A method for optimizing a technical path of energy saving, carbon reduction and efficiency improvement of a steel production system

Abstract

The application provides a technical path optimization method for energy saving, carbon reduction and efficiency increase of a steel production system, comprising the following steps: establishing a mechanism and data driven hybrid model of a steel production process; based on the mechanism and data driven hybrid model of the steel production process, accounting for heat, energy, quantity and pollutants, and forming a steel production system simulation model by connecting processes; embedding a value flow analysis model in the steel production system simulation model to perform value flow accounting of the steel production system; embedding low-carbon, zero-carbon and negative-carbon technologies in the steel production system simulation model in a material or energy connection mode to establish a steel production system coupling carbon reduction technology simulation model; setting an optimization target of the steel production system coupling carbon reduction technology simulation model; and using a U-NSGA-III method to optimize material and energy input parameter variables, process operation parameter variables and technology application parameter variables to obtain a steel production system technology optimization path.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of steel production, and relates to a technical path optimization method for energy saving, carbon reduction and efficiency increase of a steel production system. BACKGROUND

[0002] China is the largest steel producer in the world, with a crude steel output accounting for 53.9% of the world's total. The steel industry is a typical resource and energy-intensive industry, with energy consumption accounting for about 13.6% of the country's total energy consumption, and carbon emissions accounting for about 15% of the country's total carbon emissions. The steel industry has the characteristics of large output, high consumption and high emissions. The steel industry urgently needs to be transformed into a low-carbon industry. Therefore, how to effectively promote the energy saving, carbon reduction and efficiency increase of steel enterprises is an inherent requirement for industrial transformation and upgrading and the realization of green and high-quality development.

[0003] Traditional steel production long-process enterprises are complex and large production systems with multiple devices, multiple levels, and multiple technology integrations, and multiple flow interactions such as material flow, energy flow, and value flow. They involve the transformation of materials, the conversion and utilization of energy, the coordination of carbon emissions, and the change of value. They rely on the physical and chemical changes of various devices at the bottom, the energy utilization and material transformation processes, and the connection and integration of different devices, technologies, and processes to form a steel production system. Energy structure zero-carbon transformation, energy efficiency improvement to help carbon reduction, and carbon capture and utilization can directly or indirectly reduce energy consumption and carbon emissions of enterprises, or increase enterprise fund income or carbon assets. Therefore, a scientific, effective, comprehensive, and systematic low-carbon transformation decision optimization and analysis method for steel production systems is needed. This method can reveal the coupling relationship between material-energy-carbon emission-value in the steel production system, simulate and analyze the production process from crude steel to different steel products, and optimize the energy structure of the steel production system, the energy consumption of the production process, the energy consumption of the production process, and the energy consumption of the production process. The indicators, carbon emission indicators, and cost-effectiveness indicators are used for calculation, evaluation, optimization, and analysis. From a system perspective, changes in a certain factor will lead to changes in multiple directions of the production system. The coupling relationship between the steel production system and low-carbon-zero-carbon-negative-carbon reduction technologies needs to be revealed, so as to explore the development path of energy saving and carbon reduction for steel enterprises with market and policy changes or trends, provide scientific and orderly low-carbon transformation decisions for enterprises, respond to the requirements of national economic development, and promote the energy saving and low-carbon development of the steel industry and the overall economic development of the steel industry.

[0004] The current steel enterprise energy saving and carbon reduction transformation decision management and low carbon development path are more in the way of "headache medicine head, foot pain medicine foot", and the energy saving and carbon reduction technology transformation is more dependent on technical experience, and there is strong subjectivity and experience level problem, and there is a lack of source-process-end comprehensive system optimization management problem. At the same time, the matching relationship and synergistic mechanism of different energy saving and carbon reduction technologies of steel enterprises under the system perspective are not clear, which is not conducive to the combination and application of carbon reduction, zero carbon and negative carbon technologies, and it is difficult to provide scientific and effective transformation scheme for the integration of production operation status and environmental-economic benefit maximization development path. SUMMARY

[0005] To solve the above technical problems, the purpose of the present application is to provide a steel production system energy saving and carbon reduction synergistic technical path optimization method.

[0006] The present application provides a steel production system energy saving and carbon reduction synergistic technical path optimization method, comprising:

[0007] Step 1: Establish a steel production process mechanism and data-driven hybrid model;

[0008] Step 2: Based on the steel production process mechanism and data-driven hybrid model, the heat, energy, mass and pollutant accounting is carried out, and the process connection is formed to form a steel production system simulation model;

[0009] Step 3: Based on the steel production system simulation model, embed the value flow analysis model to carry out the value flow accounting of the steel production system;

[0010] Step 4: Based on the steel production system simulation model, embed low-carbon, zero-carbon and negative-carbon technologies in a material or energy linkage manner to establish a steel production system coupled carbon reduction technology simulation model;

[0011] Step 5: Set the optimization target, variable and constraint condition of the steel production system coupled carbon reduction technology simulation model;

[0012] Step 6: Use the U-NSGA-III method to optimize the material and energy input parameter variable, process operation parameter variable and technology application parameter variable, and obtain the optimized steel production technology path.

[0013] The steel production system energy saving and carbon reduction synergistic technical path optimization method of the present application has the following beneficial effects:

[0014] (1) Improve energy use efficiency: by flexibly adjusting the production process to adapt to market changes, this method can reduce energy waste and promote efficient conversion and utilization of energy; ​

[0015] (2) Reducing carbon emission level: This method focuses on the integrated application of low-carbon technologies or measures, aiming to significantly reduce the emission of carbon dioxide and other greenhouse gases in the steel production process;

[0016] (3) Quantitative evaluation of multiple technology synergistic effect: By accurately quantifying the application effect of different technology combinations, it provides strong support for formulating a scientific and reasonable energy-saving and carbon-reducing path.

[0017] (4) Promote win-win of energy saving and carbon reduction and economic sustainable development: While promoting enterprise energy saving and emission reduction, ensure the sustainable growth of economic benefits, effectively avoid the contradiction between energy saving and carbon reduction and economic benefit improvement. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 is a flow chart of a steel production system energy saving and carbon reduction synergistic technology path optimization method of the present application. DETAILED DESCRIPTION

[0019] As shown in Figure 1 A steel production system energy saving and carbon reduction synergistic technology path optimization method of the present application, comprising:

[0020] Step 1: Establish a hybrid model of steel production process mechanism and data driving, specifically:

[0021] Step 1.1: Based on the physical and chemical reaction mechanism of steel production equipment and the principle of material balance, a steel production mechanism model is constructed to calculate the production property parameters.

[0022] In specific implementation, based on the physical and chemical process principle of steel smelting and the law of conservation of mass and energy, some production property parameters or indicators with complete data can be obtained. The production property parameters are calculated more accurately by mathematical mechanism model, such as: theoretical air volume, steam generation, oxygen demand, theoretical combustion temperature, power generation, slag basicity, hearth temperature, etc.

[0023] Step 1.2: Time granularity normalization or regularization preprocessing is performed on historical production data; historical production data includes equipment parameters, material and energy input parameters, material and energy output parameters, temperature parameters, process operation parameters, composition parameters and pressure parameters.

[0024] Step 1.3: Integrate LASSO regression analysis method, screen and fit part of the pretreated historical production data, and then accurately simulate the main production equipment running process to predict the production process key parameters or product performance indicators. Taking coking process as an example, such as total coke yield of coke oven, yield of tar, crude benzene and ammonia, COG generation, steam demand, and product performance indicators of M10, M40, CSR and CRI of coke.

[0025] The LASSO regression analysis formula is as follows:

[0026]

[0027] In the formula, L lasso is the square error, y a is the a-th production process key parameter or product performance index to be fitted, r represents the number of production process key parameters or product performance indexes to be fitted, x ab is the pretreated historical production data, β0 is a constant, β b is a regression coefficient, λ is a weight factor, and q represents the number of input historical production data.

[0028] Step 1.4: Use the statistical period average or experience value of the enterprise to estimate the indexes or parameters that are difficult to accurately calculate and predict by the mechanism model and regression analysis. For example, excess air coefficient, compressed air consumption, nitrogen consumption, and a few temperature parameters. These parameters or indexes usually have small fluctuations, and the average value or experience value effectively reflects the average level or typical state of the production system under normal working conditions.

[0029] In implementation, before establishing the hybrid model, the research boundary needs to be set according to the research object and requirements. The boundary is related to the equipment, process or system of the steel production. The original data of the enterprise needs to be pretreated, including abnormal value detection and processing, and missing value processing.

[0030] Step 2: Based on the steel production process mechanism and data-driven hybrid model, the heat, energy, and pollutant accounting is carried out, and the process connection is formed to form a steel production system simulation model, which is specifically as follows:

[0031] Step 2.1: Based on the physical and chemical reaction mechanism and material input and output, the energy utilization, conversion and dissipation process of the reaction process is calculated, and the heat balance, energy conservation and conservation are used to account for the material production and consumption and energy dissipation based on the above steel production process mechanism and data-driven hybrid model.

[0032] Step 2.2: According to the hybrid model and the device heat balance formula, the steel production process heat utilization is calculated, and the device heat balance formula is as follows:

[0033] H energy，in +H material，in =H byprodcut，out +H product，out +H loss

[0034] In the formula, H energy，in is the device heat input for producing a unit product; H material,inH represents the sensible heat input of the equipment per unit of product produced. producty，out Removes heat from equipment that produces a unit of product; H byprodcut，out Heat is removed from byproducts of production; H loss This refers to the heat loss of equipment per unit of product produced.

[0035] Step 2.3: Based on the hybrid model, equipment / process Balance formula calculation Conversion process, equipment / process The equilibrium formula is as follows:

[0036] Ex energy，in +Ex material，in =Ex byproduct，out +Ex product，out +Ex iner，loss +Ex exter，loss

[0037] In the formula, Ex energy，in Energy per unit product of equipment / process Input amount; Ex material,in Unit product material for equipment / process Input amount; Ex producty，out Unit product of equipment / process Output; Ex byprodcut,out byproducts of equipment / processes Output; Ex iner，loss For the internal of equipment / process Loss amount; Ex exter，loss For the outside of equipment / process Amount of loss.

[0038] Step 2.4: Calculate the atmospheric pollutants and greenhouse gases.

[0039] In practice, the formula for calculating CO2 emissions from the equipment is as follows:

[0040]

[0041] CO2 I =[M em，in [C] em ] T

[0042] In the formula, M r，in For the material input matrix, C r，in Let M be the C element content matrix. r，out Let C be the mass output matrix. r,out M is the mass ratio matrix of C elements; GHG Other greenhouse gas emissions (GHG); GWPGHG This is to account for the global warming potential of other greenhouse gases, such as GHG.

[0043] Step 2.5: Calculate energy utilization based on the hybrid model and the process energy balance formula. The process energy balance formula is as follows:

[0044] En energy,in +En material,in =En prodct，out +En byproduct，out +En loss

[0045] In the formula, En energy,in Energy input per unit product in the process; En material,in Energy input per unit of product material in the process; En producty,out Energy output per unit product in the process; En byprodcut,out Energy output of byproducts of the process; En loss This represents the energy loss during the process.

[0046] Step 2.6: The entire steel production process is simulated by connecting the processes using the steel ratio coefficient, thus forming a simulation model of the steel production system.

[0047] Step 3: Based on the steel production system simulation model, embed a value stream analysis model to perform value stream accounting for the steel production system, specifically as follows:

[0048] (1) The basic expression for the value output of the internal process of the system is as follows:

[0049]

[0050] In the formula, Vi n,out,i The total value of the output of the i-th process in the production system's value flow is determined by the internal circulation of the i-th process. value External product value of process i The value of waste management in process i The components and expressions are as follows:

[0051]

[0052] In the formula, V eq,en Units from a system perspective Its value equivalent To loop between "nodes" within the system The stream f contains quantity; For the production process from materials to product p Change in value; V added,pThe added value of a product (P) resulting from market or customer demand; V added,po V is the cost of managing or disposing of waste generated due to policy. market,p The market value of product p at different times; The waste contained in PO Value; V manage,po The management value of waste includes waste disposal costs, management expenses, market assets, and resource recovery benefits; from a systemic perspective, the unit... The value equivalent expression is as follows:

[0053]

[0054] In the formula, V eq,en (t) represents the value equivalent of the internal cycle within period t; V sum,en (t) represents the total energy value of the system input outside the boundary during period t; Ex sum,en (t) represents the total energy contained in the energy input to the system outside the boundary during period t. quantity;

[0055] (2) The basic expression for the value input of the internal process of the system is as follows:

[0056]

[0057] In the formula, V i,in The total value invested in the i-th process of the production system's value flow is generated by the internal circulation. value External material input value V m,i composition;

[0058] V m,i =V m,main,i +V m,sub,i

[0059] In the formula, V m,main,i V represents the value of the main materials. m,sub,i Indicates the value of auxiliary materials;

[0060] (3) Value equivalent V of a unit product produced in the auxiliary process eq,au The expression for (t) is as follows:

[0061]

[0062] In the formula, Ex en,in Indicates the internal circulation energy of auxiliary processes (au). Input amount; P out,au This indicates the output of product au in the auxiliary process.

[0063] Step 4: Based on the simulation model of the steel production system, low-carbon, zero-carbon, and negative-carbon technologies are embedded in the system in a material or energy linkage manner to establish a simulation model of the steel production system coupled with carbon reduction technologies, specifically:

[0064] Step 4.1: By changing different material and energy input parameters to affect energy consumption and pollutant emissions in steel production, source structure type carbon reduction technology embedding is achieved.

[0065] (1) The formula for the direct carbon emission reduction per ton of steel after embedding the source structure type carbon reduction technology system is as follows:

[0066]

[0067] wherein, is the direct carbon emission reduction per ton of steel for the source structure type carbon reduction technology; M i,en,te is the mass change of energy or energy medium en in process i after technology application, M i,m,te is the mass change of material m; C en is the carbon emission coefficient of energy or energy medium en, C m is the carbon emission coefficient of material m; P i is the material ratio coefficient of steel production process i.

[0068] Step 4.2: By changing the equipment parameters, process operation parameters, and composition parameters in the steel production process to account for their impact on production, process node type carbon reduction technology embedding is achieved.

[0069] (2) The formula for the direct carbon emission reduction per ton of steel after embedding the process node type carbon reduction technology system is as follows:

[0070]

[0071] wherein, is the direct carbon emission reduction per ton of steel for the process node type carbon reduction technology; MC i，te，m is the mass change of material m in process i after technology application, MC i,te,en is the mass change of energy or energy medium en in process i after technology application; U i,te,m is the net consumption of material m caused by technology application, U i,te,en is the net consumption of energy or energy medium en caused by technology application.

[0072] Step 4.3: By changing energy consumption, composition parameters, pollutant emissions, energy recovery, and material output parameters to account for their impact on production, end capture type carbon reduction technology embedding is achieved.

[0073] (3) The formula for the direct carbon emission reduction per ton of steel after embedding the end capture type carbon reduction technology system is as follows:

[0074]

[0075] In the formula, The direct carbon emission reduction per ton of steel by end-of-pipe capture carbon reduction technology; CA i，te，m The amount of carbon captured by the technology.

[0076] Step 4.4: Based on the above production process simulation and technology embedding, the technological transformation will directly affect the material and energy changes of the preceding and following processes, and establish a simulation model of coupled carbon reduction technology for the steel production system.

[0077] Unit product operating cost caused by embedding energy-saving and carbon-reduction technologies Lifecycle unit product investment cost Cost-effectiveness per ton of steel with technology CB te The formula is as follows:

[0078]

[0079] In the formula, The cost of operating technology per unit of product; The investment cost per unit of product lifecycle; is the investment cost; int is the periodic discount rate; L te The time cycle of technology operation; TL te For the technology's lifespan; Pr TLte CB represents the projected crude steel production volume over a given time period. te For cost-effectiveness per ton of steel; M m,te V represents the change in material consumption m after the application of the technology; eq,m V represents the market value of a unit of material. eq,en as a unit Value equivalent; Ex te,c To save ton of steel after the application of the technology Quantity; CO2 te,R V represents the carbon emission reduction per ton of steel after the application of the technology. eq,CO2 For carbon price; PO2 te,R For the synergistic changes of other pollutants; V eq,PO The cost of treating a unit of pollutant PO emissions.

[0080] From a systems perspective, the marginal cost-effectiveness of carbon reduction technology varies with changes in source-end production conditions, emission reduction technologies, and the economic market. The formula for the marginal cost-effectiveness of carbon reduction technology is as follows:

[0081]

[0082] In the formula, MCB(t) is the carbon reduction cost benefit in the time period t, which is the marginal cost benefit if equal to 1, and the carbon reduction synergy benefit if greater than 1, and vice versa; CR CO2 is the carbon reduction amount; V CO2,ep is the carbon emission value; CVP after is the cost of the production system after the preset multiple emission reduction technologies; CVP base is the cost of the production process before the modification.

[0083] Step 5: Set the optimization target, variable and constraint condition of the steel production system coupled with the carbon reduction technology simulation model, specifically:

[0084] Step 5.1: Based on the steel production system coupled with the carbon reduction technology simulation model, three optimization targets are set for the steel production system:

[0085] Target one: production of unit product as the energy saving optimization target of the steel production system, referred to as product intensity, product intensity objective function Ex intensity as follows:

[0086]

[0087] In the formula, P z is the steel ratio or material ratio coefficient of the main process z, that is, the amount of process product consumed to produce one ton of crude steel or steel product; Ex in,z is the input amount of the main process z; Ex in,au is the input amount of the auxiliary process au; M p,z is the product p output amount in the main process z; M p,au is the product p output amount in the auxiliary process au; is the effective output amount of the main process z; is the effective output amount of the auxiliary process au;

[0088] Target two: carbon emission amount per unit steel product is set as the optimization target of the system carbon reduction, referred to as carbon emission intensity, carbon emission intensity objective function CO2 intensity as follows:

[0089]

[0090] In the formula, CO2 D,z is the direct carbon emission amount of the enterprise in the main process z; CO2 D,au is the direct carbon emission amount of the enterprise in the auxiliary process au; CO2 I,purM represents the indirect carbon emissions caused by enterprises purchasing power energy (pur) from the power grid or heating network. p,pur p represents the amount of energy (pur) that a company purchases from the power grid or heating network. pur The energy consumption per unit of steel (pure).

[0091] Objective 3: The overall cost-benefit of steel products produced per unit is set as the objective of economic benefit optimization. The overall cost-benefit objective function CB is as follows:

[0092]

[0093] Among them, molecule M m，p V p Let p be the market economic value of product p; the denominator is M. m，in V m Raw material costs, M en，in V en Energy costs, M pol，in V pol，ad Environmental costs, M CO2，in V CO2，eq CO2 emission costs and M bp，out V bp The economic value composition of by-products;

[0094] Step 5.2: The optimization model variables include: basic production operation parameters such as the composition and structure of input materials and energy composition and structure; steel production equipment parameters such as temperature, pressure, and element yield; process parameters such as product structure, oxygen enrichment rate, and material ratio coefficient; and Boolean variables for selecting and optimizing the application of low-carbon, zero-carbon, and negative-carbon technologies.

[0095] Step 5.3: The boundary settings for variables should conform to the actual requirements of normal operation. Other constraints include product quality constraints, process parameter constraints, and material and energy conservation constraints.

[0096] The constraints on equipment operating parameters and pollutant emission standards are as follows:

[0097] x j,min ≤x j ≤x j，max

[0098] 0≤P O ≤P O,max

[0099] Where, x j Let x be the operating parameter of the j-th device. j,min Let x be the historical lowest value of the j-th equipment operating parameter under normal operating conditions. j,max P represents the historical highest value of the j-th equipment operating parameter under normal operating conditions. OP O,max is the maximum value of the pollutant emission standard.

[0100] materials, heat, energy and Balance constraints: The input-output materials, heat, energy and balance constraints are established based on the laws of thermodynamics and mass conservation theory. However, the heat such as furnace heat dissipation cannot be accurately calculated in the simulation process, so a series of uncalculated energy will be constrained by the actual production experience value to make the model more consistent with the actual production.

[0101]

[0102] where E other is the uncalculated energy loss; E in,sum and E out,sum are the input and output energy, respectively; σ Q，av is the ratio of uncalculated loss to total energy, which is constrained by the actual production experience value.

[0103] Stable production condition constraints: In order to meet the stable production conditions of steel, some key production process parameters will be limited, such as the basicity of slag, the combustion temperature of the furnace, the water content, the oxygen content of flue gas, etc. In addition, considering the challenge of fine coal or ore matching in actual working conditions, the minimum threshold of the proportion of single type of coal or ore is maintained at more than 3% when matching coal or ore. Taking the slag basicity constraint as an example, it is shown as follows:

[0104] R r,min ≤ R r ≤ R r，max

[0105] where R r represents the basicity of slag and explicitly constrains its upper and lower limits.

[0106] Product quality index constraints: Ensuring the quality of the production products is an important prerequisite for achieving enterprise benefits, so the trace element composition constraints of different steel products are necessary. In the smelting process, element composition and quality index constraints are implemented for all process products and final products.

[0107] Equipment power constraints: In this study, the upper and lower limits of the power of auxiliary equipment are constrained within the design and normal working condition range, including boilers, power plants, oxygen plants, lime kilns, etc.

[0108]

[0109] where GA e,cs is the product output of equipment e per ton of crude steel. GC min,e,tmmin represents the minimum product output of the device e during tm; M cs,tm max represents the maximum product output of the device e during tm.

[0110] Reliability constraints: The iron and steel production interaction network involves the production and cross utilization of a variety of energy and materials to meet the needs of iron and steel production for a variety of materials and energy media. Therefore, the input and output of these materials and energy need to meet the reliability condition constraints, that is, the output of the auxiliary process product is greater than the system utilization, including energy media such as electricity, steam, blast, compressed air, oxygen, hydrogen, and auxiliary materials, flux. And the steam is divided into three types according to the pressure and temperature, which need to meet the reliability constraint conditions, respectively, S1 steam (2.5-3.0 MPa, 250-300℃), S2 steam (0.7-1.0 MPa, 170-300℃) and S3 steam (0.3-0.5 MPa, 200-250℃). The constraint expression is as follows:

[0111]

[0112] In the formula, min represents the production amount of the process l; max represents the purchased amount of the auxiliary material or energy medium g; max represents the utilization amount of the auxiliary material or energy medium g of the iron and steel production system, max represents the loss amount of the auxiliary material or energy medium g of the iron and steel production system.

[0113] Step 6: The U-NSGA-III method is used to optimize the material and energy input parameter variables, process operation parameter variables and technical application parameter variables to obtain the optimized steel production technology path, which is specifically:

[0114] Step 6.1: The material and energy input parameter variables, process operation parameter variables and technical application parameter variables are combined to form a chromosome. The material and energy input parameter variables and the process operation parameter variables use real number coding mode, and the technical application parameter variables use integer coding mode.

[0115] Step 6.2: Initialize the population, and set the initial population size, initial value, crossover, mutation operator, maximum iteration step number and variable upper and lower boundary.

[0116] Step 6.3: Calculate the optimization target value of the model, and calculate the fitness according to the optimization target value, which is used as the basis for the next step of stratification and selection. At the same time, the violation degree is used to measure the satisfaction of the variable to the constraint condition.

[0117] Step 6.4: According to the fitness, the GA genetic algorithm of non-dominated sorting and tournament selection is used to perform crossover and mutation genetic processing on the initial population to generate a child population.

[0118] Step 6.5: After the offspring population is combined with the parent population, elite handling is performed to generate a new generation population.

[0119] Step 6.6: Repeat steps 6.3-6.5 for the new generation population to perform non-dominated sorting and genetic handling until a maximum number of iterations is reached to obtain an optimal population.

[0120] The above description is only the preferred embodiment of the present application, and is not intended to limit the idea of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for optimizing the technical path of energy conservation, carbon reduction, and efficiency improvement in a steel production system, characterized in that: include: Step 1: Establish a hybrid model of steel production process mechanism and data-driven approach; Step 1.1: Construct a steel production mechanism model based on the physicochemical reaction mechanism and material balance principle of steel production equipment, and calculate the production physical property parameters; Step 1.2: Perform time-granularity normalization or regularization preprocessing on historical production data; historical production data includes equipment parameters, material and energy input parameters, material and energy output parameters, temperature parameters, process operation parameters, composition parameters, and pressure parameters; Step 1.3: Integrate the LASSO regression analysis method to screen and fit some preprocessed historical production data, and then accurately simulate the operation process of the main production equipment to predict key parameters of the production process or product performance indicators. Step 1.4: Use the company's statistical period average or empirical value to estimate indicators or parameters that are difficult to accurately calculate and predict through mechanistic models and regression analysis; Step 2: Based on the hybrid model of steel production process mechanism and data-driven approach, analyze heat, energy, and... The calculation of quantities and pollutants is used to connect processes and form a simulation model of the steel production system; Step 3: Based on the steel production system simulation model, embed the value stream analysis model to perform value stream accounting for the steel production system; (1) The basic expression for the value output of the internal process of the system is as follows: In the formula, V in,out,i The total value of the output of the i-th process in the production system's value flow is determined by the internal circulation of the i-th process. value External product value of process i The value of waste management in process i The components and expressions are as follows: In the formula, V eq,en Units from a system perspective Its value equivalent To loop between "nodes" within the system The stream f contains quantity; For the production process from materials to product p Change in value; V added,p The added value of a product (P) resulting from market or customer demand; V added,po V is the cost of managing or disposing of waste generated due to policy. market,p The market value of product p at different times; The waste contained in PO Value; V manage,po The management value of waste includes waste disposal costs, management expenses, market assets, and resource recovery benefits; from a systemic perspective, the unit... The value equivalent expression is as follows: In the formula, V eq,en (t) represents the value equivalent of the internal cycle within period t; V sum,en (t) represents the total energy value of the system input outside the boundary during period t; Ex sum,en (t) represents the total energy contained in the energy input to the system outside the boundary during period t. quantity; (2) The basic expression for the value input of the internal process of the system is as follows: In the formula, V i,in The total value invested in the i-th process of the production system's value flow is generated by the internal circulation. value External material input value V m,i composition; V m,i =V m,main,i +V m,sub,i In the formula, V m,main,i V represents the value of the main materials. m,sub,i Indicates the value of auxiliary materials; (3) Value equivalent V of a unit product produced in the auxiliary process eq,au The expression for (t) is as follows: In the formula, Ex en,in Indicates the internal circulation energy of auxiliary processes (au). Input amount; P out,au This indicates the output of product au in the auxiliary processes; Step 4: Based on the simulation model of the steel production system, embed low-carbon, zero-carbon, and negative-carbon technologies in a material or energy linkage manner to establish a simulation model of coupled carbon reduction technologies for the steel production system. Step 4.1: By changing the energy input parameters of different materials, the energy consumption and pollutant emissions of steel production are affected, thereby realizing the embedding of source-based structural carbon reduction technologies; Step 4.2: By changing the equipment parameters, process operation parameters and composition parameters in the steel production process, the impact on production is calculated to realize the embedding of process node carbon reduction technology; Step 4.3: By changing energy consumption, composition parameters, pollutant emissions, energy recovery, and material output parameters, the impact on production is calculated to achieve the embedding of end-of-pipe carbon reduction technologies; Step 4.4: Based on the above production process simulation and technology embedding, the technological transformation will directly affect the material and energy changes of the preceding and following processes, and establish a simulation model of coupled carbon reduction technology in the steel production system; Step 5: Set the optimization objective, variables, and constraints for the simulation model of coupled carbon reduction technology in the steel production system; Step 6: Use the U-NSGA-III method to optimize the material and energy input parameters, process operation parameters, and technology application parameters to obtain the optimized steel production technology path.

2. The technical path optimization method for energy conservation, carbon reduction, and efficiency improvement in steel production systems as described in claim 1, characterized in that, The LASSO regression analysis formula is as follows: In the formula, L lasso It is the variance, y a It is the a-th key production process parameter or product performance index that needs to be fitted, r represents the number of key production process parameters or product performance indicators that need to be fitted, x ab This is preprocessed historical production data, where β0 is a constant and β... b λ is the regression coefficient, λ is the weighting factor, and q represents the amount of historical production data input.

3. The technical path optimization method for energy conservation, carbon reduction, and efficiency improvement in steel production systems as described in claim 1, characterized in that... Step 2 specifically involves: Step 2.1: Based on the physicochemical reaction mechanism and material input and output, calculate the energy utilization, conversion, and dissipation processes of the reaction, taking into account heat balance, energy conservation, and... Based on conservation, the material production and energy dissipation are calculated on the basis of the above steel production process mechanism and data-driven hybrid model. Step 2.2: Calculate the heat utilization in the steel production process based on the hybrid model and the equipment heat balance formula. The equipment heat balance formula is as follows: H energy,in +H material,in =H byproduct，out +H product，out +H loss In the formula, H energy,in H represents the heat input of equipment per unit of product produced. material,in H represents the sensible heat input of the equipment per unit of product produced. product,out Removes heat from equipment that produces a unit of product; H byproduct,out Heat is removed from byproducts of production; H loss Heat loss of equipment per unit of product produced; Step 2.3: Based on the hybrid model, equipment / process Balance formula calculation Conversion process, equipment / process The equilibrium formula is as follows: E xenergy,in +Ex material,in =Ex byproduct，out +Ex product,out +Ex iner，loss +Ex exter,loss In the formula, Ex energy,in Energy per unit product of equipment / process Input amount; Ex material,in Unit product material for equipment / process Input amount; Ex product,out Unit product of equipment / process Output; Ex byproduct,out byproducts of equipment / processes Output; Ex iner,loss For the internal of equipment / process Loss amount; Ex exter,loss For the outside of equipment / process Loss amount; Step 2.4: Calculate the atmospheric pollutants and greenhouse gases; Step 2.5: Calculate energy utilization based on the hybrid model and the process energy balance formula. The process energy balance formula is as follows: And energy,in +And material,in =And product，out +And byproduct，out +And loss In the formula, En energy,in Energy input per unit product in the process; En material,in Energy input per unit of product material in the process; En product,out Energy output per unit product in the process; En byproduct,out Energy output of byproducts of the process; En loss This refers to the energy loss during the process. Step 2.6: The entire steel production process is simulated by connecting the processes using the steel ratio coefficient, thus forming a simulation model of the steel production system.

4. The technical path optimization method for energy conservation, carbon reduction, and efficiency improvement in steel production systems as described in claim 1, characterized in that: (1) The formula for the direct carbon emission reduction per ton of steel after the source structural carbon reduction technology is embedded in the system is as follows: In the formula, For the direct carbon emission reduction of ton of steel source structural carbon reduction technology; M i,en,te M represents the change in the mass of energy or energy medium en in process i after the application of the technology. i,m,te C represents the change in mass of material m; en C is the carbon emission factor of energy or energy medium en. m p is the carbon emission coefficient of material m; i The material ratio coefficient for steel production process i; (2) The formula for the direct carbon emission reduction per ton of steel after process node-type carbon reduction technology is embedded in the system is as follows: In the formula, Direct carbon emission reduction for process node-type carbon reduction technologies per ton of steel; MC i，te，m MC represents the change in mass of material m in the subsequent process after the application of the technology. i,te,en The change in the mass of energy or energy medium en in the subsequent process after the application of technology; U i,te,m U represents the net consumption of material m resulting from the application of the technology. i,te,en The net consumption of energy or energy medium (en) resulting from the application of technology; (3) The formula for the direct carbon emission reduction per ton of steel after the end-of-pipe capture carbon reduction technology is embedded in the system is as follows: In the formula, The direct carbon emission reduction per ton of steel by end-of-pipe capture carbon reduction technology; CA i，te，m The amount of carbon captured by the material m in the subsequent process after the application of the technology; (4) Unit product technology operating costs caused by the embedding of energy-saving and carbon-reduction technologies Lifecycle unit product investment cost Cost-effectiveness per ton of steel with technology CB te The formula is as follows: In the formula, The cost of operating technology per unit of product; The investment cost per unit of product lifecycle; is the investment cost; int is the periodic discount rate; L te The time cycle of technology operation; TL te This refers to the lifespan of the technology. Pr TLte CB represents the projected crude steel production volume over a given time period. te For cost-effectiveness per ton of steel; M m,te V represents the change in material consumption m after the application of the technology; eq,m V represents the market value of a unit of material. eq,en as a unit Value equivalent; Ex te,c To save ton of steel after the application of the technology Quantity; CO2 te,R V represents the carbon emission reduction per ton of steel after the application of the technology. eq,CO2 For carbon price; PO te,R For the synergistic changes of other pollutants; V eq,PO The cost of treating a unit of pollutant PO emissions.

5. The technical path optimization method for energy conservation, carbon reduction, and efficiency improvement in steel production systems as described in claim 1, characterized in that... Step 5 specifically involves: Step 5.1: Based on the simulation model of coupled carbon reduction technology in the steel production system, three optimization objectives are set for the steel production system: Objective 1: To produce a unit of product Energy consumption is used as the energy-saving optimization target of the steel production system, referred to as product energy consumption. Strength, Product Intensity objective function Ex intensity as follows: In the formula, p z The steel ratio or material ratio coefficient for the main process z, i.e., the amount of product consumed in the process to produce one ton of crude steel or steel product; Ex in,z Input for the main process z Quantity; Ex in,au For the input of public auxiliary processes au Quantity; M p,z M represents the output of product p in the main process z; p,au The output of product p in the auxiliary process au; For the effective output of the main process z quantity; Effective output of auxiliary processes (au) quantity; Objective 2: The carbon emissions per unit of steel product produced are set as the target for system carbon reduction optimization, referred to as carbon emission intensity. The objective function for carbon emission intensity is CO2. intensity as follows: In the formula, CO2 D,z This refers to the direct carbon emissions of the enterprise in its main processes (z); CO2 D,au This refers to the company's direct carbon emissions (CO2) in auxiliary processes (au). I,pur M represents the indirect carbon emissions caused by enterprises purchasing power energy (pur) from the power grid or heating network. p,pur p represents the amount of energy (pur) that a company purchases from the power grid or heating network. pur The energy consumption per unit of steel (pure). Objective 3: The overall cost-benefit of steel products produced per unit is set as the objective of economic benefit optimization. The overall cost-benefit objective function CB is as follows: Among them, molecule M m，p V p Let p be the market economic value of product p; the denominator is M. m，in V m Raw material costs, M en，in V en Energy costs, M pol,in V pol,ad Environmental costs, M CO2,in V CO2,eq CO2 emission costs and M bp,out V bp The economic value composition of by-products; Step 5.2: The optimization model variables include: basic production operation parameters, steel production equipment parameters, process parameters, and Boolean variables for selecting and optimizing low-carbon, zero-carbon, and negative-carbon technologies; Step 5.3: The constraints for equipment operating parameters and pollutant emission standards are as follows: x j,min ≤x j ≤x j,max 0≤P O ≤P O,max Where, x j Let x be the operating parameter of the j-th device. j,min Let x be the historical lowest value of the j-th equipment operating parameter under normal operating conditions. j,max P represents the historical highest value of the j-th equipment operating parameter under normal operating conditions. O For pollutant emissions, P O,max This represents the maximum value of the pollutant emission standard.

6. The technical path optimization method for energy conservation, carbon reduction, and efficiency improvement in steel production systems as described in claim 1, characterized in that, Step 6 specifically involves: Step 6.1: Combine the material and energy input parameters, process operation parameters, and technology application parameters to form a chromosome. The material and energy input parameters and process operation parameters are encoded using real numbers, while the technology application parameters are encoded using integers. Step 6.2: Initialize the population, and set the initial population size, initial values, crossover, mutation operators, maximum number of iterations, and upper and lower boundaries of variables; Step 6.3: Calculate the model optimization objective value and calculate the fitness based on the optimization objective value to serve as the basis for the next step of stratification and selection. At the same time, use the violation degree to measure the degree to which the variables satisfy the constraints. Step 6.4: Based on fitness, perform crossover and mutation genetic processing on the initial population using a GA genetic algorithm with non-dominated sorting and tournament selection to generate the offspring population; Step 6.5: After merging the offspring population with the parent population, perform elite processing to generate a new generation population; Step 6.6: Repeat steps 6.3-6.5 to perform non-dominated sorting and genetic processing on the new generation population until the maximum number of iterations is reached and the optimal population is obtained.

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