Energy-saving, carbon-reducing and synergistic technical path optimization method for steel production system

Abstract

The invention provides an energy-saving, carbon-reducing and synergistic technical path optimization method for a steel production system. The method comprises the following steps: establishing a steel production process mechanism and data driving hybrid model; accounting heat, energy, # imgabs0 # quantity and pollutants on the basis of a steel production process mechanism and a data driving hybrid model, and performing process connection to form a steel production system simulation model; on the basis of the steel production system simulation model, a value flow analysis model is embedded, and steel production system value flow accounting is carried out; on the basis of the steel production system simulation model, embedding low-carbon, zero-carbon and negative-carbon technologies in a substance or energy connection mode, and establishing a steel production system coupling carbon reduction technology simulation model; setting an optimization target of the coupling carbon reduction technology simulation model of the steel production system; and optimizing the material energy input parameter variable, the process operation parameter variable and the technical application parameter variable by adopting a U-NSGA-III method to obtain a technical optimization path of the steel production system.

Description

Technical Field

[0001] The present invention belongs to the technical field of steel production, and relates to a technical path optimization method for synergistic energy conservation, carbon reduction and efficiency improvement of a steel production system. Background Art

[0002] my country is the world's largest steel producer, with 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 carbon emissions. It has the characteristics of large output, high consumption and high emissions. The steel industry is in urgent need of low-carbon transformation. Therefore, how to effectively promote the coordinated development of energy conservation, carbon reduction and efficiency improvement in steel enterprises is an inherent requirement for industrial transformation and upgrading and achieving green and high-quality development.

[0003] Traditional long-process steel production enterprises are complex and large-scale production systems with multiple equipment, multiple levels, and multiple technologies integrated in a compact structure and coupled with multiple flows such as material flow, energy flow, and value flow, which involve the transformation of materials, the conversion and utilization of energy, and the coordinated carbon emissions and value changes. The energy utilization and material transformation process based on the physical and chemical changes of various underlying equipment, and the connection and integration of different equipment, technologies, and processes together constitute the steel production system, while the zero-carbon transformation of the energy structure, energy efficiency improvement to help carbon reduction, and carbon capture and utilization can directly or indirectly reduce the energy consumption and carbon emissions of enterprises, or increase corporate financial returns or carbon assets. Therefore, it is necessary to develop a scientific, effective, comprehensive, and systematic decision-making optimization and analysis method for the low-carbon transformation of steel production systems. In this way, the coupling relationship between material, energy, carbon emissions, and value in the steel production system can be clarified, and the simulation analysis of the production process from crude steel to different steel products can be realized. At the same time, the equipment, processes, processes, and energy of enterprises can be analyzed. Calculation, evaluation, optimization and analysis are performed using indicators such as indicators, carbon emission indicators and cost-effectiveness. From a system perspective, changes in a certain factor will lead to multi-faceted changes in the production system. The coupling relationship between the steel production system and various carbon reduction technologies such as low-carbon, zero-carbon and negative carbon needs to be revealed, so as to explore the development path of energy conservation and carbon reduction synergy and efficiency improvement of steel enterprises with market and policy changes or trends, provide scientific and orderly low-carbon transformation decisions for enterprises, respond to national economic development requirements, and promote energy conservation, low carbon and comprehensive economic development in the steel industry.

[0004] At present, the decision-making management of energy-saving and carbon-reduction transformation and the formulation of low-carbon development paths in steel enterprises are more of a "headache-treating the head, foot pain-treating the foot" approach, with energy-saving and carbon-reduction technology transformation for local production systems, and the selection of energy-saving and carbon-reduction technology transformation is more dependent on technical experience, with strong subjectivity and different experience levels, and lack of comprehensive system optimization management from source to process to end. At the same time, the matching relationship and coordination mechanism of different energy-saving and carbon-reduction technologies of steel enterprises from a system perspective are unclear, which is not conducive to the combined application and promotion of carbon reduction, zero carbon, and negative carbon technologies, and it is difficult to provide enterprises with scientific and effective transformation plans that integrate production and operation conditions and development paths that maximize environmental and economic benefits. Summary of the invention

[0005] In order to solve the above technical problems, the purpose of the present invention is to provide a technical path optimization method for synergistic energy conservation, carbon reduction and efficiency improvement in steel production systems.

[0006] The present invention provides a technical path optimization method for synergistic energy conservation, carbon reduction and efficiency improvement of a steel production system, comprising:

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

[0008] Step 2: Based on the steel production process mechanism and data-driven hybrid model, heat, energy, Calculate the amount and pollutants, and connect the processes to form a simulation model of the steel production system;

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

[0010] Step 4: Based on the simulation model of the steel production system, low-carbon, zero-carbon and negative-carbon technologies are embedded in the form of material or energy connection to establish a simulation model of coupled carbon reduction technology for the steel production system;

[0011] Step 5: Set the optimization objectives, variables and constraints 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 variables, process operation parameter variables, and technology application parameter variables to obtain the optimized steel production technology path.

[0013] The technical path optimization method for energy saving, carbon reduction and efficiency improvement of a steel production system of the present invention has the following beneficial effects:

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

[0015] (2) Reducing carbon emissions: This approach focuses on the integrated application of low-carbon technologies or measures to significantly reduce carbon dioxide and other greenhouse gas emissions during steel production;

[0016] (3) Quantitative evaluation of the synergistic effects of multiple technologies: By accurately quantifying the application effects of different technology combinations, strong support can be provided for formulating scientific and reasonable energy-saving and carbon-reduction paths.

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

[0018] Figure 1 It is a flow chart of a technical path optimization method for synergistic energy conservation, carbon reduction and efficiency improvement in a steel production system according to the present invention. DETAILED DESCRIPTION

[0019] like Figure 1 As shown, a technical path optimization method for synergistic energy conservation, carbon reduction and efficiency improvement of a steel production system of the present invention comprises:

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

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

[0022] In specific implementation, based on the physical and chemical process principles of steelmaking and the law of conservation of matter and energy, some production physical parameters or indicators with complete data can be obtained. Through mathematical mechanism models, production physical parameters such as theoretical air volume, steam generation, oxygen demand, theoretical combustion temperature, power generation, slag basicity, furnace temperature, etc. can be calculated more accurately.

[0023] 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.

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

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

[0026]

[0027] Where, L lasso is the square error, y a is the ath 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 preprocessed historical production data, β0 is a constant, β b is the regression coefficient, λ is the weight factor, and q represents the amount of historical production data input.

[0028] Step 1.4: Use the average value or empirical value of the enterprise's statistical period to estimate the indicators or parameters that are difficult to accurately calculate and predict through mechanism models and regression analysis. For example, excess air coefficient, compressed air consumption, nitrogen consumption, and a few temperature parameters. These parameters or indicators usually fluctuate less, and the average value or empirical value effectively reflects the average level or typical state of the production system under normal conditions.

[0029] In the specific implementation, before establishing the hybrid model, it is necessary to set up research boundaries based on the research object and needs. The boundaries include equipment, processes or systems related to steel production, and pre-process the original data of the enterprise, including outlier detection and processing, and processing of missing values.

[0030] Step 2: Based on the steel production process mechanism and data-driven hybrid model, heat, energy, The calculation of the amount and pollutants is carried out, and the process is connected to form a simulation model of the steel production system, specifically:

[0031] Step 2.1: Based on the physical and chemical reaction mechanism and material input and output, calculate the energy utilization, conversion and dissipation process of the reaction process, using heat balance, energy conservation and Based on the law of conservation, material production and energy dissipation are calculated on the basis of the above steel production process mechanism and data-driven hybrid model.

[0032] Step 2.2: Calculate the heat utilization of the steel production process based on the hybrid model and the equipment heat balance formula. The equipment 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 H is the heat input of equipment for producing unit product; material,inH is the sensible heat input of equipment material for producing unit product; producty，out Take away the heat from the equipment producing the unit product; H byprodcut，out To remove heat from the byproducts of the production unit product; H loss It is the heat loss of equipment producing unit product.

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

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

[0037] Where, Ex energy，in Unit product energy of equipment / process Input amount; Ex material,in Unit product material of the equipment / process Input amount; Ex producty，out Unit product of equipment / process Output; Ex byprodcut,out By-product of equipment / process Output; Ex iner，loss For the internal part of the equipment / process Loss amount; Ex exter，loss External to the equipment / process Amount of loss.

[0038] Step 2.4: Account for atmospheric pollutants and greenhouse gases.

[0039] In specific implementation, the equipment CO2 emission calculation formula is as follows:

[0040]

[0041] CO2 I =[M em，in ][C em ] T

[0042] Where M r，in is the material input matrix, C r，in is the C element content matrix, M r，out is the material output matrix, C r,out is the mass ratio matrix of C elements; M GHG is the emission of other greenhouse gases; GWPGHG is the global warming potential of other greenhouse gases (GHGs).

[0043] Step 2.5: Calculate energy utilization based on the hybrid model and 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 En is the energy input per unit product of the process; material,in En is the energy input value of the unit product material in the process; producty,out En is the unit product energy output of the process; byprodcut,out En is the energy output of the by-product of the process; loss is the energy loss of the process.

[0046] Step 2.6: The whole process simulation of steel production connects the processes with the steel ratio coefficient to form a simulation model of the steel production system.

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

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

[0049]

[0050] Where, Vi n,out,i is the comprehensive value of the output of the i-th process of the production system value flow, which is composed of the internal circulation of the i-th process value External product value of process i and the waste management value of process i Composition; the expressions are as follows:

[0051]

[0052] Where V eq,en For units in the system perspective The value equivalent, To circulate between "nodes" within the system The stream f contains quantity; For the production process from materials to products Value change; V added,pV is the added value of product P resulting from market or customer demand; added,po is the management or treatment cost of waste po generated by the policy, V market,p is the market value of product p at different periods; The waste contained in Value; V manage,po The management value of waste is given, including waste treatment cost, management fee, market assets and resource benefits; The equivalent expression of value is as follows:

[0053]

[0054] Where V eq,en (t) is the value equivalent of the internal cycle in period t; V sum,en (t) is the total value of energy input into the system outside the boundary within period t; Ex sum,en (t) is the total energy contained in the system outside the boundary during period t quantity;

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

[0056]

[0057] Where V i,in The comprehensive value of the input of the i-th process of the production system value flow is composed of 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] Where V m,main,i Indicates the value of the main material, V m,sub,i Indicates the value of auxiliary materials;

[0060] (3) The value equivalent of a unit product produced by the public and auxiliary processes V eq,au (t) is expressed as follows:

[0061]

[0062] Where, Ex en,in Indicates the internal circulation energy of the public and auxiliary processes au Input quantity; P out,au Represents the output of public and auxiliary process products au.

[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 form of material or energy connection to establish a simulation model of steel production system coupled carbon reduction technology, specifically:

[0064] Step 4.1: By changing the input parameters of different materials and energy, the energy consumption and pollutant emissions of steel production can be affected, and the source structural carbon reduction technology can be embedded.

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

[0066]

[0067] In the formula, Direct carbon emission reduction of structural carbon reduction technology per ton of steel source; M i,en,te is the mass change of energy or energy medium en in process i after technical 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, calculate their impact on production and embed process node-based carbon reduction technology.

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

[0070]

[0071] In the formula, is the direct carbon emission reduction of node-based carbon reduction technology per ton of steel; MC i，te，m is the mass change of material m in process i after technology application, MC i,te,en The quality change of energy or energy medium en in the process i after the technical application; U i,te,m is the net consumption of material m caused by the application of technology, U i,te,en The net consumption of energy or energy medium en caused by the application of technology.

[0072] Step 4.3: Calculate the impact of production by changing energy consumption, composition parameters, pollutant emissions, energy recovery, and material output parameters to embed end-of-pipe capture carbon reduction technology.

[0073] (3) The formula for 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:

[0074]

[0075] In the formula, The direct carbon emission reduction of tons of steel end-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 succeeding processes, and a simulation model of the steel production system coupled with carbon reduction technology is established.

[0077] Unit product technology operating cost caused by embedding energy-saving and carbon-reduction technologies Life cycle unit product investment cost With technology tons of steel cost-effective CB te The formula is as follows:

[0078]

[0079] In the formula, is the unit product technology operating cost; is the unit product investment cost for the life cycle; is the investment cost; int is the periodic discount rate; L te The time period for the technology to operate; TL te Pr is the technology life cycle time; TLte Crude steel production estimated for the time period; CB te Cost-effectiveness of technology tons of steel; M m,te V is the change in consumption of material m after technology application; eq,m is the market value of the unit material; V eq,en Unit The value equivalent of Ex te,c Tons of steel saved after technical application Amount; CO2 te,R V is the carbon emission reduction per ton of steel after the technology is applied; eq,CO2 is the carbon price; PO2 te,R is the synergistic variation of other pollutants; V eq,PO is the treatment cost of unit pollutant PO emission.

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

[0081]

[0082] Where MCB(t) is the cost-effectiveness of carbon reduction in time period t. If it is equal to 1, it is marginal cost-effectiveness. If it is greater than 1, it indicates carbon reduction synergy benefit, and vice versa. CR CO2 is the carbon emission reduction; V CO2,ep CVP is the carbon emission value after The cost of the production system after assuming various emission reduction technologies; CVP base is the cost of the production process before transformation.

[0083] Step 5: Set the optimization objectives, variables and constraints of the steel production system coupled carbon reduction technology simulation model, specifically:

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

[0085] Goal 1: Production unit products The energy consumption is taken as the energy-saving optimization target of the steel production system, referred to as product Strength, product Strength objective function Ex intensity as follows:

[0086]

[0087] Where P z Ex is the steel ratio or material ratio coefficient of the main process z, that is, the amount of process products consumed to produce one ton of crude steel or steel products; in,z is the input of main process z Quantity; Ex in,au Investment in public auxiliary processes Quantity; M p,z is the output of product p in the main process z; M p,au is the output of product p in the auxiliary process au; is the effective output of the main process z quantity; Effective output of public and auxiliary processes quantity;

[0088] Objective 2: The carbon emission per unit of steel product production is set as the goal of system carbon reduction optimization, referred to as carbon emission intensity. The carbon emission intensity objective function is CO2 intensity as follows:

[0089]

[0090] In the formula, CO2 D,z is the direct carbon emissions of the enterprise in the main process z; CO2 D,au is the direct carbon emissions of the enterprise in the public auxiliary process au; CO2 I,purThe indirect carbon emissions caused by the purchase of power energy from the power grid or heat network by the enterprise, M p,pur The amount of power energy pur purchased by the enterprise from the power grid or heat network, p pur is the power energy pur consumption per unit of steel;

[0091] Objective 3: The comprehensive cost-effectiveness of the production unit steel product is set as the goal of economic benefit optimization, and the comprehensive cost-effectiveness objective function CB is as follows:

[0092]

[0093] Among them, the molecule M m，p V p is the market economic value of product p; the denominator is M m，in V m The raw material cost, M en，in V en Energy cost, M pol，in V pol，ad Environmental cost, M CO2，in V CO2，eq The CO2 emission cost 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 input material composition and structure, energy composition and structure, steel production equipment parameters such as temperature, pressure, element recovery rate, etc., process parameters such as product structure, oxygen enrichment rate, material ratio coefficient, and Boolean variables for optimizing the application selection of low-carbon, zero-carbon, and negative-carbon technologies.

[0095] Step 5.3: The variable boundary setting meets the actual requirements of operation under normal conditions. Other constraints include product quality constraints, process parameter constraints, material and energy conservation constraints, etc.

[0096] The equipment operating parameters and pollutant emission standard constraints are:

[0097] x j,min ≤x j ≤x j，max

[0098] 0≤P O ≤P O,max

[0099] Among them, x j is the operating parameter of the jth device, x j,min is the historical minimum value of the jth equipment operating parameter under normal equipment operation conditions, x j,max is the historical maximum value of the jth equipment operating parameter under normal equipment operation conditions, P Ois the pollutant emission, P O,max It is the maximum value of the pollutant emission standard.

[0100] Materials, heat, energy and Balance constraints: Based on the laws of thermodynamics and the theory of conservation of mass, the input-output of materials, heat, energy and Balance constraints. However, in the simulation process, heat such as heat dissipation from the furnace cannot be accurately calculated, so a series of incalculable energies will be constrained by empirical values ​​of actual production conditions to make the model more consistent with actual production.

[0101]

[0102] In the formula, E other is the incalculable energy loss; E in,sum and E out,sum are input and output energy respectively; σ Q，av It is the ratio of incalculable losses to total energy, and it uses actual production experience as a constraint.

[0103] Stable production condition constraints: In order to meet the stable production conditions of steel, some key production process parameters will be restricted, such as slag basicity, furnace combustion temperature, water content, flue gas oxygen content, etc. In addition, considering the challenge of fine ore blending in actual working conditions, the minimum threshold of the proportion of a single type of coal or ore is kept above 3% when blending coal or ore. Take the slag basicity constraint as an example, as shown below:

[0104] R r,min ≤R r ≤R r，max

[0105] In the formula, R r Indicates the basicity of slag and clearly constrains its upper and lower limits.

[0106] Product quality index constraints: Ensuring the quality of production products is an important prerequisite for achieving corporate benefits, so it is necessary to constrain the trace element composition of different steel products. During 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 equipment power of public auxiliary equipment are constrained within the design and normal operating conditions, including boilers, power plants, oxygen production, lime kilns and other equipment.

[0108]

[0109] In the formula, GA e,cs Refers to the product output of equipment e when producing one ton of crude steel. GC min,e,tmIt is expressed as the minimum product output of equipment e during tm; M cs,tm is the crude steel output during the period tm. Similarly, max means the maximum.

[0110] Reliability constraints: The steel production interactive network involves the production and cross-utilization of multiple energy sources and materials to meet the needs of steel production for multiple materials and energy media. Therefore, the input and output of these materials and energy must meet the reliability constraints, that is, the output of public and auxiliary process products is greater than the system utilization, including energy media such as electricity, steam, blast, compressed air, oxygen, hydrogen, and auxiliary materials and flux. Steam is divided into three types according to different pressures and temperatures, and all of them must meet the reliability constraints, namely S1 steam (2.5~3.0MPa, 250~300℃), S2 steam (0.7~1.0MPa, 170~300℃) and S3 steam (0.3~0.5MPa, 200~250℃). The constraint expression is as follows:

[0111]

[0112] In the formula, Expressed as the amount produced in process 1; The amount of auxiliary materials or energy media g purchased; is the auxiliary material or energy medium g utilization in the steel production system, It is the loss of auxiliary materials or energy media in the steel production system.

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

[0114] Step 6.1: Combine the material and energy input parameter variables, process operation parameter variables and technology application parameter variables to form a chromosome. The material and energy input parameter variables and process operation parameter variables are coded in real numbers, and the technology application parameter variables are coded in integers.

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

[0116] Step 6.3: Calculate the model optimization target value and calculate the fitness based on the optimization target value as the basis for the next stratification and selection. At the same time, the violation degree is used to measure the satisfaction of the variables with the constraints.

[0117] Step 6.4: Perform crossover and mutation genetic processing on the initial population according to the fitness through the GA genetic algorithm of non-dominated sorting and tournament selection to generate a descendant population.

[0118] Step 6.5: Merge the offspring population with the parent population and perform elite processing to generate a new generation population.

[0119] Step 6.6: Repeat steps 6.3 to 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.

[0120] The above description is only a preferred embodiment of the present invention and is not intended to limit the concept of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A technical path optimization method for energy saving, carbon reduction and efficiency improvement in steel production system, characterized in that: include: Step 1: Establish a hybrid model of steel production process mechanism and data-driven; Step 2: Based on the steel production process mechanism and data-driven hybrid model, heat, energy, Calculate the amount and pollutants, and connect the processes to form a simulation model of the steel production system; Step 3: Based on the simulation model of the steel production system, embed the value stream analysis model to perform value stream accounting of the steel production system; Step 4: Based on the simulation model of the steel production system, low-carbon, zero-carbon and negative-carbon technologies are embedded in the form of material or energy connection to establish a simulation model of coupled carbon reduction technology for the steel production system; Step 5: Set the optimization objectives, variables and constraints of the steel production system coupled carbon reduction technology simulation model; Step 6: Use the U-NSGA-III method to optimize the material and energy input parameter variables, process operation parameter variables, and technology application parameter variables to obtain the optimized steel production technology path.

2. The method for optimizing the technical path for energy conservation, carbon reduction and efficiency improvement in the steel production system according to claim 1, characterized in that: The step 1 is specifically as follows: Step 1.1: Construct a steel production mechanism model based on the physical and chemical reaction mechanism of steel production equipment and the material balance principle, and calculate 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 energy input parameters, material 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 pre-processed historical production data, and then accurately simulate the operation process of the main production equipment to predict the key parameters of the production process or product performance indicators. The LASSO regression analysis formula is as follows: Where, L lasso is the square error, y a is the ath 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 preprocessed historical production data, β0 is a constant, β b is the regression coefficient, λ is the weight factor, and q represents the amount of historical production data input; Step 1.4: Use the company's statistical period average or empirical value to estimate indicators or parameters that are difficult to accurately calculate through mechanism models and regression analysis predictions.

3. The method for optimizing the technical path for energy conservation, carbon reduction and efficiency improvement in the steel production system according to claim 1, characterized in that: The step 2 is specifically as follows: Step 2.1: Based on the physical and chemical reaction mechanism and material input and output, calculate the energy utilization, conversion and dissipation process of the reaction process, using heat balance, energy conservation and Based on the law of conservation, 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 of the steel production process based on the hybrid model and the equipment heat balance formula. The equipment heat balance formula is as follows: H energyin +H material，in =H byprodcutout +H produc，ou t+H loss In the formula, H energy，in H is the heat input of equipment for producing unit product; material,in H is the sensible heat input of equipment material for producing unit product; producty,out Take away the heat from the equipment producing the unit product; H byprodcut，out To remove heat from the byproducts of the production unit product; H loss The heat loss of the equipment producing a unit of product; Step 2.3: Based on the mixed model, equipment / process Balance formula calculation Conversion process, equipment / process The balance formula is as follows: Ex energy，in +Ex material，in =Ex byproducct，out +Ex product，out +Ex iner，loss +Ex exter，loss Where, Ex energy，in Unit product energy of equipment / process Input amount; Ex material,in Unit product material of the equipment / process Input amount; Ex producty,out Unit product of equipment / process Output; Ex byprodcut,out By-product of equipment / process Output; Ex iner,loss For the internal part of the equipment / process Loss amount; Ex exter,loss External to the equipment / process Amount of loss; Step 2.4: Accounting for atmospheric pollutants and greenhouse gases; Step 2.5: Calculate energy utilization based on the hybrid model and 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 En is the energy input per unit product of the process; material,in En is the energy input value of the unit product material in the process; producty,out En is the unit product energy output of the process; byprodcut,out En is the energy output of the by-product of the process; loss is the energy loss of the process; Step 2.6: The whole process simulation of steel production connects the processes with the steel ratio coefficient to form a simulation model of the steel production system.

4. The method for optimizing the technical path for energy conservation, carbon reduction and efficiency improvement in the steel production system according to claim 1, characterized in that: The step 3 specifically includes embedding a value stream analysis model based on the steel production system simulation model; (1) The basic expression of the value output of the internal process of the system is as follows: Where V in,out,i is the comprehensive value of the output of the i-th process of the production system value flow, which is composed of the internal circulation of the i-th process value External product value of process i and the waste management value of process i Composition; the expressions are as follows: Where V eq,en For units in the system perspective The value equivalent, To loop between "nodes" within the system The stream f contains quantity; For the production process from materials to products Value change; V added,p V is the added value of product P resulting from market or customer demand; added,po is the management or treatment cost of waste po generated by the policy, V market,p is the market value of product p at different periods; The waste contained in Value; V manage,po The management value of waste is given, including waste treatment cost, management fee, market assets and resource benefits; The equivalent expression of value is as follows: Where V eq,en (t) is the value equivalent of the internal cycle in period t; V sum,en (t) is the total value of energy input into the system outside the boundary in period t; Ex sum,en (t) is the total energy contained in the system outside the boundary during period t quantity; (2) The basic expression of the value input of the internal process of the system is as follows: Where V i,in The comprehensive value of the input of the i-th process of the production system value flow is composed of the internal circulation value External material input value V m,i composition; V m,i =V m,main,i +V m,sub,i Where V m,main,i Indicates the value of the main material, V m,sub,i Indicates the value of auxiliary materials; (3) The value equivalent of a unit product produced by the public and auxiliary processes V eq,au (t) is expressed as follows: Where, Ex en,in Indicates the internal circulation energy of the public and auxiliary processes au Input quantity; P out,au Represents the output of public and auxiliary process products au.

5. The method for optimizing the technical path for energy conservation, carbon reduction and efficiency improvement in the steel production system according to claim 1, characterized in that: The step 4 is specifically as follows: Step 4.1: By changing the input parameters of different materials and energy, the energy consumption and pollutant emissions of steel production can be affected, and the source structural carbon reduction technology can be embedded; Step 4.2: By changing the equipment parameters, process operation parameters and composition parameters in the steel production process, calculate their impact on production and embed process node-based carbon reduction technology; Step 4.3: Calculate the impact of production by changing energy consumption, composition parameters, pollutant emissions, energy recovery, and material output parameters to embed end-of-pipe capture 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 succeeding processes, and a simulation model of the steel production system coupled with carbon reduction technology is established.

6. The method for optimizing the technical path for energy conservation, carbon reduction and efficiency improvement in a steel production system according to claim 5, characterized in that: (1) The formula for 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, Direct carbon emission reduction of structural carbon reduction technology per ton of steel source; M i,en,te is the mass change of energy or energy medium en in process i after technical 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; (2) The formula for direct carbon emission reduction per ton of steel after the process node carbon reduction technology is embedded in the system is as follows: In the formula, is the direct carbon emission reduction of node-based carbon reduction technology per ton of steel; MC i，te，m is the mass change of material m in process i after technology application, MC i,te,en The change in mass of energy or energy medium en after the process of technical application; U i,te,m is the net consumption of material m caused by the application of technology, U i,te,en The net consumption of energy or energy medium en caused by the application of the technology; (3) The formula for 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 of tons of steel end-capture carbon reduction technology; CA i，te，m The amount of carbon captured by the technology; (4) Unit product technology operating costs caused by embedding energy-saving and carbon-reduction technologies Life cycle unit product investment cost With technology tons of steel cost-effective CB te The formula is as follows: In the formula, is the unit product technology operating cost; is the unit product investment cost for the life cycle; is the investment cost; int is the periodic discount rate; L te The time period for the technology to operate; TL te is the technology life cycle time; Pr TLte Crude steel production estimated for the time period; CB te For technical ton steel cost effectiveness; M m,te V is the change in consumption of material m after technology application; eq,m is the market value of the unit material; V eq,en For units The value equivalent of Ex te,c Tons of steel saved after technical application Amount; CO2 te,R V is the carbon emission reduction per ton of steel after the technology is applied; eq,CO2 is the carbon price; PO2 te,R is the synergistic variation of other pollutants; V eq,PO is the treatment cost of unit pollutant PO emission.

7. The method for optimizing the technical path for energy conservation, carbon reduction and efficiency improvement in the steel production system according to claim 1, characterized in that: The step 5 is specifically as follows: Step 5.1: Based on the simulation model of the steel production system coupled with carbon reduction technology, three optimization objectives are set for the steel production system: Goal 1: Production unit products The energy consumption is taken as the energy-saving optimization target of the steel production system, referred to as product Strength, product Strength objective function Ex intensity as follows: Where P z Ex is the steel ratio or material ratio coefficient of the main process z, that is, the amount of process products consumed to produce one ton of crude steel or steel products; in,z is the input of main process z Quantity; Ex in,au Investment in public auxiliary processes Quantity; M p,z is the output of product p in the main process z; M p,au is the output of product p in the auxiliary process au; is the effective output of the main process z quantity; Effective output of public and auxiliary processes quantity; Objective 2: The carbon emission per unit of steel product production is set as the goal of system carbon reduction optimization, referred to as carbon emission intensity. The carbon emission intensity objective function is CO2 intensity as follows: In the formula, CO2 D,z is the direct carbon emissions of the enterprise in the main process z; CO2 D,au is the direct carbon emissions of the enterprise in the public auxiliary process au; CO2 I,pur The indirect carbon emissions caused by the purchase of power energy from the power grid or heat network by the enterprise, M p,pur The amount of power energy pur purchased by the enterprise from the power grid or heat network, p pur is the power energy pur consumption per unit of steel; Objective 3: The comprehensive cost-effectiveness of the production unit steel product is set as the goal of economic benefit optimization, and the comprehensive cost-effectiveness objective function CB is as follows: Among them, the molecule M m，p V p is the market economic value of product p; the denominator is M m，in V m The raw material cost, M en，in V en Energy cost, M pol，in V pol，ad Environmental cost, M CO2，in V CO2，eq The CO2 emission cost 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 optimizing the application of low-carbon, zero-carbon, and negative-carbon technologies; Step 5.3: Equipment operating parameters and pollutant emission standard constraints are: x j,min ≤x j ≤x j，max 0≤P O ≤P O,max Among them, x j is the operating parameter of the jth device, x j,min is the historical minimum value of the jth equipment operating parameter under normal equipment operation conditions, x j,max is the historical maximum value of the jth equipment operating parameter under normal equipment operation conditions, P O is the pollutant emission, P O,max It is the maximum value of the pollutant emission standard.

8. The method for optimizing the technical path for energy conservation, carbon reduction and efficiency improvement in the steel production system according to claim 1, characterized in that: The step 6 is specifically as follows: Step 6.1: Combine the material and energy input parameter variables, process operation parameter variables and technology application parameter variables to form a chromosome. The material and energy input parameter variables and process operation parameter variables are coded in real numbers, and the technology application parameter variables are coded in integers. Step 6.2: Initialize the population, and set the initial population size, initial value, crossover, mutation operator, maximum number of iterations, and upper and lower bounds of the variable; Step 6.3: Calculate the model optimization target value, and calculate the fitness based on the optimization target value as the basis for the next stratification and selection. At the same time, the violation degree is used to measure the satisfaction of the variables with the constraint conditions; Step 6.4: Perform crossover and mutation genetic processing on the initial population according to the fitness through the GA genetic algorithm of non-dominated sorting and tournament selection to generate a progeny population; Step 6.5: Merge the offspring population with the parent population and perform elite processing to generate a new generation population; Step 6.6: Repeat steps 6.3 to 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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