Double-target steel industry decarburization path process optimization method and system considering key boundary constraint
By constructing an LP linear optimization system with dual environmental and economic objectives, planetary boundary constraints are integrated into the steel industry, resolving the contradiction between environmental sustainability and economic feasibility in the traditional decarbonization path of the steel industry. This achieves a multi-dimensional reduction in environmental load and an improvement in economic adaptability of the steel production process, supporting the industry in achieving its "dual carbon" goals.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-04
- Publication Date
- 2026-04-03
AI Technical Summary
In the process of achieving deep decarbonization, the steel industry faces difficulties in finding a balance between environmental sustainability and economic feasibility with existing technologies. There is a lack of systematic methods to scientifically transform global-scale ecological security constraints into industry-level process path optimization decisions, resulting in insufficient environmental sustainability and economic feasibility of the optimized paths.
Using life cycle assessment (LCA) and linear programming (LP) models, an LP linear optimization system with dual environmental and economic objectives is constructed. By performing a second-order downscaling process, key planetary boundary constraints are integrated into the steel industry, establishing a dual environmental and economic optimization model, outputting the optimal process combination, and performing dynamic optimization by combining multi-dimensional environmental constraints and economic costs.
Under critical boundary constraints, the environmental impact of the steel production process has been significantly reduced, and the resulting technology combination path has good economic adaptability and environmental sustainability, supporting the steel industry in achieving its "dual carbon" goals.
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Figure CN121787630A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of carbon emission optimization technology, and more specifically to a dual-objective method and system for optimizing decarbonization pathways in the steel industry, taking into account critical boundary constraints. Background Technology
[0002] Currently, with the escalation of global climate change and the introduction of "dual carbon" targets, the green transformation of high-carbon emission industries has become a major issue that countries urgently need to address. As a typical high-energy-consuming and high-emission basic industry, the steel industry accounts for approximately 15% of China's total carbon emissions, far exceeding the global average, and plays a crucial role in achieving deep decarbonization goals. However, the steel industry still faces significant limitations in exploring and managing pathways for quality improvement, efficiency enhancement, and sustainable decarbonization.
[0003] The main problems in the steel industry are as follows: On the one hand, steel production processes heavily rely on the traditional blast furnace-converter (BF-BOF) process (accounting for as much as 90%). This process primarily uses fossil fuels such as coal, resulting in high carbon emission intensity and a lack of efficient end-of-pipe treatment methods, making it particularly vulnerable to pressure from critical planetary boundary dimensions such as climate change. On the other hand, although emerging low-carbon technologies such as electric arc furnaces (EAF), hydrogen-based direct reduced iron (H2-DRI) combined with electric furnaces, and carbon capture, utilization and storage (CCUS) have shown emission reduction potential, their large-scale industrial application is still severely constrained by factors such as insufficient technological maturity, high costs (e.g., green hydrogen production and storage), lack of infrastructure, and an imperfect industry standard system. Current improvements in traditional steel industry decarbonization research and management often focus on single objectives (such as cost minimization or carbon emission reduction) and lack a systematic approach. In particular, there is a lack of effective methods to scientifically and quantitatively transform and integrate global-scale ecological security constraints (such as planetary boundary theory) into industry-level process optimization decisions. Existing models (such as single economic optimization models) are unable to simultaneously take into account multi-dimensional environmental constraints (such as climate change, nitrogen and phosphorus cycles, freshwater resources, biodiversity, and land use) and economic feasibility. They cannot dynamically respond to changes in policies, technologies, and resource prices, nor can they fully consider the differences in resource endowments and technological adaptability in different regions, leading to doubts about the environmental sustainability or insufficient economic feasibility of the optimization path.
[0004] Therefore, how to systematically optimize the steel production process under the constraints of critical planetary boundaries, identify and promote economically feasible and environmentally sustainable combinations of quality improvement, efficiency enhancement, and decarbonization technologies, and effectively support the steel industry in achieving its "dual carbon" goals, is a core issue that urgently needs to be addressed by those skilled in the art. This urgently requires the development of dynamic optimization decision-making methods that integrate multiple boundary environmental constraints and economic objectives. Summary of the Invention
[0005] In view of this, the present invention provides a dual-objective decarbonization pathway process optimization method and system for the steel industry that considers critical boundary constraints. It integrates life cycle assessment (LCA) and linear programming (LP) models to construct an LP linear optimization system with dual environmental and economic objectives, which solves the problem that traditional process pathways are difficult to balance between environmental sustainability and cost control.
[0006] To achieve the above objectives, the present invention adopts the following technical solution: On the one hand, this invention provides a dual-objective method for optimizing decarburization pathways in the steel industry, considering critical boundary constraints, comprising: Acquire production process flow and data in the steel industry, and construct scenario groups with multiple process path combinations based on the production process flow; The environmental boundary occupancy rate of the steel industry at the national level was obtained through secondary downscaling. Establish an environmental impact database covering the entire lifecycle of steel production; With the optimization objectives of minimizing total life cycle cost and minimizing environmental boundary occupancy, and based on actual needs, a dual-objective LP linear optimization model of economic and environmental impact is established. The economic-environmental bi-objective LP linear optimization model is solved to output the scenario group with the optimal process combination.
[0007] Preferably, the environmental boundary occupancy rate of each process path is obtained through secondary downscaling, including: An allocation method combining per capita and ability-to-pay approaches was adopted, and the environmental boundary occupancy rate of each process path was obtained through a two-stage downscaling process of global-national-industry.
[0008] Preferably, an allocation method combining the per capita approach and the ability-to-pay approach is used. This method employs a two-stage downscaling process—global-national-industry—to obtain the environmental boundary occupancy rate for each technological path, including: Determine the key planetary boundary dimensions, and based on the key planetary boundary dimensions, determine the environmental load factor of the steel industry and the environmental load corresponding to the environmental load factor. Determine global planetary boundary thresholds based on the aforementioned key planetary boundary dimensions; Based on the planetary boundary principle and a pre-set resource allocation strategy, the global planetary boundary threshold is allocated to the steel industry scale using the per capita method and the ability to pay method, so as to obtain the corresponding environmental safety boundary threshold for the steel industry. Based on the actual environmental load of the industry environmental load factor for each process path and the corresponding environmental safety boundary threshold for the steel industry, the environmental boundary occupancy rate of each process path in different environmental boundary dimensions is calculated.
[0009] Preferably, an economic-environmental bi-objective LP linear optimization model is established with the optimization objectives of minimizing total life cycle cost and minimizing environmental boundary occupancy, including: The objective function constructed to minimize the total lifecycle cost is:
[0010] Where P is the set of process paths, This represents the unit cost of the corresponding process path p; It represents the annual output of process path p; The objective function constructed to minimize the environmental boundary occupancy rate is:
[0011] in, This represents the total annual environmental load calculated using a multiple linear regression model. This represents the annual environmental safety boundary threshold for the steel industry; The normalized weight coefficient representing the dimension of the k-th planetary boundary; The objective functions for total lifecycle cost and environmental boundary occupancy are integrated into a comprehensive optimization objective function through linear weighting:
[0012] in, The weighting coefficients of the objective function for total life cycle cost. These are the weighting coefficients of the objective function for environmental boundary occupancy rate; This serves as a benchmark for cost targets. This serves as a reference benchmark for environmental impact targets.
[0013] Preferably, the constraints for constructing the economic-environmental bi-objective LP linear optimization model include: Construct input-output constraint equations, specifically including: Material conservation equation
[0014] Where P is the set of process paths, It represents the annual output of process path p. It represents the iron input per unit output of process path p. It represents the iron output per unit of production in process path p. It is the iron loss per unit output of process path p; For the hydrogen-based reduced iron production process, the hydrogen balance equation is established as follows:
[0015] in, It is a subset of process routes that require hydrogen. It is the total supply of hydrogen produced by electrolysis. This is the total supply of imported hydrogen energy. This represents the hydrogen consumption per unit output of the process route p. This refers to the amount of hydrogen recovered. This is the total amount of hydrogen energy lost during transportation; The balance constraint equations for energy demand and energy supply throughout the entire process are established as follows:
[0016] in, For electricity self-production, For energy storage, This refers to electricity imports. Electricity consumption; For natural gas self-production, For energy storage, This refers to the volume of natural gas imports. This refers to natural gas consumption. Establish a balance equation among CO2 production, capture, and escape / emission rates in the process:
[0017] in, For the unit output of process path p Production volume Total catch For final emissions; Production and resource capacity constraints include: Capacity constraints:
[0018] in, This represents the forecast for total crude steel demand.
[0019] Energy supply constraints:
[0020] in, It represents the unit energy consumption of path p. It is the upper limit of available energy; Infrastructure constraints; Planetary boundary environment rigidity constraints include: Climate use constraints:
[0021] Nitrogen emission usage constraints:
[0022] Phosphorus emission usage constraints:
[0023] Freshwater use restrictions:
[0024] Constraints on biodiversity loss:
[0025] Land resource use constraints:
[0026] in, This represents the environmental load per unit output of process path p in dimension k. Represents the annual environmental occupancy rights for dimension k; After normalizing the individual loads, a weighted sum is used to establish a constraint for the comprehensive environmental impact score:
[0027] in, For comprehensive environmental boundary constraints.
[0028] Preferably, the economic-environmental bi-objective LP linear optimization model is solved to output a set of scenarios with the optimal process combination, including: By using a linear programming algorithm, the above-mentioned input-output constraints, production and resource capacity constraints, planetary boundary environment rigidity constraints, and comprehensive optimization objective function are co-embedded in the optimization solution process. Through variable iteration and multi-scenario simulation, the optimal process combination that satisfies economic-environmental balance is output as a scenario group.
[0029] Preferably, the method further includes: generating a dynamic optimization roadmap to show a comparison of scenario groups with different process path combinations in terms of environmental boundary occupancy, production life cycle cost, and emission reduction potential.
[0030] On the other hand, the present invention provides a dual-objective decarburization path process optimization system for the steel industry that considers critical boundary constraints, comprising: The data acquisition module is used to acquire the production process flow and production process data of the steel industry, and to construct scenario groups with multiple process path combinations based on the production process flow. The planetary boundary calculation module is used to obtain the environmental boundary occupancy rate of the steel industry at the national level for each process path through secondary downscaling. The LCA database is used to build and store an environmental impact database covering the entire lifecycle of steel production. The LP optimization engine is used to establish an economic-environmental dual-objective LP linear optimization model with the optimization objectives of minimizing total life cycle cost and minimizing environmental boundary occupancy, and solve it to output the scenario group of optimal process combination; A visualization decision-making platform is used to generate dynamic optimization roadmaps, showing the comparison of different process path combinations in terms of environmental boundary occupancy, production life cycle cost, and emission reduction potential.
[0031] As can be seen from the above technical solutions, compared with the prior art, this invention discloses a dual-objective decarbonization pathway process optimization method and system for the steel industry that considers key boundary constraints. It integrates Life Cycle Assessment (LCA) and Linear Programming (LP) models to construct an LP linear optimization system with dual environmental and economic objectives, solving the problem of traditional process pathways struggling to balance environmental sustainability and cost control. Furthermore, this invention scientifically scales down global-scale planetary boundary indicators to the steel industry, establishing a mapping mechanism between regional environmental capacity limits and industry resource allocation, achieving quantification of environmental boundary occupancy and refined management of resource quotas at the industry level. During the optimization process, by incorporating key environmental boundary constraints such as climate change, nitrogen and phosphorus cycles, freshwater resources, and biodiversity into the optimization model, the multi-dimensional environmental load throughout the steel production process is significantly reduced. Simultaneously, the constructed minimum cost objective function comprehensively considers multiple costs, including raw materials, energy, and carbon trading, and the output technology combination path exhibits good economic adaptability. Attached Figure Description
[0032] 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 only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.
[0033] Figure 1 This is a flowchart of the specific method disclosed in this embodiment; Figure 2 This is a specific steel industry production process diagram used in this embodiment.
[0034] Figure 3 This is a schematic diagram of the system disclosed in an embodiment of the present invention. Detailed Implementation
[0035] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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 are within the scope of protection of the present invention.
[0036] This invention discloses a dual-objective decarburization pathway process optimization method for the steel industry that considers critical boundary constraints, such as... Figure 1 As shown, it includes: Step 1: Obtain the production process flow and data of the steel industry, and construct scenario groups with multiple process path combinations based on the production process flow.
[0037] Before formal modeling, this embodiment first outlines the pathways of various steel production technologies, determining the specific process flow for each technology. Figure 2 As shown, scenario groups are established, and lifecycle inventories for all processes are collected.
[0038] The steel industry's production processes and technologies include blast furnace ironmaking, converter steelmaking, electric arc furnace steelmaking, CCUS carbon sequestration and storage technology, and hydrogen-based reduced iron production. Specific steel production processes also include sintering, coking, casting and rolling, and pelletizing.
[0039] Based on the above production process, and considering the raw material, energy, and emission aspects, optimized processes for decarbonization and efficiency improvement in the steel industry are developed. Through feasibility analysis of the optimized solutions, the following scenario groups are proposed for the entire steel industry: BAU Scenario Group: Blast furnace ironmaking + converter steelmaking accounts for 90%, blast furnace ironmaking + electric arc furnace steelmaking accounts for 10%; This scenario group is based on existing scenarios in the steel industry, where 90% of the steel industry uses the blast furnace ironmaking + converter steelmaking (BF-BOF) process route, and 10% uses the blast furnace ironmaking + electric arc furnace steelmaking (BF-EAF) process. The calculation results will serve as a basic reference for the optimization directions of other scenario groups.
[0040] Extended Scenario Group 1: Blast furnace ironmaking + converter steelmaking + CCUS account for 90%, blast furnace ironmaking + electric arc furnace steelmaking account for 10%; Currently, the blast furnace ironmaking + converter steelmaking technology, which accounts for the majority of production, is accompanied by a large amount of carbon dioxide tail gas emissions and pollutant emissions. Carbon capture and storage (CCUS) technology can effectively reduce the impact of carbon emissions from steel production processes on climate change and reduce the risk of steel production crossing planetary boundary limits.
[0041] Extended Scenario Group 2: The proportion of blast furnace ironmaking + electric arc furnace steelmaking increases, while the proportion of blast furnace ironmaking + converter steelmaking + CCUS decreases. Currently, in some regions, the proportion of electric arc furnace (EAF) steelmaking is higher than the overall average. EAF steelmaking has several advantages over converter steelmaking. Current EAF technology primarily utilizes green energy sources such as wind and solar power, resulting in significantly lower carbon dioxide emissions compared to converters. Furthermore, EAFs produce significantly less air pollution than converter steelmaking, have higher raw material recovery rates, and offer more flexible layout options. Therefore, substantially increasing the proportion of converter steelmaking in the steel industry has a significant effect on improving quality and efficiency and promoting decarbonization within the steel sector.
[0042] Extended Scenario Group 3: Introduce hydrogen-based reduced iron + electric arc furnace steelmaking technology, while keeping the proportion of blast furnace ironmaking + electric arc furnace steelmaking unchanged; Hydrogen-based reduced iron is a technology that uses pure hydrogen instead of traditional carbon monoxide or coal gas as a reducing agent to reduce iron ore in iron smelting. This technology can reduce carbon dioxide emissions by over 90%, achieving near-zero carbon iron and steel production. It perfectly aligns with the green energy economy, and hydrogen energy is currently a target of strong policy support in countries worldwide, representing a crucial production path for improving the quality and efficiency of steel production and achieving sustainable decarbonization. However, large-scale promotion of hydrogen energy faces the problem of excessively high costs and is difficult to achieve in the short term. Therefore, this invention only addresses the partial application of hydrogen-based reduced iron technology in production.
[0043] In this embodiment, the production process and current status of the steel industry are thoroughly analyzed through the scenario groups set above. Targeting industry pain points, and combining emerging technologies, targeted optimization methods and feasible paths are proposed, providing a foundation for subsequent model building.
[0044] Step 2: Obtain the environmental boundary occupancy rate of the steel industry at the national level for each process path through secondary downscaling.
[0045] Step 3: Establish an environmental impact database covering the entire life cycle of steel production; Step 4: With the optimization objectives of minimizing the total life cycle cost and minimizing the environmental boundary occupancy rate, set constraints according to actual needs, and establish an economic-environmental dual-objective LP linear optimization model; Step 5: Solve the economic-environmental bi-objective LP linear optimization model and output the scenario group of optimal process combinations. Specifically, use Python or MATLAB programming to solve the LP model, output the optimal process combination scheme, and verify it with actual production data.
[0046] Furthermore, the environmental boundary occupancy rate for each process path is obtained through secondary downscaling, including: An allocation method combining per capita and ability-to-pay approaches was adopted, and the environmental boundary occupancy rate of each process path was obtained through a two-stage downscaling process of global-national-industry.
[0047] Preferably, an allocation method combining the per capita approach and the ability-to-pay approach is used. This method employs a two-stage downscaling process—global-national-industry—to obtain the environmental boundary occupancy rate for each technological path, including: Determine the critical planetary boundary dimensions, and based on the critical planetary boundary dimensions, determine the environmental load factor for the steel industry and the corresponding environmental load.
[0048] Based on the specific steel industry production process, this embodiment selects the following six key global planetary boundary dimensions as the environmental benchmarks for this invention: climate change (characterized by atmospheric CO2 concentration), nitrogen cycle (reactive nitrogen release), phosphorus cycle (phosphorus release), freshwater resource consumption, biosphere integrity (biodiversity loss), and land system change.
[0049] The specific environmental load factors generated by steel industry production activities in the above six dimensions include: Climate change: Greenhouse gas emissions from the industry (mainly converted to CO2 equivalent). Currently, the industry's carbon emissions mainly come from the "long process" steelmaking, which is based on the blast furnace-converter steelmaking production process.
[0050] Nitrogen cycle: the amount of reactive nitrogen released by the industry, with nitrogen oxides being the main emission.
[0051] Phosphorus cycle: The amount of phosphorus (such as phosphate) released by an industry.
[0052] Freshwater resources: Total freshwater consumption for industry production.
[0053] Biosphere integrity: the area of habitat loss or potential ecological impact index caused by industry activities.
[0054] Land system changes: The area of land directly occupied or significantly altered by industry activities.
[0055] The global planetary boundary thresholds are determined based on the key planetary boundary dimensions. Specifically, based on the global planetary boundary insurmountable thresholds established and quantified by Johan Rockström et al., the specific global security boundary values of the above six dimensions are obtained.
[0056] Based on the planetary boundary principle and a pre-defined resource allocation strategy, the global planetary boundary threshold is progressively allocated to the steel industry scale using the per capita and affordability methods, resulting in the corresponding environmental safety boundary threshold for the steel industry. The pre-defined resource allocation strategy includes: Calculate the global per capita boundary value: Divide the global planetary boundary threshold by the total global population (per capita method).
[0057] Calculate the national-scale boundary value: multiply the global per capita boundary value by the country's total population (per capita method).
[0058] Calculating the steel industry's right to use (pre-allocated share): Multiply the national-scale boundary value by the specific allocation factor for the steel industry based on its share of GDP (payability method).
[0059] Based on the actual environmental load of the industry environmental load factor for each process path and the corresponding environmental safety boundary threshold for the steel industry, the environmental boundary occupancy rate of each process path in each environmental boundary dimension is calculated. The specific formula is as follows: .
[0060] The calculated occupancy rate data is normalized (e.g., 100% is set as the safety boundary threshold) to compare the degree of exceeding limits at different boundaries on the same scale. An evaluation result matrix or chart (as in this invention) is then generated. Figure 2 As shown in the figure, the actual occupancy status of the steel industry in each dimension relative to its allocated planetary boundary safety space is clearly displayed (not exceeding the limit, critical, exceeding the limit, severely exceeding the limit).
[0061] In another embodiment, a linear programming (LP) model based on life cycle assessment (LCA) is constructed to optimize the environmental impact and economic costs of the steel industry under various process pathways. This mainly includes the following: 1. Construct input-output constraint equations: such as Figure 2 As shown, the main energy balance in the current steel industry production process is the multi-stage processing balance of iron, involving coking, sintering, ironmaking, steelmaking, casting, rolling, and sintering production steps. In addition, the energy balance in the steel production process also involves electricity and natural gas usage balances. The hydrogen-based reduction of iron step in extended scenario three involves the input-output balance equation for hydrogen energy, with the main input sources being industrial methane and water-to-hydrogen production processes. In this example, for carbon emission optimization, a carbon emission capture equation is also involved. The specific constraint equations are as follows: Multi-stage processing balancing: Establishing material conservation equations for each process (sintering, coking, ironmaking, steelmaking, rolling, etc.) from raw materials (iron ore, scrap steel) to the final product (crude steel).
[0062] Where P is the set of process paths, It represents the annual output of path p. It is the iron input per unit output of path p. It represents the iron output per unit of output along path p. It is the iron loss per unit output along path p.
[0063] Hydrogen system balance: For the hydrogen-based reduced iron production process in this embodiment, which involves a hydrogen metallurgical path, a hydrogen balance equation is established:
[0064] in, It is a subset of process routes that require hydrogen. It is the total supply of hydrogen produced by electrolysis. This is the total supply of imported hydrogen energy. It is the hydrogen consumption per unit output of path p (tons / ton of steel). This refers to the amount of hydrogen recovered. It represents the total amount of hydrogen energy lost during transportation.
[0065] Energy supply and demand balance: Establishing the energy demand and supply for the entire process. In this embodiment, the main energy sources are electricity and gas. The specific balance constraint equations are as follows:
[0066] Where 'e' represents electricity, For electricity self-production, For energy storage, This refers to electricity imports. This refers to electricity consumption. Currently, steel companies mainly rely on purchased electricity and electricity generated by their own power plants. In recent years, an increasing number of companies have adopted self-generation as an important way to save energy and reduce costs. Among these, new energy power generation, because it does not produce carbon emissions, is driving companies to continuously increase their installed capacity of renewable energy, thereby achieving the dual goals of energy conservation and emission reduction.
[0067]
[0068] Where g represents natural gas, For natural gas self-production, For energy storage, This refers to the volume of natural gas imports. This represents natural gas consumption.
[0069] Carbon capture and storage balance: Establishing a balance equation between CO2 production, capture rate (related to decision variables), and escape / emission rate in the process:
[0070] in, For the unit output of path p Production volume Total catch This refers to final emissions. Coal and coke are low-calorific-value, high-carbon dioxide emission coefficient energy sources, and are major sources of carbon dioxide emissions. In the steel industry, energy consumption can be divided into three main categories: coal, petroleum, and electricity. Coal and electricity account for the majority of energy consumption, with thermal power being the primary source of electricity supply, and coal being used in a very high proportion of thermal power generation. Compared to other energy sources, coal not only has a low calorific value and a high carbon emission coefficient, but also constitutes a huge proportion of the steel industry's energy structure, thus making a significant contribution to carbon dioxide emissions. Therefore, energy consumption structure has become one of the important factors affecting carbon dioxide emissions from the steel industry.
[0071] The processes and technologies involved in steel production are also significant factors influencing the industry's carbon dioxide emissions. Besides emissions from fossil fuel combustion, the chemical reactions occurring during production also release substantial amounts of carbon dioxide. The basic principle of steel production is to utilize minerals such as coal and iron ore, employing a series of chemical reactions to reduce iron ore into the final product using carbon as a reducing agent. In this process, the coke ovens, blast furnaces, and converters used in steelmaking primarily use coal and coke as fuels, and their combustion produces large amounts of carbon dioxide that are released into the atmosphere.
[0072] 2. Construct other real-world constraint equations: In this embodiment, under constraints of production and resource capacity, this method systematically incorporates the future steel industry's capacity, energy, and infrastructure limitations into the optimization framework to ensure the feasibility of the proposed solution. Based on forecasts of the future steel industry, regarding capacity, the total output of each production path is required to be consistent with the projected total crude steel demand in 2060, thereby meeting market demand while avoiding overcapacity or undercapacity. Regarding energy supply, upper limits are set on the use of major energy sources such as coal, natural gas, electricity, and hydrogen. These limits are derived from national energy development plans and technological potential forecasts, reflecting the physical boundaries of energy supply and the trend of energy structure transformation. This ensures that while meeting production needs, excessive pressure is not placed on the energy system. Regarding infrastructure, the availability of key resources such as scrap steel recycling capacity and hydrogen transportation capacity is considered. By incorporating these capacity, energy, and infrastructure constraints into the model, the generated production paths are guaranteed to be feasible and forward-looking in terms of environmental, resource, and technological conditions, providing a scientific basis for the long-term planning of the steel industry under the "dual carbon" target. The specific constraint equations are as follows: Production and resource capacity constraints: Capacity constraint: Set the sum of decision variables (output along each path) to equal the predicted total crude steel demand in 2060:
[0073] in, This represents the projected total crude steel demand in 2060.
[0074] Energy supply constraints: The total energy consumed through all pathways (coal, gas, electricity, hydrogen, etc.) must not exceed the upper limit of available resources in 2060 based on national energy planning and technological potential projections.
[0075] in, It represents the unit energy consumption of path p for energy e. It is the upper limit of available energy e.
[0076] Infrastructure constraints: Considering the upper limit of the availability of critical resources, this embodiment includes the amount of scrap steel recycled and hydrogen delivery capacity.
[0077] Planetary boundary environment rigidity limit constraints: Under the rigid constraints of planetary boundary environmental limits, this method first employs a calculation method combining per capita and affordability approaches in step two. Based on population size, economic level, and the overall global planetary boundary limits, it quantifies the environmental resource occupancy rights of the six planetary boundary dimensions, thereby obtaining safety thresholds applicable to the steel industry in each dimension. These safety thresholds serve as reference benchmarks for subsequent optimization and evaluation, defining the available resource space within the industry's environmental carrying capacity. Subsequently, based on the established multiple linear regression prediction model, the decision variables of the steel production system are input into the model. These decision variables include the activity levels of different process paths (such as output allocation, process ratios, etc.) and technology choices (such as CCUS technology application, fuel substitution ratios, etc.). Through model calculations, these decision variables can be transformed into life-cycle environmental load prediction values under each planetary boundary dimension, and the annual total load can be quantitatively calculated. The calculation results can be compared with the aforementioned safety thresholds to determine the extent to which the steel industry occupies each planetary boundary dimension under a given production plan and whether there is a risk of exceeding limits, providing a scientific basis for subsequent sustainable optimization path selection and emission reduction measures.
[0078] Constraint equations: Independent hard constraints are established for each planetary boundary dimension to ensure that the optimization result does not exceed its occupancy limit. In this embodiment, the planetary boundary dimensions include: Climate use constraints (with) (Mainly)
[0079] Nitrogen emission usage constraints:
[0080] Phosphorus emission usage constraints:
[0081] Freshwater use restrictions:
[0082] Constraints on biodiversity loss:
[0083] Land resource use constraints:
[0084] in, This represents the unit output environmental load of path p in dimension k. Represents the annual environmental occupancy rights of dimension k.
[0085] Comprehensive Constraints: Explore establishing a comprehensive environmental impact score (EIS) constraint by normalizing and weighting the above individual loads and summing them.
[0086] in, For comprehensive environmental boundary constraints.
[0087] 3. Construct the minimum cost objective function
[0088] This invention takes minimizing the total cost of a unit product throughout its entire life cycle as one of the optimization objectives. The objective function mainly consists of a dual-objective optimization, which is composed of minimizing the economic cost function and minimizing the comprehensive influence function of the new boundary. Based on the actual situation, a comprehensive dual-objective moral evaluation objective function can be constructed.
[0089] Minimize total lifecycle cost:
[0090] in, The unit cost for path p includes: raw material cost (iron ore, scrap steel, etc.), energy cost (electricity, hydrogen, fossil fuel), operation and maintenance cost (O&M), environmental governance cost (desulfurization, wastewater treatment, CCUS), and carbon trading cost (based on the predicted carbon price in 2060).
[0091] Construct a function that minimizes the combined influence of planetary boundaries: Minimize the combined effects of planetary boundaries:
[0092] in, Representing the six planetary boundary dimensions (climate change, nitrogen cycle, phosphorus cycle, freshwater resources, biodiversity, and land system); This represents the total annual environmental load (such as total CO2 emissions and total Nr release) calculated using a multiple linear regression model. This represents the annual environmental and resource security threshold for the steel industry calculated through the aforementioned steps (per capita method + ability to pay method) in the claims / description. The normalized weight coefficient representing the boundary dimension of the k-th term ( This reflects the relative importance of different environmental factors (which can be determined through expert scoring, analytic hierarchy process (AHP), or policy prioritization).
[0093] Construct a comprehensive evaluation objective function (Min Z): Economic and environmental goals are integrated into a single optimal objective through linear weighting:
[0094] in, , The weighting coefficients for economic and environmental factors ( This reflects the decision-makers' balance between cost and environmental preferences; The cost target references the benchmark value (the industry average cost at BAU) and is used for dimensionless calculation. It serves as a reference benchmark for environmental impact targets (such as the industry's overall environmental impact score before optimization) and is used for dimensionless transformation.
[0095] This function also drives the system to optimize for lower costs and less pressure on the planetary boundary.
[0096] Furthermore, the economic-environmental bi-objective LP linear optimization model is solved to output the scenario group of optimal process combinations, including: By using a linear programming algorithm, the aforementioned input-output constraints, production and resource capacity constraints, planetary boundary environment rigidity constraints, and comprehensive optimization objective function are collaboratively embedded into the optimization solution process. Through variable iteration and multi-scenario simulation, the optimal process combination that satisfies the economic-environmental balance is output as a scenario group, thereby realizing the dynamic optimal path identification under the green and low-carbon transformation of the steel industry.
[0097] In another embodiment, to enhance the understandability, transparency, and practical application value of the model results of this invention, a visualization and decarbonization roadmap planning tool for policy formulation and industry management is further developed during implementation. This tool is based on the aforementioned LP optimization model and planetary boundary assessment results, combined with the "dual-carbon" target strategy, integrating modeling results, policy paths, and industry evolution trends to output an intuitive, modular, and interactive graphical deduction interface. Specifically, it includes the following three functional modules: (1) Multi-path comparison analysis map module: This module is used to compare the comprehensive performance of different process routes under multi-dimensional environmental and economic indicators. In this embodiment, the specific implementation is as follows: Using typical pathways as the basis for horizontal comparison, including the BAU basic route, the CCUS enhanced converter route, the electric arc furnace dominant process, and the H2-DRI+EAF combined process, the environmental occupancy of each pathway across multiple planetary boundary dimensions is standardized, and environmental occupancy radar charts (such as carbon emissions, nitrogen cycle, phosphorus emissions, water consumption, and biodiversity impact) are generated. Simultaneously, bar charts of unit product lifecycle cost composition and total cost are output to identify cost-dominant factors and differences in pathway economics.
[0098] This mapping tool supports multi-path overlay, dynamic switching, and weight adjustment, helping policymakers and industry participants intuitively understand the trade-offs between different technology combinations.
[0099] (2) Timeline-based transformation roadmap module: This module constructs a timeline for the phased green transformation of the steel industry, specifically including: By demonstrating the dynamic evolution of the current baseline scenario (BAU) and the future low-carbon and zero-carbon path, key nodes of technological breakthroughs (such as the converter-electric furnace replacement point, the critical point of large-scale CCUS application, and the breakthrough window of hydrogen metallurgy) are mapped onto the timeline. In addition, resource shift inflection points (such as the peak of coking coal reduction and the critical proportion of green hydrogen penetration) and related investment return cycles are marked to achieve forward-looking and controllable transformation progress.
[0100] The timeline-style roadmap output by this module can be dynamically updated based on actual scenario settings, policy objective revisions, and market fluctuations.
[0101] (3) Modular output tools: To improve the engineering usability and management adaptability of the deliverables, this module provides modular output formats for chart data, path parameters, key node events, and other content, mainly including: The data interface supports multiple formats such as EXCEL, CSV, and JSON, making it easy to integrate into existing systems of governments, enterprises, or think tanks. It also supports the generation of policy recommendation reports, strategic simulation maps, and path databases, providing highly adaptable support for policy research, strategic consulting, and investment evaluation. It can be embedded in GIS platforms for green assessment and resource scheduling simulation of regional steel production patterns.
[0102] This scheme breaks through the limitations of traditional single-objective optimization and creatively integrates Earth system science, LCA and operations research to form three core innovations; Boundary Quantification Innovation: Through a three-level downscaling model of "global-national-industry", planetary boundaries are transformed into actionable industry environmental quotas for the first time; Dynamic coupling innovation: using regression models to realize the dynamic transmission of process parameters to boundary loads, responding to technology iteration; Innovative decision-making mechanism: ensuring a safety baseline with rigid ecological constraints, and exploring the optimal trade-off point with a flexible objective function (Min Z).
[0103] In this embodiment, the model solution shows that when the price of green hydrogen drops to $2 / kg, the proportion of the hydrogen metallurgical pathway increases to 35%, driving a 62% reduction in carbon emission intensity and a 48% reduction in biodiversity impact. This system can be embedded in the intelligent management and control platform of steel enterprises, dynamically generating a low-carbon transformation roadmap by updating energy prices, carbon trading data, and process parameters in real time, and is estimated to reduce the industry's decarbonization costs by 120 billion yuan by 2040.
[0104] On the other hand, this invention provides a dual-objective decarburization path process optimization system for the steel industry that considers critical boundary constraints, such as... Figure 3 As shown, it includes: The data acquisition module is used to acquire the production process flow and production process data of the steel industry, and to construct scenario groups with multiple process path combinations based on the production process flow. The planetary boundary calculation module is used to obtain the environmental boundary occupancy rate of the steel industry at the national level for each process path through secondary downscaling. The LCA database is used to build and store an environmental impact database covering the entire lifecycle of steel production. The LP optimization engine is used to establish an economic-environmental dual-objective LP linear optimization model with the optimization objectives of minimizing total life cycle cost and minimizing environmental boundary occupancy, and solve it to output the scenario group of optimal process combination; A visualization decision-making platform is used to generate dynamic optimization roadmaps, showing the comparison of different process path combinations in terms of environmental boundary occupancy, production life cycle cost, and emission reduction potential.
[0105] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.
[0106] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims
1. A dual-objective decarburization path optimization method for the steel industry considering critical boundary constraints, characterized in that, include: Acquire production process flow and data in the steel industry, and construct scenario groups with multiple process path combinations based on the production process flow; The environmental boundary occupancy rate of the steel industry at the national level was obtained through secondary downscaling. Establish an environmental impact database covering the entire lifecycle of steel production; With minimizing the total life cycle cost and minimizing the environmental boundary occupancy rate as optimization objectives, and setting constraints according to actual requirements, an economic-environmental dual-objective LP linear optimization model is established. The economic-environmental bi-objective LP linear optimization model is solved to output the scenario group with the optimal process combination.
2. The method for optimizing a dual-objective decarburization path in the steel industry considering critical boundary constraints as described in claim 1, characterized in that, The environmental boundary occupancy rate for each process path was obtained through secondary downscaling, including: An allocation method combining per capita and ability-to-pay approaches was adopted, and the environmental boundary occupancy rate of each process path was obtained through a two-stage downscaling process of global-national-industry.
3. The method for optimizing a dual-objective decarburization path in the steel industry considering critical boundary constraints, as described in claim 2, is characterized in that... An allocation method combining per capita and ability-to-pay approaches was adopted, and the environmental boundary occupancy rate for each technological path was obtained through a two-stage downscaling process involving global, national, and industry levels. This included: Determine the key planetary boundary dimensions, and based on the key planetary boundary dimensions, determine the environmental load factor of the steel industry and the environmental load corresponding to the environmental load factor. Determine global planetary boundary thresholds based on the aforementioned key planetary boundary dimensions; Based on the planetary boundary principle and a pre-set resource allocation strategy, the global planetary boundary threshold is allocated to the steel industry scale using the per capita method and the ability to pay method, so as to obtain the corresponding environmental safety boundary threshold for the steel industry. Based on the actual environmental load of the industry environmental load factor for each process path and the corresponding environmental safety boundary threshold for the steel industry, the boundary environment occupancy rate of each process path in each environmental boundary dimension is calculated.
4. The method for optimizing the decarburization path process in the steel industry considering critical boundary constraints as described in claim 1, characterized in that, With the optimization objectives of minimizing total lifecycle cost and minimizing environmental boundary occupancy, an economic-environmental bi-objective LP linear optimization model is established, including: The objective function constructed to minimize the total lifecycle cost is: Where P is the set of process paths, This represents the unit cost of the corresponding process path p; It represents the annual output of process path p; The objective function constructed to minimize the environmental boundary occupancy rate is: in, This represents the total annual environmental load calculated using a multiple linear regression model. This represents the annual environmental safety boundary threshold for the steel industry; The normalized weight coefficient representing the dimension of the k-th planetary boundary; The objective functions for total lifecycle cost and environmental boundary occupancy are integrated into a comprehensive optimization objective function through linear weighting: in, The weighting coefficients of the objective function for total life cycle cost. These are the weighting coefficients of the objective function for environmental boundary occupancy rate; This serves as a benchmark for cost targets. This serves as a reference benchmark for environmental impact targets.
5. The method for optimizing a dual-objective decarburization path in the steel industry considering critical boundary constraints, as described in claim 4, is characterized in that... The constraints for constructing the economic-environmental bi-objective LP linear optimization model include: Construct input-output constraint equations, specifically including: Material conservation equation Where P is the set of process paths, It represents the annual output of process path p. It represents the iron input per unit output of process path p. It represents the iron output per unit of production in process path p. It is the iron loss per unit output of process path p; For the hydrogen-based reduced iron production process, the hydrogen balance equation is established as follows: in, It is a subset of process routes that require hydrogen. It is the total supply of hydrogen produced by electrolysis. This is the total supply of imported hydrogen energy. This represents the hydrogen consumption per unit output of the process route p. This refers to the amount of hydrogen recovered. This is the total amount of hydrogen energy lost during transportation; The balance constraint equations for energy demand and energy supply throughout the entire process are established as follows: in, For electricity self-production, For energy storage, This refers to electricity imports. Electricity consumption; For natural gas self-production, For energy storage, This refers to the volume of natural gas imports. This refers to natural gas consumption. Establish a balance equation among CO2 production, capture, and escape / emission rates in the process: in, For the unit output of process path p Production volume Total catch For final emissions; Production and resource capacity constraints include: Capacity constraints: in, This represents the forecast for total crude steel demand. Energy supply constraints: in, It represents the unit energy consumption of path p. It is the upper limit of available energy; Infrastructure constraints; Planetary boundary environment rigidity constraints include: Climate use constraints: Nitrogen emission usage constraints: Phosphorus emission usage constraints: Freshwater use restrictions: Constraints on biodiversity loss: Land resource use constraints: in, This represents the environmental load per unit output of process path p in dimension k. Represents the annual environmental occupancy rights for dimension k; After normalizing the individual loads, a weighted sum is used to establish a constraint for the comprehensive environmental impact score: in, For comprehensive environmental boundary constraints.
6. The method for optimizing a dual-objective decarburization path in the steel industry considering critical boundary constraints as described in claim 5, characterized in that, The economic-environmental bi-objective LP linear optimization model is solved to output the scenario group of optimal process combinations, including: By using a linear programming algorithm, the above-mentioned input-output constraints, production and resource capacity constraints, planetary boundary environment rigidity constraints, and comprehensive optimization objective function are co-embedded in the optimization solution process. Through variable iteration and multi-scenario simulation, the optimal process combination that satisfies economic-environmental balance is output as a scenario group.
7. The method for optimizing the decarburization path process in the steel industry considering critical boundary constraints according to claim 1, characterized in that, The method also includes generating a dynamic optimization roadmap to show a comparison of different process path combinations in terms of environmental boundary occupancy, production life cycle cost, and emission reduction potential.
8. A dual-objective decarburization path optimization system for the steel industry considering critical boundary constraints, characterized in that, include: The data acquisition module is used to acquire the production process flow and production process data of the steel industry, and to construct scenario groups with multiple process path combinations based on the production process flow. The planetary boundary calculation module is used to obtain the environmental boundary occupancy rate of the steel industry at the national level for each process path through secondary downscaling. The LCA database is used to build and store an environmental impact database covering the entire lifecycle of steel production. The LP optimization engine is used to establish an economic-environmental dual-objective LP linear optimization model with the optimization objectives of minimizing total life cycle cost and minimizing environmental boundary occupancy, and solve it to output the scenario group of optimal process combination; A visualization decision-making platform is used to generate dynamic optimization roadmaps, showing a comparison of different process path combinations in terms of environmental boundary occupancy, production life cycle cost, and emission reduction potential.