A method and system for cross-industry collaborative utilization of steel by-products based on component pair matching and energy quality matching

Abstract

The present disclosure provides a method and system for cross-industry collaborative utilization of steel by-products based on component matching and energy quality matching, which comprises collecting source end data of steel enterprise by-products and external industry demand data; constructing source end by-product attribute library, sink end demand parameter library and scene access threshold library; calculating at least one of component matching index, energy quality matching index, spatial transportation constraint index, environmental risk constraint index and carbon emission reduction benefit index; generating cross-industry collaborative utilization candidate path according to five-element structure; risk screening of candidate path; risk disposal of candidate path; determining target path; outputting cross-industry collaborative utilization scheme of steel by-products. The present disclosure can solve the problems of extensive judgment of cross-industry utilization path of steel by-products, component mismatch, energy quality mismatch, environmental risk transfer, unclear threshold source and difficult choice of multiple paths in the prior art.

Description

Technical Field

[0001] This disclosure belongs to the technical fields of resource recycling, cross-industry symbiosis, collaborative treatment of industrial solid waste and energy cascade utilization in the iron and steel industry, specifically involving a method and system for cross-industry collaborative utilization of iron and steel by-products based on component matching and energy quality matching. Background Technology

[0002] The steel industry has a long production process with complex material and energy flows. During ironmaking, steelmaking, rolling, energy conversion, and environmental remediation, various byproducts are generated, including steel slag, blast furnace slag, iron-containing dust and sludge, by-product gas, waste heat and pressure, and coking by-products. These byproducts have both resource value and are constrained by their composition, phase composition, energy quality, pollution risk, and transportation conditions.

[0003] Steel slag is rich in Ca, Fe, Mg, and other components, and can be used as a raw material for building materials, road materials, mine backfill, and for carbon dioxide mineralization. However, the risk of leaching of free CaO, free MgO, and some heavy metals limits its direct utilization. Blast furnace slag is rich in Ca, Si, Al, Mg, and other components. When the glass phase content is high, it can be used for slag powder and green building materials. Iron-containing dust and sludge are rich in Fe, C, Zn, and other components, but some dust and sludge contain limiting components such as Pb, Cl, and alkali metals. By-product gas and waste heat and pressure have energy utilization value, but their utilization efficiency depends on calorific value, temperature, pressure, flow rate, and supply and demand timing.

[0004] Existing utilization methods mostly revolve around a single byproduct or a single industry. For example, steel slag is used in road materials, blast furnace slag is used in slag powder, byproduct gas is used for power generation, and waste heat is used for steam production or heating. These methods can achieve a certain degree of resource utilization, but they still have problems such as crude judgment of utilization paths, mismatch of components or energy quality, transfer of environmental risks, unclear threshold sources, and difficulty in choosing between multiple paths. Summary of the Invention

[0005] To address these issues, this disclosure provides a method and system for cross-industry collaborative utilization of steel by-products based on component matching and energy quality matching, in order to solve the problems in existing technologies such as crude judgment of cross-industry utilization paths for steel by-products, component mismatch, energy quality mismatch, environmental risk transfer, unclear threshold sources, and difficulty in choosing multiple paths.

[0006] The first aspect of this disclosure provides a method for cross-industry synergistic utilization of steel by-products based on component matching and energy-mass matching, comprising the following steps: S1. Collect data on the source of by-products of steel enterprises and demand data from external industries. The steel by-products include one or more of the following: steel slag, blast furnace slag, iron-containing dust and sludge, by-product gas, waste heat and pressure, and coking by-products. S2. Construct a source-end by-product attribute library based on the source-end data of the steel enterprise, construct a pooling-end demand parameter library based on the external industry pooling-end demand data, and construct a scenario access threshold library based on the target pooling industry access requirements, historical operating data of steel enterprises, and demonstration operation feedback. S3. Based on the source-end by-product attribute library, sink-end demand parameter library and scenario access threshold library, calculate at least one of the following: component matching index, energy quality matching index, spatial transportation constraint index, environmental risk constraint index and carbon emission reduction benefit index. S4. Generate cross-industry collaborative utilization candidate paths according to the five-element structure of "source industry - by-product - collaborative utilization path - adaptation technology - sinking industry"; S5. Based on the environmental risk constraint index and the scenario access threshold library, risk screening is performed on the candidate paths; S6. When a candidate path has environmental risk exceeding the limit or restricted component exceeding the limit, the candidate path is eliminated or a matching preprocessing technology is used. After preprocessing, the by-product attribute data is collected or updated again. The process returns to steps S3 to S5 to re-screen the candidate path for risk until the candidate path does not have environmental risk exceeding the limit or restricted component exceeding the limit. When the candidate path does not have environmental risk exceeding the limit or restricted component exceeding the limit, the process proceeds to step S7. S7. Determine the number of candidate paths corresponding to the same steel by-product. If there are two or more, rank the candidate paths based on the comprehensive synergy index and determine the highest-ranked candidate path as the target path. If there is only one, determine the candidate path corresponding to the steel by-product as the target path. S8. Output a cross-industry collaborative utilization scheme for steel by-products based on the target path.

[0007] A second aspect of this disclosure provides a cross-industry collaborative utilization system for steel by-products based on component matching and energy-mass matching, for implementing the method of this disclosure, the cross-industry collaborative utilization system comprising: The data acquisition module is used to collect data on by-product sources from steel enterprises and demand data from external industries. The database construction module is used to construct a source-end by-product attribute library based on the source-end data of the steel enterprise, construct a pooling-end demand parameter library based on the external industry pooling-end demand data, and construct a scenario access threshold library based on the target pooling industry access requirements, historical operating data of steel enterprises, and demonstration operation feedback. The calculation module is used to calculate at least one of the following based on the source-end by-product attribute library, sink-end demand parameter library, and scenario access threshold library: component matching index, energy-quality matching index, spatial transportation constraint index, environmental risk constraint index, and carbon emission reduction benefit index. The candidate path construction module is used to generate cross-industry collaborative utilization candidate paths according to the five-element structure of "source industry - by-product - collaborative utilization approach - adaptation technology - sink industry". The risk screening module is used to screen the candidate paths based on the environmental risk constraint index and the scenario access threshold library. The risk handling module is used to eliminate candidate paths or match preprocessing techniques when there are environmental risks or restricted components exceeding limits in candidate paths. After preprocessing, it re-collects or updates by-product attribute data and returns to the candidate path construction module, risk screening module, and risk handling module to re-screen the candidate paths for risk until no candidate paths have environmental risks or restricted components exceeding limits. When no candidate path has environmental risks or restricted components exceeding limits, it enters the target path determination module. The target path determination module is used to determine the number of candidate paths corresponding to the same steel by-product. If there are two or more, the candidate paths are sorted based on the comprehensive synergy index, and the candidate path with the highest ranking is determined as the target path; if there is only one, the candidate path corresponding to the steel by-product is determined as the target path. The solution output module is used to output a cross-industry collaborative utilization solution for steel by-products based on the target path.

[0008] This disclosure constructs a source-end byproduct attribute library, a sink-end demand parameter library, and a scenario access threshold library. It calculates the component matching index, energy quality matching index, spatial transportation constraint index, environmental risk constraint index, and carbon emission reduction benefit index. Candidate paths are generated according to a five-element structure of "source industry - byproduct - collaborative utilization path - matching technology - sink industry". Cross-industry collaborative utilization solutions are output through risk screening, pre-processing and re-evaluation, and comprehensive ranking.

[0009] This disclosure has the following beneficial effects.

[0010] First, this disclosure calculates the component matching index based on the attributes of by-products at the source and the needs of industries at the sink, avoiding path mismatch caused by determining utilization direction solely based on by-product names. Second, this disclosure sets an energy-quality matching index, which can evaluate the matching relationship between by-product gas, waste heat and pressure, and the energy needs of electricity, chemicals, heating, or external processes, reducing energy grade mismatch. Third, this disclosure constructs a scenario access threshold library, ensuring that the judgment of components, phases, environmental risks, energy-quality parameters, and transportation conditions has a clear data source, avoiding thresholds relying entirely on human experience. Fourth, this disclosure generates candidate paths according to a five-element structure of "source industry—by-product—co-utilization pathway—adaptive technology—sink industry," providing a unified basis for expressing, screening, and ranking cross-industry co-utilization paths. Fifth, this disclosure sets an environmental risk screening mechanism before path ranking, reducing the transfer of heavy metal leaching, chloride migration, dust release, gaseous pollutant release, and stability risks to external industries. Sixth, this disclosure establishes a pretreatment and re-evaluation process, enabling byproducts treated through stabilization, magnetic separation, dechlorination, selective reduction, gas purification, or heat exchange to re-enter pathway matching, thereby increasing resource utilization opportunities. Seventh, this disclosure ranks multiple candidate pathways using a comprehensive synergy index, simultaneously considering component compatibility, energy and quality matching, transportation distance, environmental risk, carbon emission reduction benefits, and economic benefits, thus improving the feasibility of cross-industry collaborative utilization schemes. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of this disclosure 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 this disclosure. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0012] Figure 1 This invention discloses a flowchart of a method for cross-industry collaborative utilization of steel by-products based on component matching and energy quality matching.

[0013] Figure 2 This is a schematic diagram of the candidate path generation relationship for the five-element structure provided in the embodiments of this disclosure.

[0014] Figure 3 This disclosure presents a schematic diagram of a cross-industry collaborative utilization system for steel by-products based on component matching and energy quality matching. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of this disclosure clearer, the technical solutions of this disclosure will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort are within the scope of protection of this disclosure.

[0016] In related technologies, the resource utilization of steel by-products mostly revolves around a single by-product or a single industry. For example, steel slag is used in road materials, blast furnace slag in slag powder production, by-product gas is used for power generation, and waste heat is used for steam production or heating. These methods can achieve a certain degree of resource utilization, but the following problems still exist: First, existing methods lack the ability to accurately identify the compositional match between source-end by-products and end-end industry demands. When steel by-products enter industries such as building materials, roads, mine restoration, and chemicals, it's not enough to simply check if they contain usable components; it's also necessary to determine whether the proportions of key components, phase structure, restricted components, and the target industry's access requirements match. Determining utilization solely based on the by-product name or a single chemical composition can easily lead to product quality fluctuations or mismatched utilization pathways.

[0017] Second, existing methods lack energy quality matching judgment for energy-type by-products. Whether by-product gas and waste heat and pressure can enter power generation, chemical industry, district heating, or external process preheating systems depends on calorific value, temperature, pressure, flow rate, supply sequence, and terminal load. If the utilization direction is determined solely based on the total energy amount, it is easy to cause energy quality mismatch and low-value utilization.

[0018] Third, existing methods lack cross-industry candidate path generation rules. Steel slag, blast furnace slag, iron-containing dust and sludge, by-product gas, and waste heat and pressure may all correspond to multiple external utilization paths. If the relationship between source industries, by-products, co-utilization pathways, adapting technologies, and sink industries cannot be described with a unified structure, it will be difficult to form comparable, rankable, and executable cross-industry co-utilization solutions.

[0019] Fourth, existing methods are insufficient for controlling the transfer of environmental risks. When steel by-products are utilized across industries, risks such as heavy metal leaching, chloride migration, dust release, gaseous pollutant release, and stability issues may transfer from steel companies to building materials, roads, mine remediation, or chemical systems. Without setting industry access conditions and risk screening mechanisms, secondary pollution or engineering quality risks can easily occur.

[0020] Fifth, existing methods lack traceable sources of thresholds. Determining components, energy quality, risk, and transportation conditions requires combining target industry access requirements, historical enterprise operating data, and feedback from demonstration operations. If thresholds are determined solely by human experience, they cannot reliably support engineering applications and pathway sequencing.

[0021] Therefore, this disclosure proposes a cross-industry collaborative utilization method for steel by-products based on component matching and energy quality matching. It incorporates by-product attributes at the source end, industry demand at the sink end, scenario access thresholds, environmental risks, transportation constraints, and carbon emission reduction benefits into the same matching framework. Through candidate path generation, risk screening, pre-processing re-evaluation, and comprehensive ranking, an executable cross-industry collaborative utilization scheme is formed.

[0022] This disclosure provides a method for cross-industry synergistic utilization of steel by-products based on component matching and energy-mass matching. See [link to relevant documentation]. Figure 1 This includes the following steps: S1. Collect data on the source of by-products of steel enterprises and demand data from external industries. The steel by-products include one or more of the following: steel slag, blast furnace slag, iron-containing dust and sludge, by-product gas, waste heat and pressure, and coking by-products. S2. Construct a source-end by-product attribute library based on the source-end data of the steel enterprise, construct a pooling-end demand parameter library based on the external industry pooling-end demand data, and construct a scenario access threshold library based on the target pooling industry access requirements, historical operating data of steel enterprises, and demonstration operation feedback. S3. Based on the source-end by-product attribute library, sink-end demand parameter library and scenario access threshold library, calculate at least one of the following: component matching index, energy quality matching index, spatial transportation constraint index, environmental risk constraint index and carbon emission reduction benefit index. S4. Generate cross-industry collaborative utilization candidate paths according to the five-element structure of "source industry - by-product - collaborative utilization path - adaptation technology - sinking industry"; S5. Based on the environmental risk constraint index and the scenario access threshold library, risk screening is performed on the candidate paths; S6. When a candidate path has environmental risk exceeding the limit or restricted component exceeding the limit, the candidate path is eliminated or a matching preprocessing technology is used. After preprocessing, the by-product attribute data is collected or updated again. The process returns to steps S3 to S5 to re-screen the candidate path for risk until the candidate path does not have environmental risk exceeding the limit or restricted component exceeding the limit. When the candidate path does not have environmental risk exceeding the limit or restricted component exceeding the limit, the process proceeds to step S7. S7. Determine the number of candidate paths corresponding to the same steel by-product. If there are two or more, rank the candidate paths based on the comprehensive synergy index and determine the highest-ranked candidate path as the target path. If there is only one, determine the candidate path corresponding to the steel by-product as the target path. S8. Output a cross-industry collaborative utilization scheme for steel by-products based on the target path.

[0023] Figure 1 In this context, Y represents yes, N represents no, and AND represents and.

[0024] Step S1 involves collecting source and aggregate data. Specifically, the source data for by-products from steel enterprises includes one or more of the following parameters: by-product name, source process, production quantity, production frequency, spatial location, chemical composition, phase composition, particle size distribution, moisture content, calorific value, temperature, pressure, flow rate, supply sequence, supply stability, and pollution risk parameters. The external industries include one or more of the following: building materials industry, chemical industry, power industry, mine restoration industry, road engineering industry, and cement kiln co-processing industry. The aggregate demand data from the external industries includes one or more of the following parameters: raw material composition requirements, phase requirements, particle size requirements, moisture content requirements, fuel calorific value requirements, temperature requirements, pressure requirements, flow rate requirements, load sequence, process access conditions, pollutant limits, acceptable quantity, spatial location, and transportation radius.

[0025] Step S2 constructs a source-end byproduct attribute library, a sink-end requirement parameter library, and a scenario access threshold library based on the data collected in step S1.

[0026] The source-end by-product attribute library includes material-type by-product attribute fields and energy-type by-product attribute fields. The material-type by-product attribute fields include source process, production quantity, chemical composition, phase composition, particle size distribution, moisture content, limiting components, pollution risk parameters, and pretreatment requirements; the energy-type by-product attribute fields include source process, calorific value, temperature, pressure, flow rate, supply sequence, supply stability, spatial location, and purification requirements.

[0027] The pooled demand parameter library includes material demand parameters and energy demand parameters. The material demand parameters include target industry, target product or process, raw material composition requirements, phase requirements, particle size requirements, moisture content requirements, pollutant limits, receiving capacity, and transportation radius; the energy demand parameters include target industry, energy-consuming equipment, minimum calorific value requirements, temperature requirements, pressure requirements, flow rate requirements, load curve, continuous supply requirements, and spatial location.

[0028] It is understandable that "convergence end" refers to the final or downstream aggregation point of the cross-industry collaborative utilization method of steel by-products. "Convergence industry" refers to the industry situation of convergence or aggregation, and the target convergence industry is the aggregation industry that is considered as the target for cross-industry collaborative utilization matching.

[0029] The scenario access threshold library is jointly determined by the target sink industry access requirements, historical operating data of steel enterprises, and feedback from demonstration operations. The target sink industry access requirements include requirements for composition, phase, particle size, moisture content, pollution risk, calorific value, temperature, pressure, flow rate, and transportation radius for one or more scenarios among building materials, road engineering, mine restoration, cement kiln co-processing, chemical utilization, power utilization, and district heating utilization. The historical operating data of steel enterprises includes one or more of the following: by-product composition fluctuations, phase fluctuations, production fluctuations, gas calorific value fluctuations, waste heat temperature fluctuations, pressure fluctuations, flow rate fluctuations, transportation distance, and pretreatment effect data. The demonstration operation feedback includes one or more of the following: post-pretreatment limitation component changes, environmental risk changes, sink receiving stability, product quality feedback, energy utilization stability, and actual carbon emission reduction feedback.

[0030] In step S3, the method for calculating the component matching index, energy-quality matching index, spatial transportation constraint index, environmental risk constraint index, and carbon emission reduction benefit index based on the source-end by-product attribute library, sink-end demand parameter library, and scenario access threshold library is as follows: 1. Component matching index The Component Matching Index (CMI) is used to evaluate the degree of matching between the key components, phase structure, and limiting components in steel by-products and the raw material requirements of target sink industries. The key components include one or more of Ca, Si, Al, Mg, Fe, C, Zn, Pb, Cl, S, and P. In one embodiment, the Component Matching Index (CMI) is calculated using the following formula: CMI = α1Cm + α2Pm + α3Ir α4Rm α5Pr Where Cm represents the principal component matching degree, Pm represents the phase compatibility degree, Ir represents the proportion of substitute raw materials, Rm represents the deviation degree of the limiting component, Pr represents the intensity of pretreatment requirements, and α1 to α5 are weighting coefficients. It is understood that the sub-parameters in the formula are dimensionless and can be represented as percentages. The component matching index CMI obtained after linear weighted summation is a numerical value.

[0031] The principal component matching degree (Cm) is used to evaluate the degree of similarity between the main usable components of the by-product and the raw material requirements of the target sink industry. The phase compatibility degree (Pm) is used to evaluate whether the glassy phase, metallic iron phase, silicate phase, iron oxide phase, carbonate phase, or other functional phases in the by-product meet the utilization requirements of the target sink industry. The proportion of substitute raw materials (Ir) is used to evaluate the proportion of primary mineral raw materials, natural aggregates, fuels, or chemical raw materials that the by-product can substitute. The deviation of limiting components (Rm) is used to evaluate the degree of deviation between the leaching risk factors of free CaO, free MgO, Zn, Pb, Cl, S, P, alkali metals, or heavy metals and the access limits of the target sink industry. The intensity of pretreatment requirements (Pr) is used to evaluate the intensity of stabilization, magnetic separation, dechlorination, grinding, drying, heavy metal fixation, or other pretreatments required before the by-product enters the target sink industry.

[0032] When the target industry is the building materials industry, the matching degree of the principal components is determined based on the deviation between one or more of the components Ca, Si, Al, and Mg and the raw material requirements of the target building materials industry; when the target industry is the road engineering industry or the mine restoration industry, the matching degree of the principal components is determined based on the deviation between the properties of Ca, Si, Fe, Mg, and the stabilized material and the application scenario requirements of the target road engineering industry or the mine restoration industry; when the target industry is the chemical industry, the matching degree of the principal components is determined based on the deviation between the by-product gas components, coking by-product components, or carbon-containing components and the raw material requirements of the target chemical industry.

[0033] 2. Energy-quality matching index The Energy Quality Matching Index (EQMI) is used to evaluate the degree of matching between by-product gas, waste heat and pressure, or other energy-type by-products and the energy needs of external industries.

[0034] In one implementation, the Energy Quality Matching Index (EQMI) is calculated using the following formula: EQMI = β1Hv + β2Tm + β3Prm + β4Fm + β5Lm + β6Sm Wherein, Hv represents the matching degree of calorific value or waste heat grade, Tm represents the temperature matching degree, Prm represents the pressure matching degree, Fm represents the flow rate or load matching degree, Lm represents the spatial distance matching degree, Sm represents the supply and demand time sequence matching degree, and β1 to β6 are weighting coefficients. It can be understood that the sub-parameters in the formula are dimensionless and can be represented as percentages. The Energy Quality Matching Index (EQMI) obtained after linear weighted summation is a numerical value. For by-product gas, the matching degree of calorific value or waste heat grade Hv is determined based on the difference between the calorific value of the by-product gas and the minimum calorific value requirement of the target energy-consuming equipment; the pressure matching degree Prm is determined based on the difference between the pressure of the by-product gas and the inlet pressure requirement of the target energy-consuming equipment; and the supply and demand time sequence matching degree Sm is determined based on the degree of overlap between the by-product gas supply time sequence and the load curve at the confluence end. For waste heat and waste pressure, the temperature matching degree Tm and the pressure matching degree Prm are determined based on the difference between the waste heat and waste pressure parameters and the power generation, heating, or process preheating requirements at the confluence end.

[0035] 3. Spatial Transportation Constraint Index The Spatial Transport Constraint Index (STI) is used to evaluate the spatial accessibility and transport economics of steel by-products from source to destination. Before calculation, transport distance, transport mode, unit transport cost, environmental risk during transport, and destination receiving stability are normalized to dimensionless parameters within the range of 0 to 1. The STI is calculated using the following formula: STI = λ1Dm + λ2Mm + λ3Ct + λ4Rt + λ5Rs Where Dm is the acceptable transport distance, Mm is the adaptability of transport mode, Ct is the acceptable transport cost, Rt is the acceptable transport environment risk, and Rs is the stability of receiving at the receiving end; λ1~λ5 are weighting coefficients, and λ1+λ2+λ3+λ4+λ5=1.

[0036] The acceptable transport distance (Dm) is determined based on the actual transport distance (d) and the maximum allowable transport distance (dmax) at the target terminal. When d ≤ dmax, Dm = 1 - d / dmax; when d > dmax, Dm = 0. The transport mode suitability (Mm) is determined based on the degree to which the adopted transport mode meets applicable transport requirements such as sealing, dust prevention, leakage prevention, and rain protection. Assuming there are n applicable transport requirements, and nm of them are met, then Mm = nm / n. The acceptable transport cost (Ct) is determined based on the actual unit transport cost (c) and the maximum allowable unit transport cost (cmax) of the target route. When c ≤ cmax, Ct = 1 - c / cmax; when c > cmax, Ct = 0. cmax can be determined based on the company's historical transport costs, the economic benefits of the target route, or a preset economic transport boundary. The acceptable transport environmental risk (Rt) is determined based on the degree to which applicable risk indicators such as dust, leakage, and volatile organic compounds meet the corresponding control limits. Let there be m applicable risk indicators, of which mr are satisfied with the corresponding control limits, then Rt = mr / m. The receiver reception stability Rs is determined based on the actual receiver reception within the evaluation period. Let the evaluation period include k statistical periods, of which kr are the number of statistical periods where the actual reception volume reaches the agreed or planned reception volume, then Rs = kr / k.

[0037] Dm, Mm, Ct, Rt, and Rs are all dimensionless parameters ranging from 0 to 1, with larger values ​​indicating more favorable conditions. A larger STI indicates better spatial transportation conditions for the corresponding candidate path. When the actual transportation distance exceeds the maximum allowable transportation distance at the target terminal, the corresponding candidate path is directly eliminated.

[0038] 4. Environmental Risk Constraint Index The Environmental Risk Constraint Index (ERI) is used to evaluate the environmental and access risks of steel by-products entering target sink industries. Before calculation, the risks of heavy metal leaching, chloride migration, dust release, gaseous pollutant release, and stability are normalized to dimensionless parameters in the range of 0 to 1. The ERI is calculated according to the following formula: ERI = μ1Hr + μ2Cr + μ3Dr + μ4Gr + μ5Sr Wherein, Hr is the risk value for heavy metal leaching, Cr is the risk value for chloride migration, Dr is the risk value for dust release, Gr is the risk value for gaseous pollutant release, and Sr is the risk value for stability; μ1 to μ5 are weighting coefficients, and μ1+μ2+μ3+μ4+μ5=1.

[0039] Each risk value is determined based on the ratio of the corresponding actual detected value to the target industry access limit. When the actual detected value is less than or equal to the corresponding access limit, the corresponding risk value is determined according to the ratio of the actual detected value to the access limit; when the actual detected value is greater than the corresponding access limit, the corresponding risk value is 1. For candidate paths where there are no corresponding risk items, the corresponding risk value is 0.

[0040] Specifically, the heavy metal leaching risk (Hr) is determined based on the ratio of the leaching concentration of heavy metals such as Pb, Cd, Cr, and Zn in by-products to the corresponding limit for the target industry; the chloride migration risk (Cr) is determined based on the ratio of Cl content or chloride ion leaching amount to the corresponding access limit; the dust release risk (Dr) is determined based on the ratio of dust concentration during transportation, loading and unloading, or utilization to the corresponding control limit; the gaseous pollutant release risk (Gr) is determined based on the ratio of SO2, NOx, volatile organic compounds, or other gaseous pollutant emission concentrations to the corresponding control limit; and the stability risk (Sr) is determined based on the degree of deviation between the free CaO and free MgO content or volume stability test results and the limit for the target utilization scenario.

[0041] The higher the ERI, the higher the environmental risk of the corresponding candidate path. When the ERI exceeds the preset risk threshold of the target industry, the candidate path is eliminated or transferred to the pre-processing and re-evaluation process; when a certain restrictive indicator directly exceeds the mandatory access limit of the target industry, it is not affected by the comprehensive calculation result of ERI, and the candidate path is directly eliminated or transferred to the pre-processing and re-evaluation process.

[0042] 5. Carbon Emission Reduction Benefit Index The Carbon Benefit Index (CBI) is used to evaluate the carbon benefit of replacing primary resources, fossil fuels, or purchased energy with steel by-products. First, the net carbon reduction ΔC of the candidate pathway is calculated: ΔC = ΣQi×(EF0,i-EF1,i)-Cp-Ct Where Qi × (EF0,i-EF1,i) represents the product of the amount of the substituted raw material, fuel, or energy of type i and the difference in unit carbon emission factor before and after substitution with steel by-products. The total emission reduction generated by substitution is obtained by summing over all substituted objects. Qi is the amount of the substituted raw material, fuel, or energy of type i; EF0,i is the unit carbon emission factor of primary resources, fossil fuels, or purchased energy; EF1,i is the unit carbon emission factor after substitution with steel by-products; Cp is the carbon emission generated during the by-product pretreatment process; and Ct is the carbon emission generated during the by-product transportation process. All carbon emissions use the same accounting boundary, and the unit is unified as t CO2e.

[0043] To facilitate comparisons between different candidate pathways, ΔC is converted into a dimensionless carbon emission reduction benefit index (CBI) in the range of 0 to 1: CBI = max[0, min(1, ΔC / ΔCref)] Wherein, ΔCref is the baseline carbon emission reduction for the corresponding candidate utilization path of by-products, which can be determined based on the company's historical best utilization path, demonstration operation data, or a preset baseline value. The larger the CBI, the higher the carbon emission reduction benefit of the candidate path.

[0044] In step S4, candidate paths are generated through cross-industry collaborative utilization.

[0045] Based on the component matching index and energy quality matching index, candidate paths for cross-industry collaborative utilization of steel by-products are generated according to the five-element structure of "source industry - by-product - collaborative utilization path - matching technology - sink industry".

[0046] The five-element structure is represented as follows: Path = {S, B, R, T, D} Where S represents the source industry, B represents steel by-products, R represents co-utilization pathways, T represents adaptation technologies, and D represents the sink industry.

[0047] In one embodiment, S represents the iron and steel industry; B represents one or more of steel slag, blast furnace slag, iron-containing dust and sludge, by-product gas, waste heat and pressure, or coking by-products; R represents one or more of material substitution, energy substitution, material-energy coupling utilization, or co-processing; T represents one or more of stabilization, magnetic separation, granulation, grinding, dechlorination, selective reduction, Zn / Pb co-separation, gas purification, pressure stabilization, blending, heat exchange, heat storage, or heat network coupling; and D represents one or more of the building materials industry, chemical industry, power industry, mine restoration industry, road engineering industry, or cement kiln co-processing industry.

[0048] For example, see Figure 2 The schematic diagram of the candidate path generation relationship of the five-element structure shown in the embodiment is as follows: the first column is the source industry: iron and steel industry; the second column is the by-products: steel slag, blast furnace slag, iron-containing dust and sludge, by-product gas, waste heat and waste pressure, coking by-products; the third column is the synergistic utilization pathways: material substitution, energy substitution, material-energy coupling utilization, synergistic treatment; the fourth column is the adaptation technology: stabilization, magnetic separation, granulation, grinding, dechlorination, selective reduction, Zn / Pb synergistic separation, gas purification, pressure stabilization, mixing, heat exchange, heat storage, heat network coupling; the fifth column is the sink industry: building materials industry, chemical industry, power industry, mine restoration industry, road engineering industry, cement kiln synergistic treatment industry.

[0049] Step S6, based on the candidate path risk screening results of Step S5, if a candidate path has an environmental risk exceeding the limit (i.e., when the environmental risk constraint index is greater than the risk threshold corresponding to the target industry), the candidate path is eliminated or transferred to preprocessing for re-evaluation. After preprocessing, the by-product attribute data is re-collected or updated, and the process returns to Steps S3-S5 to re-screen the candidate path for risk until no candidate path has an environmental risk exceeding the limit. Similarly, if a candidate path has a restricted component exceeding the limit (i.e., when the restricted component in the steel by-product exceeds the access limit of the target industry), the candidate path is eliminated or transferred to preprocessing for re-evaluation. After preprocessing, the by-product attribute data is re-collected or updated, and the process returns to Steps S3-S5 to re-screen the candidate path for risk until no candidate path has a restricted component exceeding the limit. In other words, only when a candidate path has neither an environmental risk exceeding the limit nor a restricted component exceeding the limit can the process proceed to Step S7.

[0050] In one implementation, when the restricted component in the steel by-product exceeds the target sink industry access limit but is less than or equal to the warning threshold, the candidate path is transferred to preprocessing for further evaluation; when the restricted component in the steel by-product exceeds the target sink industry access limit but is greater than the warning threshold, the candidate path is eliminated. The target sink industry access limit is a value specified in the relevant industry standards, and the warning threshold can be 1.2 times the target sink industry access limit.

[0051] When a candidate path is transferred to the pretreatment and re-evaluation process, a pretreatment technology is matched based on the by-product attributes and the target sink industry access conditions. The pretreatment technologies include one or more of the following: stabilization, magnetic separation, particle size control, grinding, drying, dechlorination, heavy metal fixation, selective reduction, Zn / Pb synergistic separation, gas purification, pressure stabilization, blending, heat exchange, and heat storage. After pretreatment, one or more data from the source-end by-product attribute library, including chemical composition, phase composition, particle size distribution, moisture content, calorific value, temperature, pressure, flow rate, and pollution risk parameters, are updated. One or more of the following components matching index, energy-quality matching index, or environmental risk constraint index are recalculated: If the recalculated environmental risk constraint index is not greater than the target sink industry risk threshold, and the components matching index is not lower than the preset minimum matching threshold CMImin; for energy-type candidate paths, if the energy-quality matching index is not lower than the preset minimum matching threshold EQMImin, the candidate path proceeds to the next stage; otherwise, the candidate path is eliminated or continues pretreatment.

[0052] In step S7, when the same steel by-product corresponds to more than two candidate paths, the candidate paths are ranked based on the comprehensive synergy index.

[0053] The Comprehensive Synergy Index (CSI) is calculated using the following formula: CSI = γ1CMI + γ2EQMI + γ3CBI + γ4Ec γ5ERI γ6TC Among them, CMI represents the component matching index, EQMI represents the energy quality matching index, CBI represents the carbon emission reduction benefit index, Ec represents the economic benefit index, ERI represents the environmental risk constraint index, TC represents the transportation and pretreatment cost index, and γ1 to γ6 are weighting coefficients.

[0054] To ensure the comparability of the sub-parameters in the comprehensive synergy index, CMI, EQMI, CBI, Ec, ERI, and TC all use dimensionless values ​​in the range of 0 to 1, and γ1+γ2+γ3+γ4+γ5+γ6=1. In one implementation, the weights of CMI, CBI, Ec, ERI, and TC in the material substitution candidate path are 0.35, 0.15, 0.15, 0.20, and 0.15, respectively, while the weight of EQMI (which is not applicable) is 0; the weights of EQMI, CBI, Ec, ERI, and TC in the energy substitution candidate path are 0.35, 0.20, 0.15, 0.15, and 0.15, respectively, while the weight of CMI (which is not applicable) is 0. These weights can also be adjusted based on historical operating data or demonstration operation results. When the candidate path is a material substitution path, the component matching index (CMI) is used as the main positive evaluation indicator, and the carbon emission reduction benefit index (CBI), economic benefit index (Ec), environmental risk constraint index (ERI), and transportation and pretreatment cost index (TC) are also considered. When the candidate path is an energy substitution path, the energy quality matching index (EQMI) is used as the main positive evaluation indicator, and the carbon emission reduction benefit index (CBI), economic benefit index (Ec), environmental risk constraint index (ERI), and transportation and pretreatment cost index (TC) are also considered. When the candidate path is a material-energy coupled path, both the component matching index (CMI) and the energy quality matching index (EQMI) are used as the main evaluation indicators, and the weights of each evaluation indicator are determined according to the specific utilization scenario.

[0055] In step S8, the cross-industry collaborative utilization scheme for steel by-products is output in one or more forms, such as a path list, resource allocation table, supply and demand matching table, cross-industry symbiotic network diagram, scheduling instructions or process control parameters. The output content includes one or more of the following: by-product source, target sink industry, collaborative utilization path, adaptable technology, pretreatment process, substitute object, transportation distance, collaborative utilization amount, carbon emission reduction benefits, environmental risk level and path priority.

[0056] Example 1: Comprehensive Scenario of Cross-Industry Collaborative Utilization of By-products from Steel Enterprises A steel company generates steel slag, blast furnace slag, blast furnace gas, converter gas, and waste heat from steel rolling. The surrounding area has external industry demands, including slag powder production lines, road construction projects, mine restoration projects, power plants, chemical plants, and district heating systems. After collecting source-end data, the attributes of steel slag, blast furnace slag, blast furnace gas, converter gas, and waste heat from steel rolling are input into a source-end byproduct attribute database. After collecting external industry demands, the component requirements, calorific value requirements, temperature requirements, pressure requirements, receiving volume, transportation radius, and pollutant access conditions for building materials, road construction, mine restoration, power, chemical, and district heating systems are input into a demand parameter database. A scenario access threshold database is constructed based on the target industry access requirements, historical operating data of the steel company, and feedback from demonstration operations. The scenario access threshold library includes: stability thresholds, free CaO thresholds, and leaching risk thresholds for steel slag entering mine restoration and road engineering projects; glass phase content and main component requirements for blast furnace slag entering the slag powder pathway; calorific value, pressure, and supply stability requirements for by-product gas entering the power and chemical pathways; and temperature and supply-demand timing requirements for waste heat from steel rolling entering the district heating pathway. Calculations show that steel slag, due to its high free CaO content, has an initial environmental risk constraint index exceeding the road engineering access threshold and is therefore transferred to a stabilization pretreatment process before evaluation. Blast furnace slag, with its Ca-Si-Al-Mg composition matching the requirements of the slag powder production line and a low environmental risk constraint index, directly enters the candidate pathway ranking. Blast furnace gas, due to its good supply continuity, is prioritized for matching with power companies. Converter gas, due to its large flow fluctuations, needs to be stabilized and stored before entering the chemical or power candidate pathways. Waste heat from steel rolling, due to its temperature and load timing matching the district heating system, enters the district heating candidate pathway. The final output is a cross-industry collaborative utilization solution: after stabilization and magnetic separation, steel slag is given priority to mine restoration projects, and the remaining part is used for road materials; blast furnace slag is first subjected to waste heat recovery, and then granulated and ground into slag powder production line; blast furnace gas is given priority to power generation system; converter gas is pressure stabilized and stored before entering chemical conversion path, and when chemical consumption is insufficient, it enters the power generation backup path; waste heat from steel rolling is used in district heating system.

[0057] Example 2: Path matching for cross-industry collaborative utilization of steel slag Steel slag is generated during the converter steelmaking process of a steel company. Data collected from the source of this steel slag shows: CaO content 42.0%, Fe content 18.5%, MgO content 8.3%, free CaO content 5.8%, metallic iron phase content 7.6%, particle size distribution 0-50 mm, and heavy metal leaching risk below the hazardous waste identification limit. Simultaneously, demand-side data were collected from surrounding building materials companies, road construction projects, and mine remediation projects. Building materials companies require raw materials with a stable Ca-Si-Al-Mg composition and free CaO content not exceeding the access limit. Road construction projects require sufficient volume stability. Mine remediation projects require materials that meet leaching risk and particle size requirements and have a large disposal capacity. Based on step S3 of the technical solution disclosed in this publication, the component compatibility index and environmental risk constraint index between the steel slag and different sinking industries are calculated. The CMI for steel slag in the building materials industry is 0.67, and the ERI is 0.58; the CMI for steel slag in road engineering is 0.71, and the ERI is 0.55; and the CMI for steel slag in mine remediation projects is 0.76, and the ERI is 0.46. Since the environmental risk constraint indices for the building materials and road engineering paths are higher than their corresponding risk thresholds, they are first subjected to stabilization pretreatment before evaluation. After stabilization treatment, the free CaO content of the steel slag is reduced to 2.1%, and the volume stability meets the requirements for both road engineering and mine remediation scenarios. After recalculation, the ERI for the building materials path decreases to 0.39, the ERI for the road engineering path decreases to 0.36, and the ERI for the mine remediation path decreases to 0.34.

[0058] Candidate paths are generated based on step S4: Steel industry—steel slag—material substitution—stabilization / magnetic separation—mine restoration industry; Steel industry—steel slag—material substitution—stabilization / crushing and screening—road engineering industry; Steel industry—steel slag—material substitution—stabilization / grinding—building materials industry.

[0059] The comprehensive synergy index is calculated according to step S7. The mine remediation route ranks first due to its large disposal capacity, short transportation distance, and low environmental risk; the road engineering route ranks second; and the building material utilization route ranks third. The final output steel slag is preferentially used for mine remediation and backfilling, with the remainder used for road material utilization, in a synergistic utilization scheme.

[0060] Example 3: Path matching for the material-energy coupling utilization of blast furnace slag Blast furnace slag is produced during the blast furnace ironmaking process of a steel company. Data from the blast furnace slag source side was collected: CaO content is 38.6%, SiO2 content is 34.1%, Al2O3 content is 14.2%, MgO content is 8.4%, and glass phase content is 88.0%. The slag discharge temperature is higher than the target waste heat recovery system's access temperature, and the heavy metal leaching risk is lower than the building material utilization access limit. Demand-side data from surrounding slag powder production lines and district heating systems were collected. The slag powder production line has access requirements for Ca-Si-Al-Mg composition and glass phase content. The district heating system has requirements for waste heat temperature, heat capacity, supply timing, and spatial distance. According to step S3, the component compatibility index (CMI) between the blast furnace slag and the slag powder production line is 0.86, and the environmental risk constraint index (ERI) is 0.18; the energy-quality matching index (EQMI) between the blast furnace slag waste heat and the district heating system is 0.74, and the spatial transportation constraint index meets the preset threshold. Candidate paths were generated according to step S4. Steel industry—Blast furnace slag—Material substitution—Granulation / grinding—Building materials industry; Steel industry—blast furnace slag waste heat—energy substitution—heat exchange / heat network coupling—district heating system; Steel industry—blast furnace slag—material-energy coupling utilization—waste heat recovery / granulation / grinding—building materials industry and district heating system.

[0061] Because the material-energy coupling utilization path simultaneously meets the requirements of component matching and energy-mass matching, and does not exceed environmental risk limits, it has the highest comprehensive synergy index. The final output blast furnace slag undergoes waste heat recovery before being granulated and ground to prepare slag powder, resulting in a synergistic utilization scheme.

[0062] Example 4: Cross-industry energy and quality matching of by-product coal gas A steel company produces blast furnace gas and converter gas. Data on blast furnace gas calorific value, flow rate, pressure, supply timing, and spatial location are collected; data on converter gas calorific value, flow rate fluctuations, and pressure are also collected. Demand-side data from power plants, chemical plants, and district heating systems are also collected. The energy quality matching index (EQMI) is calculated according to step S3. The EQMI for blast furnace gas with the power generation system is 0.82, with the chemical conversion system is 0.56, and with the district heating system is 0.61. The EQMI for converter gas with the power generation system is 0.69, with the chemical conversion system is 0.73, and with the district heating system is 0.48.

[0063] Candidate paths are generated based on step S4: Steel industry—Blast furnace gas—Energy substitution—Gas purification / pressure stabilization—Power industry; Steel industry—blast furnace gas—energy substitution—gas purification / blending—district heating system; Steel industry—converter gas—energy substitution—pressure stabilization and storage—chemical industry; Steel industry—converter gas—energy substitution—gas purification / pressure stabilization—power industry.

[0064] The gas is sorted according to step S7. Blast furnace gas has a high energy quality matching index with the power generation system and good supply continuity, so it is prioritized for matching with the power generation system. Converter gas has a high energy quality matching index with the chemical conversion system and higher carbon emission reduction benefits from the chemical pathway, so it is prioritized for matching with the chemical conversion system. When the chemical conversion system's absorption capacity is insufficient, the remaining converter gas enters the power generation candidate pathway. The final output is a cross-industry collaborative utilization scheme for by-product gas, including gas source allocation ratio, target sink industries, gas purification and pressure stabilization processes, supply sequence, and pathway priority.

[0065] This disclosure also provides a cross-industry collaborative utilization system for steel by-products based on component matching and energy-mass matching, for implementing the method of this disclosure, see [link to relevant documentation]. Figure 3 The cross-industry collaborative utilization system 3 includes: Data acquisition module 301 is used to collect data on by-products from steel enterprises and demand data from external industries. The database construction module 302 is used to construct a source-end by-product attribute library based on the source-end data of the steel enterprise by-products, construct a pooling-end demand parameter library based on the external industry pooling-end demand data, and construct a scenario access threshold library based on the target pooling industry access requirements, historical operating data of steel enterprises, and demonstration operation feedback. Calculation module 303 is used to calculate at least one of the following based on the source-end by-product attribute library, sink-end demand parameter library and scenario access threshold library: component matching index, energy quality matching index, spatial transportation constraint index, environmental risk constraint index and carbon emission reduction benefit index. The candidate path construction module 304 is used to generate cross-industry collaborative utilization candidate paths according to the five-element structure of "source industry - by-product - collaborative utilization approach - adaptation technology - sink industry". The risk screening module 305 is used to screen the candidate paths based on the environmental risk constraint index and the scenario access threshold library. The risk handling module 306 is used to eliminate candidate paths or match preprocessing technologies when there are environmental risks or restricted components exceeding limits in candidate paths. After preprocessing, it re-collects or updates by-product attribute data and returns to the candidate path construction module 304, risk screening module 305, and risk handling module 306 to re-screen the candidate paths until no environmental risks or restricted components exceed limits in the candidate paths. When no environmental risks or restricted components exceed limits in the candidate paths, it proceeds to the target path determination module 307. The target path determination module 307 is used to determine the number of candidate paths corresponding to the same steel by-product. If there are two or more, the candidate paths are sorted based on the comprehensive synergy index, and the candidate path with the highest ranking is determined as the target path. If there is only one, the candidate path corresponding to the steel by-product is determined as the target path. The solution output module 308 is used to output a cross-industry collaborative utilization solution for steel by-products based on the target path.

[0066] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, and are not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or improvement of the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for cross-industry collaborative utilization of steel by-products based on component matching and energy-mass matching, characterized in that, Includes the following steps: S1. Collect data on the source of by-products of steel enterprises and demand data from external industries. The steel by-products include one or more of the following: steel slag, blast furnace slag, iron-containing dust and sludge, by-product gas, waste heat and pressure, and coking by-products. S2. Construct a source-end by-product attribute library based on the source-end data of the steel enterprise, construct a pooling-end demand parameter library based on the external industry pooling-end demand data, and construct a scenario access threshold library based on the target pooling industry access requirements, historical operating data of steel enterprises, and demonstration operation feedback. S3. Based on the source-end by-product attribute library, sink-end demand parameter library and scenario access threshold library, calculate at least one of the following: component matching index, energy quality matching index, spatial transportation constraint index, environmental risk constraint index and carbon emission reduction benefit index. S4. Generate cross-industry collaborative utilization candidate paths according to the five-element structure of "source industry - by-product - collaborative utilization path - adaptation technology - sinking industry"; S5. Based on the environmental risk constraint index and the scenario access threshold library, risk screening is performed on the candidate paths; S6. When a candidate path has environmental risk exceeding the limit or restricted component exceeding the limit, the candidate path is eliminated or a matching pretreatment technology is used. After pretreatment, the by-product attribute data is collected or updated again. Then, return to steps S3 to S5 to re-screen the candidate path for risk until the candidate path no longer has environmental risk exceeding the limit or restricted component exceeding the limit. If no environmental risk or restricted component exceeds the limit in the candidate path, proceed to step S7; S7. Determine the number of candidate paths corresponding to the same steel by-product. If there are two or more, rank the candidate paths based on the comprehensive synergy index and determine the highest-ranked candidate path as the target path. If there is only one, determine the candidate path corresponding to the steel by-product as the target path. S8. Output a cross-industry collaborative utilization scheme for steel by-products based on the target path.

2. The method according to claim 1, characterized in that: In step S3, the component matching index is used to evaluate the degree of matching between the key components, phase structure and limiting components in steel by-products and the raw material requirements of the target sink industry. The key components include one or more of Ca, Si, Al, Mg, Fe, C, Zn, Pb, Cl, S and P.

3. The method according to claim 2, characterized in that: The component matching index (CMI) is calculated using the following formula: CMI = α1Cm + α2Pm + α3Ir α4Rm α5Pr Wherein, Cm represents the matching degree of the main components, Pm represents the phase compatibility, Ir represents the proportion of substitute raw materials, Rm represents the deviation of the limiting components, Pr represents the intensity of pretreatment requirements, and α1 to α5 are weighting coefficients.

4. The method according to claim 3, characterized in that: When the target industry is the building materials industry, the matching degree of the principal components is determined based on the deviation between one or more of the components Ca, Si, Al, and Mg and the raw material requirements of the target building materials industry; when the target industry is the road engineering industry or the mine restoration industry, the matching degree of the principal components is determined based on the deviation between the properties of Ca, Si, Fe, Mg, and the stabilized material and the application scenario requirements of the target road engineering industry or the mine restoration industry; when the target industry is the chemical industry, the matching degree of the principal components is determined based on the deviation between the by-product gas components, coking by-product components, or carbon-containing components and the raw material requirements of the target chemical industry.

5. The method according to claim 1, characterized in that: In step S3, the energy quality matching index is used to evaluate the degree of matching between by-product gas, waste heat and pressure or other energy-type by-products and the energy demand of external industries. The Energy Quality Matching Index (EQMI) is calculated using the following formula: EQMI = β1Hv + β2Tm + β3Prm + β4Fm + β5Lm + β6Sm Wherein, Hv represents the matching degree of calorific value or waste heat grade, Tm represents the matching degree of temperature, Prm represents the matching degree of pressure, Fm represents the matching degree of flow rate or load, Lm represents the matching degree of spatial distance, Sm represents the matching degree of supply and demand time sequence, and β1 to β6 are weighting coefficients.

6. The method according to claim 1, characterized in that: In step S4, the five-element structure is represented as follows: Path = {S, B, R, T, D} Wherein, S represents the source industry, B represents steel by-products, R represents co-utilization pathways, T represents adaptation technologies, and D represents the sink industry; The source industry is the steel industry; the co-utilization methods include one or more of material substitution, energy substitution, material-energy coupling utilization, and co-processing; the adaptation technologies include one or more of stabilization, magnetic separation, granulation, grinding, dechlorination, selective reduction, Zn / Pb co-separation, gas purification, pressure stabilization, mixing, heat exchange, heat storage, and heat network coupling; the sinking industries include one or more of the building materials industry, chemical industry, power industry, mine restoration industry, road engineering industry, and cement kiln co-processing industry.

7. The method according to claim 6, characterized in that: In step S7, the Comprehensive Synergy Index (CSI) is calculated using the following formula: CSI = γ1CMI + γ2EQMI + γ3CBI + γ4Ec γ5ERI γ6TC Among them, CMI represents the component matching index, EQMI represents the energy quality matching index, CBI represents the carbon emission reduction benefit index, Ec represents the economic benefit index, ERI represents the environmental risk constraint index, TC represents the transportation and pretreatment cost index, and γ1 to γ6 are weighting coefficients.

8. The method according to claim 7, characterized in that: When the candidate path is a material substitution path, the component matching index (CMI) is used as the main positive evaluation indicator, and the carbon emission reduction benefit index (CBI), economic benefit index (Ec), environmental risk constraint index (ERI), and transportation and pretreatment cost index (TC) are also considered. When the candidate path is an energy substitution path, the energy quality matching index (EQMI) is used as the main positive evaluation indicator, and the carbon emission reduction benefit index (CBI), economic benefit index (Ec), environmental risk constraint index (ERI), and transportation and pretreatment cost index (TC) are also considered. When the candidate path is a material-energy coupled path, both the component matching index (CMI) and the energy quality matching index (EQMI) are used as the main evaluation indicators, and the weights of each evaluation indicator are determined according to the specific utilization scenario.

9. The method according to claim 1, characterized in that: The cross-industry collaborative utilization scheme for steel by-products is output in one or more forms, such as a path list, resource allocation table, supply and demand matching table, cross-industry symbiotic network diagram, scheduling instructions or process control parameters. The output content includes one or more of the following: by-product source, target sink industry, collaborative utilization path, suitable technology, pretreatment process, substitute object, transportation distance, collaborative utilization amount, carbon emission reduction benefits, environmental risk level and path priority.

10. A cross-industry collaborative utilization system for steel by-products based on component matching and energy-mass matching, characterized in that, For implementing the method according to any one of claims 1 to 9, the cross-industry collaborative utilization system comprises: The data acquisition module is used to collect data on by-products from steel enterprises and demand data from external industries. The database construction module is used to construct a source-end by-product attribute library based on the source-end data of the steel enterprise, construct a pooling-end demand parameter library based on the external industry pooling-end demand data, and construct a scenario access threshold library based on the target pooling industry access requirements, historical operating data of steel enterprises, and demonstration operation feedback. The calculation module is used to calculate at least one of the following based on the source-end by-product attribute library, sink-end demand parameter library, and scenario access threshold library: component matching index, energy-quality matching index, spatial transportation constraint index, environmental risk constraint index, and carbon emission reduction benefit index. The candidate path construction module is used to generate cross-industry collaborative utilization candidate paths according to the five-element structure of "source industry - by-product - collaborative utilization approach - adaptation technology - sink industry". The risk screening module is used to screen the candidate paths based on the environmental risk constraint index and the scenario access threshold library. The risk handling module is used to eliminate candidate paths or match preprocessing techniques when there are environmental risks or restricted components exceeding limits in candidate paths. After preprocessing, it re-collects or updates by-product attribute data and returns to the candidate path construction module, risk screening module, and risk handling module to re-screen the candidate paths for risk until no candidate paths have environmental risks or restricted components exceeding limits. When no candidate path has environmental risks or restricted components exceeding limits, it enters the target path determination module. The target path determination module is used to determine the number of candidate paths corresponding to the same steel by-product. If there are two or more, the candidate paths are sorted based on the comprehensive synergy index, and the candidate path with the highest ranking is determined as the target path; if there is only one, the candidate path corresponding to the steel by-product is determined as the target path. The solution output module is used to output a cross-industry collaborative utilization solution for steel by-products based on the target path.

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