Joint control method of material flow, energy flow and carbon emission flow in long-process steel enterprises

Abstract

The method for joint regulation of material flow, energy flow and carbon emission flow of long-process steel enterprises belongs to the technical field of intelligent regulation of power systems in the steel industry. The present invention couples the material-energy characteristic model of each production process of a steel enterprise and the carbon emission model of the steel enterprise to construct a coupling model of material flow, energy flow and carbon emission flow of steel materials in a long-process steel enterprise. The model is constructed by taking the production of 1 ton of steel by a long-process steel enterprise as an example, and the objective function is to minimize the sum of the power purchase cost of the superior power grid, the carbon emission cost of the park and the cost of production raw materials, and solve the optimal operation mode of the joint regulation of the material flow, energy flow and carbon emission flow of the steel enterprise. Then, the joint regulation of the material flow, energy flow and carbon emission flow of the steel enterprise is realized with the daily, weekly, monthly, quarterly and annual delivery volume of steel materials of the enterprise as the task volume, so as to improve the economy and energy efficiency utilization rate of the system, realize low-carbon operation, and facilitate the subsequent research on the construction and interaction of the electricity-carbon-green certificate market.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent control of electric power systems in the steel industry, and in particular relates to a method for joint control of material flow, energy flow and carbon emission flow in a long-process steel enterprise. Background Art

[0002] As an energy-intensive pillar industry, the steel industry generates a large amount of carbon dioxide and other pollutant emissions. In 2020, carbon emissions from the steel industry accounted for 15% of the country's total carbon emissions, second only to carbon emissions from the power industry. The EU passed the Carbon Border Adjustment Mechanism Act in 2022, which will impose carbon tariffs of hundreds of euros per ton on imported steel. Therefore, this bill has brought huge challenges to my country's traditional steel manufacturing technology.

[0003] my country's steel industry is undergoing a low-carbon transformation, which covers production structure, production materials, process technology, energy utilization, carbon capture and other aspects. The traditional steel production process is long and complex, and the low-carbon transformation of enterprises makes the internal integrated energy system structure more complex, which also brings great challenges to production scheduling. With the improvement of industrial automation level and the development of industrial Internet of Things, efficient industrial load management and optimized scheduling have become a new trend.

[0004] At present, in the modeling of production scheduling in the steel industry, either only the material-energy characteristic model of each production process of the steel enterprise is constructed, or only the carbon emission model of the steel enterprise is constructed. There is still a lack of methods to couple the two to construct a coupled model of material flow, energy flow and carbon emission flow of the steel enterprise for joint regulation.

[0005] Therefore, a new technical solution is urgently needed in the prior art to solve this problem. Summary of the invention

[0006] The technical problem to be solved by the present invention is to provide a method for jointly regulating the material flow, energy flow and carbon emission flow of a long-process steel enterprise to solve the technical problem that the current steel industry has not coupled and jointly regulated the material-energy characteristic model of each production process with the carbon emission model in production scheduling.

[0007] A method for jointly regulating material flow, energy flow and carbon emission flow of a long-process steel enterprise. In the method, the production of 1 ton of steel by the long-process steel enterprise is used as the calculation base, and each model is constructed to jointly regulate the material flow, energy flow and carbon emission flow. The production process of the long-process steel enterprise includes a coking process, a sintering process, a pelletizing process, a blast furnace ironmaking process, a converter steelmaking process, an electric arc furnace steelmaking process, a steel rolling process and an air compressor oxygenation process for providing oxygen in the corresponding process, and the corresponding process numbers are from 1 to 7 in sequence, among which the coking and scrap steel electric arc furnace short-process steelmaking processes are intermittent output processes, and sintering, pelletizing, blast furnace ironmaking, converter steelmaking and steel rolling are continuous output processes; the air compressor oxygenation process is an auxiliary process, including the following steps, and the following steps are performed in sequence.

[0008] Step 1: Construct a material flow model for the short-process electric arc furnace steelmaking process. The model consists of the following constraints:

[0009] 1) Construct continuity constraints for the start and stop of the short-process electric arc furnace steelmaking production process:

[0010] ds 6,t +dc 6,t ≤1 (1)

[0011] ds 6,t -dc 6,t =op 6,t -op 6,t-1 (2)

[0012] Among them: ds 6,t is the starting variable of the production process; dc 6,t is the stop variable of the production process; op 6,t is the operating variable of the production process; 6 is the sequence number of the electric arc furnace steelmaking process;

[0013] 2) Construct the minimum operating time constraint of the short-process electric arc furnace steelmaking production process:

[0014]

[0015] Among them: Minop6 is the minimum operating time of the production process;

[0016] 3) Construct the maximum operating time constraint of the short-process electric arc furnace steelmaking production process:

[0017]

[0018] Among them: Maxop6 is the maximum operating time of the production process;

[0019] 4) Construct the minimum downtime constraint of the short-process electric arc furnace steelmaking production process:

[0020]

[0021] Where: Dp6 is the minimum downtime of the production process;

[0022] 5) Construct material flow constraints for the short-process electric arc furnace steelmaking production process:

[0023]

[0024] Where: G 6,t is the output of the product in production process 6; G6 is the unit output of production process 6;

[0025] Step 2: Establish the following conventional long-process steel enterprise process material model:

[0026] 1) Constructing the material flow constraint model of coking production process:

[0027]

[0028] Where: Y 1,t The input raw material quantity for process 1 is coking coal; k is the coking ratio; G 1,t is the product output of process 1; jt2 is the coke supply-demand ratio of process 2; jt3 is the coke supply-demand ratio of process 3; jt4 is the coke supply-demand ratio of process 4; M jt2 is the amount of coke in input process 2; M jt3 is the amount of coke in input process 3; M jt4 G is the amount of coke in step 4; min1 and G max1 They are the lower and upper limits of the output of process 1 respectively;

[0029] 2) Construct material flow constraint model for sintering production process:

[0030]

[0031] Where: Y 2,t Input raw material quantity for process 2, i.e. iron ore; v2 is material conversion rate for process 2; λ tks2 G is the iron ore supply and demand ratio of process 2; 2,t is the product output of process 2; sjk4 is the supply-demand ratio of sintered ore in process 4; M sjk4 is the amount of sintered ore in step 4, G min2 and G max2 They are the lower and upper limits of the output of process 2 respectively;

[0032] 3) Constructing the material flow constraint model of pellet production process:

[0033]

[0034] Where: v3 is the material conversion rate of process 3; G 3,t is the product output of process 3; tks3 is the iron ore supply and demand ratio of process 3; qtk4 is the supply-demand ratio of pellets in process 4; M qtk4 G is the amount of pellets in step 4; min3 and G max3 They are the lower and upper limits of the output of process 3 respectively;

[0035] 4) Construct a material flow constraint model for the blast furnace ironmaking production process:

[0036]

[0037] Where: v4 is the material conversion rate of process 4; G 4,t is the product output of process 4; G min4 and G max4 They are the lower and upper limits of the output of process 4 respectively;

[0038] 5) Construct material flow constraint model for converter steelmaking production process:

[0039]

[0040] Where: v5 is the material conversion rate of process 5; G 5,t is the product output of process 5; G min5 and G max5 They are the lower and upper limits of the output of process 5 respectively;

[0041] 6) Construct material flow constraint model for steel rolling production process:

[0042]

[0043] Where: v7 is the material conversion rate of process 7; G 7,t is the product output of process 7; G min7 and G max7 They are the lower and upper limits of the output of process 7 respectively;

[0044] Step 3: Combine the constraints in step 2 to build a warehouse storage model, an air compression system model, a gas system model, and a CHP unit and dry quenching waste heat recovery model. A total of three warehouse storage models are built, namely, a coke warehouse storage model, a sintered ore warehouse storage model, and a pellet warehouse storage model:

[0045] 1) Build a warehouse storage model:

[0046]

[0047] Among them, equations (24) and (25) are the balance equations of the storage link; equation (26) is the upper and lower limit equations of the storage link; C is the warehouse number; S 0,c is the initial storage volume of warehouse c; M m,c,t is the output of the mth process before warehouse c at time t; Y n,c,t S is the material required by the nth process after warehouse c at time t; c,t is the capacity of warehouse c at time t; S min,c is the lower limit of the storage capacity of the cth warehouse; S max,c is the upper limit of storage capacity of the cth warehouse;

[0048] 2) Construct air compression system model:

[0049] S ca,t =SA ca,t-1 -SU ca,t Δt+α ca P ca,t Δt (27)

[0050] 0.8SA ca,ini ≤SA ca,end ≤1.2SA ca,ini (28)

[0051] V ca p ca,min ≤SA ca,t ≤V ca p ca,max (29)

[0052] S ca,t +op ca,t V ca p ca,max ≤1.1V ca p ca,max (30)

[0053] 1.1V ca p ca,min ≤SA ca,t +op ca,t V ca p ca,min (31)

[0054] Among them, formula (27) is the air storage balance equation; formula (28) and (29) are the upper and lower limit equations of air storage; formula (30) and (31) are the air compressor start and stop equations; SA ca,t is the gas storage capacity of the system gas tank at time t; SU ca,t is the gas consumption of the system at time t; α ca is the air compressor efficiency; P ca,tis the air compressor output power at time t; Δt is the optimization step length, which is 1 hour; SA ca,ini SA is the gas storage capacity of the gas tank at the initial moment; ca,end V is the gas storage capacity of the gas tank at the end time; ca is the volume of the gas tank; p ca,min and p ca,max is the minimum and maximum pressure allowed in the gas tank; op ca,t is the air compressor operating variable at time t;

[0055] 3) Construct gas system model:

[0056]

[0057] V min,o ≤V o,t ≤V max,o (36)

[0058] Where: μ COG , μ BFG , μ LDG are by-product gas yields, respectively. The gas yield is proportional to the corresponding production process load; o is by-product gas; V o,t For gas reserves; is the gas output; f j,o,t is the demand for gas o in process j; V is the gas volume input to the CHP unit; min,o and V max,o The lower and upper limits of the capacity of the corresponding gas storage tank;

[0059] 4) Construct CHP unit model:

[0060]

[0061] Wherein: Formula (37) is the conversion formula for converting the heat energy of the CHP unit using byproduct gas as fuel into electrical energy; Formula (38) is the upper and lower limit constraints of the CHP unit output; Formula (39) is the conversion formula for converting the electrical energy of the CHP unit into thermal energy; P is the steam heat generated by the CHP unit; t chp is the CHP unit power; δ is the optimized cycle length; and are the lower and upper limits of CHP unit output respectively; θ chp is the electrical efficiency of the CHP unit; θ chp,heat is the thermal efficiency of the CHP unit; β o is the calorific value of gas o;

[0062] 5) Construct a low-carbon process CDQ waste heat recovery model:

[0063] P t cdq =λ cdp P 1,t (40)

[0064]

[0065] Where: P t cdq is the waste heat power generation power; cdp is the power generation coefficient; P 1,t is the coking power; and They are the lower and upper limits of the output of the CDQ waste heat unit respectively;

[0066] Step 4: Based on the above model, the short-process electric arc furnace steelmaking process is introduced as a low-carbon process measure into the process material model of the conventional long-process steel enterprise to form a new long-process steel enterprise production process material-energy characteristic model, where the energy consumption includes electrical energy, thermal energy and oxygen energy;

[0067] The constraints of the material-energy characteristics model of the long-process steel enterprise are as follows:

[0068]

[0069] P t pv +P t chp +P t grid +P t cdq =P ca,t +P total,t +P load (43)

[0070]

[0071] G 7,t ≥F sum,task (47)

[0072] Among them: formula (42) and (43) are active power balance constraints; formula (44) is the gas energy balance constraint; formula (45) is the thermal energy balance constraint; formula (46) is the compressed air energy balance constraint; formula (47) is the total production task constraint; G j,t is the output of production process j, j∈{1,2,3,4,5,6,7}; P total,t is the total production load of the process; P j is the fixed production load per unit of process j, j∈{1,2,3,4,5,6,7}; P t pvis the photovoltaic output power; P t chp is the output power of CHP unit; P t grid Power purchased from the upper power grid; P load Operate the remaining electrical load for the enterprise; Q j is the heat energy required for process j, j∈{1,2,3,4,5,6,7}; H load Operate the remaining heat load for the enterprise; ca A is the efficiency of converting electricity into gas; j is the unit gas consumption of process j, j∈{1,2,3,4,5,6,7}; F sum,task is the total task volume of the process;

[0073] Step 5: Construct a carbon emission model for steel enterprises, which is divided into the following four parts: an indirect carbon emission model for purchased electricity consumption, a direct carbon emission model for coke consumption, a carbon emission model for the production process after calculation, and a carbon emission model for by-product gas;

[0074] 1) Construct an indirect carbon emission model for purchased electricity consumption:

[0075]

[0076] in: is the carbon emission of purchased electricity; elec,t is the carbon content coefficient of purchased electricity;

[0077] 2) Construct a direct carbon emission model for coke consumption:

[0078]

[0079] in: is the coke carbon emission; coke is the carbon content coefficient of coke; F t coke is the amount of coke used;

[0080] 3) Construct a carbon emission model for the production process after calculation:

[0081]

[0082] in: is the total carbon emission of the production process; j is the fixed carbon emission coefficient of process j;

[0083] 4) Constructing a carbon emission model for by-product gas:

[0084]

[0085] in: Carbon emissions for by-product gas; is the carbon content coefficient of by-product gas;

[0086] 5) Constructing a total carbon emission model for steel enterprises:

[0087]

[0088] in: Total carbon emissions of steel enterprises.

[0089] The carbon emission coefficient data used in the production process of the above-mentioned steel enterprises are set with reference to the actual data of real long-process steel enterprises;

[0090] Step 6: Construct a coupling model of material flow, energy flow and carbon emission flow for long-process steel enterprises, establish an objective function, and take the minimum sum of the power purchase cost of the superior power grid, the carbon emission cost of the industrial park and the cost of production raw materials as the goal, solve and obtain the optimal operation mode of the joint regulation of material flow, energy flow and carbon emission flow of steel enterprises, and realize the joint regulation of material flow, energy flow and carbon emission flow of new long-process steel enterprises;

[0091] Among them, the objective function is:

[0092]

[0093] Where: C min Indicates that the sum of the power purchase cost of the upper power grid, the carbon emission cost of the park and the material cost is the smallest; C gird,t is the time-of-use electricity price E pei,t Free carbon emission quota; C carbon is a fixed carbon price; C ljm is the price per ton of production raw material No. 1, i.e. the price per ton of coking coal; C tks The price per ton of production raw material No. 2, i.e. the price per ton of iron ore; C fg It is the price per ton of production raw material number 3, that is, the price per ton of scrap steel.

[0094] Through the above design scheme, the present invention can bring the following beneficial effects:

[0095] 1. At present, in the modeling of the steel industry, either only the material-energy characteristic model of each production process of the steel enterprise is constructed to carry out material-energy management and regulation, or only the carbon emission model of the steel enterprise is constructed to carry out carbon emission reduction and regulation. The present invention couples the two to construct a long-process steel enterprise steel material flow-energy flow-carbon emission flow coupling model for joint regulation. The basic model is constructed by taking the production of 1 ton of steel by a long-process steel enterprise as an example, and then the daily, weekly, monthly, quarterly and annual delivery volumes of steel are used as the task volume to carry out the joint regulation of the material flow-energy flow-carbon emission flow of the steel enterprise. This can more accurately obtain the optimal production operation plan, improve the economy and energy efficiency of the system, and achieve low-carbon operation.

[0096] 2. The current existing research has failed to fully consider the market development and industry policy status of the domestic electricity market, carbon market, green certificate market, etc. The analysis of potential bottlenecks in the interaction between the three is still not sufficient, and the market connection mechanism has not yet been described. This invention will facilitate subsequent research on the construction and interaction of mechanisms such as the electricity market, carbon market, and green certificate market. BRIEF DESCRIPTION OF THE DRAWINGS

[0097] The present invention is further described below with reference to the accompanying drawings and specific embodiments:

[0098] Figure 1 The present invention is a flow chart of the method for jointly regulating material flow, energy flow and carbon emission flow of a long-process steel enterprise.

[0099] Figure 2 It is a schematic diagram of the production process model of a new type of long-process steel enterprise in the present invention;

[0100] Figure 3 It is a schematic diagram of the energy consumption framework model of a new long-process steel enterprise in the present invention;

[0101] Figure 4 This is a schematic diagram of the carbon emission sources of the new long-process steel enterprise in the present invention;

[0102] Figure 5 This is a schematic diagram of the material flow-energy flow-carbon emission flow coupling model for producing 1 ton of steel in a new long-process steel enterprise in the present invention;

[0103] Figure 6 It is a time-of-use electricity price parameter diagram in an embodiment of the present invention;

[0104] Figure 7 This is a parameter diagram of the dynamic carbon emission factor of the power grid according to an embodiment of the present invention;

[0105] Figure 8 It is the output graph outputted after the optimization of the new long-process steel enterprise in the embodiment of the present invention;

[0106] Fig. 9 This is a graph of the total carbon emissions of a new type of long-process steel enterprise after optimization in an embodiment of the present invention;

[0107] Fig.10 This is the total industrial load diagram of the park after optimization of the new long-process steel enterprise in an embodiment of the present invention. DETAILED DESCRIPTION

[0108] In order to make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention are described in further detail below.

[0109] Example

[0110] The joint control method of material flow, energy flow and carbon emission flow in long-process steel enterprises is shown in Figure 1 , the method comprises the following steps:

[0111] Firstly, based on the actual production data of a long-process steel enterprise in Jiangsu Province and taking the daily delivery of 28,000 tons of steel as an example, the energy consumption framework of long-process steel enterprises is outlined, and the various industrial production processes of the enterprise are introduced. For example, industrial production includes product production and product storage. Typical production process categories are divided into continuous processes, discrete processes, etc. At the same time, electric arc furnace short-process steelmaking is added as a low-carbon process measure; then, based on the start-stop continuity constraints, minimum operating time constraints, maximum operating time constraints, minimum downtime constraints of the production process of short-process electric arc furnace steelmaking, and the principle of conservation of steelmaking materials throughout the process, short-process electric arc furnace steelmaking is introduced into conventional steel enterprises to construct a new material flow model for the production process of long-process steel enterprises; at the same time, based on the energy consumption characteristics constraints of long-process steel enterprises, product storage models, air compression system models, gas system models, and CHP unit and dry quenching waste heat recovery technology models are constructed. Finally, based on the framework of carbon emission sources of long-process steel enterprises, an enterprise carbon emission model is constructed. On the basis of the former, the material flow-energy flow model of the production process of long-process steel enterprises is coupled with the carbon emission model. With the goal of minimizing the sum of the power purchase cost of the superior power grid, the carbon emission cost of the park and the cost of production raw materials, joint regulation is carried out to solve and obtain the optimal operating mode of the joint regulation of material flow, energy flow and carbon emission flow of steel enterprises.

[0112] To sum up, the embodiments of the present invention can more accurately obtain the optimal production operation plan through the above steps, improve the economy and energy efficiency of the system, achieve low-carbon operation, and facilitate subsequent research on the trading mechanism between long-process steel enterprises and the electricity-carbon market.

[0113] The above scheme is described in detail below with specific calculation formulas and examples. See the following description for details:

[0114] First, combine Figure 2The production process model of a long-process steel enterprise in Jiangsu Province is briefly introduced, including coking, sintering, pelletizing, blast furnace ironmaking, converter steelmaking, steel rolling, and oxygen production processes of air compressors that provide oxygen in the corresponding processes.

[0115] (1) Coking process

[0116] The calculation scope of the coking process starts from the entry of raw coking coal, electricity and oxygen into the coking plant and ends with the output of product coke, coke oven gas and by-product tar and steam.

[0117] (2) Sintering process

[0118] The calculation scope of the sintering process starts from the entry of iron ore, coke and blast furnace gas into the sintering plant and ends at the output of the product sintered ore.

[0119] (3) Pelletizing process

[0120] The calculation range of the pelletizing process starts from the entry of raw materials such as iron ore, coke, blast furnace gas, and coke oven gas into the pelletizing plant and ends at the output of the product pellets.

[0121] (4) Blast furnace ironmaking process

[0122] The calculation scope of the blast furnace ironmaking process starts from the entry of raw materials such as sintered ore, pelletized ore, electricity, oxygen, steam, etc. into the blast furnace ironmaking plant, and ends with the output of products such as molten iron and external blast furnace gas.

[0123] (5) Converter steelmaking process

[0124] The calculation range of the converter steelmaking process is from the raw material 100% molten iron and by-product gas, oxygen, electricity, etc. entering the steelmaking workshop as the starting point, and the product billet, external gas, steam, etc. are output as the end point.

[0125] (6) Electric Arc Furnace Steelmaking Process

[0126] The calculation range of the electric arc furnace process starts from the time when 100% scrap steel, by-product gas, oxygen, electricity, etc. enter the electric arc furnace workshop and ends at the output of the product billet.

[0127] At present, electric arc furnace steelmaking is a relatively mature low-carbon metallurgical process. The raw material is scrap steel. It uses melted scrap steel and removes impurities to directly produce molten steel. It is an emerging short-process steelmaking process parallel to the traditional long-process blast furnace-converter process. Since electric arc furnace steelmaking has very little demand for raw materials such as coke and a high demand for electricity, introducing electric arc furnaces into the production procedures of typical long-process steel enterprises will reduce the output and production load of processes such as coking, sintering, and pelletizing, and then reduce the amount of various by-product coal gases, thereby reducing the company's carbon emissions.

[0128] (7) Steel rolling process

[0129] The calculation scope of the steel rolling process starts from the entry of raw steel billets, by-product gas, electricity, etc. into the steel rolling workshop and ends with the output of finished rolled steel products.

[0130] (8) Air compressor oxygen generation process

[0131] Providing the oxygen required for each process is an auxiliary process.

[0132] In summary, the above processes can be classified into the following categories: coking and scrap steel electric arc furnace short-process steelmaking processes belong to intermittent output processes; sintering, pelletizing, blast furnace ironmaking, converter steelmaking and steel rolling processes belong to continuous output processes; and air compressor oxygen production belongs to auxiliary processes.

[0133] Based on the continuity constraints, minimum operating time constraints, maximum operating time constraints, minimum downtime constraints of the short-process electric arc furnace steelmaking production process, and the material flow constraints between each process, the short-process electric arc furnace steelmaking production process is introduced as a low-carbon process measure into the steelmaking process model of conventional long-process steel enterprises, and a new material flow model of the production process of steel enterprises is constructed.

[0134] Among them, include:

[0135] 1) Construct continuity constraints for the start and stop of the short-process electric arc furnace steelmaking production process:

[0136] ds 6,t +dc 6,t ≤1 (1)

[0137] ds 6,t -dc 6,t =op 6,t -op 6,t-1 (2)

[0138] Among them: ds 6,t is the starting variable of the production process; dc 6,t is the stop variable of the production process; op 6,t is the operating variable of the production process; 6 is the sequence number of the electric arc furnace steelmaking process.

[0139] 2) Construct the minimum operating time constraint of the short-process electric arc furnace steelmaking production process:

[0140]

[0141] Among them: Minop6 is the minimum operating time of the production process.

[0142] 3) Construct the maximum operating time constraint of the short-process electric arc furnace steelmaking production process:

[0143]

[0144] Among them: Maxop6 is the maximum operating time of the production process.

[0145] 4) Construct the minimum downtime constraint of the short-process electric arc furnace steelmaking production process:

[0146]

[0147] Where: Dp6 is the minimum downtime of the production process.

[0148] 5) Construct material flow constraints for the short-process electric arc furnace steelmaking production process:

[0149]

[0150] Where: G 6,t is the product output of production process 6; G6 is the unit output of production process 6.

[0151] 6) Construct material flow constraints for coking production process:

[0152]

[0153] Where: Y 1,t The input raw material quantity for process 1 is coking coal; k is the coking ratio; G 1,t is the product output of process 1; jt2 is the coke supply-demand ratio of process 2; jt3 is the coke supply-demand ratio of process 3; jt4 is the coke supply-demand ratio of process 4; M jt2 is the amount of coke in input process 2; M jt3 is the amount of coke in input process 3; M jt4 G is the amount of coke in step 4; min1 and G max1 They are the lower and upper limits of the output of process 1 respectively.

[0154] 7) Establish material flow constraints for sintering production process:

[0155]

[0156] Where: Y 2,t Input raw material quantity for process 2, i.e. iron ore; v2 is material conversion rate for process 2; λ tks2 G is the iron ore supply and demand ratio of process 2; 2,t is the product output of process 2; sjk4 is the supply-demand ratio of sintered ore in process 4; M sjk4 is the amount of sintered ore in step 4, G min2 and G max2 They are the lower and upper limits of the output of process 2 respectively.

[0157] 8) Construct material flow constraints for pelletizing production process:

[0158]

[0159] Where: v3 is the material conversion rate of process 3; G 3,t is the product output of process 3; tks3 is the iron ore supply and demand ratio of process 3; qtk4 is the supply-demand ratio of pellets in process 4; M qtk4 G is the amount of pellets in step 4; min3 and G max3 They are the lower and upper limits of the output of process 3 respectively.

[0160] 9) Construct material flow constraints for blast furnace ironmaking production process:

[0161]

[0162] Where: v4 is the material conversion rate of process 4; G 4,t is the product output of process 4; G min4 and G max4 They are the lower and upper limits of the output of process 4 respectively.

[0163] 10) Construct material flow constraints for converter steelmaking production process:

[0164]

[0165] Where: v5 is the material conversion rate of process 5; G 5,t is the product output of process 5; G min5 and G max5 They are the lower and upper limits of the output of process 5 respectively.

[0166] 11) Construct material flow constraints for steel rolling production process:

[0167]

[0168] Where: v7 is the material conversion rate of process 7; G 7,t is the product output of process 7; G min7 and G max7 They are the lower and upper limits of the output of process 7 respectively.

[0169] Based on the above constraints, the material flow model of the relevant production process is constructed.

[0170] Based on the above models, the product storage model, air compression system model, gas system model, and CHP unit and dry quenching waste heat recovery technology model are constructed.

[0171] Among them, include:

[0172] 12) Build warehouse storage model:

[0173]

[0174] Among them, equations (24) and (25) are the balance equations of the storage link; equation (26) is the upper and lower limit equations of the storage link; C is the warehouse number; S 0,c is the initial storage volume of warehouse c; M m,c,t is the output of the mth process before warehouse c at time t; Y n,c,t S is the material required by the nth process after warehouse c at time t; c,t is the capacity of warehouse c at time t; S min,c is the lower limit of the storage capacity of the cth warehouse; S max,c is the upper limit of the storage capacity of the cth warehouse. A total of three warehouses are constructed, namely coke warehouse, sintered ore warehouse, and pellet warehouse.

[0175] 13) Construct an air compression system model:

[0176] S ca,t =SA ca,t-1 -SU ca,t Δt+α ca P ca,t Δt (27)

[0177] 0.8SA ca,ini ≤SA ca,end ≤1.2SA ca,ini (28)

[0178] V ca p ca,min ≤SA ca,t ≤V ca p ca,max (29)

[0179] S ca,t +op ca,t V ca p ca,max ≤1.1V ca p ca,max (30)

[0180] 1.1V ca p ca,min ≤SA ca,t +op ca,t V ca p ca,min (31)

[0181] Among them, formula (27) is the air storage balance equation; formula (28) and (29) are the upper and lower limit equations of air storage; formula (30) and (31) are the air compressor start and stop equations; SA ca,t is the gas storage capacity of the system gas tank at time t; SU ca,t is the gas consumption of the system at time t; α ca is the air compressor efficiency; P ca,t is the air compressor output power at time t; Δt is the optimization step length, which is 1 hour; SA ca,ini SA is the gas storage capacity of the gas tank at the initial moment; ca,end V is the gas storage capacity of the gas tank at the end time; ca is the volume of the gas tank; p ca,min and p ca,max is the minimum and maximum pressure allowed in the gas tank; op ca,t is the air compressor operating variable at time t.

[0182] 14) Construct gas system model:

[0183]

[0184] V min,o ≤V o,t ≤V max,o (36)

[0185] Where: μ COG , μ BFG , μ LDG are the by-product gas yields, which are proportional to the corresponding production process load; o is the by-product gas, which includes COG coke oven gas, BFG blast furnace gas and LDG converter gas; V o,t For gas reserves; is the gas output; f j,o,t is the demand for gas o in process j; V is the gas volume input to the CHP unit; min,o and V max,o It corresponds to the lower and upper limits of the capacity of the gas storage tank.

[0186] 15) Construct CHP unit model:

[0187]

[0188] Wherein: Formula (37) is the conversion formula for converting the heat energy of the CHP unit using byproduct gas as fuel into electrical energy; Formula (38) is the upper and lower limit constraints of the CHP unit output; Formula (39) is the conversion formula for converting the electrical energy of the CHP unit into thermal energy; P is the steam heat generated by the CHP unit; t chpis the CHP unit power; δ is the optimized cycle length; and are the lower and upper limits of CHP unit output respectively; θ chp is the electrical efficiency of the CHP unit; θ chp,heat is the thermal efficiency of the CHP unit; β o is the calorific value of gas o.

[0189] 16) Construct a low-carbon process dry quenching waste heat recovery technology model:

[0190] P t cdq =λ cdp P 1,t (40)

[0191]

[0192] Where: P t cdq is the waste heat power generation power; cdp is the power generation coefficient; P 1,t is the coking power; and They are the lower and upper limits of the CDQ waste heat unit output respectively.

[0193] Based on the above model, a new long-process steel enterprise production process material-energy characteristic model is constructed. The energy consumption includes electrical energy, thermal energy and oxygen energy, as follows:

[0194] 17) Constraints on material-energy characteristics of production processes in long-process steel enterprises:

[0195]

[0196] P t pv +P t chp +P t grid +P t cdq =P ca,t +P total,t +P load (43)

[0197]

[0198] G 7,t ≥F sum,task (47)

[0199] Among them: formula (42) and (43) are active power balance constraints; formula (44) is the gas energy balance constraint; formula (45) is the thermal energy balance constraint; formula (46) is the compressed air energy balance constraint; formula (47) is the total production task constraint; G j,t is the output of production process j, j∈{1,2,3,4,5,6,7}; P total,t is the total production load of the process; P j is the fixed production load per unit of process j, j∈{1,2,3,4,5,6,7}; P t pv is the photovoltaic output power; P t chp is the output power of CHP unit; P t grid Power purchased from the upper power grid; P load Operate the remaining electrical load for the enterprise; Q j is the heat energy required for process j, j∈{1,2,3,4,5,6,7}; H load Operate the remaining heat load for the enterprise; ca A is the efficiency of converting electricity into gas; j is the unit gas consumption of process j, j∈{1,2,3,4,5,6,7}; F sum,task is the total task volume of the process.

[0200] The heat, electricity data, and oxygen consumption used in the production processes of the above-mentioned steel enterprises are all based on the actual data of a long-process steel enterprise in Jiangsu Province.

[0201] Finally, combine Figure 4 This article introduces the sources of carbon emissions from steel enterprises, including indirect carbon emissions caused by consumption of purchased electricity, direct carbon emissions caused by consumption of fossil energy (coke, coal, etc.), and direct carbon emissions caused by other raw materials in the production process.

[0202] Among them, under the existing mechanism, the carbon emissions of the power system are calculated based on the fossil fuel consumption in the region, and it is impossible to accurately implement carbon emissions to each power user. The power system carbon emission flow theory can determine the carbon emissions of power users at any node based on the power flow and network structure, and then clarify their low-carbon responsibilities. Based on this, a dynamic carbon emission factor is introduced to calculate the carbon emissions generated by power consumption in different time periods.

[0203] Although the consumption of fossil energy will lead to direct carbon emissions, materials such as coke and coal will be partially converted into by-product gas during the production process. The storability of by-product gas leads to the time-varying nature of carbon emissions.

[0204] Direct carbon emissions caused by other raw materials in the production process are mainly related to the output of the process and are relatively fixed.

[0205] Based on this, a carbon emission model for steel enterprises is constructed;

[0206] Table 1 Carbon emission coefficients of processes / materials in steel enterprises

[0207]

[0208] 1) Construct an indirect carbon emission model for purchased electricity consumption:

[0209]

[0210] in: is the carbon emission of purchased electricity; elec,t is the carbon content coefficient of purchased electricity;

[0211] 2) Construct a direct carbon emission model for coke consumption:

[0212]

[0213] Since coke is produced by the coking process, carbon emissions need to be calculated separately. Carbon emissions from other fuels can be attributed to the fixed carbon emission coefficient of the production process through the calculation method provided by WSA.

[0214] in: is the coke carbon emission; coke is the carbon content coefficient of coke; F t coke is the amount of coke used;

[0215] 3) Construct a carbon emission model for the production process after calculation:

[0216]

[0217] in: is the total carbon emission of the production process; j is the fixed carbon emission coefficient of process j; G j,t is the output of production process j, j∈{1,2,3,4,5,6,7};

[0218] 4) Constructing a carbon emission model for by-product gas:

[0219]

[0220] in: Carbon emissions for by-product gas; is the carbon content coefficient of by-product gas;

[0221] 5) Constructing a total carbon emission model for steel enterprises:

[0222]

[0223] in: Total carbon emissions of steel enterprises.

[0224] The carbon emission coefficient data used in the production process of the above-mentioned steel enterprises are set based on the actual data of a long-process steel enterprise in Jiangsu Province to construct a process carbon emission model.

[0225] 6) Construct a coupling model of material flow, energy flow and carbon emission flow for long-process steel enterprises, establish an objective function, and take the minimum sum of the power purchase cost of the superior power grid, the carbon emission cost of the industrial park and the cost of production raw materials as the goal, solve and obtain the optimal operation mode of the joint regulation of material flow, energy flow and carbon emission flow of steel enterprises, and realize the joint regulation of material flow, energy flow and carbon emission flow of new long-process steel enterprises;

[0226] Among them, the objective function is:

[0227]

[0228] Where: C min Indicates that the sum of the power purchase cost of the upper power grid, the carbon emission cost of the park and the material cost is the smallest; C gird,t is the time-of-use electricity price E pei,t Free carbon emission quota; C carbon is a fixed carbon price; C ljm is the price per ton of production raw material No. 1, i.e. the price per ton of coking coal; C tks The price per ton of production raw material No. 2, i.e. the price per ton of iron ore; C fg It is the price per ton of production raw material number 3, that is, the price per ton of scrap steel.

[0229] Based on the new long-process steel enterprise material flow-energy flow model, the enterprise carbon emission flow is introduced to construct a new long-process steel enterprise material flow-energy flow-carbon emission flow coupling model, as described below:

[0230] Each production process of a long-process steel enterprise consumes heat and electricity. Based on actual data, an energy consumption model is constructed by taking the production of 1 ton of steel as an example, as shown in Table 2. The thermal demand of the production process is converted into the thermodynamic limit energy consumption, which only considers the energy that must be consumed in the process of raw materials undergoing a series of physical and chemical reactions to form products, mainly including sensible heat of heating, phase change heat, reaction heat, dissolution heat and rolling deformation work.

[0231] Table 2 Energy parameters of steel enterprises in producing 1 ton of steel

[0232]

[0233]

[0234] Based on the energy consumption model of a low-carbon long-process steel enterprise producing one ton of steel, the carbon emission model is added to construct a new energy flow-carbon emission flow coupling model for a new long-process steel enterprise producing one ton of steel, as shown in Table 3.

[0235] Table 3 Energy flow and carbon emission flow data of steel enterprises producing 1 ton of steel

[0236]

[0237] After this model is constructed, the energy flow and carbon emission flow can be jointly regulated by regulating the material flow and starting and stopping the short-process electric arc furnace steelmaking under the premise of responding to the time-of-use electricity price signal and the carbon price signal; usually, the operation mode of the long-process steel enterprise can be optimized and dispatched accordingly according to the daily, weekly, monthly, quarterly and annual delivery of steel products of the enterprise. Taking a long-process steel enterprise in Jiangsu Province with a daily delivery of 28,000 tons of steel as an example, the objective function is to minimize the power purchase cost of the superior power grid, the carbon emission cost of the park and the material cost, and to respond to the time-of-use electricity price signal and the carbon price signal for optimized dispatching. By regulating the material flow and the start and stop of the electric arc furnace, the material flow, energy flow and carbon emission flow can be jointly regulated. Time-of-use electricity price parameters are as follows: Figure 6 The dynamic carbon emission factor parameters of the upper grid are shown as follows: Figure 7 As shown; the final results of material flow and start-up and shutdown of short-process electric arc furnace steelmaking are as follows Figure 8 As shown, analysis Figure 8 The arc furnace steelmaking process production line 1 starts running at 4:00-7:00 in the morning, and then runs at 8:00-11:00 in the evening, resulting in an increase in the production of the rolling process production line 1 at the corresponding time; the arc furnace steelmaking process production line 2 starts running at 2:00-5:00 in the morning, then stops for 1 hour, and starts running at 7:00-10:00 in the morning, resulting in an increase in the production of the rolling process production line 2 at the corresponding time. The total carbon emissions of the park are as follows: Fig. 9 As shown; the total industrial load of the park is as follows Fig.10 This joint regulation method will explore the carbon emission reduction potential of enterprises, reduce operation and carbon emission costs, promote the low-carbon transformation of steel production processes, and facilitate subsequent research on the electricity-carbon-green certificate market trading mechanism.

Claims

1. A method for jointly regulating material flow, energy flow and carbon emission flow in a long-process steel enterprise, wherein the method takes the production of 1 ton of steel by the long-process steel enterprise as the calculation base, constructs various models and performs joint regulation of material flow, energy flow and carbon emission flow, and the production processes of the long-process steel enterprise include coking process, sintering process, pelletizing process, blast furnace ironmaking process, converter steelmaking process, electric arc furnace steelmaking process, steel rolling process and air compressor oxygenation process for providing oxygen in the corresponding processes, and the corresponding process numbers are from 1 to 7, among which coking and scrap steel electric arc furnace short-process steelmaking processes are intermittent output processes, sintering, pelletizing, blast furnace ironmaking, converter steelmaking and steel rolling are continuous output processes; the air compressor oxygenation process is an auxiliary process, and is characterized in that: it includes the following steps, and the following steps are performed sequentially, Step 1: Construct a material flow model for the short-process electric arc furnace steelmaking process. The model consists of the following constraints: 1) Construct continuity constraints for the start and stop of the short-process electric arc furnace steelmaking production process: ds 6,t +dc 6,t ≤1 (1) ds 6,t -dc 6,t =op 6,t -op 6,t-1 (2) Among them: ds 6,t is the starting variable of the production process; dc 6,t is the stop variable of the production process; op 6,t is the operating variable of the production process; 6 is the sequence number of the electric arc furnace steelmaking process; 2) Construct the minimum operating time constraint of the short-process electric arc furnace steelmaking production process: Among them: Minop6 is the minimum operating time of the production process; 3) Construct the maximum operating time constraint of the short-process electric arc furnace steelmaking production process: Among them: Maxop6 is the maximum operating time of the production process; 4) Construct the minimum downtime constraint of the short-process electric arc furnace steelmaking production process: Where: Dp6 is the minimum downtime of the production process; 5) Construct material flow constraints for the short-process electric arc furnace steelmaking production process: Where: G 6,t is the output of the product in production process 6; G6 is the unit output of production process 6; Step 2: Establish the following conventional long-process steel enterprise process material model: 1) Constructing the material flow constraint model of coking production process: Where: Y 1,t The input raw material quantity for process 1 is coking coal; k is the coking ratio; G 1,t is the product output of process 1; jt2 is the coke supply-demand ratio of process 2; jt3 is the coke supply-demand ratio of process 3; jt4 is the coke supply-demand ratio of process 4; M jt2 is the amount of coke in input process 2; M jt3 is the amount of coke in input process 3; M jt4 G is the amount of coke in step 4; min1 and G max1 They are the lower and upper limits of the output of process 1 respectively; 2) Construct material flow constraint model for sintering production process: Where: Y 2,t Input raw material quantity for process 2, i.e. iron ore; v2 is material conversion rate for process 2; λ tks2 G is the iron ore supply and demand ratio of process 2; 2,t is the product output of process 2; sjk4 is the supply-demand ratio of sintered ore in process 4; M sjk4 is the amount of sintered ore in step 4, G min2 and G max2 They are the lower and upper limits of the output of process 2 respectively; 3) Constructing the material flow constraint model of pellet production process: Where: v3 is the material conversion rate of process 3; G 3,t is the product output of process 3; tks3 is the iron ore supply and demand ratio of process 3; qtk4 is the supply-demand ratio of pellets in process 4; M qtk4 G is the amount of pellets in step 4; min3 and G max3 They are the lower and upper limits of the output of process 3 respectively; 4) Construct a material flow constraint model for the blast furnace ironmaking production process: Where: v4 is the material conversion rate of process 4; G 4,t is the product output of process 4; G min4 and G max4 They are the lower and upper limits of the output of process 4 respectively; 5) Construct material flow constraint model for converter steelmaking production process: Where: v5 is the material conversion rate of process 5; G 5,t is the product output of process 5; G min5 and G max5 They are the lower and upper limits of the output of process 5 respectively; 6) Construct material flow constraint model for steel rolling production process: Where: v7 is the material conversion rate of process 7; G 7,t is the product output of process 7; G min7 and G max7 They are the lower and upper limits of the output of process 7 respectively; Step 3: Combine the constraints in step 2 to build a warehouse storage model, an air compression system model, a gas system model, and a CHP unit and dry quenching waste heat recovery model. A total of three warehouse storage models are built, namely, a coke warehouse storage model, a sintered ore warehouse storage model, and a pellet warehouse storage model: 1) Build a warehouse storage model: Among them, equations (24) and (25) are the balance equations of the storage link; equation (26) is the upper and lower limit equations of the storage link; C is the warehouse number; S 0,c is the initial storage volume of warehouse c; M m,c,t is the output of the mth process before warehouse c at time t; Y n,c,t S is the material required by the nth process after warehouse c at time t; c,t is the capacity of warehouse c at time t; S min,c is the lower limit of the storage capacity of the cth warehouse; S max,c is the upper limit of storage capacity of the cth warehouse; 2) Construct an air compression system model: SA ca,t =SA ca,t-1 -SU ca,t Δt+α ca P ca,t Δt (27) 0.8SA ca,ini ≤SA ca,end ≤1.2SA ca,ini (28) V ca p ca,min ≤SA ca,t ≤V ca p ca,max (29) SA ca,t +on ca,t V ca p ca,max ≤1.1V ca p ca,max (30) 1.1V ca p ca,min ≤SA ca,t +on ca,t V ca p ca,min (31) in, Formula (27) is the air storage balance equation; Formulas (28) and (29) are the upper and lower limit equations of air storage; Formulas (30) and (31) are the air compressor start and stop equations; SA ca,t is the gas storage capacity of the system gas tank at time t; SU ca,t is the gas consumption of the system at time t; α ca is the air compressor efficiency; P ca,t is the air compressor output power at time t; Δ t The optimization step length is 1 hour; SA ca,ini SA is the gas storage capacity of the gas tank at the initial moment; ca,end V is the gas storage capacity of the gas tank at the end time; ca is the volume of the gas tank; p ca,min and p ca,max The minimum and maximum pressures allowed in the gas tank; op ca,t is the air compressor operating variable at time t; 3) Construct gas system model: In min,o ≤V o,t ≤V max,o (36) Where: μ COG , μ BFG , μ LDG are the by-product gas yields of COG coke oven gas, BFG blast furnace gas and LDG converter gas, respectively. The gas yield is proportional to the load of the corresponding production process; o is the by-product gas; V o,t For gas reserves; is the gas output; f j,o,t is the demand for gas o in process j; V is the gas volume input to the CHP unit; min,o and V max,o The lower and upper limits of the capacity of the corresponding gas storage tank; 4) Construct CHP unit model: Wherein: Formula (37) is the conversion formula for converting the heat energy of the CHP unit using byproduct gas as fuel into electrical energy; Formula (38) is the upper and lower limit constraints of the CHP unit output; Formula (39) is the conversion formula for converting the electrical energy of the CHP unit into thermal energy; The steam heat generated by the CHP unit; is the CHP unit power; δ is the optimized cycle length; and are the lower and upper limits of CHP unit output respectively; θ chp is the electrical efficiency of the CHP unit; θ chp,heat is the thermal efficiency of the CHP unit; β o is the calorific value of gas o; 5) Construct a low-carbon process CDQ waste heat recovery model: in: is the waste heat power generation power; cdp is the power generation coefficient; P 1,t is the coking power; and They are the lower and upper limits of the output of the CDQ waste heat unit respectively; Step 4: Based on the above model, the short-process electric arc furnace steelmaking process is introduced as a low-carbon process measure into the process material model of the conventional long-process steel enterprise to form a new long-process steel enterprise production process material-energy characteristic model, where the energy consumption includes electrical energy, thermal energy and oxygen energy; The constraints of the material-energy characteristics model of the long-process steel enterprise are as follows: G 7,t ≥F sum,task (47) Among them: formula (42) and (43) are active power balance constraints; formula (44) is the gas energy balance constraint; formula (45) is the thermal energy balance constraint; formula (46) is the compressed air energy balance constraint; formula (47) is the total production task constraint; G j,t is the output of production process j, j∈{1,2,3,4,5,6,7}; P total,t is the total production load of the process; P j is the fixed production load per unit of process j, j∈{1,2,3,4,5,6,7}; is the photovoltaic output power; is the output power of the CHP unit; Power purchased from the upper power grid; P load Operate the remaining electrical load for the enterprise; Q j is the heat energy required for process j, j∈{1,2,3,4,5,6,7}; H load Operate the remaining heat load for the enterprise; ca A is the efficiency of converting electricity into gas; j is the unit gas consumption of process j, j∈{1,2,3,4,5,6,7}; F sum,task is the total task volume of the process; Step 5: Construct a carbon emission model for steel enterprises, which is divided into the following four parts: an indirect carbon emission model for purchased electricity consumption, a direct carbon emission model for coke consumption, a carbon emission model for the production process after calculation, and a carbon emission model for by-product gas; 1) Construct an indirect carbon emission model for purchased electricity consumption: in: is the carbon emission of purchased electricity; elec,t is the carbon content coefficient of purchased electricity; 2) Construct a direct carbon emission model for coke consumption: in: is the coke carbon emission; coke is the carbon content coefficient of coke; is the amount of coke used; 3) Construct a carbon emission model for the production process after calculation: in: is the total carbon emission of the production process; j is the fixed carbon emission coefficient of process j; 4) Constructing a carbon emission model for by-product gas: in: Carbon emissions for by-product gas; is the carbon content coefficient of by-product gas; 5) Constructing a total carbon emission model for steel enterprises: in: is the total carbon emissions of steel enterprises; The carbon emission coefficient data used in the production process of the above-mentioned steel enterprises are set with reference to the actual data of real long-process steel enterprises; Step 6: Construct a coupling model of material flow, energy flow and carbon emission flow for long-process steel enterprises, establish an objective function, and take the minimum sum of the power purchase cost of the superior power grid, the carbon emission cost of the industrial park and the cost of production raw materials as the goal, solve and obtain the optimal operation mode of the joint regulation of material flow, energy flow and carbon emission flow of steel enterprises, and realize the joint regulation of material flow, energy flow and carbon emission flow of new long-process steel enterprises; Among them, the objective function is: Where: C min It means that the sum of the power purchase cost of the upper power grid, the carbon emission cost of the park and the material cost is the smallest; C gird,t is the time-of-use electricity price E pei,t Free carbon emission quota; C carbon is a fixed carbon price; C ljm is the price per ton of production raw material No. 1, i.e. the price per ton of coking coal; C tks The price per ton of production raw material No. 2, i.e. the price per ton of iron ore; C fg It is the price per ton of production raw material number 3, that is, the price per ton of scrap steel.

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