint optimization model.

[0084] In the above embodiments, for various types of steel products in steel enterprises characterized by a blast furnace-converter long process, carbon footprint management of steel products at the production equipment level is achieved based on life cycle assessment methods, the transformation and flow relationships of material and energy flows, and the process units they are connected to. The adopted carbon footprint management method enables precise assessment of the impact of changes in the production path on the life cycle carbon footprint of steel products, overcoming the shortcomings of existing management methods that only analyze carbon footprint results, and achieving targeted, interconnected, and systematic life cycle carbon footprint management of steel products.

[0085] like Figure 2 As shown, this embodiment of the invention provides a carbon footprint management system for steel products based on life cycle assessment, including:

[0086] Data Acquisition Module M1: Collects information on the enterprise's steel products, as well as the material and energy flows and flow paths of unit processes in the steel product production process; imports and updates the life cycle background database of material and energy flows that have exchange relationships with external entities during the production process; imports and updates the carbon emission factor database of material and energy flows in the "cradle to gate" life cycle process; collects relevant information on the entered data, including data acquisition time, data source, data calculation type, and other data quality-related information.

[0087] Data Management Module M2: Verifies the material and energy flow data of the unit process collected in Data Acquisition Module M1 using metal balance and carbon balance methods; updates the background database and carbon emission factor database; selects and discards unit process data according to selection principles; and classifies and stores data corresponding to different steel grades, different material flows, and energy flows.

[0088] Model building module M3: Determines the functional units for carbon footprint calculation and the boundaries of the calculation system for different types of steel products; selects data allocation principles; and establishes a life cycle carbon footprint calculation, prediction, and optimization model.

[0089] The carbon footprint analysis module M4 is used to obtain carbon footprint results for different types of steel products based on the life cycle carbon footprint calculation model, analyze and store the carbon footprint results; when the production path of steel products and the input material flow and energy flow in the unit process are known, the carbon footprint of steel products after production can be predicted through the life cycle carbon footprint prediction model; when the original production path of steel products is known, based on the life cycle carbon footprint optimization model and combined with the historical input and output data of the unit process, the carbon footprint of the product can be reduced by adjusting the path of this type of steel product.

[0090] The data acquisition module M1 includes: steel product information acquisition module M1.1, production path determination module M1.2, background database import module M1.3, and carbon emission factor database import module M1.4.

[0091] The steel product information collection module M1.1 is specifically used to, within the enterprise, subdivide all steel products into different product types based on their intended use, shape, processing technology, and performance, and collect basic information including product name, carbon content, and processing technology.

[0092] The smallest unit of data collection is the unit process, which is the production process of each piece of equipment included in the main processes and energy system within the plant. The material flow includes the raw materials (iron ore, iron concentrate, alloys, etc.) and auxiliary raw materials (limestone, dolomite, refractory materials, etc.) input to the unit process, as well as the products and by-products (molten iron, molten steel, scrap steel, etc.) output from the unit process. The energy flow includes the energy (washed coal, anthracite, coke, etc.) input to the unit process, the energy medium (electricity, coke oven gas, industrial water, etc.), and the energy and energy medium output from the unit process.

[0093] Within the enterprise, steel products are meticulously classified according to all steel products produced, based on production process (e.g., cold / hot rolled products), processing technology (grate annealing / continuous annealing), shape (e.g., round bar / square bar), size (e.g., hot rolled thin steel strip / medium-thick plate), properties (e.g., plasticity / hardness), and application (e.g., automotive materials / building materials).

[0094] The production path determination module M1.2 is specifically used to: determine the time granularity of information collection; determine the basic information and key physical quantities of the input and output material flow and energy flow of unit processes in the production process of a certain type of steel product; collect the basic information of all unit processes in the production process; determine all unit processes included in the enterprise's production process and the flow relationship of material flow and energy flow between unit processes; and determine the flow path of material flow and energy flow between unit processes in the production process of a certain type of steel product.

[0095] Based on the technological process and metallurgical mechanism in the production process, and according to the input of raw materials and the output of products in each unit process, the conversion relationship between the input raw materials and the output products in each unit process is determined.

[0096] Based on the technological process and metallurgical mechanism in the production process, all unit processes through which the intermediate flow passes in the steel product production process are traced. Among them, the intermediate flow includes products or by-products from the upstream unit process flowing into the downstream unit process as raw materials or auxiliary raw materials, and usually the main product flowing out of the 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 refining equipment.

[0097] Based on different types of steel products, and using the unit production process as the data collection granularity, all material and energy flows input and output in the production process of each type of steel product are collected.

[0098] Based on different steel product types, and using the unit production process as the data collection granularity, the system collects and identifies the inputs and outputs of all carbon-containing material and energy flows in the production process of each type of steel product.

[0099] The time granularity of data collection includes one or more of the following units: year, month, day, shift, hour, furnace number, and real-time. Basic information on the input and output material and energy flows of the unit process includes the type, properties, units of measurement, and constraints of the material and energy flows. Key physical quantities for the input and output material and energy flows of the unit process are consumption, production, recovery, emission, external sales, steam pressure, and carbon content. Basic information on all unit processes in the production flow is collected, including the status of equipment in each unit process (stable operation, fluctuation, maintenance, shutdown, etc.), the status of materials in the unit process (processing, transportation, residence, stagnation, etc.), and furnace numbers for steelmaking, refining, and continuous casting equipment.

[0100] Based on a certain type of steel product, and based on the input and output of material and energy flows between the unit processes in the production process, determine the connection between intermediate products in the unit processes of the production process of a certain type of steel product, as well as the conversion relationship of material and energy flows within the unit processes.

[0101] The enterprise's production process includes a production system and an energy system; each unit process is a piece of equipment within the production system and the energy system; the unit processes within the production system include one or more of the following: coke oven, dry quenching unit, sintering machine, annular cooler, roasting equipment, cooling equipment, blast furnace, hot blast stove, converter, ladle, refining furnace, continuous casting machine, heating furnace, roughing mill, finishing mill, annealing furnace, and cold rolling mill; the unit processes within the energy system include one or more of the following: gas generator, combustion chamber, waste heat boiler, coal-fired boiler, gas-fired boiler, generator set, oxygen generator, compressor, blower, pressurizer, and water treatment equipment.

[0102] The material and energy flows between unit processes include, from the perspective of the production system, material flows such as sintered ore, pellets, molten iron, molten steel, steel billets, hot-rolled coils, scrap steel, and slag steel, and energy flows such as coke and coke powder; from the perspective of the energy system, energy flows such as fresh water, clean circulating water, waste circulating water, electricity, low-pressure steam, high-pressure steam, oxygen, nitrogen, argon, coke oven gas, blast furnace gas, and converter gas; and from the perspective of the flow between the production system and the energy system, energy flows such as fresh water, clean circulating water, waste circulating water, electricity, low-pressure steam, high-pressure steam, oxygen, nitrogen, argon, coke oven gas, blast furnace gas, and converter gas.

[0103] The flow paths of material and energy flows between unit processes include: from which unit processes do the raw materials, auxiliary raw materials, and energy of a certain unit process flow out, the types and corresponding quantities of each unit process flowing out of these upstream unit processes, and all unit processes into which the product flow and by-product flow of this unit process flow, including the types and corresponding quantities of each unit process flowing into these unit processes.

[0104] Of course, in some other embodiments, each unit process also includes: other equipment (lime kiln, slag powder device, etc.), energy (natural gas, diesel, etc.), other systems (waste treatment system, etc.). In practical applications, the system, equipment and energy type should be determined according to actual needs, and no restrictions are imposed here.

[0105] The background database import module M1.3 is specifically used to import the background database and related data information of material and energy flows that have exchange relationships with the outside world during the production process, according to different steel product types.

[0106] The material flow that has an exchange relationship with external entities includes raw materials and auxiliary raw materials purchased by the enterprise from external manufacturers, and by-products sold by the enterprise to other enterprises; the energy flow that has an exchange relationship with external entities includes energy and energy medium purchased by the enterprise from external manufacturers, and energy and energy medium sold by the enterprise to other enterprises.

[0107] The background database includes all inputs and outputs of the extraction and processing of various energy sources, as well as all inputs and outputs of the transportation process.

[0108] Among them, there is a complete background database related to all external raw materials input by steel enterprises, including all inputs and outputs of the raw material mining, processing and production process, and all inputs and outputs of the transportation process; and a complete background database related to the production process of industrial products replaced by exported by-products, including the mining and processing of energy and raw materials required for the production process of the replaced industrial products, and all inputs and outputs of the production process of the industrial products.

[0109] This includes a complete background database of all energy inputs and external energy sources for steel enterprises, encompassing all inputs and outputs in the energy extraction, processing, and production processes, as well as all inputs and outputs in the transportation processes. It also includes a complete background database related to the production processes of industrial products substituted by exported secondary energy sources, including the energy processing required for the production of these industrial products and all inputs and outputs in the production process.

[0110] The background database includes energy and raw material lifecycle inventory factors provided by the enterprise's purchasing manufacturers, energy and raw material transportation process inventory factors from the purchasing manufacturers to the factory gate provided by the transportation department, and relevant energy and raw material inventory factors imported from various lifecycle background databases.

[0111] Specifically, the data provided by the enterprise to the manufacturer includes data from the upstream product life cycle assessment report provided by the upstream supplier, which meets the standard requirements and has been independently verified by a third party.

[0112] Specifically, life cycle background data should include publicly available life cycle assessment data representing the average domestic production level, and technical data on the production of similar energy and raw materials abroad.

[0113] When data in the background database needs to be updated, check if there are any updates in each database. If so, replace the original data in the database with the updated data to complete the update.

[0114] Specifically, background data information includes: detailed sources of the background data, such as those provided by upstream suppliers, extracted from the National Bureau of Statistics database, or obtained from the CLCD database; the statistical year of the data (the closer the statistical year of the background data is to the year of calculation, the more accurate the calculation result will be); the calculation type of the data, i.e., whether the data comes from on-site measurements, calculations based on formulas, or estimations based on actual conditions; and the geographical representativeness of the data, i.e., whether the data is product-level, enterprise-level, industry-level, regional-level, or country-level, and specifically for a certain type of product, a certain name of enterprise, a certain type of industry, a certain region, or a certain country. If any information is missing, the reason for the missing information should be explained.

[0115] Specifically, based on the relevant information in the background database, a data evaluation index system is established. The priority of carbon emission factor selection is established based on the data index system and updated in real time as the background database is updated. The data quality is judged based on the index system to help enterprises select more suitable background data for product carbon footprint management.

[0116] The data evaluation index system specifically includes a comprehensive scoring of data based on 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 geographical scope (within the factory, the province, the country), to determine the overall quality of data in each background database.

[0117] The carbon emission factor library import module M1.4 is specifically used to import carbon emission factor libraries and related information on material and energy flows throughout the "cradle to gate" life cycle of different steel products, based on their different product types.

[0118] The "cradle to gate" life cycle refers to the process of steel products from the extraction of resources and energy to the production and manufacturing of products before they are sold to external manufacturers. This includes the mining and processing of upstream energy and raw materials for steel products, the transportation of upstream raw materials for steel products, and the steel product production process. Carbon emission factors are the carbon input of carbon-containing substances in the "cradle to gate" life cycle, the carbon released into the system or atmosphere by the use of carbon-containing energy, and the carbon fixed by carbon-containing products and by-products due to their carbon content.

[0119] The library of direct carbon emission factors for all external carbon-containing energy sources, raw materials, products and by-products that are inputted and output throughout the life cycle of steel products from cradle to gate includes direct carbon emission factors from the fuel combustion process, carbon emission factors obtained based on the carbon content of carbon-containing solvents, carbon-containing alloys, carbon additives, etc. in the input materials, and carbon emission deduction factors obtained based on the carbon content of carbon-containing products and carbon-containing by-products during the process.

[0120] Specifically, when the composition or carbon content of the fuel used by the enterprise is available, the carbon emission factor of the fuel is obtained using the formula for calculating the carbon emission factor of direct combustion of fuel. When the carbon content of the carbon-containing materials, carbon-containing products, and by-products used by the enterprise is available, the amount of carbon dioxide obtained when a unit of material is completely decomposed is calculated based on elemental balance, thereby obtaining its carbon emission factor; for unavailable components, the carbon emission factors provided in the guidelines of the National Development and Reform Commission or the IPCC guidelines integrated in the module can be used.

[0121] Information regarding carbon emission factors should be recorded in detail in the database management module. This includes the detailed source of the carbon emission factors, such as calculations based on formulas or data from the National Development and Reform Commission's guidelines. The statistical year of the data should be provided; the closer the statistical year is to the year of calculation, the more accurate the result. The calculation type should be specified, such as calculation based on formulas, estimation based on actual data, or using national averages. The geographical representativeness of the data should be indicated, specifying whether it is product-level, enterprise-level, industry-level, regional-level, or national-level, and further specifying a particular type of product, enterprise name, industry category, region, or country. If any information is missing, the reason for the missing information should be explained.

[0122] The carbon emission factor database is an integration of multiple different databases. During the integration process, the data source, data type, and data collection time of each database should be clearly defined. When the data in the carbon emission factor database needs to be updated, check whether the data in each database has been updated. If so, replace the original data in the database with the updated data to complete the update.

[0123] Specifically, based on relevant information in the carbon emission factor database, a data evaluation index system is established, and data quality is judged according to the index system. The priority of carbon emission factor selection is established based on the data index system and updated in real time as the carbon emission factor database is updated, helping enterprises select more appropriate carbon emission factor data for product carbon footprint management.

[0124] The data evaluation index system comprehensively considers indicators such as data source (field data, literature, background database, etc.), data type (measured, calculated, averaged, estimated, etc.), data year (real-time, within the past year, within the past five years), and data collection geographical scope (within the factory, within the province, within the country) to comprehensively score the data and determine the overall quality of the data in each carbon emission factor database.

[0125] The data management module M2 includes: data verification and modification module M2.1, data selection and discarding module M2.2, and data classification and storage module M2.3;

[0126] Specifically, the data verification and modification module M2.1 is used to check for problems in the input and output data of each unit process. If a problem is found, the problematic data is fed back to the data acquisition module M1 for re-acquisition and updating of the modified data. It also checks whether the input and output data of each unit process meet the requirements of metal balance and carbon balance. If any data problems are found, the problematic data is fed back to the acquisition module M1 for re-acquisition and updating of the modified data. Finally, it updates the background database and carbon emission factor database.

[0127] The problems with the input and output data of each unit process include: whether each unit process is representative, i.e., whether the production data is collected according to the scope of unit process data collection; whether the field data is complete, whether there is data on partial material flow and energy flow of any unit process or missing information on key physical quantities; and whether the field data is consistent, including that the names of the same type of material flow and energy flow involved in all unit processes should be uniformly processed, and the names of the same type of material flow and energy flow data in the background database and the carbon emission factor library should be consistent.

[0128] Specifically, the input and output data of each unit process must satisfy the requirements for metal balance and carbon balance. Metal balance refers to the balance of ferrite flow, which means determining whether the iron content in the iron-containing materials input to the unit process and the iron-containing products, by-products and residues output are balanced or within the allowable error range. Carbon balance refers to whether the carbon content in the input energy, raw materials and auxiliary raw materials is balanced or within the allowable error range with the carbon content in the output CO2, carbon-containing products, by-products and solid waste.

[0129] The updated and modified data includes updating the data in the background database and carbon emission factor database according to the collection time requirements of the background database and carbon emission factor database; the update method is to feed back the data information of the earlier years in the background database and carbon emission factor database to M1 based on the data year information in the data evaluation index system.

[0130] The data selection module M2.2 is specifically used to identify, analyze, and filter the data of each unit process in the steel product production process according to the data selection principle of steel product input and output, and select the input and output data that meet the requirements. The data selection principle refers to the principle of reducing the complexity of the unit process data based on the proportion of raw material and energy input and output to the total amount of that type in each unit process of steel product production.

[0131] The selection of input and output data that meet the requirements of the selection and rejection principles includes the accurate location, precise identification and analysis of raw materials, auxiliary raw materials, energy input data, products, by-products and energy output in the steel product production unit process. Based on the selection and rejection principles formulated according to the proportion of raw material and energy input and output, the input and output data related to the steel product production unit process are scientifically processed, thereby simplifying the complex unit process composed of multiple materials and multiple outputs using the selection and rejection principles specified in the standard.

[0132] The data classification and storage module M2.3 is specifically used to match the collected unit process material flow and energy flow data with the imported background database and carbon emission factor database based on the information of different steel grades, forming a complete dataset of steel products of that type, and storing it in this module according to its classification.

[0133] The information on different steel grades includes key physical quantities of the input and output material and energy flows of a certain type of steel product in the unit process of production, as well as the flow paths of material and energy flows between unit processes in the production process of a certain type of steel product. Combined with the material and energy flows that have exchange relationships with the outside world in the production unit process of this type of steel product, the life cycle background database of by-products and energy products sold to external enterprises, and the relevant carbon emission factors of all inputs and outputs in the "cradle to gate" life cycle of this type of steel product, the comprehensive data quality evaluation results of each data form the life cycle carbon footprint dataset of this type of steel product.

[0134] Based on the lifecycle carbon footprint dataset of the company's steel products, these datasets are classified and stored in this module according to steel product type, data collection time granularity, data collection scope, data quality, and other types.

[0135] The model building module M3 includes: carbon footprint calculation model module M3.1, carbon footprint prediction model module M3.2, and carbon footprint scheduling model module M3.3;

[0136] The carbon footprint calculation model module M3.1 is specifically used to determine the calculation scope and allocation principles in combination with the requirements of carbon footprint calculation for steel products. Based on the nature, quantity and use of steel products, appropriate functional units are selected. Based on the connection between intermediate products in each unit process of steel product production and the conversion relationship of material flow and energy flow within the unit process, a carbon footprint calculation model for steel products is established.

[0137] The requirements for calculating the carbon footprint of steel products include that before conducting the calculation, enterprises should clarify the application intention of the carbon footprint results, the reasons for conducting the calculation and management, the method of using the results, and the target audience for communicating the results, so as to determine the classification method and level of detail of the steel product types for carbon footprint calculation, the carbon footprint analysis method to be adopted, and whether to conduct specific analysis and evaluation of the carbon footprint results.

[0138] The feasibility of selecting the actual calculation scope is based on the requirements of carbon footprint calculation for steel products. Considering that the carbon footprint calculation object is a certain type of steel product, the selectable range for 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-plant energy production (including coking, power, oxygen, water, gas, and steam systems) and the steel product production stages (including sintering, pelletizing, ironmaking, steelmaking, hot rolling, and cold rolling). The "cradle-to-gate" life cycle process includes the extraction, production, and transportation stages of raw materials and energy, and the steel product production stage, specifically including raw material extraction, production, auxiliary material extraction, production, energy extraction, scrap steel collection and processing, transportation (transportation of raw materials, energy, auxiliary raw materials, and scrap steel), in-plant energy production, and in-plant steel product production.

[0139] Among them, the functional unit refers to the numerical value of the carbon footprint calculation result expressed in cumulative terms throughout the life cycle process of producing "a given quantity of a certain steel product";

[0140] Among them, selecting an appropriate functional unit based on the nature, quantity, and use of steel products specifically includes: in order to compare different types of steel products obtained by further subdividing a large category of steel products, it is necessary to select a comparable functional unit. For example, based on the nature, quantity, and use of steel products, a 1kg mass of steel products is usually selected as the functional unit, and the carbon footprint results of different steel products are compared on this basis.

[0141] The reason for determining the allocation principle is that a unit process produces two or more products at the same time, and the input of raw materials and energy is not collected separately. There are also cases where the input of a unit process has multiple raw materials, but the output is only one, and cases where the input of a unit process has multiple raw materials and the output of multiple products. In these cases, the required input and output data of the unit process cannot be obtained directly. It is necessary to further recombine and allocate the data of these unit processes to form a new unit process.

[0142] The allocation principles include: based on unit processes, when a unit process has multiple products, data needs to be allocated; unit processes with multiple products include similar functional unit processes and multifunctional unit processes; similar functional unit processes refer to unit processes that output multiple products with similar properties and functions; multifunctional unit processes refer to unit processes that output multiple products with significant differences in properties and functions.

[0143] Based on similar functional unit processes, the allocation principles include: first, identifying similar functional unit processes and other unit processes related to these unit processes; splitting the unit processes; and allocating the material flow and energy flow of the unit process to different products according to the proportion of the mass, energy, and volume of the unit process products; for the split unit processes, it should be checked whether the sum of the input and output of the unit process before allocation and after allocation are equal.

[0144] Based on multifunctional unit processes, the allocation principle includes including some unit processes that produce by-products into the system boundary according to the actual use of the by-products.

[0145] Specifically, including certain unit processes that produce by-products within the system boundary based on their actual use means that, if the by-products are sold to external companies and used as a substitute for a certain product, the carbon footprint of the production process is effectively reduced based on the actual use of that substitute. Therefore, including certain unit processes that produce by-products within the system boundary is equivalent to offsetting the carbon footprint of that product's production. For example, if blast furnace slag is sold for use as cement clinker, the carbon footprint benefit from its recycling by external companies is equivalent to the carbon footprint of the corresponding cement clinker production process that it replaces.

[0146] Based on the transformation relationships of each unit process and the flow of materials and energy within the unit process, as well as the flow relationships of intermediate products between different unit processes, a "door-to-door" production process model for steel products is established.

[0147] Based on the steel product production process model and combined with the life cycle background database of material flow and energy flow related to the production process in the background database, a "cradle to gate" life cycle process model of steel products is constructed.

[0148] Based on the steel product production process model and the steel product "cradle to gate" life cycle process model, the material flow and energy flow in the process are converted into carbon flow according to the carbon emission factors of the material flow and energy flow related to the production process, and a carbon footprint calculation model for steel products is constructed.

[0149] The carbon footprint prediction model M3.2 is specifically used to establish a carbon footprint prediction model for steel products based on the steel product carbon footprint calculation model and combined with the historical production path of a specific type of steel product.

[0150] Based on the carbon footprint calculation model module of M3.1, the historical production path in the carbon footprint calculation results of a certain steel product is obtained. The appropriate path information is analyzed and selected based on the input material flow, energy flow data and the process and path information of the flowing unit.

[0151] Based on the conversion relationship and efficiency of material flow and energy flow in unit process and path information, as well as the intermediate flow of each unit process and its upstream and downstream correspondence, when the type and quantity of input energy and raw materials, the unit process to which they are input, and the production path between unit processes are known, the output of material flow and energy flow of unit processes and the quantity and correspondence of intermediate flow between each unit process in the entire production process can be obtained.

[0152] Based on the output of material and energy flows in a unit process and the quantity and correspondence of intermediate flows between various unit processes in the entire production process, the conversion relationship and conversion efficiency of material and energy flows in a unit process are derived.

[0153] Based on the conversion relationship and efficiency of material flow and energy flow in unit processes, as well as the intermediate flow of each unit process and its upstream and downstream correspondence, and according to the historical production paths of the steel grade to be predicted in similar time periods, combined with the basic information of each unit process, we analyze and select appropriate paths and path information as information and data to train the carbon footprint prediction model.

[0154] The carbon footprint optimization model M3.3 is specifically used to establish an optimization model for the carbon footprint of steel products by setting the objective function and constraints based on the calculation model of the carbon footprint of steel products.

[0155] The carbon footprint calculation model based on unit processes introduces objective functions and constraints for carbon footprint results, based on the conversion relationship and efficiency of material flow and energy flow in unit processes, the quantity and correspondence of intermediate flows between each unit process.

[0156] In the establishment of the carbon footprint optimization model, the objective function for minimizing the product's carbon footprint depends on the design variables. Each design variable can be written as a function related to the design variables, which is called the objective function.

[0157] In the establishment of the carbon footprint optimization model, the selection of each design variable is not arbitrary. For example, the selection of unit processes is subject to various constraints, such as the number of unit processes and the distribution of intermediate flows among unit processes. There are certain selection tendencies and ranges, which are called constraints.

[0158] Based on the carbon footprint calculation model, historical production path information, production data, and carbon footprint results data of a certain steel product that needs to be optimized are obtained. Combined with the objective function and constraints, a carbon footprint prediction model is established.

[0159] The carbon footprint analysis module M4 includes: carbon footprint calculation and analysis module M4.1, carbon footprint prediction and analysis module M4.2, and carbon footprint optimization and analysis module M4.3.

[0160] The carbon footprint calculation and analysis module M4.1 calculates the carbon footprint results and uncertainty range of steel products based on relevant data and data quality. It analyzes the differences in carbon footprints among different products and the contributions of various factors to the results. Based on the carbon footprint calculation model, it analyzes the corresponding changes in carbon footprint when a factor changes within the actual production range, thus obtaining the sensitivity of carbon footprint to that factor. The calculation and analysis results are then categorized and stored in this module.

[0161] The uncertainty in the calculated carbon footprint of steel products is due to inaccurate data in lifecycle carbon footprint management. This inaccuracy stems from several factors: some data in the background database originates from the lifecycle processes of similar products, and the data collection year, region, and coverage do not fully represent the actual data within the industrial chain; carbon emissions from individual enterprise processes cannot be directly measured, but are calculated using formulas based on the inputs and outputs of the process and carbon emission factors in a carbon emission factor library; the calculation process itself involves inherent uncertainties; and the types of substances and energy in various carbon emission factor libraries do not fully cover the entire lifecycle of steel production, with differences in the collection year, region, and coverage of carbon emission factors from different sources. Therefore, it is necessary to analyze the uncertainty in the carbon footprint results for steel products.

[0162] The uncertainty range of the results includes the comprehensive score of each data in the data evaluation index system, obtaining a certain range of values ​​for the data in the model, inputting the data into the carbon footprint calculation model of steel products in the form of a certain range of values, and transforming the uncertainty of each input and output data into the uncertainty of the final carbon footprint value of steel products.

[0163] Based on the carbon footprint calculation model, this study compares and analyzes the carbon footprint results of steel products across different steel products, between sub-grades of the same category of steel products, and within different calculation time ranges for the same steel grade. Based on carbon footprint calculation models with different calculation boundaries, this study compares and analyzes the carbon footprint results of the same steel grade within different calculation boundary ranges, helping companies to understand their actual carbon footprint situation.

[0164] Based on the carbon footprint data of steel products, the process contribution of the carbon footprint data of steel products is classified and calculated from three aspects: direct emissions during the production process, indirect emissions from the consumption of externally input electricity and heat, and emissions in the supply chain. This demonstrates the composition ratio and potential contribution impact of the carbon footprint of steel products at each stage.

[0165] Based on the carbon footprint data of steel products, the process contribution of the carbon footprint data of steel products is classified and calculated from three dimensions: each production process, the equipment included in each process, and the input and output of each piece of equipment. This demonstrates the composition ratio and potential contribution impact of the carbon footprint of each link in the steel product process.

[0166] Among them, the sensitivity of carbon footprint to a certain factor includes, based on the factors and combinations of factors that contribute to the carbon footprint results, and based on the carbon footprint calculation results of steel products, the evaluation method based on sensitivity analysis is used to evaluate the differences in the impact of changes in various aspects of the entire life cycle of industrial products on the carbon footprint results, so as to compare the sensitivity of the carbon footprint of steel products to changes in different influencing factors, rank the sensitivity, select the influencing factors with higher sensitivity results and analyze the reasons.

[0167] Among them, the combined influencing factors are: based on the carbon footprint data of steel products, the carbon footprint influencing factors of different aspects and dimensions can be recombined through their transformation, inclusion and influence relationships to form new influencing factor types. These new influencing factors can reflect some proportional relationships, technical parameters, recycling methods and other aspects in the steel product production process, and the carbon footprint of steel products is also sensitive to changes in these factors.

[0168] The evaluation method for sensitivity analysis involves inputting the range of changes in each hypothetical factor and combination of influencing factors into the model to obtain the range of the product's carbon footprint results. The sensitivity results are expressed as the amount or percentage change in the carbon footprint results when the influencing factors change by the same range. The hypothetical range of data change is the actual input data in the model, which changes within a certain percentage range based on the actual production situation of the data.

[0169] The module categorizes and stores the calculation and analysis results, including carbon footprint results, factor analysis results, sensitivity analysis results, and uncertainty analysis results, according to data acquisition time granularity, system boundary range, and steel product type.

[0170] The carbon footprint prediction and analysis module M4.2 is specifically used to: based on the carbon footprint prediction model of steel products, it can predict the carbon footprint of steel products after production by means of the carbon footprint prediction model of steel products during the input of energy and raw materials or during the production process.

[0171] The purpose of predicting the carbon footprint of steel products after production, either during energy and raw material input or production processes, is to address the fact that changes in production scheduling due to order updates during steel production mean that the production path may be subject to change at various stages. Predicting the carbon footprint of steel products after production, whether using known paths or when production paths change, provides information related to the carbon footprint in the production process, serving as a reference for enterprises regarding the potential carbon footprint of this type of steel product. It also provides decision-making objectives, references, and alternative solutions for steel production decisions, ensuring that carbon footprint prediction is achievable under both existing and adjusted production paths. Furthermore, the prediction provides a basis for the rational allocation and effective utilization of resources.

[0172] When the type and quantity of input energy and raw materials, and the unit process to which they are input are known, but the final steel product has not yet been produced through the production process, the input data of energy and raw materials of a certain type of steel product to be predicted can be imported, along with background data and carbon emission factor data. Based on the carbon footprint prediction model, the input and output of material flow and energy flow in each unit process of the steel product, as well as the prediction results of intermediate flow and corresponding relationships between each unit process in the entire production process can be obtained.

[0173] When the path of a certain link in the steel production process changes, the unit process in which the link is located and the downstream unit processes connected to the unit process are adjusted in a timely manner based on the steel product carbon footprint prediction model, so as to realize the real-time updated carbon footprint prediction when the path changes in the production process.

[0174] The corresponding relationships selected in the carbon footprint prediction model are combined with the input and output data of each unit process to obtain the carbon footprint prediction results; the accuracy of the prediction results is compared and analyzed with the carbon footprint prediction results calculated based on the data collected from each unit process after the production of this type of steel product; based on the relevant data of steel products and the quality of the relevant data, the carbon footprint prediction results of steel products and the uncertainty range of the results are calculated.

[0175] Based on the carbon footprint prediction results and their uncertainty intervals, a comparative analysis is conducted on the carbon footprint calculation results and their uncertainty intervals calculated based on the data of each unit process after the production of the same steel product through the same production path. Through the comparison of the results, the accuracy of the prediction results is analyzed. Through the comparison of the results within the uncertainty interval, the accuracy of the prediction results within the uncertainty interval is analyzed.

[0176] The carbon footprint optimization analysis module M4.3 is specifically used to optimize the carbon footprint of steel products by comparing the adjusted production path of the steel product with the carbon footprint of the same type of product produced through the original production path, based on the carbon footprint optimization model of steel products.

[0177] The purpose of carbon footprint optimization analysis for steel products is that steel production often involves many functionally identical pieces of equipment, and the carbon footprint of steel products produced through different combinations of these equipment often varies. The rationality of the production path selection has a significant impact on energy efficiency, production rate, and product carbon footprint. Efficient and rational path selection can achieve energy conservation and carbon reduction in the steel production process. However, steel production involves numerous unit processes and complex production paths, making it difficult to analyze each path individually. Therefore, establishing a carbon footprint optimization model for steel products allows for direct solution to find the optimal production path.

[0178] The purpose of obtaining the adjustment results of the production path for this type of steel product and the optimization results of the product's carbon footprint is to address the fact that, in the steel product production process, the production schedule is determined by the updating of product orders. This means that the production path may be subject to change at various stages of the production process. When the product order type is relatively simple and the production between equipment with similar functions can be adjusted within a certain range, optimizing the carbon footprint of steel product production during energy and raw material input or production can serve as relevant information for production scheduling of this type of steel product when the type and quantity of energy and raw material inputs are known. This provides a reference for enterprises to select suitable production paths; it provides decision-making objectives, references, and alternative solutions for steel product production decisions, facilitating the selection of appropriate production paths and ensuring the achievement of carbon footprint optimization goals; furthermore, the path adjustment and optimization results can provide a basis for achieving rational resource allocation and reducing the carbon footprint of steel products.

[0179] Based on relevant data and data quality of steel products, background data and carbon emission factor data are imported to calculate the carbon footprint optimization results and uncertainty range of steel products.

[0180] When the path of a certain link in the steel production process can be optimized and adjusted, based on the carbon footprint optimization model, path optimization can be carried out in the unit process that can be optimized and the downstream unit processes connected to that unit process. This enables real-time carbon footprint optimization when the path changes during the production process. By comparing the changes in the steel product production path and the carbon footprint results after the original production path and the adjusted production path, the carbon emission reduction can be obtained, thereby analyzing the potential for optimizing the carbon footprint of steel products.

[0181] After the product manufacturing process is completed, based on the carbon footprint optimization model, we can analyze whether the product's production path is a low-carbon production path by comparing the optimized carbon footprint results with the actual results. If there is room for optimization, we can analyze the optimization potential of the production path.

[0182] Based on the carbon footprint optimization results and their uncertainty range, a comparative analysis was conducted with the carbon footprint calculation results and their uncertainty range based on the steel products produced through the original production path. Through result comparison and result comparison within the uncertainty range, the effect of the optimization results based on the result comparison and the optimization results within the uncertainty range on reducing carbon footprint was analyzed.

[0183] In this embodiment of the invention, the carbon footprint management system combines various carbon footprint management methods such as carbon footprint calculation, analysis, prediction, and optimization. It can accurately assess the impact of changes in the production path on the carbon footprint of steel products in different states before, during, and after product production, thus making up for the shortcomings of existing management systems that only analyze carbon footprint results.

[0184] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for managing the carbon footprint of steel products based on life cycle assessment, characterized in that, include: Collect information on the steel products of enterprises, as well as the material flow, energy flow, and flow path of unit processes in the steel product production process; Import a lifecycle background database of material and energy flows that have exchange relationships with external entities during the production process; Import a library of carbon emission factors for material and energy flows throughout the life cycle. The material and energy flow data of each unit process in the production process were examined using metal balance and carbon balance methods. Update the background database and the carbon emission factor database; Classify and store the data corresponding to different production paths, different steel grades, and different material and energy flows; Determine the carbon footprint calculation function unit, production path, and calculation system boundary for different types of steel products; select data allocation principles; and establish a life cycle carbon footprint model. Carbon footprint management of the steel products is carried out based on the life cycle carbon footprint model; the life cycle carbon footprint model includes: a life cycle carbon footprint calculation model, a life cycle carbon footprint prediction model, and / or a life cycle carbon footprint optimization model. The establishment of the life cycle carbon footprint model includes: Based on the requirements for calculating the carbon footprint of steel products, the calculation scope and allocation principles are determined. According to the nature, quantity and use of steel products, appropriate functional units are selected. Based on the connection between intermediate products in each unit process of steel product production and the conversion relationship of material flow and energy flow within the unit process, a life cycle carbon footprint calculation model for steel products is established. Based on the aforementioned carbon footprint calculation model, the historical production path and the conversion relationships and efficiencies of material and energy flows in unit processes for the steel grade to be predicted are obtained. Appropriate path information and unit process information are analyzed and selected as information and data to train the lifecycle carbon footprint prediction model; and / or, Based on the steel product lifecycle carbon footprint calculation model, the conversion relationship and efficiency of material flow and energy flow in unit processes, as well as the quantity and corresponding relationship of intermediate flows between each unit process, are obtained. Based on the corresponding relationship, conversion relationship and efficiency, objective function and constraints are constructed so that the product carbon footprint can be minimized by optimizing the production path without changing the production process.

2. The method for managing the carbon footprint of steel products based on life cycle assessment according to claim 1, characterized in that, The establishment of the carbon footprint calculation model for the life cycle of steel products includes: Based on the transformation relationships of each unit process and the material and energy flows within the unit process, as well as the flow relationships of intermediate products between different unit processes, a steel product production process model is established. Based on the steel product production process model and the material and energy flows related to the production process in the background database, a steel product life cycle process model is constructed. Based on the steel product production process model and the steel product life cycle process model, the material flow and energy flow in the process are converted into carbon flow according to the carbon emission factors of the material flow and energy flow related to the production process, and a steel product life cycle carbon footprint calculation model is constructed. The life cycle carbon footprint prediction model is established, including: Based on the steel product lifecycle carbon footprint calculation model, all historical production paths in the steel product carbon footprint calculation results of the steel grade to be predicted are obtained, and appropriate path information is analyzed and selected. Based on the conversion relationship and efficiency of material flow and energy flow in unit process and path information, as well as the intermediate flow of each unit process and its upstream and downstream correspondence, when the type and quantity of input energy and raw materials, the unit process to which they are input, and the production path between unit processes are known, the output of material flow and energy flow of unit processes and the quantity and correspondence of intermediate flow between each unit process in the entire production process can be obtained. Based on the output of material and energy flows in unit processes and the quantity and correspondence of intermediate flows between various unit processes in the entire production process, the conversion relationship and conversion efficiency of material and energy flows in unit processes are derived. Based on the conversion relationship and efficiency of material flow and energy flow in unit processes, as well as the intermediate flow of each unit process and its upstream and downstream correspondence, and according to the historical production path of the steel grade to be predicted in a similar time period, combined with the basic information of each unit process, we analyze and select appropriate paths and path information as information and data to train the life cycle carbon footprint prediction model. The life cycle carbon footprint optimization model is established, including: Based on the life cycle carbon footprint calculation model, and according to the conversion relationship and efficiency of the material flow and energy flow in the unit process, as well as the quantity and correspondence of intermediate flows between the unit processes, an objective function and constraints are set to minimize the product carbon footprint by optimizing the production path without changing the production process. Based on the life cycle carbon footprint calculation model, the historical production path information, production data, and carbon footprint results of a certain steel product that needs to be optimized are obtained. Combined with the objective function and constraints, a carbon footprint optimization model for steel products is formed.

3. The method for managing the carbon footprint of steel products based on life cycle assessment according to claim 1 or 2, characterized in that, Carbon footprint management of the steel products based on the aforementioned lifecycle carbon footprint model includes: Based on the aforementioned steel product lifecycle carbon footprint calculation model, material and energy flow data, background data, and carbon emission factor data for the corresponding unit processes are imported. Based on relevant data and data quality, the carbon footprint results and uncertainty range of the steel products are calculated. Based on the carbon footprint results, the differences in carbon footprints among different products and the differences in the contribution of each factor to the carbon footprint results are analyzed. Based on the aforementioned steel product lifecycle carbon footprint calculation model, the corresponding changes in the carbon footprint when a certain factor changes within the actual production range are analyzed, thereby obtaining the sensitivity of the carbon footprint to that factor. The calculation and analysis results are then categorized and stored. Based on the same carbon footprint calculation model, and according to the carbon footprint results of the steel products, the carbon footprint results of the enterprise's various steel products, the sub-types of the same category of steel products, and the same type of steel in different calculation time ranges are compared and analyzed. Based on carbon footprint calculation models with different calculation boundaries, and using the carbon footprint results data of the steel products, the carbon footprint results of the same steel grade are compared and analyzed within different calculation boundary ranges to help enterprises understand their actual carbon footprint situation. Based on the carbon footprint data of steel products, the process contribution of the carbon footprint data of steel products is classified and calculated from three aspects: direct emissions during the production process, indirect emissions from the consumption of externally input electricity and heat, and emissions in the supply chain. The composition ratio and potential contribution impact of the carbon footprint data of each link of steel products are shown. Based on the carbon footprint data of the steel products, the process contribution of the carbon footprint data of steel products is classified and calculated from three dimensions: each production process, the equipment included in each process, and the input and output of each piece of equipment during the in-plant production process. This demonstrates the composition ratio and potential contribution impact of the carbon footprint data of each stage of the steel product production process.

4. The method for managing the carbon footprint of steel products based on life cycle assessment according to claim 1 or 2, characterized in that, Carbon footprint management of the steel products based on the aforementioned lifecycle carbon footprint model includes: When the production path of steel products and the input material and energy flows in the unit process are known, the life cycle carbon footprint prediction model can be used to predict the carbon footprint of steel products after production, either during energy and raw material input or during product production.

5. The method for managing the carbon footprint of steel products based on life cycle assessment according to claim 4, characterized in that, Predicting the carbon footprint of steel products after production includes: Based on the steel product lifecycle carbon footprint prediction model, the carbon footprint of steel products after production can be predicted during energy and raw material input or during the production process. When the type, quantity, and unit process of the input energy and raw materials are known, but the final steel product has not yet been produced through the production process, the carbon footprint prediction model can be used to obtain the input and output of the material flow and energy flow of each unit process of the steel product, as well as the intermediate flow and corresponding relationship between each unit process in the entire production process. When the path of a certain link in the steel production process changes, the unit process in which the link is located and the downstream unit processes connected to the unit process are adjusted in a timely manner based on the steel product carbon footprint prediction model, so as to realize the real-time updated carbon footprint prediction when the path changes in the production process. The corresponding relationships selected in the carbon footprint prediction model are combined with the input and output data of each unit process to obtain the carbon footprint prediction results; the accuracy of the prediction results is compared and analyzed with the carbon footprint prediction results calculated based on the data collected from each unit process after the production of this type of steel product; based on the relevant data of steel products and the quality of the relevant data, the carbon footprint prediction results of steel products and the uncertainty range of the results are calculated. Based on the carbon footprint prediction results and their uncertainty intervals, a comparative analysis is conducted on the carbon footprint calculation results and their uncertainty intervals calculated based on the data of each unit process after the production of the same type of steel product through the same production path. By comparing the results within the uncertainty intervals, the accuracy of the prediction results within the uncertainty intervals is analyzed.

6. The method for managing the carbon footprint of steel products based on life cycle assessment according to claim 1 or 2, characterized in that, Carbon footprint management of the steel products based on the aforementioned lifecycle carbon footprint model includes: When steel products are not yet in production or are in the production process, the carbon footprint of the product is minimized by adjusting the production path of this type of steel product based on the life cycle carbon footprint optimization model.

7. The method for managing the carbon footprint of steel products based on life cycle assessment according to claim 6, characterized in that, The adjustment to the product path for this type of steel product includes: Based on the optimized production path in the carbon footprint optimization model, and combined with the input and output data of each unit process, the carbon footprint optimization results are obtained; based on the relevant data of steel products and the relevant data quality, the carbon footprint optimization results data and the uncertainty range of the results of steel products are calculated. When the path of a certain link in the steel production process can be optimized and adjusted during the production process, based on the carbon footprint optimization model, path optimization can be realized in the unit process of the link that can be optimized and adjusted, and in the downstream unit processes connected to the unit process. Real-time carbon footprint optimization can be realized when the path changes during the production process. By comparing the changes in the steel product production path and the carbon footprint results after the original production path and the adjusted production path, the carbon footprint reduction can be obtained, thereby analyzing the potential for optimizing the carbon footprint of steel products. After the product manufacturing process is completed, based on the carbon footprint optimization model, the production path of the product is analyzed to determine whether it is a low-carbon production path by comparing the optimized carbon footprint results with the actual results. If there is room for optimization, the optimization potential of the production path is analyzed. Based on the carbon footprint optimization results and their uncertainty range, a comparative analysis was conducted with the carbon footprint calculation results and their uncertainty range based on the steel products produced through the original production path. The analysis aimed to assess the carbon footprint reduction effect of the optimization results within the uncertainty range based on the result comparison.

8. A carbon footprint management system for steel products based on life cycle assessment, characterized in that, include: The data acquisition module is used to collect information on the company's steel products, as well as the material flow, energy flow, and flow path of the unit processes in the steel product production process. Import a lifecycle background database of material and energy flows that have exchange relationships with external entities during the production process; Import a library of carbon emission factors for material and energy flows throughout the life cycle. The data management module is used to verify the material flow and energy flow data of each unit process in the production process by means of metal balance and carbon balance methods. Update the background database and the carbon emission factor database; Classify and store data corresponding to different production paths, different steel grades, and different material and energy flows; The model building module is used to determine the carbon footprint calculation functional unit, production path, and calculation system boundary for different types of steel products; select data allocation principles; and establish a life cycle carbon footprint model. The life cycle carbon footprint model includes: a life cycle carbon footprint calculation model, a life cycle carbon footprint prediction model, and / or a life cycle carbon footprint optimization model. The establishment of the life cycle carbon footprint model includes: Based on the requirements for calculating the carbon footprint of steel products, the calculation scope and allocation principles are determined. According to the nature, quantity and use of steel products, appropriate functional units are selected. Based on the connection between intermediate products in each unit process of steel product production and the conversion relationship of material flow and energy flow within the unit process, a life cycle carbon footprint calculation model for steel products is established. Based on the aforementioned carbon footprint calculation model, the historical production path and the conversion relationships and efficiencies of material and energy flows in unit processes for the steel grade to be predicted are obtained. Appropriate path information and unit process information are analyzed and selected as information and data to train the lifecycle carbon footprint prediction model; and / or, Based on the steel product life cycle carbon footprint calculation model, the conversion relationship and efficiency of material flow and energy flow in unit processes, the quantity and corresponding relationship of intermediate flow between each unit process are obtained. Based on the corresponding relationship, conversion relationship and efficiency, objective function and constraints are constructed so that the product carbon footprint can be minimized by optimizing the production path without changing the production process. The carbon footprint analysis module is used to manage the carbon footprint of the steel products based on the life cycle carbon footprint model constructed by the model building module.

9. A carbon footprint management system for steel products based on life cycle assessment as described in claim 8, characterized in that, The carbon footprint analysis module includes: The carbon footprint calculation and analysis module is used to calculate and compare the carbon footprint of products based on the carbon footprint calculation model described in claim 2. The carbon footprint prediction and analysis module is used to predict the carbon footprint of steel products after production, based on the carbon footprint prediction model as described in claim 2, when energy and raw materials are input or during the production process, and to analyze the prediction results. The carbon footprint optimization analysis module is used to optimize the carbon footprint of steel products by adjusting the specific production path of steel products based on the carbon footprint optimization model as described in claim 2, and to analyze the optimization results.

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