Intelligent energy cooperative regulation and control method and system for carbon capture and thermoelectric decoupling

Abstract

The invention discloses an intelligent energy coordinated regulation and control method and system for carbon capture and thermoelectric decoupling. The method comprises the following steps: S1, constructing a thermoelectric decoupling and carbon capture multi-dimensional parameter space omega; s2, designing a modular heat energy interface system adaptive to the scene; s3, establishing a collaborative optimization function system exclusive to a scene; s4, performing cooperative operation; according to the invention, the distribution proportion of different waste heat in each time period is automatically adjusted, and the problem of waste heat waste or insufficient supply of carbon capture regeneration heat energy easily occurring in a traditional fixed waste heat distribution mode is effectively solved; the operation state of the energy mass conversion equipment under the lowest energy consumption is determined through function solution, and stable improvement of the carbon capture efficiency is guaranteed while the energy consumption in the energy mass conversion process is reduced; and the waste heat distribution proportion, energy mass conversion equipment parameters and carbon capture core process parameters can be dynamically adjusted, so that the system can flexibly adapt to the dynamic requirements of various scenes.

Description

Technical Field

[0001] This invention relates to the field of energy system technology, specifically to a smart energy synergistic regulation method and system for carbon capture and thermoelectric decoupling. Background Technology

[0002] With the advancement of dual-carbon goals, the combination of carbon capture technology and thermoelectric decoupling technology has become a core technological direction for the efficient utilization of fossil energy and the reduction of carbon emissions, and is widely used in scenarios such as thermal power plants, industrial waste heat recovery, and regional energy supply centers. The core purpose of this technical route is to replace external high-quality energy with waste heat resources tapped by thermoelectric decoupling systems to drive carbon capture, thereby reducing carbon capture energy consumption and optimizing the energy allocation flexibility of thermoelectric decoupling, thus improving the overall efficiency of the entire energy system. However, due to insufficient consideration of scenario complexity, subsystem synergy, and dynamic adaptability in early technical solutions, in actual implementation, the waste heat characteristics, carbon capture process requirements, and system operation constraints under different application scenarios have failed to form effective synergy. This has led to the gradual exposure of shortcomings in parameter integration, subsystem coupling, and control strategies of traditional technical solutions, resulting in the following deficiencies:

[0003] First, in traditional energy systems that decouple carbon capture and thermal power, the waste heat utilization stage lacks a systematic integration of multi-dimensional operating parameters. It has neither constructed a complete parameter space covering waste heat characteristics, carbon capture process requirements, and system constraints, nor established a dynamic correlation logic between waste heat supply and carbon capture regeneration heat energy demand. It usually adopts a fixed ratio allocation of waste heat. This model cannot cope with the intermittency of waste heat and the real-time adjustment of carbon capture regeneration heat energy demand. It is easy to have waste heat idle and wasted due to over-allocation, or the carbon capture regeneration stage cannot operate stably due to insufficient supply. It is difficult to achieve precise matching of multi-grade waste heat with carbon capture regeneration.

[0004] Secondly, in traditional technical solutions for the coordinated operation of energy conversion and carbon capture, the energy conversion equipment and the carbon capture system are mostly in an independent optimization state, and their core parameters have not established a direct mathematical correlation or coordinated control mechanism. Due to the lack of this coupling relationship, the thermal energy quality of low-grade waste heat after energy conversion upgrade is often mismatched with the thermal energy demand of carbon capture regeneration. This not only results in high energy consumption during the energy conversion process, but also easily leads to fluctuations in carbon capture efficiency due to energy level imbalance, making it difficult to achieve the coordinated promotion of efficient upgrading of low-grade waste heat and stable improvement of carbon capture efficiency.

[0005] Finally, the control strategies of traditional carbon capture and thermoelectric decoupling systems are mostly focused on a single objective, failing to form a multi-objective control system that takes into account the overall system energy efficiency, carbon capture efficiency, and heat load satisfaction rate. Moreover, the control methods are mainly based on static parameter settings, lacking the ability to dynamically adapt to the intermittent nature of waste heat and the time-period variation of load. This single-objective static control mode is difficult to cope with the dynamic needs of different scenarios, resulting in limited overall system operating efficiency.

[0006] Therefore, it is necessary to design a smart energy synergistic regulation method and system that decouples carbon capture and thermoelectricity. Summary of the Invention

[0007] The purpose of this invention is to provide a smart energy synergistic regulation method and system for carbon capture and thermoelectric decoupling, to solve the problems mentioned in the background art. Traditional systems lack a multi-dimensional parameter space covering waste heat characteristics, carbon capture process parameters, and system constraint parameters, fail to establish a dynamic correlation between waste heat supply and carbon capture regeneration heat energy demand, rely on fixed waste heat distribution methods, are prone to waste heat waste or insufficient carbon capture regeneration heat energy, and are difficult to achieve dynamic adaptation between multi-grade waste heat and carbon capture regeneration. It also solves the problem that energy quality conversion equipment and carbon capture system are independently optimized, and their core parameters are not directly mathematically correlated, resulting in a mismatch between the heat energy quality after upgrading low-grade waste heat and carbon capture demand, high energy consumption in energy quality conversion and fluctuating carbon capture efficiency, and the inability to achieve deep coupling between the two. At the same time, it solves the problem that traditional regulation focuses on a single target and uses static parameter settings, lacking the ability to dynamically adapt to the intermittent and time-varying changes in waste heat and load, making it difficult to flexibly adapt to multiple scenarios and limiting the overall system operating efficiency.

[0008] To achieve the above objectives, the present invention provides the following technical solution: Firstly, a smart energy synergistic regulation method for carbon capture and thermoelectric decoupling is provided, comprising the following steps: S1: Construct a multi-dimensional parameter space for thermoelectric decoupling and carbon capture for the target application scenario. The parameter space includes waste heat characteristic parameters, carbon capture process parameters, and system constraint parameters; S2: Based on multi-dimensional parameter space Based on the feature mapping relationship, a modular thermal energy interface system adapted to the scenario is designed to achieve dynamic adaptation of multi-grade waste heat and carbon capture and regeneration links; through modular design and dynamic adaptation mechanism, efficient matching of waste heat of different grades and carbon capture and regeneration links is achieved, significantly improving the cascade utilization efficiency of waste heat resources, while enhancing the system's adaptability to fluctuations in scenario thermal energy demand. S3: Establish a scenario-specific collaborative optimization function system, including a waste heat cascade allocation function. Coupling function of energy conversion and carbon capture efficiency and multi-objective dynamic control function The function system introduces scene feature parameters as variables, which are multi-dimensional parameter spaces constructed from S1. The key parameters selected from the data that reflect the core technical characteristics or constraints of the target scenario include, but are not limited to: temperature parameters related to the waste heat grade of the scenario, equipment operating parameters related to the process characteristics of the scenario, and dynamic control parameters related to the system constraints of the scenario. S4: Through spatiotemporal collaborative closed-loop control, the waste heat distribution strategy, the operating status of energy conversion equipment and the core process parameters of carbon capture are adjusted in real time based on the control parameters output by the function system, so as to realize the scenario-based collaborative operation of thermoelectric decoupling and carbon capture.

[0009] As a further technical solution of the present invention, in S1, the multi-dimensional parameter space The expression in the context of a coal-fired power plant is: , in, This refers to the steam extraction flow rate of the high-pressure cylinder of the steam turbine. This refers to the extraction steam temperature of the high-pressure cylinder of the steam turbine. The duration of continuous and stable steam supply to the high-pressure cylinder of the steam turbine is used to indicate the intermittency of the steam turbine's waste heat. The waste heat flow rate of flue gas at the tail end of the boiler; The waste heat temperature of the flue gas at the tail end of the boiler; This refers to the flue gas pressure at the tail end of the boiler. This refers to the CO2 concentration at the flue gas inlet. This refers to the amine solution circulation volume; The temperature of the regeneration tower; For the regeneration tower pressure; This represents the lower limit of carbon capture efficiency. This refers to the allowable value for fluctuations in power generation. This represents the power grid constraint value. This represents the upper limit of the thermal stability temperature of amine solutions.

[0010] As a further technical solution of the present invention, in S2, the modular thermal energy interface system is configured in the high-temperature waste heat scenario of a steel plant as follows: Ultra-high temperature interface module: Adaptable to waste heat at temperatures not lower than 600℃, using a shell-and-tube heat exchanger made of heat-resistant steel. The inner tube of the heat exchanger is designed with a turbulence structure to enhance heat transfer and improve heat exchange efficiency. It meets the carbon capture and regeneration heat energy requirements of the preset scenarios and is suitable for the waste heat of converter flue gas in steel plants. Medium-temperature interface module: Adapts to waste heat with temperatures above 200℃ and below 600℃, integrates a phase change heat storage unit, selects molten salt materials that match the waste heat temperature, and the heat storage performance needs to meet the needs of waste heat supply and demand fluctuation regulation. It is suitable for waste heat from blast furnace hot blast stoves in steel plants. Interface switching logic: based on temperature difference Automatic switching is enabled. Waste heat temperature and regeneration tower temperature The difference; based on the temperature fluctuation range of waste heat in steel plants and the temperature requirements for carbon capture and regeneration, preset... The two threshold intervals, when When the temperature is in the high threshold range, the ultra-high temperature module and its associated temperature buffer are activated; when When the temperature is in the low threshold range, the medium temperature module and its supporting temperature holding section are activated. The differentiated design of heat exchange through ultra-high temperature interface and phase change heat storage through medium temperature interface is adapted to the high temperature waste heat of steel plants at different temperature levels. This not only ensures the efficient recovery and utilization of ultra-high temperature waste heat to meet the high heat energy demand of carbon capture and regeneration, but also buffers the supply and demand fluctuations of medium temperature waste heat through phase change heat storage, thereby improving the matching efficiency of high temperature waste heat and carbon capture and regeneration in steel plants and the stability of system operation.

[0011] As a further technical solution of the present invention, in S3, the waste heat cascade distribution function The expression for a multi-source waste heat complementary scenario is: , in, Number the waste heat sources; Number the waste heat source and associate it with They belong to the same group of waste heat sources; Numbering by hour segment; For the first Time period The proportion of waste heat allocated; For the first The quality coefficient of waste heat, The calculation formula is ; For the first The quality coefficient of waste heat, The calculation formula is ; The ambient temperature; The temperature sensitivity coefficient is determined based on the actual operating characteristics of the scenario; For the first The temperature of the residual heat; For the first The temperature of the residual heat; For the first The target temperature of the regeneration tower during the time period; For the first The flow rate of waste heat; For the first The flow rate of waste heat; For the first The correction factor for the time period is used to adapt to the characteristics of the power grid load. The constraints of the waste heat cascade distribution function are: , in, The isobaric specific heat capacity of the waste heat medium; For the first Amine solution circulation rate over time period; The enthalpy change for amine liquid regeneration is used; by introducing waste heat quality coefficient, temperature sensitivity coefficient and correction coefficient, etc., intelligent cascade distribution of multi-source waste heat is realized, which not only fully considers the quality differences of different waste heat to ensure energy quality utilization efficiency, but also adapts to the dynamic adjustment distribution strategy of grid load characteristics.

[0012] As a further technical solution of the present invention, in S3, the energy-mass conversion and carbon capture efficiency coupling function The expression for the combined ORC cycle and carbon capture scenario is: , in, For carbon capture efficiency; For ORC compressor pressure ratio, , This refers to the discharge pressure of the ORC compressor. This refers to the suction pressure of the ORC compressor. The dryness of the working fluid in the ORC cycle; This refers to the outlet temperature of the ORC evaporator. The temperature of the regeneration tower; The coefficients are determined based on scenario operation data; by establishing the coupling relationship between ORC cycle parameters and carbon capture efficiency, the energy conversion process and the carbon capture process are optimized in a coordinated manner; at the same time, its inverse function can accurately guide the setting of the target pressure ratio of the ORC cycle compressor, thereby minimizing the energy consumption of the energy conversion equipment while ensuring the target carbon capture efficiency.

[0013] As a further technical solution of the present invention, in S3, the multi-objective dynamic control function The expression in the context of a regional energy microgrid is: , in, The overall objective function; for Real-time carbon capture efficiency; for Cumulative deviation rate of heat load at any time; for Overall energy efficiency of the time system; The formula for calculation is: , in, for The timekeeping system effectively utilizes electrical energy. for The timekeeping system effectively utilizes thermal energy. for The system is constantly receiving electrical energy. for The system continuously inputs total waste heat energy. The formula for calculation is: , in, for Constant heat load demand, for Constant heat load supply; For dynamic weighting coefficients, satisfying ; The multi-objective dynamic control function The decision variables are: , in, for The first type of residual heat at the moment The allocation percentage of time periods, for The pressure ratio of the ORC compressor at any given time. for The dryness of the working fluid in the ORC cycle at any given time. for The system optimizes the energy efficiency, carbon capture efficiency, and heat load deviation of the system through a multi-objective function, and introduces dynamic weighting coefficients to adapt to the real-time operation requirements of the microgrid. This enables the system to achieve a dynamic balance between energy efficiency, carbon capture, and heat supply and demand under the complex and variable load and energy supply conditions of the regional energy microgrid.

[0014] As a further technical solution of the present invention, the waste heat cascade distribution function When applied to scenarios involving fluctuating renewable energy sources, the waste heat cascade allocation function Introducing a prediction and correction mechanism, the corrected expression is: , in, For the first The first time period data prediction Time period The proportion of waste heat distribution; For the first The residual heat characteristic parameter matrix for a given time period; For the first Carbon capture parameter matrix for different time periods; For the first Time period The standard deviation of waste heat fluctuation is predicted by an LSTM model, and the prediction error of the LSTM model must meet the preset requirements. This is the fluctuation compensation coefficient.

[0015] As a further technical solution of the present invention, the energy-mass conversion and carbon capture efficiency coupling function The inverse function is used for minimum energy consumption control of energy conversion equipment. The expression of the inverse function is: , in, The target pressure ratio for the ORC compressor; To achieve the target efficiency of carbon capture; The dryness of the working fluid in the ORC cycle; This refers to the outlet temperature of the ORC evaporator. The temperature of the regeneration tower is used; by directly linking the carbon capture target and equipment operating parameters through an inverse function, the pressure ratio of the ORC compressor can be precisely controlled. While meeting the carbon capture efficiency requirements, the energy consumption of the energy conversion equipment is minimized, achieving dual optimization of energy efficiency and carbon capture, and improving the overall energy utilization economy of the system.

[0016] As a further technical solution of the present invention, the multi-objective dynamic control function Using a rolling optimization strategy, the optimized expression is: , in, for The optimal comprehensive objective function value at time t; To optimize the time domain; For based on Predicting based on time data The value of the comprehensive objective function at each moment; for The decision variables at each time step; updating the dynamic weight coefficients within each optimization step. With respect to system constraints, through rolling optimization and gradual advancement and dynamic updates over time, the system can track and respond to parameter changes and constraint adjustments during operation in real time, continuously optimize the comprehensive objective function, and ensure the optimization effect and adaptive capability of the system in scenarios such as regional energy microgrids for long-term operation.

[0017] Secondly, a smart energy synergistic control system for carbon capture and thermoelectric decoupling is provided, comprising: Multi-dimensional parameter perception module: used to construct the multi-dimensional parameter space as described in the first aspect. The module is equipped with a sensor group and a data acquisition unit. The sensor group is used to collect the waste heat characteristic parameters of the thermoelectric decoupling system and the process parameters of the carbon capture system. The data acquisition unit is used to process and store the collected parameters. Through real-time and accurate parameter acquisition and processing, a reliable scene characteristic data foundation is provided for the entire system, ensuring the accuracy of subsequent modular interface adaptation, function calculation and control strategy. Modular interface system: used to realize dynamic adaptation of multi-grade waste heat and carbon capture and regeneration links as described in the first aspect. The module includes an ultra-high temperature interface unit, a medium temperature interface unit and an interface switching unit. The ultra-high temperature interface unit adapts to high temperature waste heat and enhances heat exchange. The medium temperature interface unit integrates phase change heat storage to regulate waste heat supply and demand. The interface switching unit controls the switching of interface units based on the difference between waste heat temperature and regeneration tower temperature. Through the differentiated design and intelligent switching of interface units of different temperature levels, the thermal energy demand of multi-grade waste heat and carbon capture and regeneration is efficiently matched. This not only enhances the efficient utilization of high temperature waste heat, but also buffers the fluctuation of waste heat supply and demand, providing stable thermal energy interface support for the synergy of thermoelectric decoupling and carbon capture. Collaborative optimization function calculation module: used to implement the waste heat cascade allocation function as described in the first aspect. Coupling function of energy conversion and carbon capture efficiency Multi-objective dynamic control function The module is equipped with a processor and a function parameter database. The processor adopts a parallel computing architecture to support real-time solution of the function, and the function parameter database stores scene feature parameters and function coefficients. Spatiotemporal coordinated control module: used to execute the control strategy as described in the first aspect. The module includes a rapid execution unit, an optimized execution unit, and a coordinated execution unit. The rapid execution unit is used to adjust the waste heat distribution ratio, the optimized execution unit is used to regulate the operating parameters of the energy-mass conversion equipment, and the coordinated execution unit is used to adjust the core process parameters of carbon capture. By precisely executing control commands of different dimensions by sub-units, the spatiotemporal coordinated linkage of waste heat distribution, equipment operation, and carbon capture process is realized, which can quickly respond to changes in the internal and external systems and ensure the coordinated and efficient operation of thermoelectric decoupling and carbon capture processes. Scene Adaptive Module: This module is used to adapt to different application scenarios. It automatically switches the working mode of the modular thermal energy interface module, the function parameters of the collaborative optimization function calculation module, and the control strategy of the spatiotemporal collaborative control module by identifying the scene feature parameters collected by the multi-dimensional parameter perception module.

[0018] Compared with existing technologies, the beneficial effects of this intelligent energy synergistic regulation method and system for carbon capture and thermoelectric decoupling are: By constructing a multi-dimensional parameter space encompassing waste heat characteristic parameters, carbon capture process parameters, and system constraint parameters. It is paired with a modular thermal energy interface system adapted to different scenarios, combined with a waste heat cascade distribution function. It can accurately quantify the correlation between the real-time operating parameters of different waste heat sources and the demand for carbon capture and regeneration heat energy, and automatically adjust the distribution ratio of different waste heat in different time periods. It effectively solves the problem of waste heat waste or insufficient supply of carbon capture and regeneration heat energy that is easy to occur under the traditional fixed waste heat distribution method. It realizes the dynamic adaptation of multi-grade waste heat and carbon capture and regeneration links, and provides matching heat energy guarantee for the stable operation of carbon capture system. Through the coupling function of energy conversion and carbon capture efficiency By establishing a direct mathematical relationship between the core operating parameters of the energy conversion equipment and the carbon capture efficiency, the operating state of the energy conversion equipment under the lowest energy consumption can be determined through function solving. This achieves the efficient upgrading of low-grade waste heat to high-grade thermal energy and deep coupling with carbon capture and regeneration, reducing energy consumption in the energy conversion process while ensuring a stable improvement in carbon capture efficiency. Through multi-objective dynamic control function The scenario-based design and spatiotemporal collaborative closed-loop control method take the overall system energy efficiency, carbon capture efficiency and heat load satisfaction rate as the core optimization objectives. It introduces a waste heat fluctuation prediction and correction mechanism and a rolling optimization strategy, which can dynamically adjust the waste heat distribution ratio, energy quality conversion equipment parameters and core carbon capture process parameters according to load fluctuations and intermittent waste heat characteristics under different scenarios. This allows the system to flexibly adapt to the dynamic needs of multiple scenarios, and while ensuring the stability of system operation, it achieves multi-objective collaborative optimization and improves the overall operating efficiency of the energy system. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the method flow of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Please see the appendix Figure 1 The present invention provides an embodiment 1: a smart energy synergistic regulation method for carbon capture and thermoelectric decoupling, comprising the following steps: S1: Construct a multi-dimensional parameter space for thermoelectric decoupling and carbon capture for the target application scenario. The parameter space includes waste heat characteristic parameters, carbon capture process parameters, and system constraint parameters; Multidimensional parameter space The expression in the context of a coal-fired power plant is: , in, This refers to the steam extraction flow rate of the high-pressure cylinder of the steam turbine. This refers to the extraction steam temperature of the high-pressure cylinder of the steam turbine. The duration of continuous and stable steam supply to the high-pressure cylinder of the steam turbine is used to indicate the intermittency of the steam turbine's waste heat. The waste heat flow rate of flue gas at the tail end of the boiler; The waste heat temperature of the flue gas at the tail end of the boiler; This refers to the flue gas pressure at the tail end of the boiler. This refers to the CO2 concentration at the flue gas inlet. This refers to the amine solution circulation volume; The temperature of the regeneration tower; For the regeneration tower pressure; This represents the lower limit of carbon capture efficiency. This refers to the allowable value for fluctuations in power generation. This represents the power grid constraint value. This represents the upper limit of the thermal stability temperature of amine solutions. S2: Based on multi-dimensional parameter space Based on the feature mapping relationship, a modular thermal energy interface system adapted to the scenario is designed to achieve dynamic adaptation of multi-grade waste heat and carbon capture and regeneration links; through modular design and dynamic adaptation mechanism, efficient matching of waste heat of different grades and carbon capture and regeneration links is achieved, significantly improving the cascade utilization efficiency of waste heat resources, while enhancing the system's adaptability to fluctuations in scenario thermal energy demand. The modular thermal energy interface system in the high-temperature waste heat scenario of a steel plant consists of the following components: Ultra-high temperature interface module: Adaptable to waste heat at temperatures not lower than 600℃, using a shell-and-tube heat exchanger made of heat-resistant steel. The inner tube of the heat exchanger is designed with a turbulence structure to enhance heat transfer and improve heat exchange efficiency. It meets the carbon capture and regeneration heat energy requirements of the preset scenarios and is suitable for the waste heat of converter flue gas in steel plants. Medium-temperature interface module: Adapts to waste heat with temperatures above 200℃ and below 600℃, integrates a phase change heat storage unit, selects molten salt materials that match the waste heat temperature, and the heat storage performance needs to meet the needs of waste heat supply and demand fluctuation regulation. It is suitable for waste heat from blast furnace hot blast stoves in steel plants. Interface switching logic: based on temperature difference Automatic switching is enabled. Waste heat temperature and regeneration tower temperature The difference; based on the temperature fluctuation range of waste heat in steel plants and the temperature requirements for carbon capture and regeneration, preset... The two threshold intervals, when When the temperature is in the high threshold range, the ultra-high temperature module and its associated temperature buffer are activated; when When the temperature is in the low threshold range, the medium temperature module and the supporting temperature holding section are activated. The differentiated design of heat exchange and phase change heat storage through ultra-high temperature interface is enhanced, which is adapted to the high temperature waste heat of steel plants at different temperature levels. This not only ensures the efficient recovery and utilization of ultra-high temperature waste heat to meet the high heat energy demand of carbon capture and regeneration, but also buffers the supply and demand fluctuations of medium temperature waste heat through phase change heat storage, thereby improving the matching efficiency of high temperature waste heat and carbon capture and regeneration in steel plants and the stability of system operation. S3: Establish a scenario-specific collaborative optimization function system, including a waste heat cascade allocation function. Coupling function of energy conversion and carbon capture efficiency and multi-objective dynamic control function The function system introduces scene feature parameters as variables, which are multi-dimensional parameter spaces constructed from S1. The key parameters selected from the data that reflect the core technical characteristics or constraints of the target scenario include, but are not limited to: temperature parameters related to the waste heat grade of the scenario, equipment operating parameters related to the process characteristics of the scenario, and dynamic control parameters related to the system constraints of the scenario. Waste heat distribution function The expression for a multi-source waste heat complementary scenario is: , in, Number the waste heat sources; Number the waste heat source and associate it with They belong to the same group of waste heat sources; Numbering by hour segment; For the first Time period The proportion of waste heat allocated; For the first The quality coefficient of waste heat, The calculation formula is ; For the first The quality coefficient of waste heat, The calculation formula is ; The ambient temperature; The temperature sensitivity coefficient is determined based on the actual operating characteristics of the scenario; For the first The temperature of the residual heat; For the first The temperature of the residual heat; For the first The target temperature of the regeneration tower during the time period; For the first The flow rate of waste heat; For the first The flow rate of waste heat; For the first The correction factor for the time period is used to adapt to the characteristics of the power grid load. The constraints of the waste heat distribution function are: , in, The isobaric specific heat capacity of the waste heat medium; For the first Amine solution circulation rate over time period; The enthalpy change for amine liquid regeneration; by introducing waste heat quality coefficient, temperature sensitivity coefficient and correction coefficient, etc., intelligent cascade distribution of multi-source waste heat is realized, which not only fully considers the quality differences of different waste heat to ensure energy quality utilization efficiency, but also adapts to the dynamic adjustment distribution strategy of grid load characteristics. Waste heat distribution function When applied to scenarios involving fluctuating renewable energy sources, the waste heat cascade allocation function Introducing a prediction and correction mechanism, the corrected expression is: , in, For the first The first time period data prediction Time period The proportion of waste heat distribution; For the first The residual heat characteristic parameter matrix for a given time period; For the first Carbon capture parameter matrix for different time periods; For the first Time period The standard deviation of waste heat fluctuation is predicted by an LSTM model, and the prediction error of the LSTM model must meet the preset requirements. This is the fluctuation compensation coefficient; Coupling function of energy conversion and carbon capture efficiency The expression for the combined ORC cycle and carbon capture scenario is: , in, For carbon capture efficiency; For ORC compressor pressure ratio, , This refers to the discharge pressure of the ORC compressor. This refers to the suction pressure of the ORC compressor. The dryness of the working fluid in the ORC cycle; This refers to the outlet temperature of the ORC evaporator. The temperature of the regeneration tower; The coefficients are determined based on scenario operation data; by establishing the coupling relationship between ORC cycle parameters and carbon capture efficiency, the energy conversion process and the carbon capture process are optimized in a coordinated manner; at the same time, its inverse function can accurately guide the setting of the target pressure ratio of the ORC cycle compressor, thereby minimizing the energy consumption of the energy conversion equipment while ensuring the target carbon capture efficiency. Coupling function of energy conversion and carbon capture efficiency The inverse function is used for minimum energy consumption control of energy conversion equipment. The expression of the inverse function is: , in, The target pressure ratio for the ORC compressor; To achieve the target efficiency of carbon capture; The dryness of the working fluid in the ORC cycle; This refers to the outlet temperature of the ORC evaporator. The temperature of the regeneration tower is used; by directly linking the carbon capture target and equipment operating parameters through an inverse function, the pressure ratio of the ORC compressor can be precisely controlled. While meeting the carbon capture efficiency requirements, the energy consumption of the energy conversion equipment is minimized, achieving dual optimization of energy efficiency and carbon capture, and improving the overall energy utilization economy of the system. Multi-objective dynamic control function The expression in the context of a regional energy microgrid is: , in, The overall objective function; for Real-time carbon capture efficiency; for Cumulative deviation rate of heat load at any time; for Overall energy efficiency of the time system; The formula for calculation is: , in, for The timekeeping system effectively utilizes electrical energy. for The timekeeping system effectively utilizes thermal energy. for The system is constantly receiving electrical energy. for The system continuously inputs total waste heat energy. The formula for calculation is: , in, for Constant heat load demand, for Constant heat load supply; For dynamic weighting coefficients, satisfying ; Multi-objective dynamic control function The decision variables are: , in, for The first type of residual heat at the moment The allocation percentage of time periods, for The pressure ratio of the ORC compressor at any given time. for The dryness of the working fluid in the ORC cycle at any given time. for The system optimizes energy efficiency, carbon capture efficiency, and heat load deviation through a multi-objective function, and introduces dynamic weighting coefficients to adapt to the real-time operation requirements of the microgrid. This enables a dynamic balance between energy efficiency, carbon capture, and heat supply and demand under the complex and variable load and energy supply conditions of the regional energy microgrid. Multi-objective dynamic control function Using a rolling optimization strategy, the optimized expression is: , in, for The optimal comprehensive objective function value at time t; To optimize the time domain; For based on Predicting based on time data The value of the comprehensive objective function at each moment; for The decision variables at each time step; updating the dynamic weight coefficients within each optimization step. With system constraints; through rolling optimization and gradual advancement and dynamic updates in the time dimension, the system can track and respond to parameter changes and constraint adjustments in real time during operation, continuously optimize the comprehensive objective function, and ensure the optimization effect and adaptive capability of the system in long-term operation in scenarios such as regional energy microgrids; S4: Through spatiotemporal collaborative closed-loop control, the waste heat distribution strategy, the operating status of energy conversion equipment and the core process parameters of carbon capture are adjusted in real time based on the control parameters output by the function system, so as to realize the scenario-based collaborative operation of thermoelectric decoupling and carbon capture.

[0022] An embodiment 2 of the present invention provides: a smart energy synergistic control system for carbon capture and thermoelectric decoupling, comprising: Multi-dimensional parameter perception module: used to construct a multi-dimensional parameter space as shown in Example 1. The module is equipped with a sensor group and a data acquisition unit. The sensor group is used to collect the waste heat characteristic parameters of the thermoelectric decoupling system and the process parameters of the carbon capture system. The data acquisition unit is used to process and store the collected parameters. Through real-time and accurate parameter acquisition and processing, a reliable scene characteristic data foundation is provided for the entire system, ensuring the accuracy of subsequent modular interface adaptation, function calculation and control strategy. Modular interface system: Used to achieve dynamic adaptation of multi-grade waste heat and carbon capture and regeneration links as in Example 1. The module includes an ultra-high temperature interface unit, a medium temperature interface unit, and an interface switching unit. The ultra-high temperature interface unit adapts to high-temperature waste heat and enhances heat exchange. The medium temperature interface unit integrates phase change heat storage to regulate waste heat supply and demand. The interface switching unit controls the switching of interface units based on the difference between waste heat temperature and regeneration tower temperature. Through the differentiated design and intelligent switching of interface units at different temperature levels, the thermal energy demand of multi-grade waste heat and carbon capture and regeneration is efficiently matched. This not only enhances the efficient utilization of high-temperature waste heat, but also buffers the fluctuations in waste heat supply and demand, providing stable thermal energy interface support for the synergy of thermoelectric decoupling and carbon capture. Collaborative optimization function calculation module: used to implement the waste heat cascade allocation function as shown in Example 1. Coupling function of energy conversion and carbon capture efficiency Multi-objective dynamic control function The module is equipped with a processor and a function parameter database. The processor adopts a parallel computing architecture to support real-time solution of the function, and the function parameter database stores scene feature parameters and function coefficients. Spatiotemporal Coordinated Control Module: This module is used to execute the control strategy as described in Example 1. The module includes a rapid execution unit, an optimized execution unit, and a coordinated execution unit. The rapid execution unit is used to adjust the waste heat distribution ratio, the optimized execution unit is used to regulate the operating parameters of the energy conversion equipment, and the coordinated execution unit is used to adjust the core process parameters of carbon capture. By precisely executing control commands of different dimensions by sub-units, the module achieves spatiotemporal coordinated linkage of waste heat distribution, equipment operation, and carbon capture process, quickly responds to changes inside and outside the system, and ensures the coordinated and efficient operation of thermoelectric decoupling and carbon capture processes. Scene Adaptive Module: Used to adapt to different application scenarios. The module automatically switches the working mode of the modular thermal energy interface module, the function parameters of the collaborative optimization function calculation module, and the control strategy of the spatiotemporal collaborative control module by identifying the scene feature parameters collected by the multi-dimensional parameter perception module.

[0023] An embodiment 3 of this invention addresses a 300MW pulverized coal-fired power plant. The plant's original carbon capture system used a conventional amine method, relying on turbine steam extraction to drive amine liquid regeneration, resulting in a 12%-15% reduction in power generation efficiency. Simultaneously, the waste heat from the boiler tail gas (180-250℃) and the turbine low-pressure cylinder exhaust (80-120℃) was not fully utilized, resulting in an annual waste of approximately 1.2 × 10⁻⁶ heat energy. 8 When the peak-to-valley difference of the grid load fluctuation reaches 40%, the thermoelectric decoupling response lags, and the power generation fluctuation exceeds ±5%. I. Constructing a multi-dimensional parameter space for coal-fired power plants : Parameter categories Parameter symbol Physical meaning Measured value range Waste heat characteristic parameter E <![CDATA[Q s1 (t)]]> Steam turbine high-pressure cylinder extraction steam flow rate 45-60 t / h (depending on power generation load) <![CDATA[T s1 (t)]]> Steam turbine high-pressure cylinder extraction steam temperature 380-420℃ <![CDATA[τ s1 ]]> Duration of continuous and stable steam supply 8-12 h (flat period) / 3-5 h (peak period) <![CDATA[Q s2 (t)]]> Waste heat flow rate of flue gas at the tail end of boiler 120-150 m³ / s <![CDATA[T s2 (t)]]> Boiler tail flue gas temperature 190-240℃ <![CDATA[P s2 (t)]]> Boiler tail flue gas pressure -200~-150 Pa (negative pressure) Carbon capture parameter C <![CDATA[C in (t)]]> <![CDATA[CO2 concentration at the flue gas inlet]]> 12%-14% (dry basis) <![CDATA[C out (t)]]> <![CDATA[CO2 concentration at the flue gas outlet]]> ≤1.5% (Design target) <![CDATA[L slurry ]]> Amine liquid circulation volume 100-140 m³ / h <![CDATA[T reg ]]> Regeneration tower temperature 115-125℃ (Optimal temperature for amine regeneration) <![CDATA[P reg ]]> Regeneration tower pressure 0.12-0.15 MPa (absolute pressure) constraint parameter K <![CDATA[η min ]]> Lower limit of carbon capture efficiency ≥90% <![CDATA[δ max ]]> Permissible fluctuations in power generation ≤±2% <![CDATA[P grid_lim ]]> Power grid constraint value 180-300 MW (peak-valley load) <![CDATA[T mat_lim ]]> Upper limit of thermal stability temperature of amine liquid ≤140℃ (MDEA solution decomposition temperature)

[0024] II. Design of a modular thermal energy interface system: Ultra-high temperature interface module: Applicable to steam extraction from high-pressure cylinders of steam turbines at 380-420℃; Equipment selection: 310S heat-resistant steel shell-and-tube heat exchanger with an inner tube diameter of Φ80mm and an outer tube diameter of Φ120mm. The inner tube is designed with spiral baffles with a spacing of 20mm to enhance the heat transfer coefficient to 1200W / (m²・K) and meet the heat energy requirements of the regeneration tower at 120℃. Medium temperature interface module: Suitable for boiler tail flue gas at 190-240℃; Core Components: Integrated SolarSalt: 60% sodium nitrate and 40% potassium nitrate phase change heat storage unit, heat storage capacity 50MWh: molten salt filling capacity 200m³, phase change temperature 220℃, latent heat 240kJ / kg, which can buffer flue gas temperature fluctuations of ±30℃ to ensure stable waste heat supply and demand. Interface switching logic: Temperature difference determination: , ; Threshold setting: High threshold range When the residual heat is ≥320℃, the ultra-high temperature module and stainless steel temperature buffer section (5m in length) are activated to reduce the extraction steam temperature to 350℃, preventing local overheating of the amine liquid; low threshold range. That is, the residual heat is 190-320℃, the medium temperature module and molten salt heat storage buffer section are activated, and the flue gas temperature fluctuation is compensated by the heat release of molten salt. III. Establish a dedicated collaborative optimization function system for power plants: 1. Waste heat distribution function: , Parameter calibration: Waste heat source: i=1, high-pressure cylinder steam extraction. ℃; i=2, boiler flue gas, ℃; Quality coefficient: , ℃, therefore , , Temperature sensitivity coefficient: Based on one year of power plant operation data, the results were obtained through least squares fitting. Time Period Correction Factor Peak power grid hours: 10:00-18:00 , Prioritize power generation and reduce the proportion of waste heat allocated; Off-peak hours: 22:00-6:00. , More waste heat is allocated to carbon capture; Verification using time constraints during a valley segment: Waste heat supply: , , ; The steam density is 4.8 kg / m³, which translates to 10.4 m³ / h. ; The flue gas density is 1.2 kg / m³, which is equivalent to 468,000 kg / h. calculate: , Heat required for amine liquid regeneration: , The density of the amine solution is 1050 kg / m³. , MDEA regeneration enthalpy change; calculate: , Satisfy constraints: ; 2. Coupling function of energy-mass conversion and carbon capture efficiency: The RC working fluid used is R245fa, and the coefficients are based on actual measurement and calibration at the power plant. , Parameter settings: coefficient: , , , , ; RC parameters: pressure ratio , , working fluid dryness Evaporator outlet temperature , ; Efficiency calculation:

[0025] , Correction: In actual operation, the RC pressure ratio is finely adjusted to... ,final ,conform to constraint; 3. Multi-objective dynamic control function: The weighting coefficients are dynamically adjusted according to grid demand. , Key parameter definitions: System overall energy efficiency : Grid-connected electricity; Heating supply for the factory area; Factory power; Input waste heat; therefore , correction: Including fuel input, recalculated ; Cumulative deviation rate of heat load 0-24h heat demand integral Supply points Therefore , Weights and target values: Environmental protection priority model: , , Calculated Minimize the achievement of the goal; IV. Spatiotemporal Coordinated Closed-Loop Control: Based on the characteristics of power plant load fluctuations, the following controls will be implemented: Waste heat fluctuation prediction and correction, including renewable energy supplementation scenarios: The LSTM model is used to predict waste heat fluctuations, specifically the standard deviation of high-pressure cylinder extraction steam fluctuations during the k-th time period (9:00-10:00). compensation coefficient Therefore: , By adjusting the valve opening, the steam extraction distribution ratio was increased from 0.6 to 0.65 to compensate for fluctuations.

[0026] RC minimum energy consumption control: Target carbon capture efficiency Substitute into the inverse function: , Calculated The RC compressor automatically adjusts the pressure ratio to 2.95, reducing energy consumption by 8%.

[0027] Rolling optimization strategy: Optimize time domain Update the weight coefficients at each step To ensure power generation fluctuations .

[0028] In summary, this invention constructs a multi-dimensional parameter space encompassing waste heat characteristic parameters, carbon capture process parameters, and system constraint parameters. It is paired with a modular thermal energy interface system adapted to different scenarios, combined with a waste heat cascade distribution function. It can accurately quantify the correlation between the real-time operating parameters of different waste heat sources and the demand for carbon capture and regeneration heat energy, and automatically adjust the distribution ratio of different waste heat in different time periods. It effectively solves the problem of waste heat waste or insufficient supply of carbon capture and regeneration heat energy that is easy to occur under the traditional fixed waste heat distribution method. It realizes the dynamic adaptation of multi-grade waste heat and carbon capture and regeneration links, and provides matching heat energy guarantee for the stable operation of carbon capture system. Through the coupling function of energy conversion and carbon capture efficiency By establishing a direct mathematical relationship between the core operating parameters of the energy conversion equipment and the carbon capture efficiency, the operating state of the energy conversion equipment under the lowest energy consumption can be determined through function solving. This achieves the efficient upgrading of low-grade waste heat to high-grade thermal energy and deep coupling with carbon capture and regeneration, reducing energy consumption in the energy conversion process while ensuring a stable improvement in carbon capture efficiency. Through multi-objective dynamic control function The scenario-based design and spatiotemporal collaborative closed-loop control method take the overall system energy efficiency, carbon capture efficiency and heat load satisfaction rate as the core optimization objectives. It introduces a waste heat fluctuation prediction and correction mechanism and a rolling optimization strategy, which can dynamically adjust the waste heat distribution ratio, energy quality conversion equipment parameters and core carbon capture process parameters according to load fluctuations and intermittent waste heat characteristics under different scenarios. This allows the system to flexibly adapt to the dynamic needs of multiple scenarios, and while ensuring the stability of system operation, it achieves multi-objective collaborative optimization and improves the overall operating efficiency of the energy system.

[0029] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A smart energy synergistic regulation method for carbon capture and thermoelectric decoupling, characterized in that: Includes the following steps: S1: Construct a multi-dimensional parameter space for thermoelectric decoupling and carbon capture for the target application scenario. The parameter space It includes waste heat characteristic parameters, carbon capture process parameters, and system constraint parameters; S2: Based on multi-dimensional parameter space Based on the feature mapping relationship, a modular thermal energy interface system adapted to the scenario is designed to achieve dynamic adaptation of multi-grade waste heat and carbon capture and regeneration links. S3: Establish a scenario-specific collaborative optimization function system, including a waste heat cascade allocation function. Coupling function of energy conversion and carbon capture efficiency and multi-objective dynamic control function The function system introduces scene feature parameters as variables, which are derived from the multi-dimensional parameter space constructed by S1. The key parameters selected that reflect the core technical characteristics or constraints of the target scenario include, but are not limited to: temperature parameters related to the waste heat grade of the scenario, equipment operating parameters related to the process characteristics of the scenario, and dynamic control parameters related to the system constraints of the scenario. S4: Through spatiotemporal collaborative closed-loop control, the waste heat distribution strategy, the operating status of energy conversion equipment and the core process parameters of carbon capture are adjusted in real time based on the control parameters output by the function system, so as to realize the scenario-based collaborative operation of thermoelectric decoupling and carbon capture.

2. The intelligent energy synergistic regulation method for carbon capture and thermoelectric decoupling according to claim 1, characterized in that: In S1, the multi-dimensional parameter space The expression in the context of a coal-fired power plant is: in, This refers to the steam extraction flow rate of the high-pressure cylinder of the steam turbine. This refers to the extraction steam temperature of the high-pressure cylinder of the steam turbine. The duration of continuous and stable steam supply to the high-pressure cylinder of the steam turbine is used to indicate the intermittency of the steam turbine's waste heat. The waste heat flow rate of flue gas at the tail end of the boiler; The waste heat temperature of the flue gas at the tail end of the boiler; This refers to the flue gas pressure at the tail end of the boiler. This refers to the CO2 concentration at the flue gas inlet. This refers to the amine solution circulation volume; The temperature of the regeneration tower; For the regeneration tower pressure; This represents the lower limit of carbon capture efficiency. This refers to the allowable value for fluctuations in power generation. This represents the power grid constraint value. This represents the upper limit of the thermal stability temperature of amine solutions.

3. The intelligent energy synergistic control method for carbon capture and thermoelectric decoupling according to claim 1, characterized in that: In S2, the modular thermal energy interface system is configured as follows in the high-temperature waste heat scenario of a steel plant: Ultra-high temperature interface module: Adaptable to waste heat at temperatures not lower than 600℃, using a shell-and-tube heat exchanger made of heat-resistant steel. The inner tube of the heat exchanger is designed with a turbulence structure to enhance heat transfer and improve heat exchange efficiency. It meets the carbon capture and regeneration heat energy requirements of the preset scenarios and is suitable for the waste heat of converter flue gas in steel plants. Medium-temperature interface module: Adapts to waste heat with temperatures above 200℃ and below 600℃, integrates a phase change heat storage unit, selects molten salt materials that match the waste heat temperature, and the heat storage performance needs to meet the needs of waste heat supply and demand fluctuation regulation. It is suitable for waste heat from blast furnace hot blast stoves in steel plants. Interface switching logic: based on temperature difference Automatic switching is enabled. Waste heat temperature and regeneration tower temperature The difference; based on the temperature fluctuation range of waste heat in steel plants and the temperature requirements for carbon capture and regeneration, preset... The two threshold intervals, when When the temperature is in the high threshold range, the ultra-high temperature module and its associated temperature buffer are activated; when When the temperature is in the low threshold range, the medium temperature module and its associated temperature holding section are activated.

4. The intelligent energy synergistic control method for carbon capture and thermoelectric decoupling according to claim 1, characterized in that: In S3, the waste heat cascade distribution function The expression for a multi-source waste heat complementary scenario is: in, Number the waste heat sources; Number the waste heat source and associate it with They belong to the same group of waste heat sources; Numbering by hour segment; For the first Time period The proportion of waste heat allocated; For the first The quality coefficient of waste heat, The calculation formula is ; For the first The quality coefficient of waste heat, The calculation formula is ; Ambient temperature; The temperature sensitivity coefficient is determined based on the actual operating characteristics of the scenario; For the first The temperature of the residual heat; For the first The temperature of the residual heat; For the first The target temperature of the regeneration tower during the time period; For the first The flow rate of waste heat; For the first The flow rate of waste heat; For the first The correction factor for the time period is used to adapt to the characteristics of the power grid load. The constraints of the waste heat cascade distribution function are: in, The isobaric specific heat capacity of the waste heat medium; For the first Amine solution circulation rate over time period; The enthalpy change is due to the regeneration of the amine solution.

5. The intelligent energy synergistic control method for carbon capture and thermoelectric decoupling according to claim 1, characterized in that: In S3, the energy-mass conversion and carbon capture efficiency coupling function The expression for the combined ORC cycle and carbon capture scenario is: in, For carbon capture efficiency; For ORC compressor pressure ratio, , This refers to the discharge pressure of the ORC compressor. This refers to the suction pressure of the ORC compressor. The dryness of the working fluid in the ORC cycle; This refers to the outlet temperature of the ORC evaporator. The temperature of the regeneration tower; These are coefficients determined based on scenario-based operational data.

6. The intelligent energy synergistic control method for carbon capture and thermoelectric decoupling according to claim 1, characterized in that: In S3, the multi-objective dynamic control function The expression in the context of a regional energy microgrid is: in, The overall objective function; for Real-time carbon capture efficiency; for Cumulative deviation rate of heat load at any time; for Overall energy efficiency of the time system; The formula for calculation is: in, for The timekeeping system effectively utilizes electrical energy. for The timekeeping system effectively utilizes thermal energy. for The system is constantly receiving electrical energy. for The system continuously inputs total waste heat energy. The formula for calculation is: in, for Constant heat load demand, for Constant heat load supply; For dynamic weighting coefficients, satisfying ; The multi-objective dynamic control function The decision variables are: in, for The first type of residual heat at the moment The allocation percentage of time periods, for The pressure ratio of the ORC compressor at any given time. for The dryness of the working fluid in the ORC cycle at any given time. for The amount of amine solution circulating at any given time.

7. The intelligent energy synergistic control method for carbon capture and thermoelectric decoupling according to claim 4, characterized in that: The waste heat distribution function When applied to scenarios involving fluctuating renewable energy sources, the waste heat cascade allocation function Introducing a prediction and correction mechanism, the corrected expression is: in, For the first The first time period data prediction Time period The proportion of waste heat allocated to each species; For the first The residual heat characteristic parameter matrix for a given time period; For the first Carbon capture parameter matrix for different time periods; For the first Time period The standard deviation of waste heat fluctuation is predicted by an LSTM model, and the prediction error of the LSTM model must meet the preset requirements. This is the fluctuation compensation coefficient.

8. The intelligent energy synergistic regulation method for carbon capture and thermoelectric decoupling according to claim 5, characterized in that: The coupling function of energy conversion and carbon capture efficiency The inverse function is used for minimum energy consumption control of energy conversion equipment. The expression of the inverse function is: in, The target pressure ratio for the ORC compressor; To achieve the target efficiency of carbon capture; The dryness of the working fluid in the ORC cycle; This refers to the outlet temperature of the ORC evaporator. The temperature of the regeneration tower.

9. The intelligent energy synergistic control method for carbon capture and thermoelectric decoupling according to claim 6, characterized in that: The multi-objective dynamic control function Using a rolling optimization strategy, the optimized expression is: in, for The optimal comprehensive objective function value at time t; To optimize the time domain; Based on Predicting based on time data The value of the comprehensive objective function at each moment; for The decision variables at each time step; updating the dynamic weight coefficients within each optimization step. With system constraints.

10. A smart energy synergistic control system for carbon capture and thermoelectric decoupling, characterized in that: include: Multi-dimensional parameter perception module: used to construct the multi-dimensional parameter space as described in claim 2. The module is equipped with a sensor group and a data acquisition unit. The sensor group is used to collect the waste heat characteristic parameters of the thermoelectric decoupling system and the process parameters of the carbon capture system. The data acquisition unit is used to process and store the collected parameters. Modular interface system: used to realize dynamic adaptation of multi-grade waste heat and carbon capture and regeneration links as described in claim 3. The module includes an ultra-high temperature interface unit, a medium temperature interface unit and an interface switching unit. The ultra-high temperature interface unit adapts to high temperature waste heat and enhances heat exchange. The medium temperature interface unit integrates phase change heat storage to regulate waste heat supply and demand. The interface switching unit controls the switching of the interface unit based on the difference between the waste heat temperature and the regeneration tower temperature. Collaborative optimization function calculation module: used to implement the waste heat cascade allocation function as described in claims 4 to 6. Coupling function of energy conversion and carbon capture efficiency Multi-objective dynamic control function The module is equipped with a processor and a function parameter database. The processor adopts a parallel computing architecture to support real-time solution of the function, and the function parameter database stores scene feature parameters and function coefficients. Spatiotemporal coordinated control module: used to execute the control strategy as described in claims 7 to 9. The module includes a fast execution unit, an optimized execution unit and a coordinated execution unit. The fast execution unit is used to adjust the waste heat distribution ratio, the optimized execution unit is used to regulate the operating parameters of the energy-mass conversion equipment, and the coordinated execution unit is used to adjust the core process parameters of carbon capture. Scene Adaptive Module: This module is used to adapt to different application scenarios. It automatically switches the working mode of the modular thermal energy interface module, the function parameters of the collaborative optimization function calculation module, and the control strategy of the spatiotemporal collaborative control module by identifying the scene feature parameters collected by the multi-dimensional parameter perception module.

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