CCUS full-process hybrid particle swarm cost optimization method and system

Abstract

The invention provides a CCUS full-process hybrid particle swarm cost optimization method and system. The optimization method comprises the following steps: S1, carrying out mathematical modeling on a CCUS subsystem; s2, establishing a CCUS full-process engineering-economic model by applying a large-scale system decomposition hierarchical and modular modeling mode; s3, converting the optimization problem containing the mixed constraints into an unconstrained optimization problem by using a penalty function; and S4, setting basic parameters of an algorithm, performing hybrid particle swarm optimization, and finally obtaining an optimal implementation scheme of the CCUS. The total cost of the CCUS full-process system is comprehensively considered, the CCUS full-process improved hybrid particle swarm optimization algorithm is perfected, and the optimization effect is obviously improved.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon dioxide treatment, and particularly to a CCUS full-process hybrid particle swarm cost optimization method and system. Background Art

[0002] Carbon dioxide capture, utilization and storage (CCUS) is one of the most effective ways to reduce carbon dioxide emissions and realize resource utilization at present. Carbon dioxide is mixed with crude oil to reduce its viscosity and injected into heavy oil, low-permeability and other reservoirs to improve oil recovery. Domestic scholars have carried out a large number of studies on the CCUS technology. Luo Ping et al. adopted second-order cone relaxation and piecewise linearization to process the non-linear terms in the objective function and constraints, and used a robust optimization algorithm to optimize the full-process cost. Zhang Wendong et al. mainly studied the storage problem of CCUS, and focused on analyzing the CO2 storage principle and the factors affecting the recovery rate of coalbed methane. Rodrigues Hydra W.L., Mackay Eric J., and Arnold Daniel P et al. evaluated the potential of associated gas carbon dioxide recovery and utilization in salt reservoirs to achieve carbon capture utilization and storage. Through underground modeling of the hypothetical field, carbon dioxide emissions were reduced to a certain extent. Wu Qian et al. An optimization introduced an optimization-based CCUS source-sink matching model and the optimization within the CCUS cluster. By connecting dispersed capture sources and storage sites, a CCUS cluster was established to achieve the minimum cost. Liu Jiajia et al. established a CCUS full-process engineering-economic model. Aiming at the goal of the lowest total cost of the CCUS full process, a robust genetic optimization algorithm and a multi-objective genetic optimization algorithm were designed respectively. Bai Hongshan et al. established a CCUS full-process engineering-economic model. Aiming at the goal of the lowest total cost of the CCUS full process, a genetic optimization algorithm and a hybrid particle swarm optimization algorithm were designed respectively. However, these studies mostly focus on the CCUS process technology, rarely involve the CCUS full-process optimization problem and optimization methods, and the computational effects of the proposed full-process optimization algorithms are poor. Summary of the Invention

[0003] In view of the above problems, the present invention is proposed to provide a CCUS full-process hybrid particle swarm cost optimization method and system that can overcome or at least partially solve the above problems.

[0004] According to one aspect of the present invention, a CCUS full-process hybrid particle swarm cost optimization method is provided. The optimization method includes:

[0005] Step S1: Perform mathematical modeling on the CCUS subsystem;

[0006] Step S2: Establish an engineering-economic model for the entire CCUS process using the hierarchical decomposition and modular modeling method of large systems;

[0007] Step S3: Use the penalty function to transform the optimization problem with mixed constraints into an unconstrained optimization problem;

[0008] Step S4: Set the basic parameters of the algorithm, perform hybrid particle swarm optimization, and finally obtain the best implementation plan for CCUS.

[0009] Optionally, the specific steps of Step S2: Establish an engineering-economic model for the entire CCUS process using the hierarchical decomposition and modular modeling method include:

[0010] Step S201: Conduct economic modeling for the entire CCUS process;

[0011] Step S202: Conduct optimization modeling for the entire process to obtain an optimization model;

[0012] Step S203: Verify the optimization model.

[0013] Optionally, the specific steps of Step S201: Conduct economic modeling for the entire CCUS process include:

[0014] CCUS subsystem technology selection, the capture system is post-combustion capture technology, the transportation system is pipeline transportation technology, and the enhanced oil recovery and storage system is CO2 enhanced oil recovery and storage fixation;

[0015] Basic assumptions for the entire CCUS process. Under the conditions of meeting actual application and design requirements, make the following assumptions: Do not consider the source-sink matching problem; Do not consider the pipeline design problems caused by terrain factors; The CO2 in the pipeline is evenly distributed, the pump station models are the same and evenly distributed;

[0016] Establish an engineering model for the entire CCUS process, and establish an engineering model for the entire process of total CO2 capture, compressor and pump energy consumption, pipe diameter and wall thickness, pump stations, and CO2 injection volume;

[0017] Establish an economic model for the entire CCUS process, and establish an economic model for the entire process of capture equipment investment, compressor and pump investment, pipeline construction investment, pump station investment, enhanced oil recovery and storage equipment investment, operation and maintenance costs, and electricity costs.

[0018] Optionally, the specific steps of Step S202: Conduct optimization modeling for the entire process to obtain an optimization model include:

[0019] Establish an economic model for the entire process, comprehensively consider the economic models of each part of CCUS, and calculate the total economic cost of the entire process;

[0020] Determine the constraints, and clarify the mass conservation, emission constraints, transportation constraints, storage constraints and their respective constraint scopes existing in the optimization modeling of the CCUS whole-process economic model;

[0021] Sensitivity analysis, perform global sensitivity analysis (GSA) on the parameters of the CCUS whole process, and obtain that the three parameters of flue gas flow rate, pipeline inlet pressure, and injection well inlet pressure are the most sensitive, and set them as decision variables;

[0022] Design of the whole-process genetic optimization algorithm, design the CCUS whole-process genetic optimization algorithm according to the economic model, constraints and decision variables.

[0023] Optionally, the step S203: verifying the optimization model specifically includes:

[0024] Set the basic parameters of the whole-process genetic optimization algorithm, input the economic model, initialize the population, and calculate the individual fitness value;

[0025] Select, crossover, and mutate the fitness value of the economic model;

[0026] Genetic optimization algorithm for optimization, judge whether the result converges, if it converges, obtain the optimal parameters.

[0027] Optionally, the step S3: using the penalty function to transform the optimization problem containing mixed constraints into an unconstrained optimization problem; specifically includes:

[0028] Construct a penalty function, and construct a reasonable penalty function form according to the economic model;

[0029] Determine the penalty factor of the penalty function, and select a reasonable penalty factor of the penalty function according to the economic model;

[0030] Combine the penalty function and the penalty factor to form an augmented objective function, and transform the constraint conditions in the whole-process engineering economic model into unconstrained for subsequent solution.

[0031] Optionally, the step S4: setting the basic parameters of the algorithm, performing hybrid particle swarm optimization, and finally obtaining the best implementation plan of CCUS specifically includes:

[0032] Step S401: Construct a hybrid particle swarm algorithm model;

[0033] Step S402: Set the hyperparameters of the hybrid particle swarm;

[0034] Step S403: Combine the penalty function and the hybrid particle swarm algorithm to calculate the fitness function of the particle swarm algorithm;

[0035] Step S404: Model iterative optimization, update the individual extreme value, update the population extreme value, judge whether it converges, if it converges, obtain the parameters of the best implementation plan of the CCUS whole-process economic model.

[0036] Optionally, step S401: constructing a hybrid particle swarm optimization algorithm model specifically includes: constructing a model based on an economic model, constraint conditions, and decision variables.

[0037] Optionally, step S402: setting the hyperparameters of the hybrid particle swarm specifically includes: setting the values of the number of particles, inertia factor, and acceleration constants.

[0038] Optionally, the CCUS full-process engineering-economic model in step S2 specifically includes:

[0039] Starting from the carbon dioxide capture system, combining the carbon dioxide capture methods, a carbon dioxide capture model is formed;

[0040] Taking the mass flow of the captured carbon dioxide as the design condition and the minimum injection pressure of the carbon dioxide injected into the oilfield as the constraint condition, a carbon dioxide transportation model including factors such as pipe diameter, pipe wall, pump station power, and number of pump stations is constructed;

[0041] Based on the outlet pressure of the carbon dioxide transportation pipeline, a carbon dioxide enhanced oil recovery and storage model is established.

[0042] Optionally, the carbon dioxide transportation model is a pipeline transportation model.

[0043] The present invention also provides a CCUS full-process hybrid particle swarm cost optimization system, applying the above-mentioned CCUS full-process hybrid particle swarm cost optimization method. The optimization system includes:

[0044] A mathematical modeling module for mathematically modeling the CCUS subsystem;

[0045] A full-process engineering-economic model establishment module for establishing a CCUS full-process engineering-economic model by using the large system decomposition hierarchical and modular modeling method;

[0046] A penalty function optimization module for converting an optimization problem with mixed constraints into an unconstrained optimization problem by using a penalty function;

[0047] A particle swarm optimization module for setting the basic parameters of the algorithm, performing hybrid particle swarm optimization, and finally obtaining the best implementation plan for CCUS.

[0048] A CCUS full-process hybrid particle swarm cost optimization method provided by the present invention, the optimization method comprising: Step S1: performing mathematical modeling on the CCUS subsystem; Step S2: establishing a CCUS full-process engineering-economic model by using a large system decomposition hierarchical and modular modeling method; Step S3: using a penalty function to transform an optimization problem containing mixed constraints into an unconstrained optimization problem; Step S4: setting basic algorithm parameters and performing hybrid particle swarm optimization to finally obtain the best implementation plan for CCUS. Considering the total cost of the CCUS full-process system comprehensively, the hybrid particle swarm optimization algorithm for improving the CCUS full process is improved, and the optimization effect is significantly improved.

[0049] The above description is only an overview of the technical solution of the present invention. In order to be able to understand the technical means of the present invention more clearly, it can be implemented according to the content of the specification. And in order to make the above and other objects, features and advantages of the present invention more obvious and understandable, the following specifically gives the specific implementation manners of the present invention. Brief Description of the Drawings

[0050] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained according to these drawings.

[0051] Figure 1 It is a flow chart of an improved CCUS full-process hybrid particle swarm optimization algorithm provided by an embodiment of the present invention;

[0052] Figure 2 It is a technical roadmap of an improved CCUS full-process hybrid particle swarm optimization algorithm provided by an embodiment of the present invention;

[0053] Figure 3 It is a full-process modeling technical roadmap of an improved CCUS full-process hybrid particle swarm optimization algorithm provided by an embodiment of the present invention. Detailed Description of the Invention

[0054] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully conveyed to those skilled in the art.

[0055] The terms "including" and "having" and any variations thereof in the description of the embodiments of the present invention and the claims and the drawings are intended to cover non-exclusive inclusion. For example, including a series of steps or units.

[0056] The technical solution of the present invention for solving its technical problems is as follows: The improved CCUS full-process hybrid particle swarm optimization algorithm includes the following steps:

[0057] Embodiment 1

[0058] The technical solution adopted by the present invention to solve its technical problems is: The improved CCUS full-process hybrid particle swarm optimization algorithm includes the following steps:

[0059] Step 1, perform mathematical modeling on the CCUS subsystem.

[0060] Step 2, use the large system decomposition hierarchical and modular modeling method to establish a CCUS full-process engineering-economic model.

[0061] Step 2-1, perform economic modeling on the CCUS full process.

[0062] Step 2-1 includes the following steps:

[0063] CCUS subsystem technology selection, the capture system is post-combustion capture technology, the transportation system is pipeline transportation technology, and the oil displacement and storage system is CO2 oil displacement and storage fixation;

[0064] CCUS full-process basic assumptions, under the conditions of meeting the actual application and design requirements, the following assumptions are made: Do not consider the source-sink matching problem; Do not consider the pipeline design problem caused by terrain factors; The CO2 in the pipeline is evenly distributed, the pump station models are the same and evenly distributed;

[0065] CCUS full-process engineering model establishment, establish a full-process engineering model for the total CO2 capture amount, compressor and pump energy consumption, pipe diameter and wall thickness, pump station, and CO2 injection amount;

[0066] CCUS full-process economic model establishment, establish a full-process economic model for capture equipment investment, compressor and pump investment, pipeline construction investment, pump station investment, oil displacement and storage equipment investment, operation and maintenance costs, and electricity costs;

[0067] Step 2-2, perform optimization modeling of the full process.

[0068] Step 2-2 includes the following steps:

[0069] Establishment of the full-process economic model, comprehensively consider the economic models of each part of CCUS, and calculate the full-process economic cost;

[0070] Determination of constraint conditions, clarify the mass conservation, emission constraints, transportation constraints, storage constraints and their respective constraint scopes existing in the optimization modeling of the CCUS full-process economic model;

[0071] Sensitivity analysis, conduct global sensitivity analysis (GSA) on the parameters of the entire CCUS process, and obtain that the sensitivities of three parameters, namely flue gas flow rate, pipeline inlet pressure, and injection well inlet pressure, are the highest, and set them as decision variables;

[0072] Design of the genetic optimization algorithm for the entire process, design the genetic optimization algorithm for the entire CCUS process according to the economic model, constraints, and decision variables;

[0073] Step 2 - 3, model verification.

[0074] Step 2 - 3 includes the following steps:

[0075] Set the basic parameters of the genetic optimization algorithm for the entire process, input the economic model, initialize the population, and calculate the individual fitness value;

[0076] Select, cross, and mutate based on the fitness value of the economic model;

[0077] Optimize using the genetic optimization algorithm, judge whether the result converges, and if it converges, obtain the optimal parameters;

[0078] Step 3, use the penalty function to transform the optimization problem with mixed constraints into an unconstrained optimization problem.

[0079] Step 3 includes the following steps:

[0080] Construct the penalty function, and construct a reasonable penalty function form according to the economic model;

[0081] Determine the penalty factor of the penalty function, and select a reasonable penalty factor of the penalty function according to the economic model;

[0082] Combine the penalty function and the penalty factor to form an augmented objective function, and transform the constraints in the economic model of the entire process into unconstrained for subsequent solution;

[0083] Step 4, set the basic parameters of the algorithm, conduct hybrid particle swarm optimization, and finally obtain the best implementation plan for CCUS.

[0084] Step 4 includes the following steps:

[0085] Construct a hybrid particle swarm algorithm model, and construct the model according to the economic model, constraints, and decision variables;

[0086] Set the hyperparameters of the hybrid particle swarm, and set the values of the number of particles, inertia factor, and acceleration constant;

[0087] Combine the penalty function and the hybrid particle swarm algorithm, and calculate the fitness function of the particle swarm algorithm;

[0088] Model iteration and optimization, individual extreme value update, population extreme value update, determine whether to converge. If converged, obtain the parameters of the best implementation plan for the CCUS full-process economic model;

[0089] In step 2, the CCUS full-process engineering-economic model includes:

[0090] Taking the carbon dioxide capture system as the starting point, combined with the carbon dioxide capture method, a carbon dioxide capture model is formed.

[0091] Taking the mass flow of carbon dioxide after capture as the design condition and the minimum injection pressure of carbon dioxide into the oilfield as the constraint condition, a carbon dioxide transportation model including factors such as pipe diameter, pipe wall, pump station power, and number of pump stations is constructed.

[0092] Based on the outlet pressure of the carbon dioxide transportation pipeline, a carbon dioxide enhanced oil recovery and storage model is established. The carbon dioxide transportation model is a pipeline transportation model.

[0093] Embodiment 2

[0094] Figures 1 to 3 It is the best embodiment of the present invention. The following further describes the present invention in conjunction with the attached Figures 1 to 3 drawings.

[0095] As Figure 1 shown, the improved CCUS full-process hybrid particle swarm optimization algorithm includes the following steps:

[0096] Step 1, perform mathematical modeling on the CCUS subsystem.

[0097] Specifically: Divide the CCUS full-process system into several subsystems, and establish the engineering model of each subsystem by using methods such as material balance, energy balance of each subsystem, and equipment investment budget model based on the 0.6th power.

[0098] Step 2, use the large system decomposition hierarchical and modular modeling method to establish the CCUS full-process engineering-economic model.

[0099] In the existing technology, the full process of CCUS technology includes carbon dioxide capture, carbon dioxide transportation, and carbon dioxide enhanced oil recovery and storage. According to the CCUS full-process modeling process, it can be divided into the following steps:

[0100] Step 2-1, perform economic modeling on the CCUS full process.

[0101] Step 2-2, perform full-process optimization modeling.

[0102] Step 2-3, model verification.

[0103] By applying the principle of modular modeling and considering the connections between subsystems, a full-process engineering-economic model is established.

[0104] Combined with Figure 3 , taking a coal-fired power plant as an example, starting from the carbon dioxide capture system of the coal-fired power plant, the modeling of the carbon dioxide capture system is completed according to the carbon dioxide capture method. Then, taking the mass flow of the captured carbon dioxide as the design condition and the minimum injection pressure of carbon dioxide into the oilfield as the constraint condition, and considering factors such as terrain, temperature, and safety, the modeling of the carbon dioxide transportation system including factors such as wall thickness, pipe diameter, and the number of pump stations is completed. In addition to the pipeline transportation model, the model of the carbon dioxide transportation system also includes vehicle transportation models, train transportation models, ship transportation models, etc. Finally, based on the pipeline outlet pressure of the carbon dioxide transportation pipeline, a carbon dioxide enhanced oil recovery and storage model is established.

[0105] During the model establishment process, comprehensively consider the service life of the power plant and pipeline, capture capacity, etc., to make it match the carbon dioxide storage capacity of the oilfield. That is, within the service life of the coal-fired power plant and pipeline, the goal is to achieve the maximum carbon dioxide storage volume and recovery rate of the oilfield. During the modeling process, also comprehensively consider market prices, taxes, national subsidies, etc., to establish a CCUS full-process engineering-economic model.

[0106] Step 3, use the penalty function to transform the optimization problem with mixed constraints into an unconstrained optimization problem.

[0107] Based on the carbon dioxide capture model, carbon dioxide transportation model, and carbon dioxide enhanced oil recovery and storage model established in the above Step 1, with multiple physical quantities as decision variables, combined with the objective function of the total full-process cost, first use the penalty function to transform the optimization problem with mixed constraints included in the full process into an unconstrained optimization problem.

[0108] Step 4, set the basic parameters of the algorithm and perform hybrid particle swarm optimization to finally obtain the best implementation plan for CCUS.

[0109] Specifically: Set the basic parameters of the hybrid particle swarm optimization algorithm, including: velocity update parameters, number of iterations, population size, maximum and minimum values of individual velocities, and inertia factor, etc. Then randomly generate a population and initial particles, and initialize their velocities and positions. Finally, start the iteration to ultimately achieve the goal of the lowest total full-process cost.

[0110] By comparing the CCUS full process to a flock of birds and the particles to birds, each particle gradually approaches the optimal value through continuous iteration to achieve individual optimality, and the overall optimality is achieved through the continuous iteration of the overall particles.

[0111] Beneficial effects: The present invention takes into account the influence of factors such as transportation distance, transportation mode, transportation terrain, and transportation volume in the full-process economic cost, resulting in a lower economic cost for the optimized result;

[0112] The present invention transforms the mixed constraint problem into an unconstrained problem by constructing a penalty function.

[0113] The present invention uses the hybrid particle swarm optimization method to optimize the parameters of the full-process economic cost.

[0114] The above specific embodiments further elaborate on the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A CCUS full-process hybrid particle swarm cost optimization method, characterized in that: The optimization method comprises: Step S1: mathematical modeling of CCUS subsystem; Step S2: Use the large system decomposition hierarchical and modular modeling method to establish the CCUS full-process engineering-economic model; Step S3: using a penalty function to transform the optimization problem containing mixed constraints into an unconstrained optimization problem; Step S4: Set the basic parameters of the algorithm, perform hybrid particle swarm optimization, and finally obtain the best CCUS implementation plan.

2. A CCUS full-process hybrid particle swarm cost optimization method according to claim 1, characterized in that: The step S2: using the large system decomposition hierarchical and modular modeling method to establish the CCUS full process engineering-economic model specifically includes: Step S201: Conduct economic modeling for the entire CCUS process; Step S202: Perform optimization modeling of the entire process to obtain an optimization model; Step S203: verifying the optimization model.

3. A CCUS full-process hybrid particle swarm cost optimization method according to claim 2, characterized in that: The step S201: economic modeling of the CCUS whole process specifically includes: The CCUS subsystem technology selection includes post-combustion capture technology for the capture system, pipeline transportation technology for the transportation system, and CO2 flooding and storage system for the oil recovery and storage fixation; Basic assumptions for the CCUS process, under the conditions of meeting actual application and design requirements, are as follows: source-sink matching is not considered; pipeline design issues caused by terrain factors are not considered; CO2 is evenly distributed in the pipeline, and the pump station models are consistent and equidistantly distributed; The CCUS full-process engineering model is established to establish the total CO2 capture, compressor and pump energy consumption, pipe diameter and wall thickness, pump station, and CO2 injection volume; The full-process economic model of CCUS is established, and a full-process economic model is established for capture equipment investment, compressor and pump investment, pipeline construction investment, pump station investment, oil recovery and storage equipment investment, operation and maintenance costs, and electricity charges.

4. A CCUS full-process hybrid particle swarm cost optimization method according to claim 2, characterized in that: The step S202: performing optimization modeling of the entire process, and obtaining the optimization model specifically includes: Establish a full-process economic model, comprehensively consider the economic models of each part of CCUS, and calculate the economic cost of the full process; Determine the constraints and clarify the mass conservation, emission constraints, transportation constraints, storage constraints and their respective constraint ranges in the optimization modeling of the CCUS full-process economic model; Sensitivity analysis: A global sensitivity analysis (GSA) was performed on the parameters of the entire CCUS process. It was found that the three parameters of flue gas flow, pipeline inlet pressure, and injection well inlet pressure had the highest sensitivity and were set as decision variables. Design of full-process genetic optimization algorithm, design of CCUS full-process genetic optimization algorithm based on economic model, constraints and decision variables.

5. A CCUS full-process hybrid particle swarm cost optimization method according to claim 2, characterized in that: The step S203: verifying the optimization model specifically includes: Set the basic parameters of the full-process genetic optimization algorithm, input the economic model, initialize the population, and calculate the individual fitness value; The fitness value of the economic model is selected, crossed, and mutated; The genetic optimization algorithm searches for the best solution and determines whether the result converges. If so, the optimal parameters are obtained.

6. A CCUS full-process hybrid particle swarm cost optimization method according to claim 1, characterized in that: The step S3: using a penalty function to transform the optimization problem containing mixed constraints into an unconstrained optimization problem; specifically includes: Construct a penalty function and construct a reasonable penalty function form based on the economic model; Determine the penalty factor of the penalty function and select a reasonable penalty factor based on the economic model; The penalty function and penalty factor are combined to form an augmented objective function, which transforms the constraints in the full-process engineering economic model into unconstrained ones for subsequent solution.

7. A CCUS full-process hybrid particle swarm cost optimization method according to claim 1, characterized in that: The step S4: setting basic algorithm parameters, performing hybrid particle swarm optimization, and finally obtaining the best CCUS implementation plan specifically includes: Step S401: constructing a hybrid particle swarm algorithm model; Step S402: setting of hybrid particle swarm hyperparameters; Step S403: combining the penalty function and the hybrid particle swarm algorithm to calculate the fitness function of the particle swarm algorithm; Step S404: The model is iterated to optimize, individual extreme values ​​are updated, group extreme values ​​are updated, and whether convergence has occurred is determined. If convergence has occurred, the optimal implementation plan parameters for the CCUS full-process economic model are obtained.

8. The CCUS full-process hybrid particle swarm cost optimization method according to claim 1, characterized in that: The step S401: constructing a hybrid particle swarm algorithm model specifically includes: constructing a model according to an economic model, constraint conditions and decision variables.

9. A CCUS full-process hybrid particle swarm cost optimization method according to claim 1, characterized in that: The step S402: setting the hyperparameters of the hybrid particle swarm specifically includes: setting the values ​​of the number of particles, the inertia factor, and the acceleration constant.

10. A CCUS full-process hybrid particle swarm cost optimization method according to claim 1, characterized in that: The CCUS full process engineering-economic model in step S2 specifically includes: Starting with the CO2 capture system, combined with the CO2 capture method, a CO2 capture model is formed; Taking the mass flow of captured carbon dioxide as the design condition and the minimum injection pressure of carbon dioxide into the oil field as the constraint condition, a carbon dioxide transportation model including pipe diameter, pipe wall, pump station power, and number of pump stations was constructed; A carbon dioxide flooding and storage model is established based on the outlet pressure of the carbon dioxide transportation pipeline.

11. A CCUS full-process hybrid particle swarm cost optimization method according to claim 10, characterized in that: The carbon dioxide transportation model is a pipeline transportation model.

12. A CCUS full-process hybrid particle swarm cost optimization system, using a CCUS full-process hybrid particle swarm cost optimization method according to any one of claims 1 to 11, characterized in that: The optimization system comprises: Mathematical modeling module, used for mathematical modeling of CCUS subsystem; The full-process engineering-economic model building module is used to build the CCUS full-process engineering-economic model by using the large-system hierarchical decomposition and modular modeling method; Penalty function optimization module, used to transform the optimization problem containing mixed constraints into an unconstrained optimization problem using penalty functions; The particle swarm optimization module is used to set the basic parameters of the algorithm, perform hybrid particle swarm optimization, and finally obtain the best implementation plan for CCUS.

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