A post-combustion carbon capture system with absorbent storage decentralized predictive control method

Abstract

The application discloses a kind of combustion post carbon capture system dispersion prediction control methods with absorbent storage, in absorption side, conventional MPC method is used to track carbon capture rate set value to adapt to flue gas fluctuation, meet emission reduction target, in desorption side, EMPC method is used, improve the economy in dynamic operation process, play the flexibility support function of carbon capture system with absorbent storage, realize the rapid adjustment to carbon capture rate to meet carbon capture rate target, maximize CO2 production in system dynamic operation process and reduce reboiler steam extraction consumption, and effectively inhibit the disturbance influence of upstream flue gas to carbon capture system, to realize the flexible, economic, efficient operation of combustion post carbon capture system with absorbent storage.

Description

Technical Field

[0001] This invention relates to the field of low-carbon economic energy system technology, and in particular to a decentralized predictive control method for a post-combustion carbon capture system with absorbent storage. Background Technology

[0002] Deep reduction of greenhouse gas emissions depends on the low-carbon transformation of the energy system. On the one hand, it requires reducing the use of fossil fuels and vigorously developing renewable energy; on the other hand, it requires promoting the clean and efficient use of fossil energy and adopting carbon capture and storage (CCS) technologies in remaining fossil fuel systems. Equipping coal-fired power units with CCS technologies allows them to continue operating while meeting climate goals. Post-combustion carbon capture (PCC) based on solvent chemical absorption has advantages such as large gas throughput, fast reaction rate, suitability for handling low partial pressure and low concentration CO2 gas sources, and relatively mature and economical technology, making it suitable for carbon capture in coal-fired power units.

[0003] To support the high-proportion absorption of intermittent renewable energy in the power grid, coal-fired power plants have gradually transformed from traditional main power sources to flexible regulating power sources that widely participate in peak shaving and frequency regulation. If carbon capture systems (CCS) cannot adapt to changes in flue gas and available extraction steam flow caused by load variations at coal-fired power plants, and cannot meet fluctuating CO2 production demands, they will not be able to fully realize their carbon emission reduction potential. Therefore, numerous scholars have focused on the operation and control of CCS systems, with the main tasks being adapting to flue gas disturbances, flexibly adjusting the carbon capture rate, and maintaining reboiler temperature. In actual operation, it is necessary not only to ensure the closed-loop control stability and operational flexibility of the CCS system, but also to consider the economic efficiency of the regulation process. Currently, economic optimization work on CCS systems focuses on steady-state parameter optimization and steady-state economic dispatch, with less attention paid to the economic operation optimization of the dynamic processes of the CCS system. Affected by upstream power plant load changes, CCS systems frequently experience operational fluctuations, and due to the system's large inertia, the transition time of dynamic processes is relatively long. Therefore, it is urgent to improve the dynamic economic efficiency of CCS operation, reducing the operating costs of dynamic regulation processes while ensuring system stability and flexibility. Summary of the Invention

[0004] Purpose of the invention: This invention provides a dispersion prediction and control method for a post-combustion carbon capture system with absorbent storage, which enables rapid adjustment of the carbon capture rate to meet the carbon capture rate target, maximizes CO2 production during the dynamic operation of the system, reduces reboiler extraction steam consumption, and effectively suppresses the disturbance effect of upstream flue gas on the carbon capture system.

[0005] Technical solution: The present invention provides a dispersion prediction and control method for a post-combustion carbon capture system with absorbent storage, comprising the following steps:

[0006] Step 1: Conduct an open-loop step experiment on the post-combustion carbon capture system with absorbent storage to obtain the dynamic characteristic experimental curve of the post-combustion carbon capture system with absorbent storage. The carbon capture system is divided into an absorption side and a desorption side.

[0007] Step 2: Based on the data corresponding to the dynamic characteristic experimental curve, the absorption side identifies the discrete state-space model characterizing the dynamic characteristics of the carbon capture system through the subspace identification method, and expands the discrete state-space model into an incremental state-space prediction model.

[0008] Step 3: On the desorption side, establish a nonlinear discrete difference algebraic equation based on the mechanism model as a prediction model to ensure sufficient economic optimization accuracy;

[0009] Step 4: Optimize the tracking target and construct a tracking objective function for the absorption side, optimize the economic target and construct an economic objective function for the desorption side, and design the corresponding absorption-desorption dispersion predictive controller for the carbon capture system in combination with the prediction model. The system achieves the control mode of tracking control on the desorption side and economic control on the desorption side through the absorption-desorption dispersion predictive controller.

[0010] Furthermore, in step 1, the control quantities on the absorption side include lean liquid flow rate, the controlled quantities include CO2 capture rate, and the measurable perturbation flue gas flow rate is considered; the control quantities on the desorption side include rich liquid flow rate and reboiler extraction steam flow rate, and the controlled quantities include reboiler temperature and CO2 production.

[0011] Furthermore, in step 2, the discrete state-space model is represented as:

[0012]

[0013] Among them, u abs (t k )=u lean (t k ), u abs (t k ) represents t k The absorption-side control quantity at time, u lean (t k ) represents t k The flow rate of the anti-fluid at any given time; y abs (t k )=y CL (t k ), y abs (t k ) represents t k The controlled quantity on the absorption side at time yCL (t k ) represents t k CO2 capture rate at time d; abs (t k )=d fg (t k ), d abs (t k ) represents t k The absorbable disturbance at time d fg (t k ) represents t k The flue gas flow rate at any given time; x abs,0 (t k ) represents t k The state vector of the absorbing side at time t; A0, B0, C0, D0, E0 and F0 all represent the system characteristic matrix.

[0014] Furthermore, in step 2, the incremental state-space prediction model is expressed as:

[0015]

[0016]

[0017] Where O represents the zero matrix; I ny Represents the identity matrix.

[0018] Furthermore, in step 3, the nonlinear discrete difference algebraic prediction model is expressed as:

[0019]

[0020] Among them, u str (t k )=[u rich (t k ),u reb (t k )], u str (t k ) represents t k Desorption-side control quantity at time, u rich (t k ) represents t k The flow rate of the rich liquid at any given time, u reb (t k ) represents t k The reboiler extraction steam flow rate at any given time; y str (t k ) represents t k The desorption-controlled quantity at time y reb (t k ) represents t kThe reboiler temperature at that moment, Indicates t k CO2 production at time x str (t k ) represents t k The desorption-side differential state vector at time z; str (t k ) represents t k The desorption-side algebraic state vector at time t.

[0021] Furthermore, in step 4, the target optimization and tracking objective function on the absorption side are expressed as follows:

[0022]

[0023] stx abs (t+1)=Ax abs (t)+B u Δu abs (t)+B d Δd abs (t)

[0024] y abs (t)=Cx abs (t)

[0025] x abs (t k )=x abs,m (t k )

[0026] y abs,min ≤y abs (t)≤y abs,max

[0027] u abs,min ≤u abs (t)≤u abs,max

[0028] Δu abs,min ≤Δu abs (t)≤Δu abs,max

[0029] Among them, y abs,r Indicates the setpoint of the controlled variable on the absorption side; N p Represents the prediction time domain; Q represents the error weight; R represents the control weight; x abs,m The state variable x on the absorption side represents the state variable. abs The measured value; y abs,min and y abs,max Representing the controlled quantity y on the absorption side respectively abs The upper and lower limits; u abs,min and u abs,max These represent the absorption-side control quantity u.abs The upper and lower limits; Δu abs,min and Δu abs,max These represent the rate of change Δu of the control quantity on the absorption side. abs The upper and lower limits.

[0030] Furthermore, in step 4, the optimization of the desorption-side economic objective is divided into two stages: steady-state objective value optimization and dynamic transition process optimization. The objective function for the steady-state objective value optimization is expressed as:

[0031]

[0032] stf(x str,s ,z str,s ,u str,s ) = 0

[0033] g(x str,s ,z str,s ,u str,s ) = 0

[0034] y str,s =h(x str,s ,z str,s ,u str,s )

[0035] y str,min ≤y str,s ≤y str,max

[0036] u str,min ≤u str,s ≤u str,max

[0037] Where α1 and α2 represent the CO2 trading price and the reboiler extraction steam price, respectively; x str,s and z str,s These represent the differential state and algebraic state at the optimal economic steady-state operating point on the desorption side, respectively; u str,s and y str,s These represent the control and controlled variables, respectively, at the economically optimal steady-state operating point on the desorption side; u str,min and u str,max They represent u respectively str,s The upper and lower limits of y; str,min and y str,max They represent y respectively str,s The upper and lower limits;

[0038] The objective function for optimizing the dynamic transition process is expressed as:

[0039]

[0040] stx str (t+1)=f(xstr (t),z str (t),u str (t))

[0041] g(x str (t),z str (t),u str (t))=0

[0042] y str (t)=h(x str (t),z str (t),u str (t))

[0043] x str (t k )=x str,m (t k )

[0044] z str (t k )=z str,m (t k )

[0045] y str,min ≤y str (t)≤y str,max

[0046] u str,min ≤u str (t)≤u str,max

[0047] Where β1, β2, and β3 represent regularization coefficients; x str,m The differential state quantity x on the desorption side str The measured value; z str,m The algebraic state variable z on the desorption side str The measured value; V f (·) represents the terminal cost function, which ensures that the predicted state of the system at the end of the prediction time domain is located in the terminal domain, thereby guaranteeing the feasibility and stability of the system under EMPC control.

[0048] Furthermore, in step 4, the carbon capture system includes an absorption-desorption dispersion prediction controller, which includes an absorption-side MPC controller and a desorption-side EMPC controller. The absorption-side MPC controller controls the CO2 capture rate on the absorption side, and the desorption-side EMPC controller controls the reboiler temperature and CO2 production on the desorption side.

[0049] Beneficial Effects: Compared with the prior art, the present invention has the following significant advantages: The present invention, through the control structure, uses the conventional MPC method on the absorption side to track the carbon capture rate setpoint to adapt to flue gas fluctuations and meet emission reduction targets, and uses the EMPC method on the desorption side to improve the economy during dynamic operation, give full play to the flexible support function of the carbon capture system with absorbent storage, realize the rapid adjustment of carbon capture rate to meet the carbon capture rate target, maximize CO2 production during the dynamic operation of the system and reduce reboiler steam extraction consumption, and effectively suppress the disturbance effect of upstream flue gas on the carbon capture system, thereby achieving flexible, economical and efficient operation of the post-combustion carbon capture system with absorbent storage. Attached Figure Description

[0050] Figure 1 This is a schematic diagram of the system structure of the present invention.

[0051] Figure 2 This is a schematic diagram of the control structure of the present invention.

[0052] Figure 3 This is a schematic diagram of the system dynamic response curve of the present invention.

[0053] Figure 4 This is a schematic diagram of the simulation results curve for scenario one of the present invention.

[0054] Figure 5 This is a schematic diagram of the simulation results curve for scenario two of the present invention. Detailed Implementation

[0055] like Figure 1 As shown, this system is a post-combustion carbon capture system with absorbent storage. The main equipment of the post-combustion carbon capture system with absorbent storage includes an absorption tower, desorption tower, reboiler, lean liquor tank, rich liquor tank, and condenser. Other auxiliary equipment includes heat exchangers, lean liquor pumps, rich liquor pumps, pipelines, and valves. Flue gas enters the absorption tower from bottom to top, contacting the lean absorbent (low CO2 loading absorbent) entering from top to bottom in a counter-current manner. After CO2 removal, the gas exits from the top of the absorption tower and is discharged into the atmosphere. The rich absorbent (high CO2 loading absorbent), containing a large amount of CO2, exits from the bottom of the absorption tower and is stored in the rich liquor tank. An interstage cooling process is installed in the middle section of the absorption tower to reduce the temperature rise caused by the exothermic reaction of CO2 absorption and to increase the reaction driving force. After heat exchange between the rich liquor and the regenerated lean liquor in the heat exchanger, the gas enters the desorption tower to complete CO2 desorption. The heat for desorption is provided by steam extracted from the generator set's steam turbine. The desorbed CO2 exits from the top of the desorption tower and enters the condenser to complete subsequent processes. The regenerated lean solution flows into the lean solution tank for storage, and is then pumped back into the absorption tower by the lean solution pump to begin the next cycle.

[0056] To improve the economy, carbon reduction benefits, and operational flexibility of post-combustion carbon capture systems with absorbent storage, this invention proposes a decentralized predictive control method for such systems. This method is implemented through a control structure. The control structure utilizes an absorption-side MPC controller and a desorption-side MPC controller, such as... Figure 2 As shown in the diagram, the physical object controlled by the absorption-side MPC controller is the lean liquor pump of the carbon capture system; the physical object controlled by the desorption-side EMPC controller is the rich liquor pump of the carbon capture system. The absorption-side MPC controller uses the lean liquor flow rate to control the CO2 capture rate, with the flue gas flow rate as a measurable disturbance; the desorption-side EMPC controller uses the rich liquor flow rate and the reboiler extraction steam flow rate to control the reboiler temperature and CO2 production.

[0057] The above-mentioned dispersion prediction and control methods for post-combustion carbon capture systems with absorbent storage include:

[0058] S1: An open-loop step experiment was conducted on a post-combustion carbon capture system with absorbent storage to obtain the dynamic characteristic experimental curves of the post-combustion carbon capture system with absorbent storage; wherein, the carbon capture system is divided into an absorption side and a desorption side; the controllable quantities on the absorption side include lean liquid flow rate, the controlled quantities include CO2 capture rate, and the measurable perturbation flue gas flow rate is considered; the controllable quantities on the desorption side include rich liquid flow rate and reboiler extraction steam flow rate, and the controlled quantities include reboiler temperature and CO2 production;

[0059] S2: Based on the data corresponding to the dynamic characteristic experimental curve, the absorption side identifies and obtains a discrete state-space prediction model characterizing the dynamic characteristics of the carbon capture system through a subspace identification method, and expands the discrete state-space model into an incremental state-space prediction model.

[0060] Specifically, the discrete state-space model is represented as:

[0061]

[0062] Among them, u abs (t k )=u lean (t k ), u abs (t k ) represents t k The absorption-side control quantity at time, u lean (t k ) represents t k The flow rate of the anti-fluid at any given time; y abs (t k )=y CL (t k ), y abs (t k ) represents t kThe controlled quantity on the absorption side at time y CL (t k ) represents t k CO2 capture rate at time d; abs (t k )=d fg (t k ), d abs (t k ) represents t k The absorbable disturbance at time d fg (t k ) represents t k The flue gas flow rate at any given time; x abs,0 (t k ) represents t k The state vector of the absorbing side at time t; A0, B0, C0, D0, E0 and F0 all represent the system characteristic matrix;

[0063] The incremental state-space model is represented as follows:

[0064]

[0065] Where O represents the zero matrix; I ny Represents the identity matrix.

[0066] S3: On the desorption side, a nonlinear discrete difference algebraic equation based on a mechanism model is established as a prediction model to ensure sufficient economic optimization accuracy;

[0067] Specifically, the nonlinear discrete difference algebraic prediction model is expressed as follows:

[0068]

[0069] S4: Optimize the tracking target for the absorption side and construct a tracking objective function; optimize the economic target for the desorption side and construct an economic objective function; combine the prediction model to design the absorption-desorption dispersion prediction controller corresponding to the carbon capture system; realize the control mode of system desorption side tracking control and desorption side economic control through the absorption-desorption dispersion prediction controller, thereby realizing the flexible, economical and efficient operation of the post-combustion carbon capture system with absorbent storage.

[0070] Specifically, the target optimization and tracking objective function on the absorption side are expressed as follows:

[0071]

[0072] stx abs (t+1)=Ax abs (t)+B u Δu abs (t)+Bd Δd abs (t)

[0073] y abs (t)=Cx abs (t)

[0074] x abs (t k )=x abs,m (t k )

[0075] y abs,min ≤y abs (t)≤y abs,max

[0076] u abs,min ≤u abs (t)≤u abs,max

[0077] Δu abs,min ≤Δu abs (t)≤Δu abs,max (5)

[0078] Among them, y abs,r Indicates the setpoint of the controlled variable on the absorption side; N p Represents the prediction time domain; Q represents the error weight; R represents the control weight; x abs,m The state variable x on the absorption side represents the state variable. abs The measured value; y abs,min and y abs,max Representing the controlled quantity y on the absorption side respectively abs The upper and lower limits; u abs,min and u abs,max These represent the absorption-side control quantity u. abs The upper and lower limits; Δu abs,min and Δu abs,max These represent the rate of change Δu of the control quantity on the absorption side. abs The upper and lower limits.

[0079] The optimization of the desorption-side economic objective is divided into two stages: steady-state objective value optimization and dynamic transition process optimization. The objective function for steady-state objective value optimization is expressed as:

[0080]

[0081] stf(x str,s ,z str,s ,u str,s ) = 0

[0082] g(x str,s ,z str,s ,u str,s ) = 0

[0083] y str,s =h(x str,s ,z str,s ,u str,s )

[0084] y str,min ≤y str,s ≤y str,max

[0085] u str,min ≤u str,s ≤u str,max (6)

[0086] Where α1 and α2 represent the CO2 trading price and the reboiler extraction steam price, respectively; x str,s and z str,s These represent the differential state and algebraic state at the optimal economic steady-state operating point on the desorption side, respectively; u str,s and y str,s These represent the control and controlled variables, respectively, at the economically optimal steady-state operating point on the desorption side; u str,min and u str,max They represent u respectively str,s The upper and lower limits of y; str,min and y str,max They represent y respectively str,s The upper and lower limits.

[0087] The objective function for optimizing the dynamic transition process is expressed as:

[0088]

[0089] stx str (t+1)=f(x str (t),z str (t),u str (t))

[0090] g(x str (t),z str (t),u str (t))=0

[0091] y str (t)=h(x str (t),z str (t),u str (t))

[0092] x str (t k )=x str,m (t k )

[0093] y str,min ≤ystr (t)≤y str,max

[0094] u str,min ≤u str (t)≤u str,max (7)

[0095] Where β1, β2, and β3 represent regularization coefficients; x str,m The differential state quantity x on the desorption side str The measured value; V f (·) represents the terminal cost function, which ensures that the predicted state of the system at the end of the prediction time domain is located in the terminal domain, thereby guaranteeing the feasibility and stability of the system under EMPC control.

[0096] The control structure of the distributed predictive control algorithm is as follows: Figure 2 As shown, specifically, the absorption-side MPC controller uses lean liquid flow rate to control CO2 capture rate, with flue gas flow rate as a measurable disturbance; the desorption-side EMPC controller uses rich liquid flow rate and reboiler extraction steam flow rate to control reboiler temperature and CO2 production.

[0097] The following calculation uses a real-world system as an example, and the specific process is as follows:

[0098] First, based on the established mechanistic model of the post-combustion carbon capture system with absorbent storage, an open-loop step experiment was conducted to obtain the corresponding dynamic characteristic experimental curves, which were then compared with the dynamic characteristic experimental curves of the post-combustion carbon capture system without absorbent storage. Figure 3 As shown, the introduction of the absorbent storage tank eliminates the influence of lean liquid flow rate on the desorption process, weakens and delays the influence of reboiler extraction steam flow rate on the absorption process, and basically achieves decoupling between the system's absorption and desorption processes.

[0099] Subsequently, with a sampling period of 20 seconds, based on the corresponding dynamic characteristic experimental data, the incremental state-space model of the absorption side was obtained after transformation using the subspace identification method, and the prediction model of the absorption side was also obtained. With a sampling period of 20 seconds, a nonlinear discrete difference algebraic equation based on the mechanism model was established as the prediction model of the desorption side.

[0100] Subsequently, based on equations (5) to (7), an optimization problem for the absorption-side MPC control and the desorption-side EMPC control of the carbon capture system can be constructed, and the optimization solution can be performed to control the post-combustion carbon capture system with absorbent storage.

[0101] The basic parameters and constraint settings of the controller in the distributed predictive control method are shown in Table 1. To verify the superiority of the distributed predictive control algorithm proposed in this embodiment, it is compared with a tracking-type MPC control algorithm that uses both the absorption and desorption sides.

[0102] Table 1 shows the parameters and constraint settings for the distributed predictive controller.

[0103]

[0104] The simulation results are as follows: Figure 4 and Figure 5 As shown. Among them, Figure 4 The graph shows a comparison of the control performance of two control methods in the following scenario. Figure 5 This is a comparison chart of the control performance of the two control methods in Scenario 2.

[0105] In this scenario, the absorption side needs to meet a higher CO2 capture rate requirement, and the desorption side needs to meet a given CO2 production requirement. Assume the initial operating conditions of the carbon capture system are as shown in the left column of Table 2, and the target optimized operating conditions are as shown in the right column of Table 2. The flue gas flow rate remains constant during the simulation. Figure 4 As shown, at 200s, the CO2 capture rate setpoint and CO2 production setpoint became 95% and 30kg / s, respectively. For the absorption side, both the dispersed predictive controller and the comparative controller can control the CO2 capture rate to quickly and stably rise to the new steady-state operating point by adjusting the lean liquor flow rate. For the desorption side, the comparative controller's control objective is to quickly track the given CO2 production and the new reboiler temperature; therefore, both the rich liquor flow rate and the reboiler extraction steam flow rate increase rapidly to meet the higher CO2 production demand as quickly as possible. The dispersed predictive controller's control objective is to minimize reboiler heat consumption during the dynamic process of meeting the higher CO2 production target, thereby reducing operating costs; therefore, compared to the comparative controller, the reboiler extraction steam flow rate increases slightly slower. Simultaneously, to offset the impact of the increased reboiler extraction steam flow rate and quickly reach the CO2 production target, the rich liquor flow rate is significantly increased to achieve higher dynamic operating economic efficiency. Economic comparison results show that during the main dynamic transition process (200s-800s), the predictive controller is 1.23% more economical than the comparative controller.

[0106] Table 2 shows the system operating conditions under scenario one.

[0107] variable Initial operating conditions Target Optimize Operating Conditions u [0.19,0.19,59.33] [0.24,0.46,114.79] y [83.91,391.51,14.78] [95.00,390.95,30.00] d 81.97 81.97

[0108] In scenario two, the absorption side is affected by upstream flue gas disturbances, requiring it to demonstrate disturbance resistance. The desorption side needs to adapt to changes in the upstream power plant load; therefore, the reboiler extraction steam flow rate is fixed. Assume the initial operating conditions of the carbon capture system are as shown in the left column of Table 3, and the target optimized operating conditions are as shown in the right column of Table 3. Figure 5As shown, at 200s, the flue gas flow rate increased by 3%. On the absorption side, the CO2 capture rate decreased with increasing flue gas flow rate, but both the controller and the control controller adjusted the lean liquid flow rate to eventually return the CO2 capture rate to its initial value and stabilize. On the desorption side, since the reboiler extraction flow rate was fixed, the control process depended on the rich liquid flow rate. The control controller gradually increased the rich liquid flow rate to achieve the final steady-state optimization result. Under the action of the distributed predictive controller, since the reboiler heat cost was fixed, the rich liquid flow rate initially increased rapidly and then gradually decreased to a stable value to improve CO2 production during dynamic operation and improve economic efficiency. Economic comparison results show that during the main dynamic transition process (200s-800s), the predictive controller improved economic efficiency by 1.28% compared to the control controller.

[0109] Table 3 System Operating Conditions under Scenario 2

[0110] variable Initial operating conditions Target Optimize Operating Conditions u [0.19,0.19,59.33] [0.20,0.22,59.33] y [83.91,391.51,14.78] [83.91,391.10,15.44] d 81.97 84.43

[0111] In summary, the dispersion prediction and control method for post-combustion carbon capture systems with absorbent storage proposed in this invention can achieve more low-carbon, economical, flexible, and safe operation of carbon capture systems.

Claims

1. A method for predictive control of dispersion in a post-combustion carbon capture system with absorbent storage, characterized in that, Includes the following steps: Step 1: Conduct an open-loop step experiment on the post-combustion carbon capture system with absorbent storage to obtain the dynamic characteristic experimental curve of the post-combustion carbon capture system with absorbent storage. The carbon capture system is divided into an absorption side and a desorption side. Step 2: Based on the data corresponding to the dynamic characteristic experimental curve, the absorption side identifies the discrete state-space model characterizing the dynamic characteristics of the carbon capture system through the subspace identification method, and expands the discrete state-space model into an incremental state-space prediction model. Step 3: On the desorption side, establish a nonlinear discrete difference algebraic equation based on the mechanism model as a prediction model to ensure sufficient economic optimization accuracy; The nonlinear discrete difference algebraic prediction model is expressed as: in, u str (t k ) represents t k Desorption-side control quantity at time, u rich (t k ) represents t k The flow rate of the rich liquid at any given time, u reb (t k ) represents t k The reboiler extraction steam flow rate at any given time; y str (t k ) represents t k The desorption-controlled quantity at time y reb (t k ) represents t k The reboiler temperature at that moment, Indicates t k CO2 production at time x str (t k ) represents t k The desorption-side differential state vector at time z; str (t k ) represents t k The desorption-side algebraic state vector at time t; Step 4: Optimize the tracking objective on the absorption side and construct a tracking-type objective function; optimize the economic objective on the desorption side and construct an economic-type objective function. The economic objective optimization on the desorption side is divided into two stages: steady-state objective value optimization and dynamic transient process optimization. The steady-state objective value optimization objective function is expressed as: Where α1 and α2 represent the CO2 trading price and the reboiler extraction steam price, respectively; x str,s and z str,s These represent the differential state and algebraic state at the optimal economic steady-state operating point on the desorption side, respectively; u str,s and y str,s These represent the control and controlled variables, respectively, at the economically optimal steady-state operating point on the desorption side; u str,min and u str,max They represent u respectively str,s The upper and lower limits of y; str,min and y str,max They represent y respectively str,s The upper and lower limits; The objective function for optimizing the dynamic transition process is expressed as: Where β1, β2, and β3 represent regularization coefficients; x str,m The differential state quantity x on the desorption side str The measured value; z str,m The algebraic state variable z on the desorption side str The measured value; N p V represents the prediction time domain; f (·) represents the terminal cost function, which ensures that the predicted state of the system at the end of the prediction time domain is within the terminal domain, thereby guaranteeing the feasibility and stability of the system under EMPC control; A predictive controller for the absorption-desorption dispersion of a carbon capture system is designed by combining a predictive model. The system achieves a control mode of tracking control on the absorption side and economic control on the desorption side through the absorption-desorption dispersion predictive controller.

2. The dispersion prediction and control method for a post-combustion carbon capture system with absorbent storage as described in claim 1, characterized in that, In step 1, the control quantities on the absorption side include lean liquid flow rate, the controlled quantities include CO2 capture rate, and the measurable perturbation flue gas flow rate is taken into account. The desorption-side control variables include the rich liquid flow rate and the reboiler extraction steam flow rate, while the controlled variables include the reboiler temperature and CO2 production.

3. The dispersion prediction and control method for a post-combustion carbon capture system with absorbent storage as described in claim 1, characterized in that, In step 2, the discrete state-space model is represented as: in, u abs (t k ) represents t k The absorption-side control quantity at time, u lean (t k ) represents t k The flow rate of the fluid at any given moment; y abs (t k ) represents t k The controlled quantity on the absorption side at time y CL (t k ) represents t k CO2 capture rate at time; d abs (t k ) represents t k The absorbable disturbance at time d fg (t k ) represents t k The flue gas flow rate at any given time; x abs,0 (t k ) represents t k The state vector of the absorbing side at time t; A0, B0, C0, D0, E0 and F0 all represent the system characteristic matrix.

4. The dispersion prediction and control method for a post-combustion carbon capture system with absorbent storage as described in claim 3, characterized in that, In step 2, the incremental state-space prediction model is expressed as: Where O represents the zero matrix; Represents the identity matrix.

5. The dispersion prediction and control method for a post-combustion carbon capture system with absorbent storage as described in claim 4, characterized in that, In step 4, the target optimization and tracking objective function on the absorption side are expressed as follows: Among them, y abs,r Indicates the setpoint of the controlled variable on the absorption side; N p Represents the prediction time domain; Q represents the error weight; R represents the control weight; x abs,m The state variable x on the absorption side represents the state variable. abs The measured value; y abs,min and y abs,max Representing the controlled quantity y on the absorption side respectively abs The upper and lower limits; u abs,min and u abs,max These represent the absorption-side control quantity u. abs The upper and lower limits; Δu abs,min and Δu abs,max These represent the rate of change Δu of the control quantity on the absorption side. abs The upper and lower limits.

6. The dispersion prediction and control method for a post-combustion carbon capture system with absorbent storage as described in claim 1, characterized in that, In step 4, the carbon capture system includes an absorption-desorption dispersion prediction controller, which includes an absorption-side MPC controller and a desorption-side EMPC controller. The absorption-side MPC controller controls the CO2 capture rate on the absorption side, and the desorption-side EMPC controller controls the reboiler temperature and CO2 production on the desorption side.

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