CN117314708A - Carbon emission reduction system optimization method, device, equipment, medium and program product - Google Patents
Carbon emission reduction system optimization method, device, equipment, medium and program product Download PDFInfo
- Publication number
- CN117314708A CN117314708A CN202311406694.4A CN202311406694A CN117314708A CN 117314708 A CN117314708 A CN 117314708A CN 202311406694 A CN202311406694 A CN 202311406694A CN 117314708 A CN117314708 A CN 117314708A
- Authority
- CN
- China
- Prior art keywords
- cost
- emission reduction
- technology
- carbon
- production
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 219
- 230000009467 reduction Effects 0.000 title claims abstract description 216
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 title claims abstract description 180
- 229910052799 carbon Inorganic materials 0.000 title claims abstract description 180
- 238000005457 optimization Methods 0.000 title claims abstract description 40
- 238000005516 engineering process Methods 0.000 claims abstract description 220
- 238000004519 manufacturing process Methods 0.000 claims abstract description 157
- 230000008569 process Effects 0.000 claims description 110
- 230000006870 function Effects 0.000 claims description 54
- 238000004590 computer program Methods 0.000 claims description 24
- 238000000926 separation method Methods 0.000 claims description 17
- 238000010521 absorption reaction Methods 0.000 claims description 16
- 238000010276 construction Methods 0.000 claims description 9
- 239000012528 membrane Substances 0.000 claims description 9
- 238000001179 sorption measurement Methods 0.000 claims description 8
- 239000000126 substance Substances 0.000 claims description 8
- 230000005540 biological transmission Effects 0.000 claims description 7
- 238000003860 storage Methods 0.000 claims description 7
- 238000004134 energy conservation Methods 0.000 claims description 5
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 abstract description 74
- 229910000831 Steel Inorganic materials 0.000 abstract description 52
- 239000010959 steel Substances 0.000 abstract description 52
- 229910052742 iron Inorganic materials 0.000 abstract description 37
- 239000000047 product Substances 0.000 description 42
- 238000010248 power generation Methods 0.000 description 30
- 239000007789 gas Substances 0.000 description 26
- 239000006227 byproduct Substances 0.000 description 23
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 description 22
- 239000002994 raw material Substances 0.000 description 18
- 239000000446 fuel Substances 0.000 description 13
- 230000005611 electricity Effects 0.000 description 12
- 229910002092 carbon dioxide Inorganic materials 0.000 description 11
- 238000012946 outsourcing Methods 0.000 description 11
- 238000011160 research Methods 0.000 description 9
- 238000005245 sintering Methods 0.000 description 9
- 230000008901 benefit Effects 0.000 description 8
- 238000004364 calculation method Methods 0.000 description 7
- 239000001569 carbon dioxide Substances 0.000 description 7
- 238000004939 coking Methods 0.000 description 7
- 238000009628 steelmaking Methods 0.000 description 7
- UHOVQNZJYSORNB-UHFFFAOYSA-N Benzene Chemical compound C1=CC=CC=C1 UHOVQNZJYSORNB-UHFFFAOYSA-N 0.000 description 6
- 239000000571 coke Substances 0.000 description 6
- 238000005265 energy consumption Methods 0.000 description 6
- 238000004891 communication Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 5
- 239000000463 material Substances 0.000 description 5
- 229910001341 Crude steel Inorganic materials 0.000 description 3
- 230000008859 change Effects 0.000 description 3
- 239000003245 coal Substances 0.000 description 3
- 238000009749 continuous casting Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 238000011156 evaluation Methods 0.000 description 3
- 238000002347 injection Methods 0.000 description 3
- 239000007924 injection Substances 0.000 description 3
- 230000003993 interaction Effects 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 230000000452 restraining effect Effects 0.000 description 3
- 238000005096 rolling process Methods 0.000 description 3
- 239000000243 solution Substances 0.000 description 3
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 230000001419 dependent effect Effects 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 238000006073 displacement reaction Methods 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 239000008188 pellet Substances 0.000 description 2
- 239000002893 slag Substances 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- FGUUSXIOTUKUDN-IBGZPJMESA-N C1(=CC=CC=C1)N1C2=C(NC([C@H](C1)NC=1OC(=NN=1)C1=CC=CC=C1)=O)C=CC=C2 Chemical compound C1(=CC=CC=C1)N1C2=C(NC([C@H](C1)NC=1OC(=NN=1)C1=CC=CC=C1)=O)C=CC=C2 FGUUSXIOTUKUDN-IBGZPJMESA-N 0.000 description 1
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 description 1
- 235000008733 Citrus aurantifolia Nutrition 0.000 description 1
- 229910001208 Crucible steel Inorganic materials 0.000 description 1
- 208000033986 Device capturing issue Diseases 0.000 description 1
- 235000011941 Tilia x europaea Nutrition 0.000 description 1
- CREMABGTGYGIQB-UHFFFAOYSA-N carbon carbon Chemical compound C.C CREMABGTGYGIQB-UHFFFAOYSA-N 0.000 description 1
- 239000011203 carbon fibre reinforced carbon Substances 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 239000011280 coal tar Substances 0.000 description 1
- 238000002485 combustion reaction Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000005262 decarbonization Methods 0.000 description 1
- 238000007599 discharging Methods 0.000 description 1
- 230000007717 exclusion Effects 0.000 description 1
- 239000003546 flue gas Substances 0.000 description 1
- 239000002803 fossil fuel Substances 0.000 description 1
- 229910021389 graphene Inorganic materials 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 238000009776 industrial production Methods 0.000 description 1
- 239000004571 lime Substances 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000009766 low-temperature sintering Methods 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000006386 neutralization reaction Methods 0.000 description 1
- 229910052757 nitrogen Inorganic materials 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000035515 penetration Effects 0.000 description 1
- 239000012466 permeate Substances 0.000 description 1
- 239000004033 plastic Substances 0.000 description 1
- 238000010791 quenching Methods 0.000 description 1
- 230000000171 quenching effect Effects 0.000 description 1
- 238000004064 recycling Methods 0.000 description 1
- 238000001356 surgical procedure Methods 0.000 description 1
- 230000009897 systematic effect Effects 0.000 description 1
- 239000011269 tar Substances 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
- 239000002918 waste heat Substances 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/04—Manufacturing
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Marketing (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Entrepreneurship & Innovation (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Game Theory and Decision Science (AREA)
- Primary Health Care (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Manufacturing & Machinery (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The application relates to a carbon emission reduction system optimization method, device, equipment, medium and program product. The method comprises the following steps: firstly, obtaining decision variable parameters of a production link of the carbon emission reduction system, then determining purchasing cost, production cost, indirect emission reduction technology cost, carbon capture technology cost, self-contained power plant technology cost and project income of the production link according to the decision variable parameters, thereby determining an objective function of an optimization model of the carbon emission reduction system, and finally optimizing the objective function according to preset constraint conditions. By adopting the method, the carbon emission reduction cost of each link of production and manufacture can be comprehensively considered, and a specific link is not adopted, so that the overall planning of the carbon emission reduction system of the iron and steel enterprise can be realized.
Description
Technical Field
The application relates to the technical field of energy conservation and emission reduction, in particular to a carbon emission reduction system optimization method, a device, equipment, a medium and a program product.
Background
The steel industry is used as an important component of industry, and has large energy consumption and high carbon emission. CO from fossil fuel consumption in the iron and steel industry 2 The (carbon dioxide) emission is about 1/8 of the national, 1/4 of the industrial and 1/2 of the process industry. In order to achieve the aim of carbon neutralization, the steel industry is under tremendous pressure and must go through the development path of "low carbon-carbon reduction-decarbonization". Therefore, research on carbon emission reduction in the steel industry is required.
However, the current carbon emission reduction system has limitations, and cannot realize the overall planning of the production and the manufacture of iron and steel enterprises.
Disclosure of Invention
Based on this, it is necessary to provide a method, an apparatus, a device, a medium and a program product for optimizing a carbon emission reduction system, which comprehensively consider each link of production and manufacture.
In a first aspect, the present application provides a method for optimizing a carbon emission reduction system, the method comprising: obtaining decision variable parameters of the production link of the carbon emission reduction system; determining purchasing cost, production cost, indirect emission reduction technology cost, carbon capture technology cost, self-contained power plant technology cost and project income of a production link according to the decision variable parameters, so as to determine an objective function of a carbon emission reduction system optimization model; and optimizing the objective function according to a preset constraint condition.
In one embodiment, the preset constraint conditions include a mass balance constraint condition, a capacity constraint condition, an exclusion constraint condition of an energy saving and emission reduction technology, a dependency constraint condition of the energy saving and emission reduction technology, a carbon emission reduction constraint condition, an integer constraint condition and a non-negative constraint condition, and optimizing the objective function according to the preset constraint conditions, including: under the condition that a preset constraint condition is met, determining the minimum value of the objective function; and determining the value of each decision variable parameter according to the minimum value of the objective function.
In one embodiment, the production cost is determined based on the investment cost of the production facility, the variable operating cost of the production facility, and the fixed operating cost of the production facility for each process of the production link.
In one embodiment, the cost of the indirect emission reduction technology is determined according to the construction investment cost of the energy saving and emission reduction technology corresponding to each process of the production link, the variable operation cost of the energy saving and emission reduction technology, and the fixed operation cost of the energy saving and emission reduction technology.
In one embodiment, the carbon capture technology includes at least one of a chemical absorption separation method, a pressure swing adsorption method, a membrane absorption and transmission method, and a low temperature separation and combination method; the carbon capture technology costs are determined based on the purchase costs of the carbon capture equipment, the variable operating costs of the carbon capture process, and the fixed operating costs.
In one embodiment, the self-contained power plant technology cost is determined based on the equipment investment cost of the generator set, the variable operating cost of the generator set, and the fixed operating cost of the generator set.
In a second aspect, the present application further provides a carbon emission reduction system optimization apparatus, the apparatus comprising:
the acquisition module is used for acquiring decision variable parameters of the production link of the carbon emission reduction system;
The determining module is used for determining purchasing cost, production cost, indirect emission reduction technology cost, carbon capture technology cost, self-contained power plant technology cost and project income of a production link according to the decision variable parameters so as to determine an objective function of the carbon emission reduction system optimization model;
and the optimization module is used for optimizing the objective function according to preset constraint conditions.
In a third aspect, the present application further provides a computer device, including a memory and a processor, where the memory stores a computer program, and the processor implements the carbon emission reduction system optimization method according to any one of the first aspects when executing the computer program.
In a fourth aspect, the present application further provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the carbon emission reduction system optimization method according to any one of the first aspects.
In a fifth aspect, the present application also provides a computer program product comprising a computer program which, when executed by a processor, implements the carbon emission reduction system optimization method of any one of the first aspects.
According to the carbon emission reduction system optimization method, device, equipment, medium and program product, firstly, the decision variable parameters of the production link of the carbon emission reduction system are obtained, then the purchasing cost, the production cost, the indirect emission reduction technology cost, the carbon capture technology cost, the self-contained power plant technology cost and the project income of the production link are determined according to the decision variable parameters, so that the objective function of the carbon emission reduction system optimization model is determined, and finally, the objective function is optimized according to the preset constraint condition. In this way, purchasing cost, production cost, indirect emission reduction technology cost, carbon capture technology cost, self-contained power plant technology cost and project income of a production link are comprehensively considered in the carbon emission reduction system to determine an objective function, then the objective function is optimized according to preset constraint conditions of the production link, and carbon emission reduction cost of each link of production and manufacture is comprehensively considered instead of a specific link, so that the overall planning of the carbon emission reduction system of a steel enterprise can be realized.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the related art, the drawings that are required to be used in the embodiments or the related technical descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to the drawings without inventive effort for a person having ordinary skill in the art.
FIG. 1 is a schematic flow chart of a method for optimizing a carbon abatement system in one embodiment;
FIG. 2 is a schematic flow chart of a method for optimizing a carbon abatement system in another embodiment;
FIG. 3 is a block diagram of a method of optimizing a carbon abatement system in one embodiment;
FIG. 4 is a block diagram of an example carbon emission reduction system optimization device;
fig. 5 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
The current research on the carbon emission system of the iron and steel enterprises still has the following problems, the research is focused on realizing the measurement, calculation and prediction of the carbon emission in the production process of the iron and steel enterprises, the main research content is an indirect energy-saving and emission-reducing technology, and the research on direct energy-saving and emission-reducing technologies such as carbon trapping is less. Most of researches are optimized aiming at specific links or processes in the production process, but carbon emission reduction is a systematic problem related to the whole production process, and the interaction influence among links needs to be comprehensively considered to realize the overall optimization rather than the local optimization of the carbon emission reduction system. The actual production environment of iron and steel enterprises is complex, a single energy-saving and emission-reduction technology often has great limitation, and different energy-saving and emission-reduction technologies mutually influence, mutually permeate, mutually promote or repel, so that the influence on the emission-reduction effect and the economic index of the energy-saving and emission-reduction technology is remarkable. Meanwhile, the existing carbon emission reduction cost model is generally biased to reflect macroscopic indexes, and has weak specific guidance on implementing carbon emission reduction for microcosmic enterprises.
Based on the above, in the embodiment of the application, the purchasing cost, the production cost, the indirect emission reduction technology cost, the carbon capture technology cost, the self-contained power plant technology cost and the project income of the production link are comprehensively considered in the carbon emission reduction system to determine the objective function, then the objective function is optimized according to the preset constraint condition of the production link, and the carbon emission reduction cost of each link of production and manufacture is comprehensively considered instead of a certain specific link, so that the overall planning of the carbon emission reduction system of the iron and steel enterprise can be realized.
In an exemplary embodiment, as shown in fig. 1, a method for optimizing a carbon emission reduction system is provided, and the method is used for a terminal to be described as an example, it is understood that the method can also be applied to a server, and can also be applied to a system including the terminal and the server, and is implemented through interaction between the terminal and the server. The method comprises the following steps:
and step 101, obtaining decision variable parameters of a production link of the carbon emission reduction system.
The decision variable parameters can be variables which can be changed according to carbon emission reduction targets in different production links of the carbon emission reduction system, and in order to realize overall planning of the carbon emission reduction system of the steel enterprise, the production links of the carbon emission reduction system and the interaction influence among the production links are comprehensively considered to realize overall optimization of the carbon emission reduction system, so that the decision variable parameters of the production links of the carbon emission reduction system are required to be obtained, and the overall planning of the carbon emission reduction system is realized by determining the values of the decision variable parameters. Optionally, different production links have different decision variable parameters, and for convenience of reference of readers, definition of decision variable used in the application is listed as follows.
1. The subscripts of the variable parameters referred to in this application are as follows.
t: project planning period (in years);
f: different production flows;
l: a production process;
j: the indirect emission reduction technology type adopted by different production procedures;
v: types of outsourcing materials, wherein the outsourcing materials can be divided into three categories: a: all depending on the raw materials purchased externally b: product outsourcing raw material category, c: electric power;
h: purchasing scale;
g: grade of outsourcing raw materials;
d: a transportation distance;
m: grade of the product/by-product produced;
n: the grade of the consumed fuel;
i: different production technology types of the same production process;
and p: the product types generated in the production procedure;
o: a trapped air source;
e: a trapping technique;
u: the trapping efficiency;
k: the type of power generation of the self-contained power plant;
s: the type of byproduct;
θ，two indirect emission reduction techniques with exclusive relationships;
alpha, beta: two indirect emission reduction techniques with a dependency relationship;
ζ: after the indirect emission reduction technology alpha is implemented, the change coefficient of the energy saving and emission reduction capacity of the beta technology.
2. The meaning of decision variables in this application is as follows.
RM t,f,l,v,h,g,d : scale g grade of process h in process flow l in t period v raw material purchase amount (ton) at d distance;
P t,f,l,i,p,m : production (ton) of m-grade p products produced by the process i technology in the process of the flow path of the t period f;
YTP t,f,l,i,p,m : whether the capacity of production equipment for producing m-grade p products by the process i technology is expanded (0-1) or not is carried out in the t period f;
YTR t,f,l,j : t period f flow l process j technique is whether to expand capacity (0-1);
C t,f,l,o,e,u : capturing the capturing amount (ton) of the e capturing technology of the f process step o gas source with u efficiency at the t period;
YTC t,f,l,o,e,u : in the t period, the e trapping technology for trapping the f process step I gas source with u efficiency is used for expanding the capacity (0-1);
E t,k,n,m : the k power generation type in the period t consumes the power generation amount (MW) of the self-contained power plant with n grade fuel power generation grade m;
YTE t,k,n,m : whether the capacity of the self-contained power plant (0-1) consumes n grade fuel power generation grade m in the period of t and the power generation type of k;
SP t,f,l,i,p,m : the sales (ton) of m grade p products produced by the process i technology in the process of the flow path of the t period;
SBP t,f,l,i,s,m : sales (ton) of m grade s by-products produced by the process i technology in the process of the flow path of the t period f;
SE t,k,n,m : the k power generation type consumes the sales (MW) of electricity of n grade fuel power generation grade m in the t period;
INP t-1,f,l,i,p,g : the stock quantity (ton) of the g-grade p product produced by the process i technology in the process of the flow path l in the period t-1;
INR t-1,f,l,v＝a,g : the process of the flow path l in the period t-1 is totally dependent on the stock quantity (ton) of the outsourced g-grade a raw materials;
INB t-1,f,l,i,s,m : the stock quantity (ton) of m grade s byproducts produced by the process i technology of the flow path l at the time of t-1;
INE t,n : the stock quantity (ton) of n-grade electricity in t period.
3. Model parameters
PRM t,f,l,v,h,g,d : distance of scale g grade d of process h in process of f flow path in t period v raw material purchase price (yuan/ton);
TP t,f,l,i,p,m : production of m grade p product by t period f process i technical plant capacity (tons/year);
ETP t,f,l,i,p,m : production of m grade p product by t period f process i capacity expansion capacity (tons/year) of technical equipment;
STP t-1,f,l,i,p,m : the loss capacity (ton/year) of i technical equipment for producing m grade p products in the process of the flow l in the period t-1;
VCP t,f,l,i,p,m : the unit variable operation cost (yuan/ton) of the m grade p product produced by the process i technology of the process i of the flow path f in the period t;
FCP t,f,l,i,p,m : the unit fixed operation cost (yuan/ton) of the m grade p product produced by the process i technology of the process i of the flow path f in the period t;
ICP t,f,l,i,p,m : the unit investment cost (yuan/ton) of the production of m grade p products by the process i technology of the process i in the t period f;
R t,f,l,j : t period f flow path l procedure j energy saving and emission reduction (ton);
TR t,f,l,j : energy saving and emission reduction of t-period f-flow l-procedure j technologyCapacity (tons/year);
ETR t,f,l,j : capacity expansion capacity (ton/year) of process j technology in the process of the flow of the process of the step f in the period of t;
STR t,f,l,j : the lost capacity (ton/year) of the process j technique of the process flow i at the t period;
VCR t,f,l,j : variable running cost per unit (yuan/ton) of the t-period f-flow l-process j technique;
FCR t,f,l,j : the unit fixed operation cost (yuan/ton) of the process j technology of the process flow of the period f;
ICR t,f,l,j : the unit investment cost (yuan/ton) of the process j technology of the process flow of the period f of t;
TC t,f,l,o,e,u : the trapping capacity (ton/year) of the e trapping technology for trapping the f-process i-process o-gas source with u-efficiency at the t-time;
ETC t,f,l,o,e,u : the capturing expansion scale (ton/year) of the e capturing technology for capturing the f process step o gas source with u efficiency in the t period;
STC t-1,f,l,o,e,u : trapping the process of f flow path l with the efficiency of o gas source e in the period of t-1 the amount of loss of capture capacity (tons/year) of the capture technology;
VCC t,f,l,o,e,u : variable operation cost (yuan/ton) of the e trapping technology for trapping the f-process/process o-gas source with u-efficiency at the t-time;
FCC t,f,l,o,e,u : the fixed operation cost (yuan/ton) of the e trapping technology for trapping the f process step o gas source with u efficiency in the t period;
ICC t,f,l,o,e,u : the construction cost (yuan/ton) of the e trapping technology for trapping the f process step o gas source with u efficiency at the t time;
TE t,k,n,m : the power generation type of the k power generation type in the t period consumes the power generation capacity (MW) of the self-contained power plant with n grade fuel power generation grade m;
ETE t,k,n,m : the power generation type k in the period t consumes the capacity expansion capacity (MW/year) of the self-contained power plant with the power generation grade m of the n-grade fuel;
STE t-1,k,n,m : the loss of the power generation capacity (MW/year) of the self-contained power plant, which consumes n grade fuel power generation grade m, of the k power generation type in the period t-1;
VCE t,k,n,m : the unit variable operation cost (meta MW) of the self-contained power plant with the k power generation type consuming n-grade fuel power generation grade m in the t period;
FCE t,k,n,m : the unit fixed operation cost (yuan/MW) of the self-contained power plant with the k power generation type consuming n-grade fuel power generation grade m in the t period;
ICE t,k,n,m : the k power generation type in the t period consumes the unit investment cost (yuan/MW) of the self-contained power plant with n grade fuel power generation grade m;
CO t : carbon dioxide emission reduction (ton) at time t;
PP t,f,l,i,p,m : the unit selling price (yuan/ton) of p products of the technology m grade of the process i in the t period f;
PBP t,f,l,i,s,m : the unit sales price (yuan/ton) of the m grade s byproduct produced by the process i technology of the process i in the t period f;
PE t,k,n,m : the k power generation type consumes the electricity unit selling price (Yuan/kWh) of the n-grade fuel power generation grade m in the t period;
PCO t : CO at t period 2 Unit price of reduced displacement (yuan/ton);
AR t,f,l,j : t period f flow l procedure j unit subsidy price (yuan/ton) of emission reduction technology;
AC t,f,l,o,e,u : the unit subsidy price (yuan/ton) of the e trapping technology for trapping the o gas source in the process u efficiency of the process f in the t period;
DP t,f,l,i,p,g : the technology of the process i of the process flow of the period f of t produces the demand (ton) of the p product with the grade of g;
ζ t,f,l,i,p,g : conversion coefficient of the product, i.e. the yield (ton) of the g-grade P product produced by the l process i technology of the unit product consumption of the production unit of the f process P process in the t period;
BP t,f,l,i,s,m : the yield (ton) of m grade s byproducts produced by the process i technology in the process of the flow path i in the t period;
CBP t,f,l,i,p,m,n : the consumption (ton) of the unit p products with m grades to the n grade byproducts is produced by the process i technology of the process i in the t period f;
CBR t,f,l,j,n : t period f flow l procedure i technical unit energy saving the consumption (ton) of n grade byproducts;
CBC t,f,l,o,e,u,n : capturing unit CO by using e capturing technology for capturing f process step o gas source in u efficiency in t period 2 Consumption (ton) of n-grade byproducts;
CBE t,k,n,m : the consumption (ton) of n-grade byproducts when the self-contained power plant generating m-grade electricity in the period of t generates electricity in the type of k generation;
η t,f,l,i,p,m,s : the byproduct quantity generated by the production of m-grade unit p products by the process i technology of the process i in the t period f, namely a byproduct conversion coefficient (ton/ton);
ER t,f,l,j,n : the generated n-grade electric quantity (ton) of the energy saving and emission reduction technology in the process j of the process flow of the period f;
CEP t,f,l,i,p,m,n : the technology of the process i of the process f of the period t produces the consumption (ton) of n-grade electricity by the unit p product of m grade;
CER t,f,l,j,n : t period f flow l procedure j technical unit the energy-saving quantity consumes n-grade electricity (ton);
CBC t,f,l,o,e,u,n : capturing unit CO by using e capturing technology for capturing f process step o gas source in u efficiency in t period 2 Consumption of n-grade electricity (ton);
OC t,f,l,o : CO in f-process l-process o-gas source captured in t period 2 Content (%);
RCO t,f,l,j : energy saving and emission reduction of process j in process f in t period technology-generated displacement reduction (ton);
OP t,f,l,i,p,m : the actual production amount (ton) of the m grade p product produced by the process i technology in the process of the flow path of the t period f;
TCO t : the target carbon dioxide emission (tons) at time t is reduced.
And 102, determining purchasing cost, production cost, indirect emission reduction technology cost, carbon capture technology cost, self-contained power plant technology cost and project income of a production link according to the decision variable parameters, so as to determine an objective function of the carbon emission reduction system optimization model.
The purchasing cost, the production cost, the indirect emission reduction technology cost, the carbon capture technology cost, the self-contained power plant technology cost and the project income of the production links can be determined according to the decision variable parameters, so that a multi-objective optimization model of the carbon emission reduction system can be determined according to the determined cost and project income of each production link, namely, each production link is optimized as a multi-objective. The objective function may be the total cost of the carbon abatement system, alternatively, the objective function may be the sum of the costs of the various production links of the carbon abatement system and the difference in project returns.
And step 103, optimizing the objective function according to a preset constraint condition.
The preset constraint conditions can be various constraint conditions of a production link, such as a mass balance constraint condition, a production capacity constraint condition or rejection constraint conditions of various energy saving and emission reduction technologies. The objective function is optimized when the preset constraint condition is satisfied, alternatively, the minimum value of the objective function may be determined when the preset constraint condition is satisfied, that is, even if the total cost of the carbon emission reduction system is minimum, or the difference between the objective function and the preset cost threshold may be determined when the preset constraint condition is satisfied to conform to a preset difference range, that is, even if the cost of the carbon emission reduction system is controlled within an acceptable range. By optimizing the objective function, the emission reduction task of the current carbon dioxide can be realized by the iron and steel enterprises according to the emission reduction technical route in the planning period, so that a scientific basis is provided for the comprehensive management of the enterprises.
According to the embodiment, firstly, the decision variable parameters of the production link of the carbon emission reduction system are obtained, then the purchasing cost, the production cost, the indirect emission reduction technology cost, the carbon capturing technology cost, the self-contained power plant technology cost and the project income of the production link are determined according to the decision variable parameters, so that the objective function of the optimization model of the carbon emission reduction system is determined, and finally, the objective function is optimized according to the preset constraint conditions. In this way, the purchasing cost, the self-contained power plant technical cost, the carbon trapping technical cost, the production cost, the indirect emission reduction technical cost and the project income of the production link are comprehensively considered in the carbon emission reduction system to determine the objective function, then the objective function is optimized according to the preset constraint condition of the production link, and the carbon emission reduction cost of each link of production and manufacture is comprehensively considered instead of a certain specific link, so that the overall planning of the carbon emission reduction system of the iron and steel enterprise can be realized.
In the embodiment of the application, in the production process of steel, raw materials, fuel and power are required to be used for the production of products in each link. Outsourcing is also required when the yields of the produced products such as coke, sinter, etc. do not meet the demands of the next process. And the purchase price of different raw materials and auxiliary materials and power is different according to the different purchase time, purchase scale, grade and transportation distance. Therefore, the main cost of purchase cost comprises purchase cost of raw materials, auxiliary materials and fuel, and the cost of raw materials purchased outside the system C 1 The specific calculation formula is shown below.
In one embodiment, the production cost is determined based on the investment cost of the production facility, the variable operating cost of the production facility, and the fixed operating cost of the production facility for each process of the production link.
The production process comprises coking, sintering, blast furnace ironmaking, converter steelmaking, electric furnace steelmaking, continuous casting, steel rolling and other processes. The products produced by each process have main products and byproducts. In the coking process, the main product is coke, and the byproducts include coke oven gas, tar, crude benzene and the like. In the sintering process, the main product is sintered ore. In the blast furnace ironmaking process, the main product is molten iron, and the byproduct mainly comprises blast furnace gas. In the steelmaking process, the main product is crude steel, and the byproduct is converter gas. In the continuous casting process, the main product is cast steel. In the rolling process, various steel products are produced. The main product of each production process is used as raw material to enter the next production process, if the rest is left, the main product is used asThe period stock is stored or sold. The secondary energy generated in each process can be used for the production process, and plays a role in energy conservation and emission reduction. For other byproducts, the byproducts can be directly sold outwards to generate benefits. From the above, the production cost is determined according to the investment cost of the production equipment in each process of the production link, the variable operation cost and the fixed operation cost in the process of producing the product by the production equipment, and the production cost C 2 The calculation of (2) is as follows.
TP t,f,l,i,p,m ＝TP t-1,f,l,i,p,m +YTP t-1,f,l,i,p,m ×ETP t-1,f,l,i,p,m -STP t-1,f,l,i,p,m (3)
In one embodiment, the cost of the indirect emission reduction technology is determined according to the construction investment cost of the energy saving and emission reduction technology corresponding to each process of the production link, the variable operation cost of the energy saving and emission reduction technology, and the fixed operation cost of the energy saving and emission reduction technology.
The indirect emission reduction technology mainly refers to emission reduction through a technology for reducing energy consumption, and is called an energy saving and emission reduction technology. At present, most of steel production mainly comprises a long process, the yield of coarse steel accounts for more than 90% of the total yield, and the carbon emission per ton of steel in the long process is far greater than the emission in the short process of an electric furnace. Therefore, research on carbon emission reduction of combined iron and steel enterprises, mainly in long-process production, is of great importance to the low-carbon development of the iron and steel industry. The iron and steel enterprises mainly produced by a long process mainly comprise the procedures of raw material purchase, coking, sintering, blast furnace ironmaking, converter steelmaking, continuous casting, steel rolling, self-contained power plants and the like, and the procedures have corresponding indirect emission reduction countermeasures. In each process of steel production, the energy consumption of coking, sintering and blast furnace ironmaking processes accounts for more than 70% of the energy consumption of the whole production flow, so that the pre-iron process is an important point of CO2 emission reduction of combined steel enterprises.
For the coking process, the selectable energy-saving and emission-reduction technologies include a coal humidifying technology, a tamping coking technology, a high-temperature high-pressure dry quenching technology and the like; for the sintering process, selectable energy-saving and emission-reduction technologies include a pellet sintering process technology, a sintering air leakage rate reducing technology, a low-temperature sintering process technology, a thick material layer sintering technology, a pellet waste heat recycling technology and the like; for the iron-making process, selectable energy-saving and emission-reducing technologies include blast furnace iron-making precision technology, blast furnace dense-phase high-efficiency coal injection technology, blast furnace top gas dry residual pressure power generation technology, blast furnace waste plastic injection technology, blast furnace coke oven gas injection technology, blast furnace slag comprehensive utilization technology and the like; for the steelmaking process, the selectable energy-saving and emission-reducing technologies include a negative energy steelmaking technology, a converter flue gas high-efficiency utilization technology and the like; and comprehensive technologies such as an energy management center, an optimal regulation technology, a gas-steam combined cycle power generation technology and the like are also provided.
When the steel enterprises select the emission reduction technology, the emission reduction effect and the economic index of the technology are often emphasized, but single emission reduction has great limitation. The various emission reduction techniques interact, interpenetrate, promote or repel each other. From a different perspective, emission reduction techniques have different structures, and thus emission reduction combinations may be classified differently. According to the technical principle and research data analysis, the relations among various technologies are divided into two main categories, namely, the dependence among technologies and the rejection among technologies. Therefore, in order to obtain better emission reduction effect, iron and steel enterprises should pay attention to develop a series of related emission reduction technologies matched with each other so as to form a group. Specifically, the cost of the indirect emission reduction technology is determined according to the construction investment cost of the energy saving and emission reduction technology corresponding to each procedure of the production link, the variable operation cost of the energy saving and emission reduction technology and the fixed operation cost of the energy saving and emission reduction technology.
Cost C of indirect emission reduction technology 3 The calculation formula of (2) is shown below.
TR t,f,l,j ＝TR t-1,f,l,j +YTR t-1,f,l,j ×ETR t-1,f,l,j -STR t-1,f,l,j (5)
In one embodiment, the carbon capture technology includes at least one of a chemical absorption separation method, a pressure swing adsorption method, a membrane absorption and transmission method, and a low temperature separation and combination method; the carbon capture technology costs are determined based on the purchase costs of the carbon capture equipment, the variable operating costs of the carbon capture process, and the fixed operating costs.
Wherein the carbon capture technology refers to the process of CO generation 2 A technique for collecting and disposing of it, avoiding its emission to the atmosphere. Currently, CO of iron and steel enterprises 2 The emission sources mainly comprise blast furnace gas, hot blast stove tail gas and lime kiln tail gas. Different economies will be exhibited for the same carbon capture technology in capturing CO2 in different exhaust gas sources, while the economies of the carbon capture technology are also affected by the capture scale. In addition, different CO2 emission sources adopt different carbon trapping technologies, and the trapping efficiency is also different. Since the three sources of CO2 emissions vary in gas composition, concentration, temperature, pressure, etc., different carbon capture technologies need to be selected for different sources of gas. Alternatively, the carbon capture technology used in the present application is mainly post-combustion capture technology, including: at least one of a chemical absorption separation method, a pressure swing adsorption method, a membrane absorption method, a membrane separation and a low temperature separation combination method. The carbon capture technology costs are determined based on the purchase costs of the carbon capture equipment, the variable operating costs of the carbon capture process, and the fixed operating costs. Specifically, the carbon capture technology cost C 4 The calculation formula of (2) is shown below.
TC t,f,l,o,e,u ＝TC t-1,f,l,o,e,u +YTC t-1,f,l,o,e,u ×ETC t-1,f,l,o,e,u -STC t-1,f,l,o,e,u (7)
In the embodiment, the carbon trapping direct emission reduction technology is introduced, so that the optimization performance of the carbon emission system is improved.
In one embodiment, the self-contained power plant technology cost is determined based on the equipment investment cost of the generator set, the variable operating cost of the generator set, and the fixed operating cost of the generator set.
In order to provide steam and electric power for iron and steel enterprises, large and medium-sized iron and steel enterprises generally build self-contained power plants, the self-contained power plants can reduce the electricity cost, and the energy conservation, the emission reduction and the economic benefit improvement are good. The technical cost of the self-contained power plant is determined according to the equipment investment cost of the generator set, the variable operation cost of the generator set and the fixed operation cost of the generator set, and is specifically the self-contained power plant cost C 5 The calculation formula of (2) is shown below.
TE t,k,n,m ＝TE t-1,k,n,m +YTE t-1,k,n,m ×ETE t-1,k,n,m ×STE t-1,k,n,m (9)
Optionally, the project benefits mainly comprise benefits generated by selling products and byproducts, benefits generated by taking carbon emission reduction measures by iron and steel enterprises, subsidies, carbon emission trading right subsidies, national fund support and the like. The products sold mainly comprise coke, sinter, molten iron and crude steel, and the byproducts mainly comprise blast furnace gas, coke oven gas, converter gas, crude benzene, coal tar, steel slag and the like. Meanwhile, the carbon trapping technology not only can trap CO 2 At the same time can separate N 2 (Nitrogen), sell N 2 But may also be a part of the revenue. Meanwhile, CO researched in carbon emission reduction system optimization model 2 Emission reduction to CO 2 The clean emission reduction is realized, and CO generated by the increase of emission reduction technology is eliminated 2 And (5) discharging. Specifically, project benefit C 6 The calculation formula of (2) is shown below.
In the embodiment of the present application, the preset constraint conditions include a mass balance constraint condition, a capacity constraint condition, an rejection constraint condition of an energy saving and emission reduction technology, a dependency constraint condition of an energy saving and emission reduction technology, a carbon emission reduction constraint condition, an integer constraint condition and a non-negative constraint condition, and optimizing the objective function according to the preset constraint conditions, as shown in fig. 2 includes:
in step 201, in case that a preset constraint condition is satisfied, a minimum value of the objective function is determined.
Wherein, the minimum value of the objective function under all preset constraint conditions, namely, the minimum cost of the carbon emission reduction system is determined, and the minimum cost is shown in the following formula.
min(C 1 +C 2 +C 3 +C 4 +C 5 -C 6 ) (11)
Step 202, determining the value of each decision variable parameter according to the minimum value of the objective function.
And determining the value of each decision variable parameter according to the minimum value of the objective function, namely determining the specific planning of each production link in the carbon emission reduction system.
Wherein the mass balance constraint conditions comprise product supply and demand balance, outsourcing raw material supply and demand balance, byproduct supply and demand balance, electric supply and demand balance and CO 2 Is a mass balance of (a). Specifically, the following is shown.
(1) The product supply and demand balance, the sum of the product yield and the outsourcing amount of the product raw materials and the stock amount in the previous period should be larger than the sum of the demand amount and sales amount of other working procedures and the stock amount in the period, and the sum is specifically shown in the following formula.
DP t,f,l,i,p,g ＝P t,f,l,i,p,m ×ζ t,f,l,i,p,g (13)
(2) The balance of supply and demand of outsourcing raw materials depends on the sum of the outsourcing amount of the outsourcing raw materials and the stock amount in the previous period, and the sum of the demand of other processes and the stock amount in the previous period is more than or equal to the sum, and the specific formula is shown below.
(3) The balance of the supply and demand of the byproducts, the sum of the yield of the byproducts (excluding electricity) and the stock quantity in the previous period is more than or equal to the sum of the consumption of each process, the sales of the energy-saving and emission-reduction technology, the consumption of the trapping technology, the sales and the stock quantity in the previous period, and the specific formula is shown in the specification.
BP t,f,l,i,p,m,n ＝P t,f,l,i,p,m ×η t,f,l,i,p,m,s (16)
(4) The sum of the electricity supply and demand balance, the electricity outsourcing amount, the self-contained power plant production amount, the energy-saving and emission-reduction technology power generation amount and the upper period stock amount is more than or equal to the sum of the product production consumption amount, the energy-saving and emission-reduction technology consumption amount, the carbon capture technology consumption amount, the sold electric quantity and the period stock amount, and the specific formula is shown below.
(5)CO 2 Is mass balanced, CO trapped by carbon trapping technology 2 The amount cannot exceed the CO in the capture source 2 The content is shown in the following formula.
Optionally, the capacity constraints include capacity constraints of production technology, capacity constraints of indirect emission reduction technology, capacity constraints of carbon capture technology, and capacity constraints of self-contained power plants.
(1) The capacity constraints of the production technology, for each period, the actual throughput of that period cannot exceed the capacity of the production facility, as shown in the following formula.
TP t,f,l,i,p,m +YTP t,f,l,i,p,m ×ETP t,f,l,i,p,m -STP t,f,l,i,p,m ≥OP t,f,l,i,p,m (19)
(2) The capacity constraint of the indirect emission reduction technology aims at the fact that the capacity of the indirect emission reduction equipment is larger than or equal to the actual emission reduction capacity of the indirect emission reduction technology in each period, and the method is specifically shown in the following formula.
TR t,f,l,j +YTR t,f,l,j ×ETR t,f,l,j -STR t-1,f,l,j ≥RCO t,f,l,j (20)
(3) Capacity constraints of carbon capture technology due to CO in the system 2 The content of (C) is gradually reduced, so that the capturing capacity of the carbon capturing device in the t-1 period is ensured to be larger than that of CO in the t period 2 The collection amount is specifically represented by the following formula.
TC t,f,l,o,e,u ≥C t,f,l,o,e,u (21)
(4) The capacity constraint of the self-contained power plant, the maximum power generation capacity of the self-contained power plant should be greater than the actual power generation capacity, as shown in the following formula.
TE t,k,n ≥E t,k,n (22)
In the face of complex actual production environment of iron and steel enterprises, a single energy-saving and emission-reduction technology often has great limitation, and the conditions of mutual influence, mutual penetration, mutual promotion or rejection exist among different energy-saving and emission-reduction technologies, so that constraint conditions exist among different energy-saving and emission-reduction technologies need to be considered.
The method comprises rejection constraint of the emission reduction technology and dependency constraint of the emission reduction technology.
And the rejection constraint of the emission reduction technology is that the technology with the rejection relation only implements one term, and the specific formula is shown in the following description, assuming that theta and phi are indirect emission reduction technologies with the rejection relation.
And the dependency constraint of the emission reduction technology is assumed that alpha and beta are indirect emission reduction technologies with dependency relationship, and when the indirect emission reduction technology alpha is implemented, the energy saving and emission reduction capacity of the indirect emission reduction technology beta is improved.
The preset constraint condition also comprises CO 2 And the sum of the emission reduction constraint, the carbon trapping amount and the emission reduction generated by the energy-saving emission reduction technology is more than or equal to the target emission reduction. Target emission reduction is target CO of carbon emission reduction system 2 Is shown in the following formula.
CO t ≥TCO t (25)
The integer constraint in the preset constraint condition is specifically shown as follows.
And restraining each procedure for the production module to expand only once in the evaluation period.
And the energy-saving and emission-reduction module is used for restraining each procedure to expand only once in the evaluation period.
And restraining each procedure of the self-contained power plant module to expand the capacity only once in the evaluation period.
The non-negative constraint in the preset constraint conditions is specifically shown as follows.
RM t,f,l,v,h,g,d ≥0,P t,f,l,i,p,m ≥0,P t,f,l,i,p,m ≥0,C t,f,l,o,e,u ≥0,E t,k,n ≥0 (31)
In one embodiment, as shown in fig. 3, the influence factors of the carbon emissions of the iron and steel industry are summarized by analyzing the carbon emissions of the iron and steel industry. The iron and steel industry is an energy-intensive industry, consumes a large amount of fossil energy, also consumes power, steam and other energy sources, and is a typical 'two-high-one-resource' industry, namely, the iron and steel industry has the characteristics of high energy consumption, high pollution and high resource consumption. The yield of the crude steel is an important factor for determining the carbon emission in the steel industry. Currently, the production of coarse steel in the steel industry still has two typical characteristics of an industrial production mode mainly comprising a long production flow and an energy structure mainly comprising coal, which have great influence on energy consumption and carbon emission in the steel industry. According to the related research, the carbon dioxide emission in the iron and steel industry mainly comes from links of blast furnace ironmaking, sintering, converter steelmaking, coking and the like from the aspect of contribution degree of production procedures, wherein the carbon dioxide emission in the procedures is respectively 72%, 13%, 9% and 5%, and the carbon dioxide emission generated in the pre-iron process accounts for about 90% of the proportion of the total emission of the iron and steel enterprises. Therefore, the pre-iron step is CO of iron and steel enterprises 2 And the key point of emission reduction. Therefore, a multi-objective optimization model of the carbon emission reduction system of the iron and steel enterprise is established to realize the overall planning of the carbon emission reduction system of the iron and steel enterprise. Optionally, the multi-objective optimization model of the carbon emission reduction system of the iron and steel enterprise can be composed of the following six modules: purchasing module, self-contained power plant technical module and carbon capturing technologyThe system comprises a surgery module, a production module, an indirect emission reduction technology module and a project income module. The modules mutually influence and feed back, and meanwhile, the technical modules comprise a plurality of different technical choices to form mutual competition or coexistence-dependent mutual relation. The cost of each module is calculated, an objective function is determined, and the total cost of the carbon emission reduction system is minimized under the condition that the preset constraint condition is met, so that decision variables in the carbon emission reduction system are determined, the minimum technical emission reduction cost under the condition that the established carbon emission reduction target is completed is realized, and a scientific basis is provided for the emission reduction decision support of iron and steel enterprises. Further, effective support can be provided for the iron and steel enterprises to determine self carbon emission reduction cost and the future iron and steel industries to participate in carbon transaction and carbon pricing. Furthermore, by establishing a multi-objective optimization model of the carbon emission reduction system of the iron and steel enterprise, the enterprise is guided to carry out decision analysis on investment of the carbon reduction project, economic benefits are considered while carbon reduction is realized, and the carbon emission reduction is achieved to the greatest extent with the minimum investment.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides a carbon emission reduction system optimizing device for realizing the carbon emission reduction system optimizing method. The implementation of the solution provided by the device is similar to the implementation described in the above method, so the specific limitation in the embodiments of the carbon emission reduction system optimization device or devices provided below may be referred to the limitation of the carbon emission reduction system optimization method hereinabove, and will not be repeated herein.
In one exemplary embodiment, as shown in FIG. 4, a carbon abatement system optimization apparatus is provided, comprising: the device comprises an acquisition module, a determination module and an optimization module, wherein:
and the acquisition module is used for acquiring the decision variable parameters of the production link of the carbon emission reduction system.
And the determining module is used for determining purchasing cost, production cost, indirect emission reduction technology cost, carbon capture technology cost, self-contained power plant technology cost and project income of the production link according to the decision variable parameters, so as to determine an objective function of the carbon emission reduction system optimization model.
And the optimization module is used for optimizing the objective function according to preset constraint conditions.
In one embodiment, the preset constraint conditions include a mass balance constraint condition, a capacity constraint condition, an rejection constraint condition of an energy saving and emission reduction technology, a dependence constraint condition of the energy saving and emission reduction technology, a carbon emission reduction constraint condition, an integer constraint condition and a non-negative constraint condition, and the optimization module is specifically configured to determine a minimum value of the objective function when the preset constraint condition is satisfied; and determining the value of each decision variable parameter according to the minimum value of the objective function.
In one embodiment, the production cost is determined based on the investment cost of the production facility, the variable operating cost of the production facility, and the fixed operating cost of the production facility for each process of the production link.
In one embodiment, the cost of the indirect emission reduction technology is determined according to the construction investment cost of the energy saving and emission reduction technology corresponding to each process of the production link, the variable operation cost of the energy saving and emission reduction technology, and the fixed operation cost of the energy saving and emission reduction technology.
In one embodiment, the carbon capture technology includes at least one of a chemical absorption separation method, a pressure swing adsorption method, a membrane absorption and transmission method, and a low temperature separation and combination method; the carbon capture technology costs are determined based on the purchase costs of the carbon capture equipment, the variable operating costs of the carbon capture process, and the fixed operating costs.
In one embodiment, the self-contained power plant technology cost is determined based on the equipment investment cost of the generator set, the variable operating cost of the generator set, and the fixed operating cost of the generator set.
The modules in the carbon emission reduction system optimizing device can be realized in whole or in part by software, hardware and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one exemplary embodiment, a computer device is provided, which may be a terminal, and an internal structure diagram thereof may be as shown in fig. 5. The computer device includes a processor, a memory, an input/output interface, a communication interface, a display unit, and an input means. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program when executed by a processor implements a method for optimizing a carbon emission reduction system. The display unit of the computer device is used for forming a visual picture, and can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be a key, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those skilled in the art that the structure shown in fig. 5 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In one exemplary embodiment, a computer device is provided comprising a memory and a processor, the memory having stored therein a computer program, the processor when executing the computer program performing the steps of: obtaining decision variable parameters of the production link of the carbon emission reduction system; determining purchasing cost, production cost, indirect emission reduction technology cost, carbon capture technology cost, self-contained power plant technology cost and project income of a production link according to the decision variable parameters, so as to determine an objective function of a carbon emission reduction system optimization model; and optimizing the objective function according to a preset constraint condition.
In one embodiment, the preset constraints include mass balance constraints, capacity constraints, rejection constraints of energy saving and emission reduction techniques, dependency constraints of energy saving and emission reduction techniques, carbon emission reduction constraints, integer constraints, and non-negative constraints, and the processor when executing the computer program further performs the steps of: under the condition that a preset constraint condition is met, determining the minimum value of the objective function; and determining the value of each decision variable parameter according to the minimum value of the objective function.
In one embodiment, the production cost is determined based on the investment cost of the production facility, the variable operating cost of the production facility, and the fixed operating cost of the production facility for each process of the production link.
In one embodiment, the cost of the indirect emission reduction technology is determined according to the construction investment cost of the energy saving and emission reduction technology corresponding to each process of the production link, the variable operation cost of the energy saving and emission reduction technology, and the fixed operation cost of the energy saving and emission reduction technology.
In one embodiment, the carbon capture technology includes at least one of a chemical absorption separation method, a pressure swing adsorption method, a membrane absorption and transmission method, and a low temperature separation and combination method; the carbon capture technology costs are determined based on the purchase costs of the carbon capture equipment, the variable operating costs of the carbon capture process, and the fixed operating costs.
In one embodiment, the self-contained power plant technology cost is determined based on the equipment investment cost of the generator set, the variable operating cost of the generator set, and the fixed operating cost of the generator set.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of: obtaining decision variable parameters of the production link of the carbon emission reduction system; determining purchasing cost, production cost, indirect emission reduction technology cost, carbon capture technology cost, self-contained power plant technology cost and project income of a production link according to the decision variable parameters, so as to determine an objective function of a carbon emission reduction system optimization model; and optimizing the objective function according to a preset constraint condition.
In one embodiment, the preset constraints include mass balance constraints, capacity constraints, rejection constraints of energy saving and emission reduction techniques, dependency constraints of energy saving and emission reduction techniques, carbon emission reduction constraints, integer constraints, and non-negative constraints, and the computer program when executed by the processor further performs the steps of: under the condition that a preset constraint condition is met, determining the minimum value of the objective function; and determining the value of each decision variable parameter according to the minimum value of the objective function.
In one embodiment, the production cost is determined based on the investment cost of the production facility, the variable operating cost of the production facility, and the fixed operating cost of the production facility for each process of the production link.
In one embodiment, the cost of the indirect emission reduction technology is determined according to the construction investment cost of the energy saving and emission reduction technology corresponding to each process of the production link, the variable operation cost of the energy saving and emission reduction technology, and the fixed operation cost of the energy saving and emission reduction technology.
In one embodiment, the carbon capture technology includes at least one of a chemical absorption separation method, a pressure swing adsorption method, a membrane absorption and transmission method, and a low temperature separation and combination method; the carbon capture technology costs are determined based on the purchase costs of the carbon capture equipment, the variable operating costs of the carbon capture process, and the fixed operating costs.
In one embodiment, the self-contained power plant technology cost is determined based on the equipment investment cost of the generator set, the variable operating cost of the generator set, and the fixed operating cost of the generator set.
In one embodiment, a computer program product is provided comprising a computer program which, when executed by a processor, performs the steps of: obtaining decision variable parameters of the production link of the carbon emission reduction system; determining purchasing cost, production cost, indirect emission reduction technology cost, carbon capture technology cost, self-contained power plant technology cost and project income of a production link according to the decision variable parameters, so as to determine an objective function of a carbon emission reduction system optimization model; and optimizing the objective function according to a preset constraint condition.
In one embodiment, the preset constraints include mass balance constraints, capacity constraints, rejection constraints of energy saving and emission reduction techniques, dependency constraints of energy saving and emission reduction techniques, carbon emission reduction constraints, integer constraints, and non-negative constraints, and the computer program when executed by the processor further performs the steps of: under the condition that a preset constraint condition is met, determining the minimum value of the objective function; and determining the value of each decision variable parameter according to the minimum value of the objective function.
In one embodiment, the production cost is determined based on the investment cost of the production facility, the variable operating cost of the production facility, and the fixed operating cost of the production facility for each process of the production link.
In one embodiment, the cost of the indirect emission reduction technology is determined according to the construction investment cost of the energy saving and emission reduction technology corresponding to each process of the production link, the variable operation cost of the energy saving and emission reduction technology, and the fixed operation cost of the energy saving and emission reduction technology.
In one embodiment, the carbon capture technology includes at least one of a chemical absorption separation method, a pressure swing adsorption method, a membrane absorption and transmission method, and a low temperature separation and combination method; the carbon capture technology costs are determined based on the purchase costs of the carbon capture equipment, the variable operating costs of the carbon capture process, and the fixed operating costs.
In one embodiment, the self-contained power plant technology cost is determined based on the equipment investment cost of the generator set, the variable operating cost of the generator set, and the fixed operating cost of the generator set.
It should be noted that, the user information (including, but not limited to, user equipment information, user personal information, etc.) and the data (including, but not limited to, data for analysis, stored data, presented data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party, and the collection, use, and processing of the related data are required to meet the related regulations.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the various embodiments provided herein may include at least one of relational databases and non-relational databases. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic units, quantum computing-based data processing logic units, etc., without being limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application shall be subject to the appended claims.
Claims (10)
1. A method for optimizing a carbon abatement system, the method comprising:
obtaining decision variable parameters of the production link of the carbon emission reduction system;
determining purchasing cost, production cost, indirect emission reduction technology cost, carbon capture technology cost, self-contained power plant technology cost and project income of the production link according to the decision variable parameters, so as to determine an objective function of the carbon emission reduction system optimization model;
And optimizing the objective function according to a preset constraint condition.
2. The method of claim 1, wherein the preset constraints include mass balance constraints, capacity constraints, rejection constraints of energy conservation and emission reduction techniques, dependency constraints of energy conservation and emission reduction techniques, carbon emission reduction constraints, integer constraints, and non-negative constraints, wherein optimizing the objective function according to the preset constraints comprises:
determining the minimum value of the objective function under the condition that the preset constraint condition is met;
and determining the value of each decision variable parameter according to the minimum value of the objective function.
3. The method of claim 1, wherein the production cost is determined based on an investment cost of production equipment for each process of the production link, a variable operating cost of the production equipment, and a fixed operating cost of the production equipment.
4. The method of claim 1, wherein the indirect emission reduction technology cost is determined according to a construction investment cost of an energy saving and emission reduction technology corresponding to each process of the production link, a variable operation cost of the energy saving and emission reduction technology, and a fixed operation cost of the energy saving and emission reduction technology.
5. The method of claim 1, wherein the carbon capture technology comprises at least one of a chemical absorption separation method, a pressure swing adsorption method, a membrane absorption and transmission method, and a low temperature separation and combination method;
the carbon capture technology cost is determined according to the purchase cost of the carbon capture equipment, the variable operating cost of the carbon capture process, and the fixed operating cost.
6. The method of claim 1, wherein the self-contained power plant technical cost is determined based on a capital cost of equipment for a generator set, a variable operating cost for the generator set, and a fixed operating cost for the generator set.
7. A carbon emission reduction system optimization device, the device comprising:
the acquisition module is used for acquiring decision variable parameters of the production link of the carbon emission reduction system;
the determining module is used for determining purchasing cost, production cost, indirect emission reduction technology cost, carbon capture technology cost, self-contained power plant technology cost and project income of the production link according to the decision variable parameters so as to determine an objective function of the carbon emission reduction system optimization model;
and the optimization module is used for optimizing the objective function according to preset constraint conditions.
8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 6 when the computer program is executed.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
10. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311406694.4A CN117314708A (en) | 2023-10-26 | 2023-10-26 | Carbon emission reduction system optimization method, device, equipment, medium and program product |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311406694.4A CN117314708A (en) | 2023-10-26 | 2023-10-26 | Carbon emission reduction system optimization method, device, equipment, medium and program product |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117314708A true CN117314708A (en) | 2023-12-29 |
Family
ID=89288314
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311406694.4A Pending CN117314708A (en) | 2023-10-26 | 2023-10-26 | Carbon emission reduction system optimization method, device, equipment, medium and program product |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117314708A (en) |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100257124A1 (en) * | 2009-04-07 | 2010-10-07 | Ramesh Srinivasan | Method for industrial energy and emissions investment optimization |
| CN109002921A (en) * | 2018-07-19 | 2018-12-14 | 北京师范大学 | A kind of Regional Energy system optimization method based on two type Fuzzy Chance Constraints |
| CN109508894A (en) * | 2018-11-27 | 2019-03-22 | 国网能源研究院有限公司 | Power System Planning method is coordinated in the storage of one provenance net lotus |
| CN110472783A (en) * | 2019-08-06 | 2019-11-19 | 国网冀北综合能源服务有限公司 | Consider the carbon capture set optimization method of generated energy quota |
| CN111435787A (en) * | 2020-04-09 | 2020-07-21 | 北京师范大学 | Regional power system optimization method and system based on interval fuzzy random |
| CN114037292A (en) * | 2021-11-11 | 2022-02-11 | 云南电网有限责任公司保山供电局 | Low-carbon optimized scheduling method of electricity-gas integrated energy system considering carbon capture |
| CN115907218A (en) * | 2022-12-24 | 2023-04-04 | 西安建筑科技大学 | Power system production distribution method and system considering carbon emission reduction situation |
| CN116029879A (en) * | 2022-12-13 | 2023-04-28 | 广西电网有限责任公司 | Multi-dimensional-based generation method and system for full-industry optimal emission reduction strategy |
| CN116131302A (en) * | 2023-02-16 | 2023-05-16 | 国网湖南省电力有限公司 | Energy storage scheduling method and system considering energy time-consuming shift characteristic of carbon capture power plant |
| WO2023134254A1 (en) * | 2022-01-11 | 2023-07-20 | 云南电网有限责任公司电力科学研究院 | Equipment model selection method for energy interconnection system |
-
2023
- 2023-10-26 CN CN202311406694.4A patent/CN117314708A/en active Pending
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100257124A1 (en) * | 2009-04-07 | 2010-10-07 | Ramesh Srinivasan | Method for industrial energy and emissions investment optimization |
| CN109002921A (en) * | 2018-07-19 | 2018-12-14 | 北京师范大学 | A kind of Regional Energy system optimization method based on two type Fuzzy Chance Constraints |
| CN109508894A (en) * | 2018-11-27 | 2019-03-22 | 国网能源研究院有限公司 | Power System Planning method is coordinated in the storage of one provenance net lotus |
| CN110472783A (en) * | 2019-08-06 | 2019-11-19 | 国网冀北综合能源服务有限公司 | Consider the carbon capture set optimization method of generated energy quota |
| CN111435787A (en) * | 2020-04-09 | 2020-07-21 | 北京师范大学 | Regional power system optimization method and system based on interval fuzzy random |
| CN114037292A (en) * | 2021-11-11 | 2022-02-11 | 云南电网有限责任公司保山供电局 | Low-carbon optimized scheduling method of electricity-gas integrated energy system considering carbon capture |
| WO2023134254A1 (en) * | 2022-01-11 | 2023-07-20 | 云南电网有限责任公司电力科学研究院 | Equipment model selection method for energy interconnection system |
| CN116029879A (en) * | 2022-12-13 | 2023-04-28 | 广西电网有限责任公司 | Multi-dimensional-based generation method and system for full-industry optimal emission reduction strategy |
| CN115907218A (en) * | 2022-12-24 | 2023-04-04 | 西安建筑科技大学 | Power system production distribution method and system considering carbon emission reduction situation |
| CN116131302A (en) * | 2023-02-16 | 2023-05-16 | 国网湖南省电力有限公司 | Energy storage scheduling method and system considering energy time-consuming shift characteristic of carbon capture power plant |
Non-Patent Citations (2)
| Title |
|---|
| 王楠: "基于碳捕集的联合钢铁企业减排系统优化研究", 中国优秀硕士学位论文全文数据库 工程科技I辑, no. 04, 15 April 2019 (2019-04-15), pages 027 - 160 * |
| 金定强 等: "温室气体减排方法下多电源区域的减排分析", 中国环境科学, vol. 37, no. 07, 20 July 2017 (2017-07-20), pages 2793 - 2800 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Zhang et al. | Integrated assessment of resource-energy-environment nexus in China's iron and steel industry | |
| Zeng et al. | A novel multi-period mixed-integer linear optimization model for optimal distribution of byproduct gases, steam and power in an iron and steel plant | |
| Wang et al. | A model on CO2 emission reduction in integrated steelmaking by optimization methods | |
| Wang et al. | Multi-objective optimization of synergic energy conservation and CO2 emission reduction in China's iron and steel industry under uncertainty | |
| Kim et al. | Simulation of CO2 emission reduction potential of the iron and steel industry using a system dynamics model | |
| Na et al. | Review of evaluation methodologies and influencing factors for energy efficiency of the iron and steel industry | |
| Tan et al. | Stochastic optimization framework of the energy-water-emissions nexus for regional power system planning considering multiple uncertainty | |
| Sun et al. | A comprehensive assessment on material, exergy and emission networks for the integrated iron and steel industry | |
| Elsheikh et al. | Assessment of variable solar-and grid electricity-driven power-to-hydrogen integration with direct iron ore reduction for low-carbon steel making | |
| Zhang et al. | Energy and exergy analyses of an integrated iron and steel making process | |
| CN116664360A (en) | Carbon emission accounting and carbon flow tracking method for key industrial enterprises | |
| Nurdiawati et al. | Towards fossil-free steel: Life cycle assessment of biosyngas-based direct reduced iron (DRI) production process | |
| Nørstebø et al. | Analysis of carbon capture in an industrial park—a case study | |
| Yuan et al. | Multi-objective optimization and analysis of material and energy flows in a typical steel plant | |
| Chen et al. | Hierarchical model to find the path reducing CO2 emissions of integrated iron and steel production | |
| Gan et al. | Three-stage coordinated operation of steel plant-based multi-energy microgrids considering carbon reduction | |
| Perpiñán et al. | High oxygen and SNG injection in blast furnace ironmaking with Power to Gas integration and CO2 recycling | |
| CN117314708A (en) | Carbon emission reduction system optimization method, device, equipment, medium and program product | |
| Sadlowski et al. | Ecologic Potential for Flexible Methanol Production from Steel Mill Off‐gases | |
| Hu et al. | The dynamic evaluation and optimization model for steel enterprise’s energy flow network operations | |
| Horst et al. | Sustainability of the Steel Industry: A Systematic Review | |
| Liu | Rotary Kiln thermal simulation model and smart supply chain logistics transportation monitoring management | |
| Wang et al. | Low-carbon economic dispatch of regional integrated energy system based on carbon-oxygen cycle | |
| Raiko et al. | Managing costs of an industrial enterprise when using secondary resources | |
| Fang et al. | Process-based life cycle inventory framework for assessing the carbon footprint of products from complex production paths: Case of dual-phase automotive strip steel |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |