Regional cement-coal power generation industry symbiotic carbon emission reduction optimization methods, equipment, and media

Abstract

This invention discloses a method, equipment, and medium for optimizing carbon emission reduction in a regional cement-coal-fired power generation industry symbiotic relationship, relating to the field of resource optimization and scheduling technology. The method includes: establishing a multi-objective optimization model based on regional supply and demand structure and a fly ash substitution strategy for cement clinker, with the objectives of minimizing economic costs and carbon emissions, and fly ash scheduling volume as the decision variable; solving the multi-objective optimization model under constraints using the NSGA-II algorithm to obtain the Pareto front solution set; processing the solutions in the Pareto front solution set using the normalized weighted distance method to select the optimal compromise solution; and evaluating the economic and environmental benefits of each scenario by comparing different blending ratios of fly ash and cement clinker and different transportation methods for the optimal compromise solution. This invention provides a decision-making basis for the resource-based scheduling of industrial solid waste and synergistic emission reduction in a regional scale, realizing the resource-based reuse of fly ash.

Description

Technical Field

[0001] This invention relates to the field of resource optimization and scheduling technology, specifically to a method, equipment, and medium for optimizing carbon emission reduction in a regional cement-coal-fired power generation industry symbiosis. Background Technology

[0002] Faced with the severe challenges of global climate change, promoting a green and low-carbon transformation has become a core issue on the strategic agenda. As typical energy-intensive industries, the cement and power industries require coordinated emission reduction pathways that are crucial for achieving the "dual carbon" goals. The cement production process includes three stages: raw material preparation, clinker calcination, and cement grinding. Among these, the carbon emissions from the clinker calcination stage account for over 95%, making carbon emission reduction a particularly challenging task for the cement industry.

[0003] The power industry is a vital energy sector for the nation. While coal-fired power plants ensure energy security, they also generate a large amount of solid waste. The combustion of coal produces substantial amounts of fly ash. Unused fly ash not only occupies valuable land resources but also poses environmental risks such as dust and water pollution, threatening the ecological environment and public health.

[0004] Under the constraints of "dual carbon" targets, how to scientifically optimize the allocation of industrial solid waste resources and promote coordinated emission reduction in high-carbon industries has become a key issue that urgently needs to be addressed in the regional green and low-carbon transformation. Among these challenges, the theory of industrial symbiosis offers a solution, defining it as an organizational model where enterprises achieve material circulation through the exchange of by-products / waste and resource sharing. Under the "dual carbon" targets, alternative fuels, due to their advantages in resource recovery and carbon emission reduction, have become an important path for the cement industry to achieve low-carbon transformation. Fly ash, rich in active components such as silica (SiO2) and possessing excellent pozzolanic properties, can serve as an ideal substitute for cement clinker. Research shows that replacing clinker with fly ash is one of the most effective ways to reduce carbon emissions in the cement industry. Currently, research on fly ash substitution mainly focuses on two dimensions: material ratio optimization and production process improvement. Summary of the Invention

[0005] This invention addresses the shortcomings of fly ash as a substitute for clinker in terms of resource recovery and carbon emission reduction by providing a regional cement-coal-fired power generation industry symbiotic carbon emission reduction optimization method, equipment, and media. This provides a decision-making basis for the resource-based scheduling of industrial solid waste and the symbiotic and collaborative emission reduction of industries at the regional scale, and realizes the resource-based reuse of fly ash.

[0006] This invention is achieved through the following technical solution:

[0007] Firstly, a method for optimizing carbon emission reduction in a region with a cement-coal-fired power generation industry symbiosis is provided, the method comprising:

[0008] Based on the regional supply and demand structure and the strategy of replacing cement clinker with fly ash, a multi-objective optimization model is established with the objectives of minimizing economic costs and carbon emissions, and the decision variable being the amount of fly ash dispatched. The multi-objective optimization model includes a cost minimization objective function and a carbon emission minimization objective function.

[0009] The multi-objective optimization model was solved under constraints using the non-dominated sorting genetic NSGA-II algorithm to obtain the Pareto front solution set.

[0010] The solutions in the Pareto front solution set are processed using the normalized weighted distance method to select the optimal compromise solution.

[0011] For the aforementioned optimal compromise, we compare different combinations of fly ash and cement clinker admixture ratios and different transportation methods to evaluate the economic and environmental benefits under each scenario.

[0012] In some embodiments, the cost minimization objective function is:

[0013] ;

[0014] ;

[0015] ;

[0016] Where S represents the set of supplying cities, and D represents the set of demanding cities. This refers to the unit transportation price for diesel trucks. For transportation distance, Let this be the total amount of fly ash to be dispatched from city i to city j. For fly ash prices, For cement clinker prices, This is the geographical distance between cities calculated using the Haversine formula, where R is the Earth's radius. , These represent the latitudes from city i to city j. , These represent the difference between latitude and longitude, respectively, and δ represents the minimum solid waste treatment rate, serving as a detour coefficient.

[0017] In some embodiments, the objective function for minimizing carbon emissions is:

[0018] ;

[0019] ;

[0020] in, To transport carbon emission factors, Carbon emissions from cement production This is the process emission factor. For fuel emission coefficient, Electricity consumption per unit of clinker This is the electricity emission factor.

[0021] In some embodiments, the solutions in the Pareto front solution set are processed using the normalized weighted distance method to select the optimal compromise, including:

[0022] Each solution in the Pareto front solution set of economic cost and the Pareto front solution set of carbon emissions is normalized separately.

[0023] Based on the Pareto front solutions of economic cost and carbon emissions after normalization, the weighted Euclidean distance between each solution and the ideal point is calculated, where the ideal point is a vector composed of the minimum economic cost and the minimum carbon emissions.

[0024] The solution corresponding to the minimum weighted Euclidean distance is selected as the optimal compromise.

[0025] In some embodiments, each solution in the Pareto front solution set of economic costs and the Pareto front solution set of carbon emissions is normalized, including:

[0026] ;

[0027] in, and Let be the minimum and maximum values ​​of the k-th objective in the Pareto solution set, respectively. This is the k-th target value.

[0028] In some embodiments, based on the Pareto front solution of normalized economic cost and the Pareto front solution of carbon emissions, the weighted Euclidean distance between each solution and the ideal point is calculated, including:

[0029] ;

[0030] in, , Indicates the weighting coefficient. , These represent the distance between economic cost and the ideal value of economic cost, and the distance between carbon emissions and the ideal value of carbon emissions, respectively.

[0031] In some embodiments, the constraints include: supply constraints, demand constraints, solid waste treatment rate constraints, and non-negativity constraints, wherein,

[0032] The supply constraint is:

[0033] ;

[0034] ;

[0035] in, The amount of fly ash transferred from outside the city. For fly ash production, S represents the amount of fly ash consumed by city i, and S represents the supply to the city's aggregate. Let D be the total amount of fly ash to be dispatched from city i to city j, and D be the set of cities in demand.

[0036] The requirement constraint is as follows:

[0037]

[0038]

[0039] in, This indicates the amount of fly ash that city j needs to transfer from outside the city.

[0040] The solid waste treatment rate constraint is:

[0041]

[0042] in, The minimum solid waste treatment rate is given by n, where n is the number of cities.

[0043] The nonnegativity constraint is:

[0044] .

[0045] Secondly, a regional cement-coal-fired power generation industry symbiotic carbon emission reduction optimization device is provided, the device comprising:

[0046] A multi-objective optimization model construction module is used to: establish a multi-objective optimization model based on regional supply and demand structure and fly ash substitution strategy for cement clinker, with the objectives of minimizing economic costs and carbon emissions, and the decision variable being the amount of fly ash dispatched. The multi-objective optimization model includes a cost minimization objective function and a carbon emission minimization objective function.

[0047] The objective optimization model solving module is used to: solve the multi-objective optimization model under constraints using the non-dominated sorting genetic NSGA-II algorithm to obtain the Pareto front solution set;

[0048] The optimal compromise solution selection module is used to: process the solutions in the Pareto front solution set using the normalized weighted distance method to select the optimal compromise solution.

[0049] The scenario benefit assessment module is used to: compare different combinations of fly ash and cement clinker admixture ratios and different transportation methods for the optimal compromise scheme, and evaluate the economic and environmental benefits under each scenario.

[0050] Thirdly, a regional cement-coal-fired power generation industry symbiotic carbon emission reduction optimization device is provided, the device comprising:

[0051] At least one processor;

[0052] At least one memory coupled to the at least one processor and storing instructions for execution by the at least one processor, the instructions implementing the method described in any of the above when executed by the at least one processor.

[0053] Fourthly, a computer-readable storage medium is provided, the computer-readable storage medium storing instructions that, when executed by a computer, cause the computer to perform the method described in any of the preceding embodiments.

[0054] Compared with existing technologies, this invention has the following advantages and beneficial effects: It sets constraints such as supply, demand, and solid waste treatment rate, with the objective functions of minimizing economic costs and carbon emissions; it employs the Non-Dominated Sorting Genetic Algorithm (NSGA-II) to solve the Pareto front solution set, combined with the normalized weighted distance method to screen the optimal compromise solution; finally, it compares different combinations of blending ratios and different transportation methods, evaluating the economic and environmental benefits under each scenario, providing a decision-making basis for regional-scale industrial solid waste resource utilization scheduling and industrial symbiotic emission reduction. Attached Figure Description

[0055] To more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0056] Figure 1 This is a schematic diagram of a symbiotic network for the resource utilization of fly ash in the cement and power industries.

[0057] Figure 2 This is a flowchart of a regional cement-coal-fired power generation industry symbiotic carbon emission reduction optimization method according to an embodiment of the present invention.

[0058] Figure 3 This is an optimized architecture diagram for the co-production of fly ash resources in the cement and power industries according to an embodiment of the present invention.

[0059] Figure 4 This is a structural block diagram of a regional cement-coal power generation symbiotic carbon emission reduction optimization device according to an embodiment of the present invention.

[0060] Figure 5 This is a schematic diagram of a regional cement-coal power generation symbiotic carbon emission reduction optimization device according to an embodiment of the present invention. Detailed Implementation

[0061] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0062] To facilitate understanding of the technical solution of this invention, the relevant terms are explained below.

[0063] Non-dominated Sorting Genetic Algorithm II (NSGA-II) is an efficient evolutionary algorithm for solving multi-objective optimization problems. By using fast non-dominated sorting and crowding distance calculation, it accurately approximates the true Pareto front while maintaining population diversity. It is suitable for multi-objective collaborative optimization under complex constraints.

[0064] Multi-Objective Optimization Problem (MOOP) - A mathematical programming problem that simultaneously optimizes two or more conflicting objective functions. Its solution set usually represents a Pareto front, and the optimal compromise solution must be selected from it through a specific decision-making method.

[0065] Industrial Symbiosis (IS) is an industrial organization model that achieves closed-loop material flow and maximizes overall system benefits through the exchange of by-products / waste, resource sharing, and cascaded energy utilization. It is the core practice of the circular economy.

[0066] Fly Ash (FA): Fine ash collected by flue gas treatment systems in coal-fired power plants. Its main components are silicon and aluminum oxides. It has pozzolanic activity and can be used as a cement admixture to replace part of the clinker, achieving the dual benefits of solid waste resource utilization and carbon emission reduction.

[0067] Pareto Optimal Solution: In multi-objective optimization, a Pareto optimal solution is a set of solutions for which no other solution can improve any objective without compromising at least one objective. Its projection onto the objective space constitutes the Pareto front, which represents the theoretical limit of the system's performance.

[0068] Solid Waste Utilization Rate (SWUR): refers to the proportion of industrial solid waste that is effectively utilized after resource recovery treatment, out of the total amount generated. It is a key indicator for measuring the level of circular economy and environmental performance.

[0069] The power and cement industries, as key sectors of energy consumption and carbon emissions, possess significant potential for coupled emission reduction in their production processes. This invention focuses on the symbiotic network of the cement-power industries at a regional scale, aiming to achieve synergistic optimization of solid waste resource utilization and carbon emission reduction through cross-industry and cross-regional scientific resource scheduling. Figure 1 As shown, if fly ash generated during coal-fired power generation is not effectively utilized, long-term stockpiling will occupy a large amount of land resources and pose a risk of heavy metal leachate pollution of the surrounding water and soil environment, constituting serious negative environmental externalities. At the same time, fly ash, as the largest solid waste produced by the coal-fired power generation industry, can replace cement clinker in cement production, thereby achieving resource recycling.

[0070] This invention focuses on the symbiotic network of the cement and power industries, aiming to construct an optimal scheme for the scheduling of fly ash resources under the trade-off between regional economy and environment. The objective function is to minimize economic cost and carbon emissions, with constraints such as supply, demand, and solid waste treatment rate. The Non-Dominated Sorting Genetic Algorithm (NSGA-II) is used to solve for the Pareto front solution set, combined with the normalized weighted distance method to select the optimal compromise scheme. Finally, different combinations of blending ratios and transportation methods are compared to evaluate the economic and environmental benefits under each scenario, providing a decision-making basis for the resource-based scheduling of industrial solid waste and synergistic emission reduction through industrial symbiosis at the regional scale. The NSGA-II algorithm, with its fast non-dominated sorting mechanism and adaptive crowding operator, demonstrates significant advantages in the field of solid waste management. This algorithm can accurately approximate the true Pareto front while maintaining population diversity, making it particularly suitable for resource scheduling problems with complex constraints.

[0071] On the one hand, the present invention provides an optimization method for carbon emission reduction in the symbiotic relationship between the regional cement and coal-fired power generation industries. Figure 2 This is a flowchart illustrating an optimization method for carbon emission reduction in a regional cement-coal-fired power generation industry symbiotic relationship according to an embodiment of the present invention. (Reference) Figure 2 The optimization method for carbon emission reduction in the symbiotic relationship between the cement and coal-fired power generation industries in this region includes: S10 to S40.

[0072] Figure 3 This is a diagram illustrating the optimized architecture for the symbiotic resource utilization of fly ash in the cement and power industries according to an embodiment of the present invention. The following references... Figure 2 and Figure 3 The present invention provides a detailed description of the regional cement-coal-fired power generation industry symbiotic carbon emission reduction optimization method.

[0073] In S10, based on the regional supply and demand structure and the strategy of replacing cement clinker with fly ash, a multi-objective optimization model is established with the objectives of minimizing economic costs and carbon emissions, and the decision variable being the amount of fly ash dispatched. The multi-objective optimization model includes a cost minimization objective function and a carbon emission minimization objective function.

[0074] Specifically, cement production can utilize solid waste from power generation. The closer the symbiotic relationship between cement and electricity, the greater the amount of material exchange and utilization, which is more conducive to environmental protection and energy conservation. When constructing the cost minimization objective function, the costs incurred in transporting fly ash and the economic costs saved by replacing cement clinker with fly ash are fully considered. The cost minimization objective function is:

[0075] (1)

[0076] (2)

[0077] (3)

[0078] Where S represents the set of supplying cities, and D represents the set of demanding cities. This refers to the unit transportation price for diesel trucks. For transportation distance, Let this be the total amount of fly ash to be dispatched from city i to city j. For fly ash prices, For cement clinker prices, This is the geographical distance between cities calculated using the Haversine formula, where R is the Earth's radius. , These represent the latitudes from city i to city j. , The latitude and longitude differences are represented by δ, which is the minimum solid waste treatment rate, and is used as a detour coefficient. The detour coefficient δ is introduced to correct the transportation distance, which is used to simulate the actual road distance.

[0079] When constructing the objective function for minimizing carbon emissions, the carbon emissions generated during fly ash transportation and the carbon emissions reduced in clinker production through fly ash substitution are considered. The latter takes into account process emissions, fuel combustion emissions, and indirect emissions during substitution. When the amount of blended materials added increases by 1%, the amount of cement clinker used can be reduced by 1% accordingly, thereby reducing the carbon emissions corresponding to clinker in cement production. The objective function for minimizing carbon emissions is:

[0080] (4)

[0081] (5)

[0082] in, To transport carbon emission factors, Carbon emissions from cement production This is the process emission factor. For fuel emission coefficient, Electricity consumption per unit of clinker This is the electricity emission factor.

[0083] In S20, the Non-Dominated Sorting Genetic Algorithm (NSGA-II) is used to solve the multi-objective optimization model under constraints to obtain the Pareto front solution set.

[0084] In some embodiments, the constraints include: supply constraints, demand constraints, solid waste treatment rate constraints, and non-negativity constraints.

[0085] The supply constraint is:

[0086] (6)

[0087] (7)

[0088] in, The amount of fly ash transferred from outside the city. For fly ash production, S represents the amount of fly ash consumed by city i, and S represents the supply to the city's aggregate. Let D be the total amount of fly ash to be dispatched from city i to city j, and D be the set of cities in demand.

[0089] The requirement constraints are:

[0090] (8)

[0091] (9)

[0092] in, This indicates the amount of fly ash that city j needs to transfer from outside the city.

[0093] The solid waste treatment rate constraint is:

[0094] (10)

[0095] in, The minimum solid waste treatment rate is given by n, where n is the number of cities.

[0096] The nonnegativity constraint is:

[0097] (11).

[0098] The Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is used to solve the multi-objective optimization model. NSGA-II can effectively identify Pareto optimal solutions through a fast non-dominated sorting mechanism. By introducing an adaptive crowding comparison operator, it can accurately approximate the true Pareto front while maintaining population diversity.

[0099] In S30, the normalized weighted distance method is used to process the solutions in the Pareto front solution set in order to select the best compromise solution.

[0100] Specifically, the normalized weighted distance method is used to select the best compromise solutions from the Pareto front solution set, including S31 to S33.

[0101] In S31, each solution in the Pareto front solution set for economic cost and the Pareto front solution set for carbon emissions is normalized separately. For example, economic cost Z1 and carbon emissions Z2 can be normalized separately.

[0102] Specifically, selecting the optimal solution from the Pareto solution set requires balancing both economic and environmental objectives. This invention employs the normalized weighted distance method to determine the optimal compromise solution:

[0103] (12)

[0104] in, and Let be the minimum and maximum values ​​of the k-th objective in the Pareto solution set, respectively. Let be the k-th target value. Normalization eliminates differences in the dimensions and orders of magnitude of different targets, making multi-target comparisons comparable.

[0105] In S32, based on the Pareto frontier solutions for economic cost and carbon emissions after normalization, the weighted Euclidean distance between each solution and the ideal point is calculated, where the ideal point is the vector composed of the minimum economic cost and the minimum carbon emissions. The weighted Euclidean distance is calculated as follows:

[0106] (13)

[0107] in, , Indicates the weighting coefficient. , These represent the distance between economic cost and the ideal economic cost, and the distance between carbon emissions and the ideal carbon emissions, respectively. Weighting coefficients. and It reflects the decision-maker's preference for different objectives. This distance metric comprehensively considers the degree of deviation between the proposed solution and the ideal state; the smaller the distance, the better the overall performance of the solution.

[0108] In S33, the solution corresponding to the minimum weighted Euclidean distance is selected as the optimal compromise:

[0109] (14)

[0110] Here, PS represents the Pareto optimal solution set. This method, while maintaining its multi-objective optimization nature, provides objective and transparent decision-making basis, effectively balancing economic and environmental benefits, and offering scientific support for the regional collaborative management of fly ash resource utilization.

[0111] In S30, for the optimal compromise, different combinations of fly ash and cement clinker admixture ratios and different transportation methods are compared to evaluate the economic and environmental benefits under each scenario.

[0112] For example, refer to Figure 3 By mixing fly ash and cement clinker in different proportions and using different transportation methods, the corresponding economic and environmental benefits can be calculated, and the economic and environmental benefits under each scenario can be evaluated.

[0113] This invention constructs a regional-scale cement-power industry symbiotic network optimization scheme, realizing cross-regional and cross-industry coordinated scheduling of solid waste resources, and providing a reference for regional industrial solid waste resource utilization and coordinated carbon reduction in high-carbon industries. Simultaneously, this invention quantitatively analyzes the comprehensive emission reduction benefits under different blending ratios and transportation mode combinations, providing decision-makers with a scientific basis for parameter selection and enhancing the adaptability and practicality of the scheme.

[0114] On the other hand, the present invention provides a regional cement-coal power generation industry symbiotic carbon emission reduction optimization device. Figure 4 This is a structural block diagram of a regional cement-coal power generation co-production carbon emission reduction optimization device according to an embodiment of the present invention. (Reference) Figure 4 The carbon emission reduction optimization equipment for the cement-coal power generation industry symbiosis in this region includes: a multi-objective optimization model construction module, an objective optimization model solving module, an optimal compromise solution screening module, and a scenario benefit assessment module.

[0115] The multi-objective optimization model construction module is used to: establish a multi-objective optimization model with the objectives of minimizing economic costs and minimizing carbon emissions, and with the amount of fly ash dispatched as the decision variable, based on the regional supply and demand structure and the strategy of replacing cement clinker with fly ash. The multi-objective optimization model includes a cost minimization objective function and a carbon emission minimization objective function.

[0116] The objective optimization model solving module is used to solve multi-objective optimization models under constraints using the non-dominated sorting genetic NSGA-II algorithm to obtain the Pareto front solution set.

[0117] The optimal compromise solution selection module is used to process the solutions in the Pareto front solution set using the normalized weighted distance method in order to select the optimal compromise solution.

[0118] The scenario benefit assessment module is used to: compare different combinations of fly ash and cement clinker admixture ratios and different transportation methods for optimal compromise solutions, and evaluate the economic and environmental benefits under each scenario.

[0119] For further details regarding the carbon emission reduction optimization equipment for the cement-coal power generation industry symbiosis in this region, please refer to the previous description of the carbon emission reduction optimization method for the cement-coal power generation industry symbiosis in this region, which will not be repeated here.

[0120] In implementing the functions of the integrated modules described above using hardware, this embodiment of the invention provides a structure for the regional cement-coal-fired power generation industry symbiotic carbon emission reduction optimization equipment involved in the above embodiments. Figure 5 This is a schematic diagram of a regional cement-coal power generation co-production carbon reduction optimization device according to an embodiment of the present invention. (Reference) Figure 5 The carbon emission reduction optimization device for the cement-coal-fired power generation industry in this region includes: at least one processor; and at least one memory. The at least one memory is coupled to the at least one processor and stores instructions for execution by the at least one processor, which, when executed by the at least one processor, implement the methods described above.

[0121] A processor can be a set of logic blocks, modules, and circuits that implement or execute the various exemplary logic blocks, modules, and circuits described in connection with embodiments of the present invention. The processor can be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in connection with embodiments of the present invention. A processor can also be a combination that implements computational functions, such as a combination of one or more microprocessors, a combination of a digital signal processor and a microprocessor, etc.

[0122] The memory may be read-only memory (ROM) or other types of static storage devices that can store static information and instructions, random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, electrically erasable programmable read-only memory (EEPROM), disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited thereto.

[0123] In one implementation, the memory can exist independently of the processor. The memory can be connected to the processor via a bus and used to store instructions or program code. When the processor calls and executes the instructions or program code stored in the memory, it can implement the method provided in the embodiments of the present invention. In another implementation, the memory can also be integrated with the processor.

[0124] On the other hand, the present invention also provides a computer-readable storage medium (e.g., a non-transitory computer-readable storage medium) storing computer program instructions that, when executed on a computer, cause the computer to perform the method as described in any of the above embodiments.

[0125] Exemplary examples show that the aforementioned computer-readable storage media may include, but are not limited to: magnetic storage devices (e.g., hard disks, floppy disks, or magnetic tapes), optical discs (e.g., compact disks (CDs), digital versatile disks (DVDs), etc.), smart cards, and flash memory devices (e.g., erasable programmable read-only memory (EPROMs), cards, sticks, or key drives, etc.). The various computer-readable storage media described in this invention may represent one or more devices and / or other machine-readable storage media for storing information. The term "machine-readable storage medium" may include, but is not limited to, wireless channels and various other media capable of storing, containing, and / or carrying instructions and / or data.

[0126] This invention provides a computer program that, when run on a computer, causes the computer to perform the method of any of the above embodiments.

[0127] This invention provides a computer program product containing instructions that, when run on a computer, cause the computer to perform the method of any of the above embodiments.

[0128] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for optimizing carbon emission reduction in a regional cement-coal-fired power generation industry symbiotic relationship, characterized in that, The method includes: Based on the regional supply and demand structure and the strategy of replacing cement clinker with fly ash, a multi-objective optimization model is established with the objectives of minimizing economic costs and carbon emissions, and the decision variable being the amount of fly ash dispatched. The multi-objective optimization model includes a cost minimization objective function and a carbon emission minimization objective function. The multi-objective optimization model was solved under constraints using the non-dominated sorting genetic NSGA-II algorithm to obtain the Pareto front solution set. The solutions in the Pareto front solution set are processed using the normalized weighted distance method to select the optimal compromise solution. For the aforementioned optimal compromise, we compare different combinations of fly ash and cement clinker admixture ratios and different transportation methods to evaluate the economic and environmental benefits under each scenario.

2. The method according to claim 1, characterized in that, The cost minimization objective function is: ； ； ； Where S represents the set of supplying cities, and D represents the set of demanding cities. This refers to the unit transportation price for diesel trucks. For transportation distance, Let this be the total amount of fly ash to be dispatched from city i to city j. For fly ash prices, For cement clinker prices, This is the geographical distance between cities calculated using the Haversine formula, where R is the Earth's radius. , These represent the latitudes from city i to city j. , These represent the difference between latitude and longitude, respectively, and δ represents the minimum solid waste treatment rate, serving as a detour coefficient.

3. The method according to claim 1, characterized in that, The objective function for minimizing carbon emissions is: ； ； in, To transport carbon emission factors, Carbon emissions from cement production This is the process emission factor. For fuel emission coefficient, Electricity consumption per unit of clinker This is the electricity emission factor.

4. The method according to any one of claims 1 to 3, characterized in that, The solutions in the Pareto front solution set are processed using the normalized weighted distance method to select the optimal compromise solution, including: Each solution in the Pareto front solution set of economic cost and the Pareto front solution set of carbon emissions is normalized separately. Based on the Pareto front solutions of economic cost and carbon emissions after normalization, the weighted Euclidean distance between each solution and the ideal point is calculated, where the ideal point is a vector composed of the minimum economic cost and the minimum carbon emissions. The solution corresponding to the minimum weighted Euclidean distance is selected as the optimal compromise.

5. The method according to claim 4, characterized in that, For each solution in the Pareto front set of economic costs and the Pareto front set of carbon emissions, normalization is performed, including: ； in, and Let be the minimum and maximum values ​​of the k-th objective in the Pareto solution set, respectively. This is the k-th target value.

6. The method according to claim 4, characterized in that, Based on the Pareto frontier solutions for normalized economic costs and carbon emissions, the weighted Euclidean distance between each solution and the ideal point is calculated, including: ； in, , Indicates the weighting coefficient. , These represent the distance between economic cost and the ideal value of economic cost, and the distance between carbon emissions and the ideal value of carbon emissions, respectively.

7. The method according to claim 1, characterized in that, The constraints include: supply constraints, demand constraints, solid waste treatment rate constraints, and non-negativity constraints, among which, The supply constraint is: ； ； in, The amount of fly ash transferred from outside the city. For fly ash production, S represents the amount of fly ash consumed by city i, and S represents the supply to the city's aggregate. Let D be the total amount of fly ash to be dispatched from city i to city j, and D be the set of cities in demand. The requirement constraint is as follows: in, This indicates the amount of fly ash that city j needs to transfer from outside. The solid waste treatment rate constraint is: in, The minimum solid waste treatment rate is given by n, where n is the number of cities. The nonnegativity constraint is: 。 8. A regional cement-coal-fired power generation industry symbiotic carbon emission reduction optimization device, characterized in that, The device includes: A multi-objective optimization model construction module is used to: establish a multi-objective optimization model based on regional supply and demand structure and fly ash substitution strategy for cement clinker, with the objectives of minimizing economic costs and carbon emissions, and the decision variable being the amount of fly ash dispatched. The multi-objective optimization model includes a cost minimization objective function and a carbon emission minimization objective function. The objective optimization model solving module is used to: solve the multi-objective optimization model under constraints using the non-dominated sorting genetic NSGA-II algorithm to obtain the Pareto front solution set; The optimal compromise solution selection module is used to: process the solutions in the Pareto front solution set using the normalized weighted distance method to select the optimal compromise solution. The scenario benefit assessment module is used to: compare different combinations of fly ash and cement clinker admixture ratios and different transportation methods for the optimal compromise scheme, and evaluate the economic and environmental benefits under each scenario.

9. A regional cement-coal-fired power generation industry symbiotic carbon emission reduction optimization device, characterized in that, The device includes: At least one processor; At least one memory coupled to the at least one processor and storing instructions for execution by the at least one processor, the instructions implementing the method of any one of claims 1 to 7 when executed by the at least one processor.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 7.

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