CN116090188A - Steel manufacturing optimization method and system for optimizing carbon emission in steel enterprises - Google Patents
Steel manufacturing optimization method and system for optimizing carbon emission in steel enterprises Download PDFInfo
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Abstract
A steel manufacturing optimization method and system for optimizing carbon emission in a steel enterprise are characterized in that the method comprises the following steps: step 1, obtaining steel manufacturing procedures and configuration parameters in a steel enterprise to generate a procedure model, and obtaining raw material proportion constraint in the steel manufacturing process based on the configuration parameters; step 2, solving a process model by taking the lowest carbon emission and the lowest raw material cost of each process in the steel manufacturing process as an objective function based on the raw material proportioning constraint so as to obtain an optimal solution of each process; and 3, taking the optimal solution of each working procedure as an optimal manufacturing method of the iron and steel enterprise, and adjusting configuration parameters of manufacturing equipment based on the optimal manufacturing method to realize iron and steel manufacturing. The method is accurate and effective, establishes the objective function aiming at the lowest carbon emission and the lowest cost of each process, and solves the problem that the processes are mutually influenced and simultaneously determines the association factors among the processes.
Description
Technical Field
The invention relates to the field of steel manufacturing, in particular to a steel manufacturing optimization method and system for optimizing carbon emission in a steel enterprise.
Background
At present, iron and steel enterprises are used as energy and resource intensive enterprises, and energy conservation and emission reduction work needs to be carried out greatly. The iron and steel enterprises as main carbon dioxide emission operators can have the potential of greatly reducing carbon emission through structural optimization in the production and manufacturing process.
Based on such background, various carbon emission optimization methods for iron and steel enterprises have appeared in the prior art. However, since the plant operation processes in the respective different process steps are relatively independent in the steel manufacturing process, it is difficult to consider the independent characteristics of the respective process steps in the steel manufacturing process if modeling is performed for the entire flow of the steel manufacturing process. On the other hand, since the material flow rates of the iron and steel manufacturing raw materials, the intermediate products and the final products in each process follow relatively fixed ratio constraint, if only each process is independently modeled, effective products, recycled waste materials and waste gases in each process are difficult to fully recycle, and the optimization model is difficult to consider the characteristic that a large amount of recycling exists in part of materials in iron and steel enterprises.
In view of the foregoing, there is a need for an optimization method and system for steel manufacturing that optimizes carbon emissions in a steel enterprise.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides an optimization method and a system for steel manufacturing for optimizing carbon emission in a steel enterprise.
The invention adopts the following technical scheme.
The first aspect of the invention relates to a steel manufacturing optimization method for optimizing carbon emission in a steel enterprise, comprising the following steps: step 1, obtaining steel manufacturing procedures and configuration parameters in a steel enterprise to generate a procedure model, and obtaining raw material proportion constraint in the steel manufacturing process based on the configuration parameters; step 2, solving a process model by taking the lowest carbon emission and the lowest raw material cost of each process in the steel manufacturing process as an objective function based on raw material proportioning constraint so as to obtain an optimal solution of each process; and 3, taking the optimal solution of each procedure as an optimal manufacturing method of the iron and steel enterprise, and adjusting configuration parameters of manufacturing equipment based on the optimal manufacturing method to realize iron and steel manufacturing.
Preferably, the steel manufacturing process includes a coking process, a blast furnace process, and a converter process; the configuration parameters in the coking process comprise power consumption, coking coal consumption, tar yield, benzene yield, gas generation rate and total coking rate; the configuration parameters in the blast furnace process comprise the total amount of blast furnace gas, the carbon dioxide content, the carbon monoxide content, the hydrogen content, the methane content, the nitrogen content and the carbon element coke ratio in the blast furnace gas, the coal injection coke ratio, the carbon content, the pig iron carburization content, the corresponding reduction carbon content of alloy elements, the corresponding reduction carbon content of iron elements, the burning carbon content in front of a tuyere, the actual carbon burning heat in front of the tuyere, the heat brought by hot air, the reduction heat absorption of iron, the corresponding reduction heat consumption of alloy elements, the water decomposition heat consumption, the desulfurization heat consumption, the heat taken away by molten iron and the heat taken away by gas; the configuration parameters in the converter process include the generation amount of the converter gas, the generation amount of carbon monoxide, the generation amount of carbon dioxide, the generation amount of sulfur dioxide, the generation amount of water, the generation amount of oxygen, the generation amount of nitrogen, the oxidation heat of smoke dust, the oxidation heat of iron element and the decomposition heat consumption of ore in the converter gas.
Preferably, the process model is obtained based on fitting actual historical production data of the iron and steel enterprise.
Preferably, the process model in the coking process is
Wherein H is the power consumption,
j1 refers to the coke yield and,
j2 refers to the consumption of coking coal,
j3 refers to the yield of tar,
t refers to the yield of tar and,
z refers to the steam requirement and is referred to as,
b refers to the yield of benzene and,
BQ refers to the blast furnace gas demand,
g refers to the gas generation rate of the gas,
c refers to the coke oven gas demand,
k refers to the total focal rate.
Preferably, the process model in the blast furnace process is
Wherein V is g Refers to the total amount of blast furnace gas,
V co refers to the actual carbon monoxide content in the blast furnace gas,
C K refers to the coke ratio of the carbon element,
C M refers to the coal injection coke ratio,
C mw refers to the carbon content of the inlet gas,
C C refers to the carburization content of pig iron,
C da refers to the corresponding reduced carbon content of the alloy element, C dFe Refers to the corresponding reduced carbon content of the iron element,
C b refers to the content of burning carbon before the tuyere,
q c refers to the actual carbon combustion heat in front of the tuyere,
q b refers to the heat brought by the hot air,
q Fe refers to the reduction heat absorption of iron,
q da representing the corresponding reduction heat consumption of the alloy element,
q f refers to the water decomposition and heat consumption,
q s represents the heat consumption of desulfurization,
q h refers to the fact that the molten iron takes away heat,
q gas refers to the heat taken away by the gas.
Preferably, the process model in the converter process is
Wherein M is 1 Refers to the generation amount of the converter gas,
M CO refers to one in converter gasThe amount of carbon oxide to be generated,
Q Fe representing the oxidation heat of the smoke dust,
H M refers to the melting point of molten iron,
T f refers to the melting point of molten iron,
Q c refers to the oxidation heat of the iron element,
Q slag refers to the heat consumption of the decomposition of the ore,
e q refers to the yield of molten steel.
Preferably, the raw material proportioning constraint is obtained by modeling and solving an input substance and an output substance of each process in the configuration parameters based on a substance flow algorithm.
Preferably, the objective function in the coking process is
Wherein f (x) refers to a multi-objective function of the coking long-flow carbon emission optimization sub-model,
PCE sj (x) Refers to the carbon emissions in the coking process,
P sj (x) Refers to the cost of the coking process,
i is the variable number in the coking process,
EF i the carbon emission factor for the i-th variable,
P i refers to the unit price corresponding to each variable,
p' refers to the processing cost of producing tons of sinter.
Preferably, the objective function in the blast furnace process is
Wherein f (x)' refers to a multi-objective function of the long-flow carbon emission optimization sub-model of the blast furnace,
PCE gl (x) Refers to the carbon emissions in the blast furnace process,
P gl (x) Refers to the cost in the blast furnace process.
Preferably, the objective function in the converter step is
Wherein f (x) "refers to a multi-objective function of the long-flow carbon emission optimization sub-model of the blast furnace,
P lt (x) Refers to the cost of the blast furnace process.
In a second aspect the invention relates to a steel manufacturing optimisation system for optimising carbon emissions in a steel enterprise, the system being arranged to carry out the steps of the method of the first aspect of the invention; the system comprises a model acquisition module, a model solving module and a manufacturing control module; the model acquisition module is used for acquiring steel manufacturing procedures and configuration parameters in a steel enterprise to generate a procedure model and acquiring raw material proportion constraint in the steel manufacturing process based on the configuration parameters; the model solving module is used for solving a process model by taking the lowest carbon emission and the lowest raw material cost of each process in the steel manufacturing process as objective functions based on raw material proportion constraint so as to obtain an optimal solution of each process; and the manufacturing control module is used for realizing steel manufacturing by taking the optimal solution of each process as the optimal manufacturing method of the steel enterprise.
Compared with the prior art, the method and the system for optimizing the steel manufacturing of the carbon emission in the steel enterprise have the advantages that the configuration parameters in the steel manufacturing process can be obtained to obtain the process model and the raw material proportioning constraint, and the model is solved based on the objective function, so that the optimal manufacturing method of the steel enterprise is generated. The method is accurate and effective, and the objective function is established according to the lowest carbon emission and the lowest cost of each process by independently setting production models of different processes, so that the solution of the optimal manufacturing method of the iron and steel enterprises is relatively and independently realized, and the problem of mutual influence of the processes is solved. On the other hand, through a material flow algorithm, the invention also defines the correlation factors among the working procedures, and prevents the contradiction problem in the solving process.
Drawings
FIG. 1 is a schematic flow chart of the steps of an optimization method for manufacturing iron and steel for optimizing carbon emissions in an iron and steel enterprise according to the present invention;
FIG. 2 is a schematic view of cost savings achieved by optimizing a method for optimizing carbon emissions in a steel enterprise in accordance with the present invention;
FIG. 3 is a schematic view of carbon emissions in a blast furnace process after optimization of a steel manufacturing optimization method for optimizing carbon emissions in a steel enterprise according to the present invention;
FIG. 4 is a schematic view of carbon emissions in a converter process after optimization of a method for optimizing carbon emissions in an iron and steel enterprise according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. The described embodiments of the invention are only some, but not all, embodiments of the invention. All other embodiments of the invention not described herein, which are obtained from the embodiments described herein, should be within the scope of the invention by those of ordinary skill in the art without undue effort based on the spirit of the present invention.
FIG. 1 is a schematic flow chart of the steps of an optimizing method for iron and steel manufacturing for optimizing carbon emissions in an iron and steel enterprise according to the present invention. As shown in fig. 1, the present invention relates in a first aspect to a steel manufacturing optimization method for optimizing carbon emissions in a steel enterprise, the method comprising steps 1 to 3.
Step 1, obtaining steel manufacturing procedures and configuration parameters in a steel enterprise to generate a procedure model, and obtaining raw material proportion constraint in the steel manufacturing process based on the configuration parameters.
Preferably, the steel manufacturing process includes a coking process, a blast furnace process, and a converter process; the configuration parameters in the coking process comprise power consumption, coking coal consumption, tar yield, benzene yield, gas generation rate and total coking rate; the configuration parameters in the blast furnace process comprise the total amount of blast furnace gas, the carbon dioxide content, the carbon monoxide content, the hydrogen content, the methane content, the nitrogen content and the carbon element coke ratio in the blast furnace gas, the coal injection coke ratio, the carbon content, the pig iron carburization content, the corresponding reduction carbon content of alloy elements, the corresponding reduction carbon content of iron elements, the burning carbon content in front of a tuyere, the actual carbon burning heat in front of the tuyere, the heat brought by hot air, the reduction heat absorption of iron, the corresponding reduction heat consumption of alloy elements, the water decomposition heat consumption, the desulfurization heat consumption, the heat taken away by molten iron and the heat taken away by gas; the configuration parameters in the converter process include the generation amount of the converter gas, the generation amount of carbon monoxide, the generation amount of carbon dioxide, the generation amount of sulfur dioxide, the generation amount of water, the generation amount of oxygen, the generation amount of nitrogen, the oxidation heat of smoke dust, the oxidation heat of iron element and the decomposition heat consumption of ore in the converter gas.
It can be understood that the invention can construct physical sub-models of three procedures of a coking procedure, a blast furnace procedure and a converter procedure, and complete the construction of physical models of production procedures of iron and steel enterprises. Under the condition that the technical level and equipment configuration of the iron and steel enterprises are certain, the material flow in a long process consisting of a plurality of working procedures can be subjected to proportioning constraint.
Preferably, the process model is obtained based on fitting actual historical production data of the iron and steel enterprise. It can be understood that specific configuration parameters of each process in model construction can be obtained, and each configuration parameter can realize the influence on model construction.
In a specific modeling process, some of the configuration parameters may be used to generate key quantities for the model, and these key quantities are typically the optimal indicators that characterize the state of the model. For example, for the coking process, the content of power consumption, steam demand, blast furnace gas demand, coke oven gas demand, etc. are respectively important indicators of the coking process model, which can be measured according to corresponding parameters collected during the historical working process of the coke oven plant. Therefore, according to the value of the historical index, the coefficient of the formula in each model can be obtained, so that the specific content of the model can be determined.
By adopting the method, the invention respectively acquires the process models of three different steps.
Preferably, the process model in the coking process is
Wherein H is power consumption, J1 is coke yield, J2 is coking coal consumption, J3 is tar yield, T is tar yield, Z is steam demand, B is benzene yield, BQ is blast furnace gas demand, G is gas generation rate, C is coke oven gas demand, and K is total coke rate.
Specifically, in the above calculation formula, the calculation of the partial configuration parameters may also be implemented according to the measurement parameters in the related art. For example, the tar yield is calculated as follows:
T＝25.30-54.20V daf +0.852V daf 2
wherein V is daf Refers to ash-free base dry volatile matters of coking coal.
In addition, the benzene yield is calculated as follows:
B＝5.248+2.514V daf +0.587V daf 2
the calculation formula of the gas generation rate is specifically as follows:
b is the correlation correction coefficient of the coking coal type.
The calculation formula of the total focal rate is specifically as follows:
A 1 refers to the fixed carbon content corresponding to the dry-based ash,
refers to the fixed carbon content corresponding to the dry basis.
Preferably, the process model in the blast furnace process is
Wherein V is g Refers to the total amount of blast furnace gas,
refers to blast furnace gasActual carbon dioxide content, V co Means the actual carbon monoxide content in the blast furnace gas, < >>
Refers to hydrogen content, < >>
Is the content of nail alkane, is->
Refers to the nitrogen content, C K Refers to the coke ratio of carbon element, C M Refers to the coke ratio of the coal injection, C mq Refers to the carbon content of the inlet gas, C C Refers to carburization content of pig iron, C da Refers to the corresponding reduced carbon content of the alloy element, C dFe Refers to the corresponding reduced carbon content of iron element, cx refers to the pre-tuyere combustion carbon content, q c Refers to the actual carbon combustion heat quantity, q, in front of the tuyere b Refers to heat quantity introduced by hot air, q Fe Refers to the reduction heat absorption of iron, q da Represents the corresponding reduction heat consumption of alloy elements, q f Refers to water decomposition and heat consumption, q s Represents desulfurization heat consumption, q h Refers to the heat taken away by molten iron, q gas Refers to the heat taken away by the gas.
Preferably, the process model in the converter process is
Wherein M is 1 Refers to the generation amount of converter gas, M CO Refers to the generation amount of carbon monoxide in the converter gas,
refers to the carbon dioxide generation amount in converter gas, < + >>
Represents the generation amount of sulfur dioxide, < >>
Indicating the occurrence of water, ++>
Refers to oxygen generation amount,/->
Refers to the generation amount of nitrogen and Q Fe Represents the oxidation heat of smoke and dust, H M Refers to the melting point of molten iron, T f Refers to the melting point of molten iron, Q c Refers to the oxidation heat of iron element, Q slag Refers to ore decomposition heat consumption, e w Refers to the yield of molten steel.
Furthermore, the invention also needs to obtain the raw material proportioning constraint according to each configuration parameter.
Preferably, the raw material proportioning constraint is obtained by modeling and solving an input substance and an output substance of each process in the configuration parameters based on a substance flow algorithm.
In particular, under the condition that the technical level and equipment configuration of the iron and steel enterprises are certain, the material flow in the long flow can be subjected to proportioning constraint, so that the material flow can be distributed to different flows in different proportions. In other words, the calculation of the material ratios in the various working procedures of the iron and steel enterprises is realized by adopting a material flow algorithm.
Specifically, in the proportioning constraint of the material flows, the fluid input in the process is divided into four types, wherein the first type is the material flow F (j, k) returned to the kth process after the reject produced in the downstream jth process; the second type is a corresponding material flow O (k) when the raw material from the outside of the process is input to the kth process; the third category is the corresponding material flow F (k, k) of the reject produced in the kth process, which returns to the material flow circularly applied in the process; the fourth type is a material flow P (k-1) in which the production material of the previous process is fed to the kth process.
Meanwhile, the output fluid is divided into four types, wherein the first type is to output a material flow P (k) of a qualified product in the kth process to the kth+1th process; the second category is a material flow F (k, l) of returning the unqualified product produced in the kth process to the material flow circularly applied in the upstream process; the third class is a corresponding material flow D (k) of the kth procedure facing the external environment for implementing waste discharge; the fourth category is the corresponding stream F (k, k) from which the reject produced in the kth process is returned to the stream cyclically used in the process.
It will be appreciated that the invention may be employed in
To represent the substance flow input vector by +.>
To represent the output vector of the material flow by
Representing the corresponding distribution coefficients of the different material flow output streams.
And then, calculating each flow by adopting a formula of a material flow algorithm, and finally, realizing a material flow distribution coefficient matrix as a raw material ratio constraint in the invention. The contents of which are not described in detail herein.
And 2, solving a process model by taking the lowest carbon emission and the lowest raw material cost of each process in the steel manufacturing process as objective functions based on raw material proportioning constraint so as to obtain an optimal solution of each process.
It will be appreciated that after the constraints on the respective raw material ratios are obtained, the objective function may also be constructed according to different objectives. In the invention, the objective function is respectively constructed based on each different procedure, and in this way, the influence of the incompletely matched running state in each procedure on the optimization algorithm can be prevented.
Preferably, the objective function in the coking process is
Wherein f (x) refers to multiple objectives of the coking long-flow carbon emission optimization submodelFunction, PCE sj (x) Refers to the carbon emission amount, P in the coking process sj (x) Refers to the cost of the coking process, i is the variable number in the coking process, EF i The carbon emission factor for the i-th variable,
refers to the corresponding carbon emission amount of power consumption, P i The unit price corresponding to each variable, and the P' is the processing cost for producing ton of sintered ore.
It will be readily appreciated that the variables herein, i.e., the materials of the material flows in the various material proportioning constraints hereinabove, are, in addition, actually the input materials, intermediate materials and output materials in the various processes. The variable content in the determination process needs to be precisely determined before each model is constructed.
For example, table 1 shows the contents of one of the optimization variables in the prior art of the present invention, and from the contents of the optimization variables in this table, various types of raw materials practically usable in the coking process can be actually known.
Table 1 optimization variable table for coking process
In addition, various constraint conditions are used for the model, such as process constraint, balance constraint, maximum ore usage constraint, waste material usage constraint, solid fuel usage constraint, non-negative constraint, aluminum oxide and magnesium oxide constraint, which are used as constraint conditions of the coking long-flow carbon emission optimization multi-objective function. The actual content of each constraint is not described in detail herein.
Preferably, the objective function in the blast furnace process is
Wherein f (x)' refers to a multi-objective function of a long-flow carbon emission optimization sub-model of the blast furnace, PCE gl (x) Refers to the carbon emission amount, P in the blast furnace process gl (x) Refers to the cost in the blast furnace process.
It is understood that the optimization variables of the long-process carbon emission optimization sub-model of the blast furnace can be defined as the blast furnace slag amount, the BFG generation amount, the blast amount, the COG consumption amount, and the BFG consumption amount in the present invention, as shown in table 2.
Table 2 optimization variable table for blast furnace process
After the contents of the models are obtained, the invention can establish an objective function according to different process models, and solve the contents of the models. The invention can also restrict the magnesia content in the slag, the alkalinity of the blast furnace slag and the product parameters as the constraint conditions of the blast furnace long-process carbon emission optimization multi-objective function.
Preferably, the objective function in the converter step is
Wherein f (x) "refers to a multi-objective function of the long-flow carbon emission optimization sub-model of the blast furnace,
refers to the carbon emission amount, P of the blast furnace procedure lt (x) Refers to the cost of the blast furnace process.
In the invention, the optimization variables of the converter long-flow carbon emission optimization submodel are defined as blast furnace slag quantity, south African lump ore, brazil lump ore, hainan lump ore, black pellet, he pellet B, russian ore and brazil ore, and are shown in Table 3.
Table 3 optimization variable table in converter process
In the invention, the converter process is positioned at the end of the long process, so that the constraint conditions of the other two sub-models can be used as the constraint conditions of the blast furnace long process carbon emission optimization sub-model. And (3) solving a formula under constraint conditions, so that the long-flow carbon emission optimization of the iron and steel enterprises under the low-carbon transformation background is completed.
And 3, taking the optimal solution of each procedure as an optimal manufacturing method of the iron and steel enterprise, and adjusting configuration parameters of manufacturing equipment based on the optimal manufacturing method to realize iron and steel manufacturing.
In one embodiment of the invention, a joint iron and steel enterprise in a certain area can be selected as an experimental enterprise, and the main output products of the enterprise are coarse steel, lime, molten iron, sinter, coke and other intermediate link products which are only used as input raw materials of other working procedures and are not sold. The production process conditions of the experimental iron and steel enterprises are specifically shown in table 4.
TABLE 4 production process conditions for iron and Steel enterprises
Before optimization, the carbon dioxide emission of the enterprise is respectively outsourced power emission: 32.58 ten thousand tons; emission of self-contained power plant: 64.52 ten thousand tons; discharge amount of hot blast stove: 452.63 ten thousand tons; lime kiln discharge: 75.36 ten thousand tons; steel-making discharge amount: 5.21 ten thousand tons; iron-making emission amount: 596.32 ten thousand tons; sintering emission amount: 256.32 ten thousand tons; coking emission amount: 105.24 ten thousand tons.
FIG. 2 is a schematic diagram showing cost savings after optimization of the optimization method for optimizing carbon emissions in iron and steel enterprises according to the present invention. As shown in fig. 2, the above-described discharge amount can be optimized using the method of the present invention. Firstly, the invention can test and reduce the cost of producing the coarse steel for the experimental steel enterprises. The test results are shown in fig. 2. According to the comparison result of the cost test of fig. 2, the cost of producing the crude steel can be remarkably reduced by constructing a model, the larger the quantity of the crude steel to be produced is, the more obvious the effect of reducing the cost of the model is, and the highest cost reduction value is 40 yuan/ton. The construction model is effectively proved to have the effect of obviously reducing the cost.
FIG. 3 is a schematic view of carbon emissions in a blast furnace process after optimization of the optimization method for manufacturing iron and steel in an iron and steel enterprise according to the present invention. FIG. 4 is a schematic view of carbon emissions in a converter process after optimization of a method for optimizing carbon emissions in an iron and steel enterprise according to the present invention. As shown in fig. 3 and 4, the present invention can also test whether the constructed model can reduce carbon dioxide emissions of the experimental iron and steel enterprises. Two procedures with larger carbon dioxide emission are selected for testing, namely a blast furnace and iron making, and the carbon dioxide emission performance of the model constructed in the two procedures is observed.
The comparison test data of carbon dioxide emission in the blast furnace process are shown in fig. 3. According to the comparative test data of carbon dioxide emission amount in fig. 3, when 10 tons of iron are contained in the blast furnace process, the carbon dioxide emission amount is reduced from 55kg to 23kg, and the reduction range is reduced along with the increase of the iron capacity, but the overall reduction trend is shown, and in conclusion, the carbon dioxide emission amount in the blast furnace process is obviously reduced after the optimization by constructing a model.
The comparative test data of carbon dioxide emissions in the ironmaking process are shown in fig. 4. According to the carbon dioxide emission comparison test data of fig. 4, after the model is constructed and optimized in the iron-making process, the carbon dioxide emission of the iron-making process is also obviously reduced, and the model has better carbon dioxide emission reducing capability. The comprehensive cost and the carbon dioxide emission can be known, and the construction model can realize the cost optimization under the long process of the iron and steel enterprises and the optimization in the aspect of carbon emission.
In a second aspect the invention relates to a steel manufacturing optimisation system for optimising carbon emissions in a steel enterprise, the system being arranged to carry out the steps of the method of the first aspect of the invention; the system comprises a model acquisition module, a model solving module and a manufacturing control module; the model acquisition module is used for acquiring steel manufacturing procedures and configuration parameters in a steel enterprise to generate a procedure model and acquiring raw material proportion constraint in the steel manufacturing process based on the configuration parameters; the model solving module is used for solving a process model by taking the lowest carbon emission and the lowest raw material cost of each process in the steel manufacturing process as objective functions based on raw material proportion constraint so as to obtain an optimal solution of each process; and the manufacturing control module is used for realizing steel manufacturing by taking the optimal solution of each process as the optimal manufacturing method of the steel enterprise.
It can be understood that, in order to implement each function in the method provided in the embodiment of the present application, the steel manufacturing optimization system includes a hardware structure and/or a software module that perform each function. Those of skill in the art will readily appreciate that the algorithm steps of the examples described in connection with the embodiments disclosed herein may be implemented as hardware or a combination of hardware and computer software. Whether a function is implemented as hardware or computer software driven hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The embodiment of the application can divide the functional modules of the steel manufacturing optimization system according to the method example, for example, each functional module can be divided corresponding to each function, and two or more functions can be integrated in one processing module. The integrated modules may be implemented in hardware or in software functional modules. It should be noted that, in the embodiment of the present application, the division of the modules is schematic, which is merely a logic function division, and other division manners may be implemented in actual implementation.
The system may be implemented by a plurality of device network connections, the devices including at least one processor, a bus system, and at least one communication interface. The processor may be a central processing unit (Central Processing Unit, CPU), or may be replaced by a field programmable gate array (Field Programmable Gate Array, FPGA), application-specific integrated circuit (ASIC), or other hardware, or the FPGA or other hardware may be used together with the CPU as a processor.
The memory may be, but is not limited to, read-only memory (ROM) or other type of static storage device that can store static information and instructions, random access memory (random access memory, RAM) or other type of dynamic storage device that can store information and instructions, as well as electrically erasable programmable read-only memory (electrically erasable programmable read-only memory, EEPROM), compact disk read-only memory (CD-ROM) or other optical disk storage, optical disk storage (including compact disk, laser disk, optical disk, digital versatile disk, blu-ray disk, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and that can be accessed by a computer. The memory may be stand alone and coupled to the processor via a bus. The memory may also be integrated with the processor.
The hard disk may be a mechanical disk or a solid state disk (Solid State Drive, SSD), etc. The interface card may be a Host Bus Adapter (HBA), a redundant array of independent disks card (Redundant Array ofIndependent Disks, RID), an Expander card (Expander), or a Network interface controller (Network InterfaceController, NIC), which is not limited by the embodiment of the present invention. The interface card in the hard disk module is communicated with the hard disk. The storage node communicates with an interface card of the hard disk module to access the hard disk in the hard disk module.
The interface of the hard disk may be a Serial attached small computer system interface (Serial Attached SmallComputer System Interface, SAS), serial Advanced TechnologyAttachment, SATA, or high speed Serial computer expansion bus standard (Peripheral ComponentInterconnect express, PCIe), etc.
In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using a software program, it may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in accordance with embodiments of the present application are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another, for example, by wired (e.g., coaxial cable, fiber optic, digital subscriber line (digital subscriber line, simply DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means from one website, computer, server, or data center. Computer readable storage media can be any available media that can be accessed by a computer or data storage devices including one or more servers, data centers, etc. that can be integrated with the media. The usable medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a Solid State Disk (SSD)), or the like.
Computer program instructions for carrying out operations of the present invention may be assembly instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, c++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions may be executed entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present invention are implemented by personalizing electronic circuitry, such as programmable logic circuitry, field Programmable Gate Arrays (FPGAs), or Programmable Logic Arrays (PLAs), with state information for computer readable program instructions, which can execute the computer readable program instructions.
Compared with the prior art, the method and the system for optimizing the steel manufacturing of the carbon emission in the steel enterprise have the advantages that the configuration parameters in the steel manufacturing process can be obtained to obtain the process model and the raw material proportioning constraint, and the model is solved based on the objective function, so that the optimal manufacturing method of the steel enterprise is generated. The method is accurate and effective, and the objective function is established according to the lowest carbon emission and the lowest cost of each process by independently setting production models of different processes, so that the solution of the optimal manufacturing method of the iron and steel enterprises is relatively and independently realized, and the problem of mutual influence of the processes is solved. On the other hand, through a material flow algorithm, the invention also defines the correlation factors among the working procedures, and prevents the contradiction problem in the solving process.
Finally, it should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.
Claims (11)
1. A steel manufacturing optimization method for optimizing carbon emissions in a steel enterprise, the method comprising the steps of:
step 1, obtaining steel manufacturing procedures and configuration parameters in a steel enterprise to generate a procedure model, and obtaining raw material proportion constraint in the steel manufacturing process based on the configuration parameters;
step 2, solving a process model by taking the lowest carbon emission and the lowest raw material cost of each process in the steel manufacturing process as an objective function based on the raw material proportioning constraint so as to obtain an optimal solution of each process;
and 3, taking the optimal solution of each working procedure as an optimal manufacturing method of the iron and steel enterprise, and adjusting configuration parameters of manufacturing equipment based on the optimal manufacturing method to realize iron and steel manufacturing.
2. A steel manufacturing optimization method for optimizing carbon emissions in a steel enterprise as claimed in claim 1, wherein:
the steel manufacturing process includes a coking process, a blast furnace process, and a converter process; and, in addition, the processing unit,
the configuration parameters in the coking process comprise power consumption, coking coal consumption, tar yield, benzene yield, gas generation rate and full coke rate;
the configuration parameters in the blast furnace process comprise the total amount of blast furnace gas, the carbon dioxide content, the carbon monoxide content, the hydrogen content, the methane content, the nitrogen content and the carbon element coke ratio in the blast furnace gas, the coal injection coke ratio, the carbon content, the pig iron carburization content, the corresponding reduced carbon content of alloy elements, the corresponding reduced carbon content of iron elements and the carbon content burnt before a tuyere, the actual carbon combustion heat before the tuyere, the heat brought by hot air, the reduction heat absorption of iron, the corresponding reduction heat consumption of alloy elements, the water decomposition heat consumption, the desulfurization heat consumption, the heat taken away by molten iron and the heat taken away by gas;
the configuration parameters in the converter process comprise the generation amount of converter gas, the generation amount of carbon monoxide, carbon dioxide, sulfur dioxide, water, oxygen, nitrogen, smoke oxidation heat, iron oxidation heat and ore decomposition heat consumption in the converter gas.
3. A steel manufacturing optimization method for optimizing carbon emissions in a steel enterprise as claimed in claim 2, wherein:
the process model is obtained based on fitting actual historical production data of the iron and steel enterprises.
4. A steel manufacturing optimization method for optimizing carbon emissions in a steel enterprise as claimed in claim 3, wherein:
the process model in the coking process is as follows
Wherein H is the power consumption,
j1 refers to the coke yield and,
j2 refers to the consumption of coking coal,
j3 refers to the yield of tar,
t refers to the yield of tar and,
z refers to the steam requirement and is referred to as,
b refers to the yield of benzene and,
BQ refers to the blast furnace gas demand,
g refers to the gas generation rate of the gas,
c refers to the coke oven gas demand,
k refers to the total focal rate.
5. A steel manufacturing optimization method for optimizing carbon emissions in a steel enterprise as claimed in claim 3, wherein:
the process model in the blast furnace process is that
Wherein V is g Refers to the total amount of blast furnace gas,
V co refers to the actual carbon monoxide content in the blast furnace gas,
C K refers to the coke ratio of the carbon element,
C M refers to the coal injection coke ratio,
C mq refers to the carbon content of the inlet gas,
C C refers to the carburization content of pig iron,
C da refers to the corresponding reduced carbon content of the alloying element,
C DFe refers to the corresponding reduced carbon content of the iron element,
C b refers to the content of burning carbon before the tuyere,
q c refers to the actual carbon combustion heat in front of the tuyere,
q b refers to the heat brought by the hot air,
q Fe refers to the reduction heat absorption of iron,
q da representing the corresponding reduction heat consumption of the alloy element,
q f refers to the water decomposition and heat consumption,
q s represents the heat consumption of desulfurization,
q h refers to the fact that the molten iron takes away heat,
q gas refers to the heat taken away by the gas.
6. A steel manufacturing optimization method for optimizing carbon emissions in a steel enterprise as claimed in claim 3, wherein:
the process model in the converter process is that
Wherein M is 1 Refers to the generation amount of the converter gas,
M CO refers to the generation amount of carbon monoxide in the converter gas,
Q Fe representing the oxidation heat of the smoke dust,
H M refers to the melting point of molten iron,
T f refers to the melting point of molten iron,
Q c refers to the oxidation heat of the iron element,
Q slag refers to the heat consumption of the decomposition of the ore,
e q refers to the yield of molten steel.
7. A steel manufacturing optimization method for optimizing carbon emissions in a steel enterprise according to any one of claims 4-6, wherein:
the raw material proportioning constraint is obtained by modeling and solving input substances and output substances of each procedure in the configuration parameters based on a substance flow algorithm.
8. A steel manufacturing optimization method for optimizing carbon emissions in a steel enterprise according to any one of claims 7, wherein:
the objective function in the coking process is that
Wherein f (x) refers to a multi-objective function of the coking long-flow carbon emission optimization sub-model,
PCE sj (x) Refers to the carbon emissions in the coking process,
P sj (x) Refers to the cost of the coking process,
i is the variable number in the coking process,
EF i the carbon emission factor for the i-th variable,
P i refers to the unit price corresponding to each variable,
p' refers to the processing cost of producing tons of sinter.
9. A steel manufacturing optimization method for optimizing carbon emissions in a steel enterprise according to any one of claims 8, wherein:
the objective function in the blast furnace process is that
Wherein f (x)' refers to a multi-objective function of the long-flow carbon emission optimization sub-model of the blast furnace,
PCE gl (x) Refers to the carbon emissions in the blast furnace process,
P gl (x) Refers to the cost in the blast furnace process.
10. A steel making optimizing method for optimizing carbon emissions in a steel enterprise according to any one of claims 9, wherein:
the objective function in the converter procedure is that
Wherein f (x) "refers to a multi-objective function of the long-flow carbon emission optimization sub-model of the blast furnace,
P lt (x) Refers to the cost of the blast furnace process.
11. An iron and steel manufacturing optimization system for optimizing carbon emissions in an iron and steel enterprise, characterized by:
the system being adapted to implement the steps of the method of any one of claims 1-10; the system comprises a model acquisition module, a model solving module and a manufacturing control module; wherein, the liquid crystal display device comprises a liquid crystal display device,
the model acquisition module is used for acquiring steel manufacturing procedures and configuration parameters in a steel enterprise to generate a procedure model and acquiring raw material proportion constraint in the steel manufacturing process based on the configuration parameters;
the model solving module is used for solving a process model by taking the lowest carbon emission and the lowest raw material cost of each process in the steel manufacturing process as objective functions based on the raw material proportioning constraint so as to obtain an optimal solution of each process;
and the manufacturing control module is used for realizing steel manufacturing by taking the optimal solution of each procedure as the optimal manufacturing method of the steel enterprise.
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