A carbon emission optimization prediction model construction method and system

Abstract

The present application belongs to the technical field of carbon emission, and discloses a kind of carbon emission optimization prediction model construction method, the present application is through the establishment of energy-saving mechanism research under thermodynamic model, reveal the energy conversion and transmission law in the process of CO2 capture by chemical absorption method, build the thermodynamic model in the process of CO2 capture by chemical absorption method, through theoretical analysis, optimize capture process, reduce energy consumption, improve energy efficiency.Through the model research of flue gas carbon dioxide capture of mixed amine absorbent: establish the mathematical model of mixed amine absorbent for capturing CO2 in flue gas, through literature data and simulation analysis, study the absorption mechanism and performance characteristics of mixed amine absorbent in the capture process, optimize the absorbent formula, improve the capture efficiency and stability.In-depth understanding of thermodynamic mechanism: through the construction and analysis of thermodynamic cycle of CO2 capture by chemical absorption method, the influence of different parameters (such as CO2 volume concentration in flue gas, liquid-gas ratio, desorption temperature, etc.) on system energy efficiency is clear.

Description

Technical Field

[0001] This invention belongs to the field of carbon emission technology, and in particular relates to a method and system for constructing an optimized prediction model for carbon emissions. Background Technology

[0002] Global climate change and carbon emission control have become major global issues of widespread concern in the international community. To achieve carbon neutrality, existing technological pathways mainly fall into two categories: one is to focus on energy supply-side reform, promoting the development of renewable energy technologies and improving the efficiency of existing energy systems; the other is to develop negative emission technologies such as carbon capture, utilization, and storage, and achieve effective control of carbon dioxide emissions through proactive management measures.

[0003] Carbon capture, utilization, and storage (CCUS) is a crucial technological guarantee for achieving the goals of low-carbon utilization of fossil energy and carbon neutrality. Among these technologies, CO2 capture technology directly determines the purity of the gas source processed and the operating cost of the CCUS system. Its process energy consumption accounts for more than 70% of the total energy consumption of a CCUS-EOR (carbon dioxide enhanced oil recovery) system, making it the core and critical link in the entire CCUS chain. Currently, commonly used industrial CO2 capture technologies mainly include absorption, adsorption, cryogenic distillation, membrane separation, and combinations of these methods.

[0004] Among them, CO2 chemical absorption selectively absorbs CO2 from a mixed gas using an alkaline absorbent solution. It utilizes the differences in solubility and reaction characteristics of different gases in a solvent to achieve gas separation and purification. Of all CO2 separation and capture technologies, CO2 chemical absorption is the most technologically mature and widely used in industrial applications. This method boasts advantages such as high separation purity, high removal efficiency, and a wide range of applicable gas sources, making it the CO2 capture technology with the greatest potential for large-scale industrial application. Currently, numerous industrial demonstration projects are in operation worldwide.

[0005] Compared to flue gas from coal-fired power plants, flue gas from gas-fired power plants is characterized by low CO2 concentration (≤5%) and high O2 content, placing higher demands on the adaptability and energy efficiency of carbon capture technologies. Post-combustion CO2 capture technology can preserve the original power generation unit's function and structure to the greatest extent. The vast majority of low-pressure, low-to-medium concentration CO2 sources come from the exhaust gas after the combustion of fuels such as coal, oil, and natural gas. Therefore, post-combustion chemical absorption is the mainstream technical route for capturing CO2 suitable for such gas sources.

[0006] Chemical absorption is the mainstream technology for CO2 capture, and its core performance depends on the development and application of highly efficient absorbents. With the continuous deepening of related scientific research, CO2 capture processes have shown considerable technological potential in improving capture efficiency and reducing operating energy consumption and costs, providing technical support for building a complete industrial chain for CO2 capture, utilization, and storage, and driving the development of related industries.

[0007] Chemical absorption is one of the earliest industrially applied CO2 capture technologies, having been implemented as early as the 1930s. This technology typically uses alkaline solvents as absorbents. During absorption, the solvent reacts chemically with the acidic CO2 gas to achieve capture. Under specific conditions, the enriched solution undergoes reverse decomposition, releasing CO2 and completing the absorption-desorption cycle. Currently, commonly used industrial absorption systems, such as organic amine solutions, offer advantages such as fast absorption reaction rates, high CO2 removal rates, and low raw material costs. However, they also suffer from drawbacks such as highly corrosive solvents and high energy consumption during desorption and regeneration processes, severely limiting their widespread application in large-scale industrial CO2 capture scenarios.

[0008] Since 2000, scholars both domestically and internationally have focused on developing novel chemical absorption systems and optimizing the design of absorption-regeneration processes to address the aforementioned shortcomings. These absorption systems primarily utilize organic amine solvents, amino acid salts, and ionic liquids, including types such as physicochemical composite absorption solvents and phase change absorbents. Simultaneously, various gas-liquid absorption mass transfer enhancement devices and novel regeneration technologies have been developed to achieve low-energy, large-scale CO2 absorption and capture. In recent years, several demonstration plants with capacities of tens of thousands and hundreds of thousands of tons have been built in China using the CO2 chemical absorption method. These demonstration plants mostly employ absorption-regeneration tower equipment, effectively reducing system energy consumption and costs through novel absorption systems and enhanced tower internal design. However, this technology still faces many key scientific and technological challenges that require breakthroughs, hindering further cost and energy reduction and failing to meet the demands of large-scale industrial CO2 capture applications.

[0009] The high energy consumption of absorption-based carbon capture systems stems primarily from the regeneration of the absorbent in the desorbent tower. Currently, most post-combustion carbon capture power plants use steam extraction from low-pressure cylinders within the power plant to provide heat to the reboiler in the desorbent tower. This extraction can reach up to 50% of the total steam volume in the cylinders, directly leading to a significant reduction in the overall power plant efficiency. The development of renewable energy technologies offers a new feasible path for energy conservation and emission reduction in absorption-based carbon capture technology. Coupled with renewable energy systems, the carbon capture system can effectively reduce energy losses during decarbonization.

[0010] Among existing technologies, Wibberley was the first to propose using solar energy as an auxiliary energy source in absorption-based carbon capture systems. Subsequently, teams such as Rochelle and the Spanish Research Centre for Environmental Energy Technology explored the coupling methods between solar energy systems and carbon capture systems. The Milani team in Australia proposed a novel coupling method for a solar-assisted carbon capture system. In this system, the rich liquid output from the absorption tower is directly heated by the solar collector after passing through a heat exchanger, and then enters a CO2 flash separation device to complete the desorption process. This system replaces the traditional desorption tower + reboiler structure, realizing the direct utilization of solar energy. Domestically, Han Zhonghe et al. from North China Electric Power University proposed various integration methods for solar energy and economizer-heated feedwater. Wang et al. from Tianjin University conducted experimental research on solar-assisted carbon capture systems, using a 30wt% MEA solution with a liquid-to-gas ratio (L / G) of 4.0 L / m³. 3 Under ideal operating conditions, the lowest regenerative energy consumption of 4.2 MJ / kg CO2 was achieved. Besides solar energy, existing technologies also include research on the coupling of renewable energy sources such as wind, biomass, and geothermal energy with carbon capture systems.

[0011] To improve the energy efficiency of absorption carbon capture systems, clarifying the internal energy conversion mechanism is crucial. In recent years, Zhao et al. from Tianjin University proposed a thermodynamic carbon pump model from a thermodynamic perspective, applying it to the energy efficiency evaluation of adsorption-based carbon capture technology. They further explored the application of this model and the concept of circulation in absorption technology. Wang et al., for typical MEA absorption carbon capture technology, introduced the concept of thermodynamic circulation, constructing isothermal and adiabatic absorption cycles, and analyzed the impact of key circulation parameters on system energy efficiency. Xu et al., based on the carbon pump model, introduced Gibbs free energy change and conducted energy efficiency evaluation of absorption-based carbon capture systems using corresponding energy efficiency indicators.

[0012] However, the existing research mentioned above is limited to the system or process level. On the one hand, due to the lack of in-depth quantitative understanding of the energy conversion mechanism inside the carbon capture system, it is impossible to reveal the theoretical upper limit of the system's energy efficiency and to achieve global optimization of the system's energy efficiency. On the other hand, existing technologies lack coupling optimization and prediction methods for the entire carbon capture process and the entire carbon emission chain. They are unable to accurately predict and globally optimize the carbon emission levels and system energy efficiency under different operating conditions, different gas sources, and different energy coupling modes. This makes it difficult to support the stable and efficient operation of large-scale carbon capture systems and the refined management of carbon emissions, and cannot meet the practical application needs of optimizing and managing the entire carbon emission process under the background of carbon neutrality. Summary of the Invention

[0013] To address the problems existing in the prior art, this invention provides a method for constructing an optimized carbon emission prediction model.

[0014] This invention is implemented as follows: A method for constructing a carbon emission optimization prediction model includes:

[0015] Step 1: Thermodynamic model construction;

[0016] Thermodynamics, starting from energy conversion efficiency and limits, reveals the macroscopic laws governing the conversion of heat with other forms of energy, serving as a tool for studying the energy efficiency of absorption carbon capture; including energy conversion in absorption carbon capture, establishing a decoupling model for absorption carbon pumps, and establishing an ideal cycle representation for thermally driven carbon capture;

[0017] Step 2, thermodynamic cycle analysis of carbon capture by absorption method;

[0018] The decoupled model of the absorption method thermodynamic carbon pump ignores the boundary conditions of carbon source and carbon sink. Due to the limitations of the actual solubility of the absorbent and the non-equilibrium of the absorption and desorption process, the actual cycle deviates further from the ideal cycle. Analyzing the actual cycle of carbon capture by absorption method helps to understand the actual state of the operation process.

[0019] Step 3: Study of absorbent properties based on the equation of state of the statistical associative fluid theory (SAFT);

[0020] Step 4: Entropy analysis of the energy consumption process of the absorption carbon capture system.

[0021] Furthermore, the energy conversion of the absorption-type carbon capture:

[0022] In the study of carbon capture cycle by absorption, the working fluid is abstracted from the perspective of energy conversion into the internal energy conversion process of the system as a "thermal-Gibbs free energy change" conversion, and the efficiency of thermal drive mode is analyzed.

[0023] Furthermore, the decoupling model for the absorption-method carbon pump is established as follows:

[0024] The ideal cycle of the thermodynamic carbon pump is decoupled to construct a thermodynamic model of "heat engine-carbon pump". The model is based on the following assumptions, and the concepts of cycle and working fluid are introduced. In actual operation, carbon capture by absorption method completes energy conversion through the cycle of working fluid.

[0025] Furthermore, the establishment of an ideal cycle image representation for thermally driven carbon capture is as follows:

[0026] Based on thermodynamic cycles, the performance analysis of the ideal cycle of the absorption thermodynamic carbon pump on the actual carbon capture cycle is carried out to clarify the upper limit of energy conversion between heat and ΔG and to evaluate the system efficiency under different operating conditions.

[0027] Furthermore, the physical properties of the absorbent based on the equation of state of the Statistical Associative Fluid Theory (SAFT) are studied:

[0028] Within the SAFT framework, key reactions in the absorption process of amine solutions are analyzed, and their influence on the reaction is characterized by the design of association parameters. Combined with thermodynamic theory, phase equilibrium calculations are performed on mixed amines to optimize the model, making it more general and expanding the applicability of the model parameters.

[0029] Furthermore, the entropy analysis of the energy consumption process of the carbon capture system based on absorption method is as follows:

[0030] A carbon capture cycle is constructed using the basic physical properties of the absorbent to decouple key energy-consuming processes, calculate and analyze process energy consumption and entropy indices; entropy analysis is performed on each process of the absorption system using thermodynamic mechanisms, and the effects of desorption temperature and CO2 concentration in the waste gas on the energy consumption and entropy production of key energy-consuming processes are considered.

[0031] Another objective of this invention is to provide a carbon emission optimization prediction model construction system comprising:

[0032] The model building module is used for thermodynamic-based model building. Thermodynamics, starting from energy conversion efficiency and limits, reveals the macroscopic laws followed when heat is converted to other forms of energy, serving as a tool for studying the energy efficiency of absorption carbon capture. It includes energy conversion in absorption carbon capture, establishing a decoupling model for absorption carbon pumps, and establishing an ideal cycle representation of heat-driven carbon capture.

[0033] The cycle analysis module is used for thermodynamic cycle analysis of carbon capture by absorption method. The decoupled model of carbon pump in absorption method ignores the boundary conditions of carbon source and carbon sink. Due to the limitations of actual solubility of absorbent and non-equilibrium of absorption and desorption process, the actual cycle deviates further from the ideal cycle. Analyzing the actual cycle of carbon capture by absorption method can help understand the actual state of the operation process.

[0034] The absorbent property analysis module is used for the study of absorbent properties based on the equation of state of the Statistical Associating Fluid Theory (SAFT). Under the SAFT framework, it analyzes the key reactions in the absorption process of amine solutions and characterizes their influence on the reaction by designing association parameters. Combining thermodynamic theory, it performs phase equilibrium calculations on mixed amines, optimizes the model, makes the model more general, and expands the applicability range of the model parameters.

[0035] The entropy analysis module is used for entropy analysis of the energy consumption process in the absorption carbon capture system. It constructs a carbon capture cycle using the basic physical properties of the absorbent, decouples key energy-consuming links, calculates process energy consumption and entropy indices, and conducts analysis. It uses thermodynamic mechanisms to perform entropy analysis on each process of the absorption system, and considers the impact of desorption temperature and CO2 concentration in the exhaust gas on the energy consumption and entropy production of key energy-consuming processes.

[0036] Another object of the present invention is to provide a computer device including a memory and a processor, the memory storing a computer program, which, when executed by the processor, causes the processor to perform the steps of the carbon emission optimization prediction model construction method.

[0037] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the carbon emission optimization prediction model construction method.

[0038] Another objective of this invention is to provide an information data processing terminal for implementing the carbon emission optimization prediction model construction system.

[0039] Based on the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solution to be protected by this invention are as follows:

[0040] By establishing a thermodynamic model to study the energy-saving mechanism, this study reveals the energy conversion and transfer laws in the process of CO2 capture by chemical absorption. A thermodynamic model of CO2 capture by chemical absorption is constructed, and through theoretical analysis, the capture process is optimized to reduce energy consumption and improve energy efficiency.

[0041] A model study on CO2 capture in flue gas using mixed amine absorbents was conducted. A mathematical model for CO2 capture in flue gas using mixed amine absorbents was established. Through literature data and simulation analysis, the absorption mechanism and performance characteristics of mixed amine absorbents during the capture process were studied, and the absorbent formulation was optimized to improve capture efficiency and stability.

[0042] In-depth understanding of thermodynamic mechanisms: By constructing and analyzing the thermodynamic cycle of CO2 capture by chemical absorption method, the influence of different parameters (such as CO2 volume concentration in flue gas, liquid-to-gas ratio, desorption temperature, etc.) on system energy efficiency is clarified.

[0043] The study reveals the energy loss mechanism during absorption and desorption, and proposes optimization schemes to reduce the energy consumption per unit of capture.

[0044] Performance optimization of mixed amine absorbents: Through model studies, the mixed amine combination with the best overall performance was screened out, which significantly improved absorption rate, absorption capacity and regeneration energy consumption compared with traditional single absorbents (such as MEA).

[0045] Clarifying the reaction mechanism and kinetic characteristics of mixed amine absorbents provides a theoretical basis for industrial applications.

[0046] The expected benefits and commercial value of the technical solution of this invention after transformation are as follows:

[0047] It has reached the international advanced level, and achieved a leading position in some areas. Through in-depth research on thermodynamic mechanisms and performance optimization of mixed amine absorbents, the overall capture efficiency and economic benefits have been improved.

[0048] With increasing global attention to climate change, carbon capture technology, as an important means of reducing greenhouse gas emissions, has broad market prospects.

[0049] With the increasing global demand for carbon emission reduction, chemical absorption CO2 capture technology will see a broad market prospect. This technology has significant application potential, especially in high-emission industries such as power generation, chemicals, and rubber.

[0050] The widespread application of the project's results will help improve the carbon emission control capabilities of relevant enterprises, reduce their operating costs, and bring them additional carbon trading revenue. Meanwhile, as the technology matures and costs decrease, chemical absorption carbon capture technology will gradually achieve commercial operation, bringing considerable economic returns to investors.

[0051] The implementation of this project will help reduce greenhouse gas emissions and mitigate global warming, playing a vital role in protecting the ecological environment and promoting sustainable development. Furthermore, the project's widespread application will drive the development of related industrial chains, creating more job opportunities and economic benefits. Attached Figure Description

[0052] Figure 1 This is a flowchart of the carbon emission optimization prediction model construction method provided in the embodiments of the present invention.

[0053] Figure 2 This is a structural block diagram of the carbon emission optimization prediction model construction system provided in the embodiments of the present invention.

[0054] Figure 3 This is a technical roadmap for the carbon emission optimization prediction model provided in the embodiments of the present invention.

[0055] Figure 4 This is a diagram illustrating the selection and optimization of different mixed absorbents provided in this embodiment of the invention; a) different system mass ratios, synergistic numbers, and mass transfer coefficients; b) CO2 desorption capacity; c) CO2 absorption capacity;

[0056] Figure 5 This is a flowchart of CO2 absorption-desorption provided in an embodiment of the present invention;

[0057] Figure 6 This is a schematic diagram of component input provided in an embodiment of the present invention;

[0058] Figure 7 This is a schematic diagram of the Basis property package provided in an embodiment of the present invention;

[0059] Figure 8 This is a schematic diagram of the absorption tower structure and parameters provided in an embodiment of the present invention;

[0060] Figure 9 This is a schematic diagram of the absorption tower operation results provided in an embodiment of the present invention;

[0061] Figure 10 This is a schematic diagram of a desorption tower simulation setup provided in an embodiment of the present invention;

[0062] Figure 11 This is a schematic diagram of regeneration energy consumption and capture rate provided in an embodiment of the present invention;

[0063] Figure 12 This is a normal probability diagram of the residuals provided in the embodiments of the present invention;

[0064] Figure 13 This is a distribution diagram of predicted values ​​and residuals provided in an embodiment of the present invention;

[0065] Figure 14 This is a distribution diagram of the running sequence and residuals provided in an embodiment of the present invention;

[0066] Figure 15 This is a response surface curve of regeneration energy consumption versus amine solution provided in an embodiment of the present invention;

[0067] Figure 16 This is a response surface curve of the capture rate versus the amine solution provided in an embodiment of the present invention. Detailed Implementation

[0068] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0069] like Figure 1 As shown, the carbon emission optimization prediction model construction method provided by this embodiment of the invention includes the following steps:

[0070] S101, model construction based on thermodynamics;

[0071] Thermodynamics, starting from energy conversion efficiency and limits, reveals the macroscopic laws governing the conversion of heat with other forms of energy, serving as a tool for studying the energy efficiency of absorption carbon capture; including energy conversion in absorption carbon capture, establishing a decoupling model for absorption carbon pumps, and establishing an ideal cycle representation for thermally driven carbon capture;

[0072] S102, thermodynamic cycle analysis of carbon capture by absorption method;

[0073] The decoupled model of the absorption method thermodynamic carbon pump ignores the boundary conditions of carbon source and carbon sink. Due to the limitations of the actual solubility of the absorbent and the non-equilibrium of the absorption and desorption process, the actual cycle deviates further from the ideal cycle. Analyzing the actual cycle of carbon capture by absorption method helps to understand the actual state of the operation process.

[0074] S103, Study on absorbent properties based on the equation of state of the statistical associative fluid theory SAFT;

[0075] S104, Entropy analysis of energy consumption process in absorption carbon capture system.

[0076] The energy conversion of absorption carbon capture provided in this embodiment of the invention:

[0077] In the study of carbon capture cycle by absorption, the working fluid is abstracted from the perspective of energy conversion into the internal energy conversion process of the system as a "thermal-Gibbs free energy change" conversion, and the efficiency of thermal drive mode is analyzed.

[0078] The decoupling model of the absorption-type carbon pump provided in this embodiment of the invention:

[0079] The ideal cycle of the thermodynamic carbon pump is decoupled to construct a thermodynamic model of "heat engine-carbon pump". The model is based on the following assumptions, and the concepts of cycle and working fluid are introduced. In actual operation, carbon capture by absorption method completes energy conversion through the cycle of working fluid.

[0080] The ideal cycle image representation of thermally driven carbon capture provided by the embodiments of the present invention:

[0081] Based on thermodynamic cycles, the performance analysis of the ideal cycle of the absorption thermodynamic carbon pump on the actual carbon capture cycle is carried out to clarify the upper limit of energy conversion between heat and ΔG and to evaluate the system efficiency under different operating conditions.

[0082] The absorption agent property study based on the statistical associative fluid theory (SAFT) equation of state provided in this embodiment of the invention:

[0083] Within the SAFT framework, key reactions in the absorption process of amine solutions are analyzed, and their influence on the reaction is characterized by the design of association parameters. Combined with thermodynamic theory, phase equilibrium calculations are performed on mixed amines to optimize the model, making it more general and expanding the applicability of the model parameters.

[0084] Entropy analysis of the energy consumption process of the absorption carbon capture system provided in this embodiment of the invention:

[0085] A carbon capture cycle is constructed using the basic physical properties of the absorbent to decouple key energy-consuming processes, calculate and analyze process energy consumption and entropy indices; entropy analysis is performed on each process of the absorption system using thermodynamic mechanisms, and the effects of desorption temperature and CO2 concentration in the waste gas on the energy consumption and entropy production of key energy-consuming processes are considered.

[0086] like Figure 2 As shown, the carbon emission optimization prediction model construction system provided in this embodiment of the invention includes:

[0087] The model building module is used for thermodynamic-based model building. Thermodynamics, starting from energy conversion efficiency and limits, reveals the macroscopic laws followed when heat is converted to other forms of energy, serving as a tool for studying the energy efficiency of absorption carbon capture. It includes energy conversion in absorption carbon capture, establishing a decoupling model for absorption carbon pumps, and establishing an ideal cycle representation of heat-driven carbon capture.

[0088] The cycle analysis module is used for thermodynamic cycle analysis of carbon capture by absorption method. The decoupled model of carbon pump in absorption method ignores the boundary conditions of carbon source and carbon sink. Due to the limitations of actual solubility of absorbent and non-equilibrium of absorption and desorption process, the actual cycle deviates further from the ideal cycle. Analyzing the actual cycle of carbon capture by absorption method can help understand the actual state of the operation process.

[0089] The absorbent property analysis module is used for the study of absorbent properties based on the equation of state of the Statistical Associating Fluid Theory (SAFT). Under the SAFT framework, it analyzes the key reactions in the absorption process of amine solutions and characterizes their influence on the reaction by designing association parameters. Combining thermodynamic theory, it performs phase equilibrium calculations on mixed amines, optimizes the model, makes the model more general, and expands the applicability range of the model parameters.

[0090] The entropy analysis module is used for entropy analysis of the energy consumption process in the absorption carbon capture system. It constructs a carbon capture cycle using the basic physical properties of the absorbent, decouples key energy-consuming links, calculates process energy consumption and entropy indices, and conducts analysis. It uses thermodynamic mechanisms to perform entropy analysis on each process of the absorption system, and considers the impact of desorption temperature and CO2 concentration in the exhaust gas on the energy consumption and entropy production of key energy-consuming processes.

[0091] Another object of the present invention is to provide a computer device including a memory and a processor, the memory storing a computer program, which, when executed by the processor, causes the processor to perform the steps of the carbon emission optimization prediction model construction method.

[0092] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the carbon emission optimization prediction model construction method.

[0093] Another objective of this invention is to provide an information data processing terminal for implementing the carbon emission optimization prediction model construction system.

[0094] The systematic methodology for constructing carbon emission optimization prediction models includes:

[0095] Specific implementation of the present invention:

[0096] This paper focuses on energy conservation and consumption reduction in carbon capture systems, and studies the mechanism of absorption-based carbon capture from different perspectives. The main research contents are as follows:

[0097] 1. Thermodynamic-based model construction

[0098] Thermodynamics, starting with energy conversion efficiency and limits, reveals the macroscopic laws governing the conversion of heat with other forms of energy, serving as a tool for studying the energy efficiency of carbon capture by absorption methods.

[0099] (1) Absorption-based carbon capture energy conversion

[0100] In the study of carbon capture cycle by absorption, the working fluid is abstracted from the perspective of energy conversion into the internal energy conversion process of the system as a "thermal-Gibbs free energy change" conversion, and the efficiency of thermal drive mode is analyzed.

[0101] (2) Establish a decoupling model for the absorption carbon pump

[0102] The ideal cycle of the thermodynamic carbon pump is decoupled to construct a thermodynamic model of "heat engine-carbon pump". The model is based on the following assumptions, and the concepts of cycle and working fluid are introduced. In actual operation, carbon capture by absorption method completes energy conversion through the cycle of working fluid.

[0103] (3) Establish an ideal cycle representation of thermally driven carbon capture

[0104] Based on thermodynamic cycles, the performance analysis of the ideal cycle of the absorption thermodynamic carbon pump on the actual carbon capture cycle is carried out to clarify the upper limit of energy conversion between heat and ΔG and to evaluate the system efficiency under different operating conditions.

[0105] 2. Thermodynamic Cycle Analysis of Carbon Capture by Absorption Method

[0106] The decoupled thermodynamic carbon pump model of the absorption method neglects boundary condition constraints such as carbon source and carbon sink. Limited by the actual solubility of the absorbent and the non-equilibrium of the absorption and desorption processes, the actual cycle deviates further from the ideal cycle. Analyzing the actual cycle of carbon capture using the absorption method helps to understand the actual state of the operation process.

[0107] 3. Study on absorbent properties based on equation of state of Statistical Associative Fluid Theory (SAFT)

[0108] Within the SAFT framework, key reactions in the absorption process of amine solutions are analyzed, and their impact on the reaction is characterized by the design of association parameters. Combined with thermodynamic theory, phase equilibrium calculations are performed on mixed amines to optimize the model, making it more general and expanding the applicability of the model parameters.

[0109] 4. Entropy Analysis of Energy Consumption Process in Absorption-Based Carbon Capture Systems

[0110] A carbon capture cycle is constructed using the basic physical properties of the absorbent, key energy-consuming processes are decoupled, and process energy consumption and entropy indices are calculated and analyzed. Entropy analysis is performed on each process of the absorption system using thermodynamic mechanisms, considering the impact of desorption temperature and CO2 concentration in the waste gas on the energy consumption and entropy production of key energy-consuming processes.

[0111] The scientific problem to be solved:

[0112] Currently, low-concentration gas source carbon capture technologies for refining and chemical enterprises can be mainly divided into physical adsorption, chemical absorption, and membrane separation. Among them, chemical absorption is the most advantageous carbon capture technology. This research focuses on energy conservation and consumption reduction in carbon capture and aims to solve the following problems in absorption-based carbon capture:

[0113] (1) It is proposed to improve the application of absorption CO2 capture technology under thermodynamic theory by establishing a thermodynamic model based on carbon pump theory and constructing a thermodynamic cycle analysis theory from ideal cycle to actual cycle.

[0114] (2) It is proposed to decouple the key energy-consuming processes of the actual cycle by constructing an actual cycle of carbon capture by absorption method; to carry out entropy analysis on the main components of the carbon capture system, and to study the energy consumption calculation and entropy analysis of key links, so as to solve the problem of optimizing process parameters and improving energy efficiency.

[0115] Research objectives:

[0116] This study investigates the thermodynamic mechanism of carbon capture through chemical absorption, aiming to enhance energy conservation and reduce consumption.

[0117] (1) It is proposed to study the energy conversion and transfer law in the process of CO2 capture by chemical absorption method by establishing a thermodynamic model, construct a thermodynamic model in the process of CO2 capture by chemical absorption method, optimize the capture process through theoretical analysis, reduce energy consumption and improve energy efficiency.

[0118] (2) Research on the carbon dioxide capture model of flue gas using mixed amine absorbent: Establish a mathematical model for capturing CO2 in flue gas using mixed amine absorbent, and study the absorption mechanism and performance characteristics of mixed amine absorbent in the capture process through literature data and simulation analysis, optimize absorbent formulation, and improve capture efficiency and stability.

[0119] 2. Proposed research methods, technical route, experimental plan, and feasibility analysis

[0120] Research Methods

[0121] (1) Literature review

[0122] By reviewing relevant domestic and international literature on the CO2 capture mechanism of chemical absorption, we can understand the latest research progress, main research results, and existing problems and challenges of this theory.

[0123] (2) Theoretical Analysis

[0124] Based on thermodynamics, kinetics and other theories, a carbon pump model is established to analyze the energy conversion process of the system.

[0125] (3) Numerical calculation and simulation

[0126] Phase equilibrium calculations and analyses were performed using the SAFT equation of state and fundamental thermodynamic relations. Based on a novel mixed amine absorbent, a chemical absorption carbon capture model was built to analyze fluid flow, mass transfer, and heat transfer processes, and to optimize the process flow and parameter settings.

[0127] Technical routes such as Figure 3 :

[0128] Figure 4 Selection and optimization of different mixed absorbents:

[0129] The non-aqueous solution performance of EMEA+DEEA is far superior to that reported in other literature, with an absorption capacity of 0.68 mol CO2 / mol EMEA and a desorption capacity of 0.62 mol CO2 / mol EMEA.

[0130] Evidence related to the technical effects obtained by the embodiments of the present invention.

[0131] 1. This invention simulates and obtains key performance data under different ratios of various amine components by changing their concentration proportions. Based on this, a response surface methodology (RSM) is introduced to model and analyze the simulation data, quantifying the impact of the concentrations of each amine component and their interactions on CO2 capture rate and regeneration energy consumption. Using the chemical process simulation software Aspen HYSYS, a carbon capture process model is established with a multi-component mixed amine solution composed of MEA, DEA, and MDEA as its core. The optimal ratio is obtained through RSM: MDEA concentration 10 wt%, DEA concentration 1 wt%, and MEA concentration 30 wt%, at which point the capture rate is 99.64% and the regeneration energy consumption is 1.77 GJ / tCO2. Compared to the baseline MEA process (capture rate 86.4%, energy consumption 1.95 GJ / tCO2), the capture rate is increased by 13.24 percentage points, and the energy consumption is reduced by 9.2%. This method provides a reference for the development and optimization of low-energy, high-efficiency carbon capture processes.

[0132] 2. Model building and simulation process

[0133] 2.1 Selection and Characteristic Analysis of Amine Absorbents

[0134] This invention selects three amine absorbents—monoethanolamine (MEA), diethanolamine (DEA), and N-methyldiethanolamine (MDEA)—to form a multi-component mixed amine system. MEA, as a representative of first-generation amine absorbents, has the advantage of fast absorption rate, but suffers from high regeneration energy consumption and strong corrosivity. DEA, as a secondary amine, has an absorption rate and regeneration energy consumption between MEA and MDEA. MDEA, a tertiary amine, reacts with CO2 through an alkaline catalytic mechanism, exhibiting low regeneration energy consumption, but with a slow absorption rate. Combining these three amines aims to leverage their respective advantages, utilizing MDEA to provide a low-energy foundation, and further adjusting and optimizing the overall performance through MEA and DEA, in order to achieve a balance between high capture rate and low regeneration energy consumption.

[0135] 2.2 Construction based on the Aspen HYSYS model

[0136] 2.2.1 Aspen Hysys Process Flow Setup

[0137] When simulating and modeling the CO2 removal process using the HYSYS software, the first step is to define the flue gas composition. This is done by adding components such as CO2, O2, N2, and H2O, and selecting appropriate property packages to ensure the accuracy of phase equilibrium behavior. Next, an absorption tower model is established, using the flue gas stream as the feed stream and the amine organic amine absorbent as another feed stream. Parameters such as the packing type and tower height are set, and the top outlet is determined to be purified gas, while the bottom outlet is rich liquid. Next, a heat exchange and regeneration system is constructed. The rich liquid from the bottom of the absorption tower is connected to a heat exchanger for heat exchange to recover heat. The heat-exchanged rich liquid then flows into a regeneration tower, where heating conditions are set, and a certain proportion of fresh amine solution is added to the lean liquid at the bottom of the tower after heat exchange to maintain system balance. The CO2 purification and liquefaction process is then simulated. The regenerated gas flowing from the top of the regeneration tower passes through a dryer to remove moisture, then enters a heat exchanger to lower its temperature. The gas stream is subsequently compressed in stages and condensed in a condenser, ultimately collecting the liquid CO2 product under specific pressure and temperature. After the entire model is built, parameter settings and solutions are required. By adjusting key parameters such as compressor power, heat exchanger area, and fluid load within the tower, the system's energy consumption and removal efficiency are optimized to ensure the simulation results meet design specifications. The specific process is as follows: Figure 5 As shown.

[0138] 2.2.2 Description of Key Processes and Parameters

[0139] 1. Physical property input

[0140] The process flow was simulated using Aspen Hysys V14.0 software. A new case was created in the software, and the physical properties were clicked. The required components for this design were searched in the source database and added as pure components. The component list is as follows: Figure 6Add the property package Basis-1, selecting the acid gas-chemical solvent property package, such as... Figure 7 .

[0141] 2. Absorption Tower Simulation

[0142] The flue gas enters from the bottom of the absorption tower and comes into countercurrent contact with the absorbent liquid descending from the top of the tower, completing the absorption process. The temperature of the absorbent liquid is 38.02 ℃, and the flow rate is 1269 m³ / s. 3 The absorption liquid concentration is 30 wt% per hour. The absorption tower has 20 trays, with the bottom pressure set at 1655 kPa and the top pressure at 1620 kPa. See [link to absorption tower structure and parameters] for details. Figure 8 After the flue gas is absorbed by the absorption tower, the purified gas is discharged from the top of the tower. The rich liquid formed after absorbing CO2 is discharged from the bottom of the tower and enters the desorption tower after heat exchange. The parameters are as follows: Figure 9 .

[0143] 3. Desorption Tower Simulation

[0144] The preheated rich solution had a temperature of 93.33 ℃, a pressure of 1650 kPa, and a flow rate of 1415 m³ / s. 3 / h is fed into the desorption tower for regeneration. The rich solution undergoes thermal regeneration at 111.6℃ and 137.9 kPa, requiring approximately 1.95 GJ / tCO2. The desorption tower parameters are set as follows: Figure 10 As shown.

[0145] 2.3 Simulation and Analysis of Multi-Amine Mixtures

[0146] Based on simulation data, the performance of different amine formulations exhibits certain variation patterns, which are consistent with some existing literature findings, while also revealing certain characteristics of this system under specific conditions. Formulation 1 (30% MEA), serving as the baseline, demonstrated an 86.4% capture rate and a regeneration energy consumption of 1.95 GJ / tCO2 in this simulation. The formulation settings for this simulation are shown in Table 1.

[0147] Table 1 Amine solution formulation

[0148]

[0149] The regeneration energy consumption and capture rate of each ratio were calculated through simulation, and the following data were obtained, such as... Figure 11 .

[0150] Based on simulation data, the performance of different amine formulations exhibits a clear variation pattern. Formulation 1 (30% MEA), serving as the baseline, demonstrated a capture rate of 86.4% and a regeneration energy consumption of 1.95 GJ / t CO2 in this simulation. Mixed amine formulations generally outperformed single-component 30% MEA solutions in capture performance. Data shows that the capture rate of a single 30% MEA solution was only 86.4%, while the capture rate significantly improved after introducing MDEA and DEA in combination. This result validates the design concept of mixed amine absorbents, namely, combining amines with different reaction characteristics to improve the CO2 capture performance of the absorbent and optimize energy consumption. Among them, formulation 5 (MEA 30% + MDEA 10% + DEA 5%) performed best, with a capture rate as high as 99.65% and a regeneration energy consumption reduced to 1.74 GJ / t CO2, achieving a good balance between high-efficiency capture and low energy consumption.

[0151] Analysis of the mechanisms of action of the formulation components reveals that MEA, as a primary amine, exhibits high reactivity but also high regeneration energy consumption; MDEA, as a tertiary amine, boasts advantages in large absorption capacity and low regeneration energy consumption; while DEA, as a secondary amine, falls between the two in terms of performance. The superior performance of Formulation 5 leverages the characteristics of different amine components, achieving performance optimization through synergistic effects. This compounding strategy effectively overcomes the challenge of balancing capture rate and regeneration energy consumption in single-component organic amine solutions, representing an effective approach to addressing the insufficient absorption performance and high regeneration energy consumption issues of single-amine solvents.

[0152] However, the data also shows that formulation optimization is not a simple linear additive process. Although formulation 2 (MEA 30% + MDEA 5%) increased the capture rate to 93.46%, its regeneration energy consumption (2.17 GJ / t CO2) was higher than that of a single MEA solution, indicating that improper proportions may lead to increased energy consumption due to interactions between components. This suggests that in the research of compound amine absorbents, finely adjusting the proportions of each component and balancing the capture rate and regeneration energy consumption is key to developing novel, highly efficient, and low-energy-consumption amine absorbents.

[0153] Simulation data revealed the significant advantages of ternary mixed amine systems compared to binary systems. Formulas 5 and 6, both ternary mixed systems, achieved capture rates exceeding 99% and energy consumption controlled at approximately 1.74 GJ / t CO2, significantly superior to binary mixed amine formulas (formulas 2 and 3). This phenomenon indicates that the introduction of multiple components helps to more comprehensively optimize the thermodynamic equilibrium of the system. The study points out that regeneration energy consumption is jointly determined by the heat of reaction, sensible heat, and latent heat. The multi-amine system may achieve energy consumption reduction by optimizing the reaction mechanisms between different amine components and balancing different heat components.

[0154] Furthermore, although formulations 5 and 6 have higher total amine concentrations, their regeneration energy consumption did not increase significantly due to the increase in solvent circulation volume. On the contrary, the unit energy consumption decreased due to the substantial improvement in capture efficiency. This indicates that, within a reasonable concentration range, appropriately increasing the amine concentration helps to improve the solution's circulation load capacity, thereby reducing regeneration energy consumption. However, this also requires consideration of engineering issues such as increased viscosity and enhanced corrosivity resulting from high-concentration amine solutions.

[0155] 3. Response Surface Methodology Optimization Design and Verification

[0156] 3.1 Response Surface Design

[0157] Based on previous simulations, three factors significantly affecting the performance of the mixed amine system were selected as independent variables: MEA concentration (A, wt%), MDEA concentration (B, wt%), and DEA concentration (C, wt%), with CO2 capture rate (Y1, %) and regeneration energy consumption (Y2, GJ / tCO2) as response values. The MEA concentration was fixed at 30 wt% as the base absorbent. An optimal design was used for the experimental scheme design, which minimizes the variance of the regression coefficients and improves model accuracy for a given number of experiments. A total of 20 experimental points were generated based on the design matrix, and the factor level ranges are shown in Table 2. The experimental order was randomly generated using Design-Expert 13 software. The experimental data were used for regression analysis and model building using this software.

[0158] Table 2. Response surface methodology factor level range

[0159]

[0160] 3.2 Simulation Experiment Results

[0161] According to the optimal design scheme, 20 sets of mixed amine systems with different ratios were simulated in Aspen HYSYS to obtain the CO2 capture rate and regeneration energy consumption at each experimental point. The results are shown in Table 3.

[0162] Table 3 Optimal Experimental Design and Simulation Results

[0163]

[0164] 3.3 Regression Model Establishment and Significance Test

[0165] Multiple regression fitting was performed on the experimental data to obtain a quadratic polynomial regression model of CO2 capture rate (Y1) and regeneration energy consumption (Y2) on the encoded independent variables:

[0166] Y1= 103.81 + 11.93A + 4.48B + 6.73C – 7.15AB – 27.11AC – 5.69BC –22.31A 2 - 11.63B 2 - 11.7C 2

[0167] Y2= 0.9057 - 0.53A - 0.7949B - 0.5715C - 0.4318AB + 3.91AC-1.07BC +2.22 A 2 + 1.15B 2 + 1.97C 2

[0168] Analysis of variance (ANOVA) was performed on the capture rate model, and the results are shown in Table 4. The model p-value < 0.0001 indicates that the quadratic regression model is highly significant, and there is a true regression relationship between the selected factors and the response values. The coefficient of variation (CV) = 4.84%, indicating good experimental reliability. Precision measures the signal-to-noise ratio (SNR), which is the ratio of model signal to noise. The precision (Adeq Precision) = 26.77, which is much greater than 4, indicating a good SNR and relatively stable operation.

[0169] Table 4. Analysis of Variance for the Capture Rate Regression Model

[0170]

[0171] Analysis of variance (ANOVA) was performed on the regenerative energy consumption model, and the results are shown in Table 5. The model p-value was <0.0001, indicating extreme significance. The coefficient of variation (CV) was 25.42%, indicating some dispersion in the experimental data. This may be due to the large number of components in the mixed amine system and the complex interactions between factors. Nevertheless, the model p-value was extremely significant (<0.0001), and the coefficient of determination R0 was [value missing]. 2 A high precision (Adeq Precision) indicates that the model can fit the overall trend of the data well and can be used for qualitative analysis and determining the direction of process optimization. A precision of 18.1755 indicates a high signal-to-noise ratio and reliable predictive ability.

[0172] Table 5. Analysis of Variance of Regenerative Energy Consumption Regression Model

[0173]

[0174] 3.4 Model Diagnosis and Hypothesis Testing

[0175] To verify the effectiveness of the regression model, the three basic assumptions of normality, homogeneity of variance, and independence were tested on the residuals.

[0176] 3.4.1 Normality Assumption

[0177] When residuals are randomly distributed around a straight line, the data can be considered to follow a normal distribution; conversely, if the residuals exhibit a clear S-shaped distribution, it indicates that the data does not satisfy the normal distribution assumption, and the existing model needs to be modified. From the normal probability plot of the residuals ( Figure 12 As can be seen, the data points are basically distributed around a straight line and there is no obvious S-shaped trend, indicating that the residuals follow a normal distribution and satisfy the normality assumption.

[0178] 3.4.2 Homogeneity of Variance Assumption

[0179] Homogeneity of variance requires that the variance of the residuals does not exhibit a regular change with the predicted values; that is, the residual points should be randomly distributed on both sides of zero with a relatively constant dispersion range. If the residuals show a diverging or contracting trend as the predicted values ​​increase, it indicates the existence of heteroscedasticity, and the model needs further modification.

[0180] From the distribution plot of predicted values ​​and residuals ( Figure 13 As can be seen, all residual points are randomly distributed within a certain range, and the distribution range of the scatter points does not significantly widen or narrow as the predicted value increases, showing no obvious divergence trend. This result indicates that the residuals of the experimental data satisfy the homogeneity of variance assumption, and the regression model has good stability and reliability.

[0181] 3.4.3 Independence Assumption

[0182] The independence assumption requires that the residuals are independent of each other and should not exhibit significant time or order correlation trends. This can be verified by analyzing the correlation between the run sequence and the residuals. (From the distribution plot of the run sequence and residuals...) Figure 14 As can be seen, the residuals do not show a significant time correlation trend and are randomly distributed, indicating that the independence assumption is satisfied.

[0183] The above test results indicate that the regression model is effective and can be used for subsequent analysis and optimization. The response surface curve obtained by simulating the trapping rate is shown below. Figure 15 The response surface curve obtained by simulating regenerative energy consumption is as follows: Figure 16 .

[0184] 3.5 Optimization and Validation

[0185] The expectation function method was used to simultaneously optimize the two response variables, setting the objectives as maximizing the capture rate and minimizing the regeneration energy consumption, with a weight allocation of capture rate: energy consumption = 1:1. The optimal ratio was obtained through optimization: MDEA concentration 10 wt%, DEA concentration 1 wt%, MEA concentration 30 wt%. Under this condition, the model predicted a capture rate of 100% and a regeneration energy consumption of 1.37 GJ / tCO2.

[0186] To verify the reliability of the model, the Aspen HYSYS simulation was run again under the optimal ratio, and the actual capture rate was 99.64%, the regeneration energy consumption was 1.77 GJ / tCO2, and the relative errors with the predicted values ​​were 0.3% and 21%, respectively.

[0187] The optimal formulation was compared with the baseline formulation (30% MEA). The optimal formulation improved the capture rate by 13.24 percentage points and reduced the regeneration energy consumption by 9.2% compared with the baseline MEA.

[0188] MDEA, as a tertiary amine, provides an energy-saving basis for the system due to its low regeneration energy consumption; DEA, as a secondary amine, plays a regulatory and balancing role in the system; and MEA, as a basic absorbent, participates in the reaction. The synergistic effect of these three components enables the system to minimize regeneration energy consumption while maintaining a high capture rate.

[0189] This invention establishes a carbon capture process model based on Aspen HYSYS, collects data, and optimizes the proportions of the MEA-MDEA-DEA multi-component mixed amine system using optimal design response surface methodology. Using 30 wt% MEA as a baseline, the capture rate and regeneration energy consumption data under different proportions are obtained through simulation, and a quadratic regression model is established. The model is highly significant (P<0.0001), with a coefficient of determination R0. 2 The values ​​were 0.9793 and 0.9367, respectively, indicating a good fit. The optimal ratio was obtained through multi-objective optimization: MDEA concentration 10 wt%, DEA concentration 1 wt%, and MEA concentration 30 wt%, achieving a collection efficiency of 99.64% and a regeneration energy consumption of 1.77 GJ / tCO2. Compared to the baseline MEA process (collection efficiency 86.4%, energy consumption 1.95 GJ / tCO2), the collection efficiency increased by 13.24 percentage points, and energy consumption decreased by 9.2%.

[0190] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions, and improvements made by those skilled in the art within the scope of the technology disclosed in the present invention, and within the spirit and principles of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for constructing an optimized carbon emission prediction model, characterized in that, Includes the following steps: (1) Based on the thermodynamic principle, an energy conversion model of the carbon capture system by absorption method is established, and the energy conversion process of the system is abstracted into the conversion relationship between heat and Gibbs free energy; (2) Establish a decoupling model of the absorption thermodynamic carbon pump, and obtain the energy efficiency limit of the system under different operating conditions by comparing the ideal cycle with the actual cycle; (3) Study the physical properties of the absorbent based on the equation of state of statistical associative fluid theory, and construct a calculation model for the thermodynamic properties of the absorbent; (4) Conduct energy consumption and entropy analysis on the absorption carbon capture system, determine the key energy consumption links and their entropy production distribution, and construct an overall carbon emission optimization prediction model for the system.

2. The method as described in claim 1, characterized in that, The energy conversion model is based on the change in the internal energy of the working fluid during the absorbent cycle. It regards the heat input as the main energy source driving the change in Gibbs free energy, and uses it to characterize the energy conversion efficiency of the absorption-desorption cycle.

3. A method for modeling an absorption thermodynamic carbon pump, implementing the carbon emission optimization prediction model construction method as described in any one of claims 1-2, characterized in that, include: (1) The absorption carbon capture process is divided into a heat engine subsystem and a carbon pump subsystem; (2) Assume that all the effective energy provided by the heat engine subsystem is used for the gas-liquid phase transfer work of the carbon pump subsystem; (3) Under the condition of ignoring the boundary constraints of carbon source and carbon sink, calculate the energy efficiency deviation between the ideal cycle and the actual cycle of carbon pump; (4) Determine the upper limit of system energy efficiency based on the relationship between the deviation and the actual operating temperature and pressure.

4. The method as described in claim 3, characterized in that, The ideal cycle is expressed through a temperature-entropy diagram and a free energy change curve, which are used to visualize the energy paths of the absorption and desorption processes and to calculate the theoretical upper limit of the thermally driven carbon capture efficiency.

5. A method for studying the physical properties of absorbents based on statistical associative fluid theory, implementing the carbon emission optimization prediction model construction method as described in any one of claims 1-2, characterized in that: (1) The equation of state for the absorption process of amine solutions was established using the framework of association theory; (2) The intermolecular interactions in solution are described by association parameters and hydrogen bond energy parameters; (3) Use phase equilibrium calculation methods to determine the phase behavior of the solution at a given temperature and pressure; (4) By fitting and correcting the parameters through experimental data, a universal model applicable to different amine solutions is obtained.

6. The method as described in claim 5, characterized in that, The absorbent includes one or more of monoethanolamine solution, diethanolamine solution, and piperazine adjuvant mixed solution, used for predicting absorption efficiency under different CO2 concentration conditions.

7. A method for analyzing the energy consumption entropy of an absorption carbon capture system implementing the carbon emission optimization prediction model construction method as described in any one of claims 1-2, characterized in that: (1) Thermodynamic boundary delineation of the absorption tower, regeneration tower and heat exchanger unit; (2) Calculate the energy input of each unit process using the specific heat capacity and latent heat of vaporization data of the absorbent; (3) Calculate the energy consumption distribution using the enthalpy difference method and obtain the total entropy production of the system; (4) Based on the relationship between entropy production and CO2 concentration and desorption temperature, evaluate the potential for energy consumption optimization of the system.

8. The method as described in claim 7, characterized in that, The energy consumption analysis indicators include: energy consumption per unit of CO2 captured, system irreversible loss ratio, and cycle efficiency, all based on the first and second laws of thermodynamics.

9. A carbon emission prediction model based on thermodynamic-statistical coupling, implementing the carbon emission optimization prediction model construction method as described in any one of claims 1-2, characterized in that: (1) The upper limit of energy efficiency and entropy production parameters obtained from thermodynamic analysis are used as inputs; (2) Use the physical property data obtained from the absorbent's equation of state as input; (3) The prediction model is trained by multiple linear regression and machine learning algorithms, and the carbon emission intensity prediction results of the system under different operating conditions are output; (4) The model is used for energy efficiency optimization and emission early warning of carbon capture systems.

10. The carbon emission prediction model as described in claim 9, characterized in that, The model training process employs a combination of gradient descent algorithm and regularization constraints, with the objective function being to minimize the sum of squared prediction errors, thereby ensuring the model's convergence stability and generalization ability.

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