Control device, co2 recovery device, control method, and program

Abstract

To provide a control method for a CO2 recovery device, which can achieve an economical operation while controlling a control amount to a target value.SOLUTION: This control device comprises: a data acquisition unit that acquires operation data of a CO2 recovery device; a target value setting unit that sets a target value of a parameter to be controlled on the basis of the operation data; a prediction model that simulates an operation and a state of the CO2 recovery device; an optimization calculation unit that aims to minimize deviation between a predicted value of the parameter and the target value while predicting the predicted value of the parameter when a prescribed operation amount is given to the CO2 recovery device on the basis of the prediction model and to optimize the predicted value based on the prediction model of a heat amount supplied to a regenerative heater provided in a regeneration tower of the CO2 recovery device, and calculates the operation amount to achieve the aim through optimization calculation; and a control unit that controls the CO2 recovery device on the basis of the calculated operation amount.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to a control device, a CO2 recovery device, a control method, and a program. [Background technology]

[0002] Patent Document 1 discloses a method for operating a CO2 capture system while controlling the CO2 concentration and CO2 capture rate toward target values. Specifically, the method simultaneously performs two control operations: maintaining the difference between the actual CO2 capture rate and the target CO2 concentration within a predetermined range by changing the circulation rate of the absorbent supplied to the absorber and the saturated steam amount supplied to the regenerator of the regenerator. Hereinafter, the CO2 concentration and CO2 capture rate of the control target (evaluation target) are referred to as the controlled variables, and the circulation rate of the lean absorbent supplied to the absorber and the saturated steam amount supplied to the regenerator for controlling the controlled variables toward the target values ​​are referred to as the manipulated variables. Patent Document 1 also discloses a method for preventing interference between the manipulated variables for controlling the CO2 concentration and the CO2 capture rate. While the control method of Patent Document 1 can control the CO2 concentration and CO2 capture rate toward target values, it does not take into account the economics of operation. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2016-16392 Summary of the Invention [Problem to be solved by the invention]

[0004] A control method for a CO2 recovery device is provided that achieves economical operation while controlling a control variable to a target value.

[0005] The present disclosure provides a control device, a CO2 recovery device, a control method, and a program that can solve the above problems. [Means for solving the problem]

[0006] The control device disclosed herein comprises a data acquisition unit that acquires operating data of a CO2 recovery device, a target value setting unit that sets target values ​​for parameters to be controlled based on the operating data, a prediction model that predicts the state of the CO2 recovery device, an optimization calculation unit that calculates the operating variables that will achieve the following objectives: predicting, based on the prediction model, the predicted values ​​of the parameters when a predetermined operating variable is applied to the CO2 recovery device, while minimizing the deviation between the predicted value of the parameters and the target value, and optimizing the predicted value based on the prediction model of the amount of heat to be supplied to a regenerative heater provided in a regenerator of the CO2 recovery device; and a control unit that controls the CO2 recovery device based on the calculated operating variables.

[0007] The CO2 recovery apparatus of the present disclosure includes an absorption tower, a regeneration tower, a regeneration heater provided in the regeneration tower, piping for delivering lean absorption liquid from the regeneration tower to the absorption tower, a valve provided in the piping, and the above-mentioned control device.

[0008] The control method disclosed herein aims to obtain operating data of a CO2 recovery device, set target values ​​for parameters to be controlled based on the operating data, predict the predicted values ​​of the parameters when a predetermined operating variable is applied to the CO2 recovery device based on a prediction model that predicts the state of the CO2 recovery device, minimize the deviation between the predicted value of the parameter and the target value, and optimize the predicted value based on the prediction model of the amount of heat to be supplied to a regenerative heater provided in a regenerator of the CO2 recovery device, calculate the operating variable that achieves the above objective by optimization calculation, and control the CO2 recovery device based on the calculated operating variable.

[0009] The program disclosed herein causes a computer to acquire operating data of a CO2 recovery device, set target values ​​for parameters to be controlled based on the operating data, predict the predicted values ​​of the parameters when a predetermined operating variable is applied to the CO2 recovery device based on a prediction model that predicts the state of the CO2 recovery device, while minimizing the deviation between the predicted value of the parameter and the target value, and optimizing the predicted value based on the prediction model of the amount of heat to be supplied to a regenerative heater provided in a regenerator of the CO2 recovery device, calculate the operating variable that achieves the above objectives through optimization calculation, and execute a process to control the CO2 recovery device based on the calculated operating variable. [Effects of the Invention]

[0010] According to the above-described control device, CO2 recovery device, control method, and program, it is possible to control the CO2 recovery device in consideration of economic efficiency while controlling the control amount to a target value. [Brief explanation of the drawings]

[0011] [Figure 1] 1 is a schematic diagram showing an example of a CO2 recovery device according to an embodiment. [Figure 2] FIG. 2 is a block diagram illustrating an example of a control device according to the embodiment. [Figure 3] FIG. 2 is a diagram illustrating an overview of control according to the embodiment. [Figure 4] FIG. 1 is a first diagram illustrating some control examples according to the embodiment. [Figure 5] FIG. 10 is a second diagram illustrating some control examples according to the embodiment. [Figure 6] FIG. 2 is a diagram illustrating an optimal operating point according to the embodiment. [Figure 7] FIG. 1 is a diagram showing an example of a test case for operating a CO2 capture device. [Figure 8A] FIG. 4 is a first diagram showing an example of a control result when the optimal operating point according to the embodiment is not taken into consideration. [Figure 8B]FIG. 10 is a second diagram showing an example of a control result when the optimal operating point according to the embodiment is not taken into consideration. [Figure 9] FIG. 10 is a diagram illustrating an example of a locus of an operating point when an optimal operating point is taken into consideration in the embodiment. [Figure 10A] FIG. 4 is a first diagram showing an example of a control result when an optimal operating point according to the embodiment is taken into consideration. [Figure 10B] FIG. 10 is a second diagram showing an example of a control result when the optimal operating point according to the embodiment is taken into consideration. [Figure 11] 4 is a flowchart showing an example of control of the CO2 recovery apparatus according to the embodiment. [Figure 12] FIG. 2 is a diagram illustrating an example of a hardware configuration of a control device according to an embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0012] Hereinafter, a control method for a CO2 recovery apparatus according to the present disclosure will be described with reference to the drawings. <Embodiment> (composition) FIG. 1 shows an example of the schematic configuration of a CO2 recovery system 100 according to an embodiment. As shown in FIG. 1, the CO2 recovery system 100 includes a control device 10, an absorber 2, a regenerator 3, a heat exchanger 4, and other components. Exhaust gas discharged from industrial combustion equipment such as a boiler or gas turbine is cooled and then sent to the absorber 2 through a pipe 80. A flue gas measuring instrument 101 is provided in the pipe 80. The flue gas measuring instrument 101 measures the flow rate and CO2 concentration of the flue gas sent to the absorber 2 and transmits the measured values ​​to the control device 10. The flue gas sent to the absorber 2 comes into contact with an absorbing solution in the absorber 2, and the CO2 in the flue gas is absorbed by the absorbing solution through a chemical reaction. The absorbing solution that has absorbed the CO2 is called a rich absorbing solution. The flue gas from which CO2 has been removed in the absorber 2 is released to the outside of the system through a pipe 81. A CO2 concentration meter 102 is provided in the pipe 81. The CO2 concentration meter 102 detects the CO2 concentration in the flue gas discharged from the absorption tower 2 and transmits the detected CO2 concentration to the control device 10. The rich absorbing liquid is pressurized by the pump 21 and sent to the heat exchanger 4 through a pipe 53. In the heat exchanger 4, the rich absorbing liquid is heated by the lean absorbing liquid regenerated in the regenerator 3 and having a higher temperature than the rich absorbing liquid, and is supplied to the regenerator 3 through a pipe 54.

[0013] The rich absorbing solution supplied to the regenerator 3 is supplied into the regenerator 3 from the top thereof. The rich absorbing solution releases CO2 through an endothermic reaction inside the regenerator 3. As the rich absorbing solution supplied to the regenerator 3 flows downward to the bottom of the tower, CO2 is removed, turning the rich absorbing solution into a semi-lean solution. A circulation line 31 is provided at the bottom of the regenerator 3 to circulate the absorbing solution that has flowed downward to the bottom of the tower. A regenerative heater 33 that heats the absorbing solution is provided in this circulation line 31. The regenerative heater 33 is, for example, an electric heater or a heat exchanger. In the case of an electric heater, the absorbing solution flowing through the circulation line 31 is heated by the heat of the electric heater and returned to the regenerator 3. In the case of a heat exchanger, for example, saturated steam or the like is supplied to the regenerative heater 33 through a pipe (not shown), and heat exchange occurs between the saturated steam supplied through this pipe and the absorbing solution flowing through the circulation line 31. The absorbing solution flowing through the circulation line 31 is heated and returned to the regenerator 3. The semi-lean solution circulates through the circulation line 31 and is heated by the regenerator heater 33 to become a lean solution. In this way, the rich absorbing solution supplied to the regenerator 3 becomes a lean absorbing solution, from which almost all of the CO2 has been removed, by the time it reaches the bottom of the regenerator 3. The lean absorbing solution is pressurized by the pump 35 and sent to the heat exchanger 4 through the pipe 51. The lean absorbing solution sent to the heat exchanger 4 through the pipe 51 is cooled by the rich absorbing solution in the heat exchanger 4 and supplied to the absorber 2 through the pipe 52. In this way, the absorbing solution circulates between the absorber 2 and the regenerator 3. Note that CO2 gas released from the rich absorbing solution in the regenerator 3 is discharged from the top of the regenerator 3 through the pipe 82. A CO2 gas meter 103 is provided on the pipe 82. The CO2 gas meter 103 detects the gas flow rate and concentration of the discharged CO2 gas and transmits the gas flow rate and concentration to the control device 10.

[0014] A rich absorbent control valve 61 is provided in the pipe 54. The control device 10 controls the aperture of the rich absorbent control valve 61 to control the flow rate of the rich absorbent sent to the regenerator 3. The flow rate of the rich absorbent flowing through the pipe 54 is measured by a flow meter 71. The flow rate measured by the flow meter 71 is sent to the control device 10. A lean absorbent control valve 62 is provided in the pipe 52. The control device 10 controls the aperture of the lean absorbent control valve 62 to control the flow rate of the lean absorbent sent to the absorber 2. The flow rate of the lean absorbent flowing through the pipe 52 is measured by a flow meter 72. The flow rate measured by the flow meter 72 is sent to the control device 10. The control device 10 controls the heat input to the regenerative heater 33. For example, if the regenerative heater 33 is an electric heater, the control device 10 controls the output of the electric heater. When the regenerative heater 33 is a heat exchanger, the control device 10 controls the flow rate of saturated steam supplied to the regenerative heater 33 by controlling the aperture of a saturated steam control valve (not shown) provided in a pipe that supplies saturated steam. A pump 21 that delivers rich absorbing liquid is provided in the pipe 53. The control device 10 controls the pump 21 to pressure-feed the rich absorbing liquid that accumulates in the lower part of the absorber 2 toward the heat exchanger 4 and the regenerator 3. A pump 35 that delivers lean absorbing liquid is provided in the pipe 51. The control device 10 controls the pump 35 to pressure-feed the lean absorbing liquid that accumulates in the lower part of the regenerator 3 toward the heat exchanger 4 and the absorber 2. The control device 10 acquires the exhaust gas flow rate, exhaust gas temperature and CO2 concentration in the exhaust gas measured by the exhaust gas measuring instrument 101, the CO2 concentration measured by the CO2 concentration meter 102, and the gas flow rate and concentration of CO2 gas measured by the CO2 gas measuring instrument 103, and based on this information, controls the heat input to the regenerative heater 33 (e.g., the output of the electric heater or the flow rate of saturated water vapor) so that the CO2 concentration and CO2 recovery rate reach predetermined target values.

[0015] Fig. 2 is a block diagram showing an example of the functions of a control device according to an embodiment. The control device 10 controls the CO2 recovery device 100 by nonlinear model predictive control (NMPC). As shown in Fig. 2, the control device 10 includes a data acquisition unit 11, an operation amount calculation unit 12, a control unit 13, and a storage unit 14.

[0016] The data acquisition unit 11 acquires the flow rate and temperature of the exhaust gas and the CO2 concentration in the exhaust gas measured by the exhaust gas measuring instrument 101, the CO2 concentration measured by the CO2 concentration meter 102, the gas flow rate and concentration of CO2 gas measured by the CO2 gas measuring instrument 103, the flow rate of the rich absorption solution measured by the flow meter 71, the flow rate of the lean absorption solution measured by the flow meter 72, etc. The data acquisition unit 11 records the acquired measurement values ​​in the memory unit 14 in association with time.

[0017] The manipulated variable calculation unit 12 calculates manipulated variables such that the difference between the measured value of the CO2 concentration and / or the CO2 capture rate and a predetermined target value (reference value) falls within a predetermined range. Alternatively, the manipulated variable calculation unit 12 may calculate manipulated variables such that not only the CO2 concentration and the CO2 capture rate approach the target values ​​but also the heater heat input Qin and the lean absorbent flow rate WL are optimized. The heater heat input Qin represents the amount of heat provided to the regenerative heater 33 and can be controlled by adjusting the output of the electric heater or the flow rate of saturated steam. The lean absorbent flow rate WL can be optimized by adjusting the aperture of the lean absorbent control valve 62, for example. The manipulated variable calculation unit 12 includes a target value setting unit 121, a prediction model 122, and an optimization calculation unit 123.

[0018] The target value setting unit 121 sets target values ​​for controlled variables (CO2 concentration, CO2 recovery rate) and manipulated variables (flow rate WL of the lean absorbing solution supplied to the absorption tower, heater heat input Qin). For example, the target value setting unit 121 sets a target value for the CO2 concentration and a target value for the CO2 recovery rate according to the flow rate and temperature of the flue gas supplied to the CO2 recovery apparatus 100, a target value for the heater heat input Qin and a target value for the lean absorbing solution flow rate WL at an optimal operating point according to the flow rate and temperature of the flue gas supplied to the CO2 recovery apparatus 100, etc. These target values ​​set by the target value setting unit 121 may be calculated in advance using a simulator (which may be a prediction model 122 described later) that simulates the operation of the CO2 recovery apparatus 100. Alternatively, the target value setting unit 121 may have a simulator, and parameters such as the flow rate and temperature of the flue gas acquired by the data acquisition unit 11 during operation of the CO2 recovery apparatus 100 may be input to the simulator to calculate the respective target values.

[0019] The prediction model 122 is a simulator that simulates the behavior of the CO2 capture system 100 when an operation is performed, and predicts the plant state resulting from that operation. The prediction model 122 is composed of numerous physical models that explain the phenomena that occur within the CO2 capture system 100. Many of the physical models are described using nonlinear mathematical expressions. An example of a physical model is shown below. This physical model takes into account the characteristics and mass transfer coefficient of the absorption liquid, the concentrations of gas and liquid, and the temperature distribution in the flow direction within the vessel. Gases include CO2, N2, and O2, and liquids include H2O, N2, and amine absorbent. t is time, i is the identification number of the gas and liquid components, j is the number of divisions in the flow direction of the gas and liquid components (the areas created by dividing the absorption tower 2, etc., at cross sections perpendicular to the flow direction of the gas or liquid are considered as "elements," and the state of the gas and liquid is modeled for each element based on the chemical changes that occur in that element. The number of divisions refers to the number of elements in this case), F j L (t) is the liquid flow rate [kg / s], x i,j (t) is the liquid mass composition [weight fraction], M i is the molecular weight [kg / kg-mol], d z is the length in the flow direction per element [m], M cpjis the heat capacity [J / K], C j PL (t) is liquid specific velocity [J / kg K], γ i (t) is the reaction heat [J / kg], Q ex If (t) is the heat transfer rate between the gas and the liquid [J / s], the liquid holdup U i,j (t) [kg] and liquid temperature T j L (t)[K] are calculated using the following formula (1).

[0020]

number

[0021] Also, k Gi,j (t) is the mass transfer coefficient [kg-mol / (sm 2 Pa)], a j is the gas / liquid contact area [m 2 ], p i,j (t) is the partial pressure of the gas [Pa], p i,j * If (t) is the equilibrium partial pressure [Pa], which is a function of temperature, liquid composition, etc., then the mass transfer rate of CO2 and H2O m i,j (t) [kg-mol / s] is calculated using the following formula (2).

[0022]

number

[0023] Also, F j G is the gas flow rate [kg-mol / s], y i,j If (t) is the gas composition [molar fraction], the gas mass balance is calculated using the following equation (3):

[0024]

number

[0025] Also, C j PGIf (t) is the gas specific velocity [J / kg-mol K], the gas temperature T j G (t) is calculated from the gas side heat balance using the following equation (4).

[0026]

number

[0027] Since the prediction model 122 is used for real-time online control of the CO2 capture system 100, it is required to be able to quickly predict the state of the CO2 capture system 100. To achieve this, the prediction model 122 reduces the order of the tens of thousands of parameters used in the physical model that constitutes the prediction model 122 to several hundred parameters to enable high-speed calculations while maintaining prediction accuracy. The order reduction is achieved by approximating the coefficients of the absorption and equilibrium reactions with polynomials and adjusting the number of divisions. It has been confirmed that simulation results of the prediction model 122, such as CO2 gas flow rate, based on the reduced-order physical model accurately match measured values ​​in an actual plant. For example, the prediction model 122 predicts the CO2 concentration, CO2 capture rate, and other parameters when the heater heat input Qin and the lean absorbent flow rate WL are given as inputs.

[0028] The optimization calculation unit 123 uses the prediction model 122 to predict control variables, such as CO2 concentration, when the CO2 capture device 100 is controlled with a certain control variable. The optimization calculation unit 123 then calculates, through optimization calculation, control variables that achieve the target values ​​of the control variables, or control variables that optimize the heater heat input Qin or the lean absorption liquid flow rate WL while achieving the target values ​​of the control variables, thereby realizing an optimal operating point. For example, the optimization calculation unit 123 provides the prediction model 122 with predicted values ​​of the control variables per second for the next few minutes, causes the prediction model 122 to predict the state of the CO2 capture device 100 per second for the next few minutes, and calculates the control variables that minimize the sum of the differences between the predicted values ​​of the control variables, such as the CO2 capture rate per second for the next few minutes, and their respective target values, as the optimal solution. The optimization calculation uses, for example, the C / GMRES method. The C / GMRES method calculates the control variables without iterative calculations by tracking the change in the optimal solution, thereby enabling efficient and fast solution calculation. By reducing the number of parameters in the prediction model 122 and performing calculations using the C / GMRES method, the manipulated variable that serves as the solution can be calculated quickly. This makes it possible to achieve real-time and online control of the CO2 recovery apparatus 100. For example, even in a situation where the operating load of the CO2 recovery apparatus 100 changes rapidly, the CO2 recovery apparatus 100 can be controlled in real time according to the speed of the load change.

[0029] The control unit 13 controls the CO2 recovery apparatus 100 using the operation amount calculated by the operation amount calculation unit 12. For example, the control unit 13 controls the opening degree of the lean absorbent adjustment valve 62 and the like so as to optimize the heater heat input Qin and the lean absorbent flow rate WL while achieving the target values ​​of the CO2 concentration and the CO2 recovery rate.

[0030] The storage unit 14 stores various information necessary for controlling the CO2 recovery apparatus 100, such as data acquired by the data acquisition unit 11 and target values ​​set by the target value setting unit 121.

[0031] FIG. 3 shows an overview of the control by the control device 10. Measured disturbance d in FIG. 3 is, for example, the flow rate or temperature of the flue gas supplied to the CO2 recovery device 100. The disturbance d is provided to a static optimizer 201 and a plant 205. Upon receiving the disturbance d, the static optimizer 201 generates a reference. The reference is, for example, a target value of a controlled variable or a manipulated variable. For example, the static optimizer 201 generates a target value of the CO2 concentration according to the disturbance d, a target value of the CO2 recovery amount according to the disturbance d, a target value of the heater heat input Qin according to the disturbance d, and a target value of the lean absorption solution flow rate WL according to the disturbance d. The static optimizer 201 provides the generated reference to a dynamic optimizer 202. The dynamic optimizer 202 uses optimization calculations to search for manipulated variables Mv that can achieve the received reference. Specifically, the Dynamic Optimizer 202 provides a certain manipulated variable Mv to the Model 203, compares the controlled variables Cv (Controlled Variables) predicted by the Model 203 with target values, and evaluates the manipulated variable Mv provided to the Model 203. The Dynamic Optimizer 202 repeats this process to search for an appropriate manipulated variable Mv. The manipulated variable Mv is the heater heat input Qin (for example, the output of an electric heater or the aperture of a valve that adjusts the flow rate of saturated steam) and the lean absorbent flow rate WL (for example, the aperture of the lean absorbent control valve 62). When an appropriate manipulated variable Mv is found, the Plant 205 is controlled by that manipulated variable Mv. As a result of controlling the Plant 205 using the manipulated variable Mv, a measured value y such as a CO2 concentration or a CO2 capture rate is measured. The Estimator 204 calculates the difference between the measured value y and the predicted value ye (Estimated Measurements) of the controlled variable that the Model 203 predicts when the same manipulated variable Mv as that given to the Plant 205 is given to the Model 203, calculates the state s of the Model 203 that compensates for this difference, and modifies the Model 203 by the amount of the state s so that the actual state of the Plant 205 can be accurately simulated.The static optimizer 201 corresponds to the target value setting unit 121 in Fig. 2. The model 203 corresponds to the prediction model 122 in Fig. 2. The dynamic optimizer 202 corresponds to the optimization calculation unit 123 in Fig. 2.

[0032] Next, some control examples will be described with reference to FIGS. (1) Conventional control For comparison, an example of conventional control will be described. In conventional control, for example, command values ​​for heater heat input Qin and lean absorption solution flow rate WL according to the exhaust gas flow rate, etc. are calculated, and feedforward control is performed to give the command values ​​to the plant. In conventional control, control using NMCP is not performed. Next, an example of control using the NMCP according to this embodiment will be described.

[0033] (2) 1 input, 1 output In the one-input, one-output system, the heater heat input Qin is used as the manipulated variable, and the CO2 flow rate at the outlet of the regenerator 3 is used as the controlled variable. The heater heat input Qin is given to the prediction model 122, and an optimization calculation is performed to determine the heater heat input Qin that can control the CO2 flow rate to the target value. In addition, a command value is calculated for the lean absorbent flow rate WL, and feedforward control is performed in which the command value is output to the CO2 recovery device 100. The relationship between the CO2 flow rate at the regenerator outlet and the CO2 recovery rate can be expressed by the following equation (A). Bringing the CO2 flow rate at the regenerator outlet close to the target value is the same as bringing the CO2 recovery rate close to the target value, with the CO2 recovery rate being the controlled variable. CO2 recovery rate = (CO2 flow rate at regeneration tower outlet) / (exhaust gas flow rate at absorption tower 2 inlet × CO2 concentration in exhaust gas at absorption tower 2 outlet) (A) In addition, the value measured by the CO2 gas measuring instrument 103 can be used for the CO2 flow rate at the outlet of the regeneration tower in equation (A), the value measured by the exhaust gas measuring instrument 101 can be used for the exhaust gas flow rate at the inlet of the absorption tower 2, and the value measured by the CO2 concentration meter 102 can be used for the CO2 concentration in the exhaust gas at the outlet of the absorption tower 2.

[0034] An example of an evaluation function in optimization calculations is shown in formula (1) in Figure 5. In formula (1) in Figure 5, x represents the controlled variable, and as shown in the "X (controlled variable)" column in the table in Figure 5, x in the case of one input and one output is x = [X6regen Qin] T x in equation (1) in Figure 5 ref represents the target value (reference value) of the controlled variable, and x ref =[X6regen_ref 0(zero)] T Similarly, u in equation (1) in Figure 5 represents the manipulated variable, and as shown in the "U (NMPC manipulated variable)" column in the table in Figure 5, u for one input and one output is u = [dQin / dt] T Also, t is time, and the T in t+T is T=T f (1-e -αt ) and T f is the prediction horizon, α is the acceleration gain. The superscript T is the transposed vector, u is the manipulated variable, Qin is the heater heat input, WL in is the flow rate of the lean absorbent supplied from the regenerator 3 to the absorber 2, and X6 regen is the CO2 flow rate at the outlet of the regeneration tower 3 (measured value of the CO2 gas measuring instrument 103), X6 regen_ref is a value calculated by multiplying the exhaust gas flow rate to the absorber 2 by the CO2 concentration at the inlet of the absorber 2 by the target value of the CO2 recovery rate, Sf is a weighting coefficient for the end, Q is a weighting coefficient for the state, and R is a weighting coefficient for control. The optimization calculation unit 123 calculates u that minimizes the value of equation (1) in Fig. 5. With one input and one output, as is clear from the definition of x and equation (1) in Fig. 5, which is the evaluation function, it calculates the manipulated variable heater heat input Qin that minimizes the deviation between the predicted value of the CO2 flow rate by the prediction model 122 and the target value of the CO2 flow rate (minimizes the deviation between the CO2 recovery rate and the target value of the CO2 recovery rate) and minimizes itself. Here, the deviation between the predicted CO2 flow rate and the target CO2 flow rate is minimized, but the evaluation function may also be configured to minimize the deviation between the predicted CO2 concentration and the target CO2 concentration.

[0035] (3) 2 inputs, 2 outputs In two-input, two-output control, the heater heat input Qin and the lean absorbent flow rate WL are used as the manipulated variables, and the CO2 flow rate at the outlet of the regenerator 3 and the CO2 concentration discharged from the absorber 2 are used as the controlled variables. The heater heat input Qin and the lean absorbent flow rate WL are given to the prediction model 122, and the heater heat input Qin and the lean absorbent flow rate WL that can control the CO2 flow rate and CO2 concentration to their respective target values ​​are found by optimization calculation. The value measured by the CO2 concentration meter 102 can be used for the CO2 concentration. The evaluation function is equation (1) in Figure 5. Unlike the one-input, one-output case, the controlled variable x is x = [X6regen Qin Y6abso] as shown in the "X (controlled variable)" column in the table in Figure 5. T Also, x ref is x as shown in the table in Figure 5. ref =[X6regen_ref 0 Y6abso_ref] T In addition, u is expressed as u = [dQin / dt,dWL] as shown in the "U (NMPC manipulated variable)" column of the table in Figure 5. in / dt] T In the two-input, two-output system, the deviation between the predicted value of the CO2 flow rate and the target value of the CO2 flow rate is minimized, the deviation between the predicted value of the CO2 concentration and the target value of the CO2 concentration is minimized, and the manipulated variable heater heat input Qin that minimizes itself is calculated.

[0036] (4) Two-input, two-output system considering the optimal operating point In a two-input, two-output system that takes into account the optimal operating point, the heater heat input Qin and lean absorbent flow rate WL are used as the manipulated variables, and the CO2 flow rate at the regenerator 3 outlet and the CO2 concentration discharged from the absorber 2 are used as the controlled variables. Then, not only are the CO2 flow rate and CO2 concentration controlled to their respective target values, but the heater heat input Qin and lean absorbent flow rate WL are determined by optimization calculations so that the difference between the predicted values ​​of the heater heat input Qin and lean absorbent flow rate WL and their respective optimal operating points is minimized. The evaluation function is equation (1) in Figure 5. Unlike the two-input, two-output system, the controlled variable x is x=[X6regen Qin Y6abso WLin] T Also, x ref is x ref=[X6regen_ref Qin_ref Y6abso_ref WLin_ref] T u is similar to a two-input, two-output system.

[0037] (Optimal operating point) Next, the optimal operating point will be described. FIG. 6 shows an example of the optimal operating point and the trajectory of the operating point when the CO2 recovery system is operated without considering the optimal operating point. The vertical axis of the graph in FIG. 6 represents Qin (kW), and the horizontal axis represents WL (kg / h). Graph 600 is a graph showing the relationship between the heater heat input Qin and the lean absorption solution flow rate WL at various operating points of the CO2 recovery system 100 when the exhaust gas flow rate is rated. Graph 601 is a graph showing the relationship between the heater heat input Qin and the lean absorption solution flow rate WL at various operating points of the CO2 recovery system 100 when the exhaust gas flow rate is 80%. Graph 602 is a graph showing the relationship between the heater heat input Qin and the lean absorption solution flow rate WL at various operating points of the CO2 recovery system 100 when the exhaust gas flow rate is 60%. These graphs 600-602 are graphs plotting the relationship between the lean absorbent flow rate WL and the heater heat input Qin when the lean absorbent flow rate WL is varied while maintaining the operating state, with rated, 80%, and 60% values ​​for the exhaust gas flow rate given as input parameters to a simulator (e.g., the prediction model 122) of the CO2 recovery device 100. The operating state is simulated to achieve target values ​​for the CO2 concentration and CO2 capture rate at each exhaust gas flow rate. The optimal operating point is the operating point at which the heater heat input Qin is minimized while achieving the target values ​​for the CO2 concentration and CO2 capture rate. For the rated graph 600, the heater heat input and lean absorbent flow rate values ​​corresponding to point 60a are the optimal operating point. Similarly, for the rated graph 601, the heater heat input and lean absorbent flow rate values ​​corresponding to point 61a are the optimal operating point, and for the rated graph 602, the heater heat input and lean absorbent flow rate values ​​corresponding to point 62a are the optimal operating point.

[0038] Graphs 63 to 65 in FIG. 6 show the trajectories of operating points obtained by an operation simulation in which the target CO2 capture rate is set to 75%, the exhaust gas flow rate supplied to the CO2 capture device 100 is reduced from the rated flow rate to 60% at a rate of 10% / min, maintained at that rate for a while, and then increased to the rated flow rate at the same rate. Note that the values ​​for the exhaust gas flow rate change rate and the flow rate reduction rate (10% / min and 60%, respectively) are merely examples and are not intended to be limiting. Graph 63 shows the trajectory of operating points obtained when the above-mentioned (1) conventional control is performed while the exhaust gas flow rate is changed under the above conditions. Graph 64 shows the trajectory of operating points obtained when the above-mentioned (2) one-input, one-output control is performed while the exhaust gas flow rate is similarly changed. Graph 65 shows the trajectory of operating points obtained when the above-mentioned (3) two-input, two-output control is performed while the exhaust gas flow rate is changed under the same conditions. In this example, the simulation is performed using an operating point based on the operation of an actual plant as the starting condition. As shown in FIG. 6, in either case, the heater heat input Qin is maintained at a higher level than the optimum operating point.

[0039] (Control results when the optimal operating point is not considered) 8A and 8B show an example of simulation results when the optimal operating point is not taken into consideration. FIG. 8A shows the results of the controlled variables. The vertical axis of graphs 801 to 803 indicates CO2 concentration, and the horizontal axis indicates time. Graph 801 shows (1) the progression of CO2 concentration in conventional control, graph 802 shows (2) the progression of CO2 concentration with one input and one output, and graph 803 shows (3) the progression of CO2 concentration with two inputs and two outputs. Graphs 801a, 802a, and 803a show target values, and graphs 801b, 802b, and 803b show control results. With one input and one output, the overshoot in the CO2 concentration becomes large after time t1. With two inputs and two outputs, the overshoot is suppressed.

[0040] The vertical axis of graphs 804 to 806 in Fig. 8A represents the CO2 flow rate, and the horizontal axis represents time. Graph 804 (1) represents the change in the CO2 flow rate under conventional control, graph 805 (2) represents the change in the CO2 flow rate under one input and one output, and graph 806 (3) represents the change in the CO2 flow rate under two inputs and two outputs. Graphs 804a, 805a, and 806a represent the target values, and graphs 804b, 805b, and 806b represent the control results. In (1) conventional control, there are time periods when there is a slight discrepancy between the target value and the control value, but in (2) one input and one output and (3) two inputs and two outputs, control is achieved almost exactly as targeted.

[0041] 8B shows the results of the operation amount and CO2 capture rate. The vertical axis of graphs 807 to 809 represents the lean absorbent flow rate WL, and the horizontal axis represents time. Graphs 807b to 809b respectively show (1) the transition of the lean absorbent flow rate WL under conventional control, (2) the transition of the lean absorbent flow rate WL under one input and one output, and (3) the transition of the lean absorbent flow rate WL under two inputs and two outputs.

[0042] The vertical axis of graphs 810-812 represents heater heat input Qin, and the horizontal axis represents time. Graphs 810a, 811a, and 812a represent the target value of heater heat input Qin (target value when exhaust gas flow rate is 60%). Graphs 810b-812b respectively represent (1) the trend of heater heat input Qin under conventional control, (2) the trend of heater heat input Qin under one input and one output, and (3) the trend of heater heat input Qin under two inputs and two outputs. In all cases, heater heat input Qin remains higher than the target value.

[0043] The vertical axis of graphs 813 to 815 represents the CO2 capture rate, and the horizontal axis represents time. Graphs 813bb to 815b respectively show (1) the change in CO2 capture rate under conventional control, (2) the change in CO2 capture rate with one input and one output, and (3) the change in CO2 capture rate with two inputs and two outputs. (2) The case of one input and one output shows the smallest transient change.

[0044] (Control results when considering the optimal operating point) Next, an example of control when the optimal operating point is considered is described. Figure 9 shows an example of the trajectory of the operating point when the optimal operating point is considered. For (1) conventional control, (2) one-input, one-output, and (3) two-input, two-output, the optimal operating point was considered by setting the operating point at the start of operation to the optimal operating point 60a. Graphs 91, 92, and 93 shown in Figure 9 show the trajectories of the operating point when the exhaust gas flow rate shown in Figure 7 is changed by performing (1) conventional control, (2) one-input, one-output, and (3) two-input, two-output control, respectively. Looking at the trajectories in Figure 9, it is clear that an operating point closer to the optimal operating point can be achieved compared to the case of Figure 6. In contrast, graph 94 shows the trajectory of the operating point when (4) two-input, two-output control considering the optimal operating point is performed. Comparing graphs 91 to 93, it can be seen that an ideal trajectory of the operating point is achieved.

[0045] 10A and 10B show an example of simulation results when the optimal operating point is taken into consideration. FIG. 10A shows the results of the controlled variables. The vertical axis of graphs 817 to 820 indicates CO2 concentration, and the horizontal axis indicates time. Graph 817 (1) shows the progress of CO2 concentration under conventional control, graph 818 (2) shows the progress of CO2 concentration with one input and one output, graph 819 (3) shows the progress of CO2 concentration with two inputs and two outputs, and graph 820 (4) shows the progress of CO2 concentration with two inputs and two outputs taking the optimal operating point into consideration. The target value of CO2 concentration was most accurately achieved in the case of (4) two inputs and two outputs taking the optimal operating point into consideration.

[0046] The vertical axis of graphs 821 to 824 represents the CO2 flow rate, and the horizontal axis represents time. Graphs 821a to 824a represent the target value of the CO2 flow rate. Graphs 821b to 824b respectively show the control results of the CO2 flow rate for (1) conventional control, (2) one input and one output, (3) two inputs and two outputs, and (4) two inputs and two outputs taking into account the optimal operating point. In all cases, control was achieved almost exactly as targeted.

[0047] Figure 10B shows the results of the operation amount and CO2 capture rate. The vertical axis of graphs 825 to 828 represents the lean absorbent flow rate WL, and the horizontal axis represents time. Graphs 825b to 828b respectively show the trends in (1) the lean absorbent flow rate WL under conventional control, (2) the lean absorbent flow rate with one input and one output, (3) the lean absorbent flow rate WL with two inputs and two outputs, and (4) the lean absorbent flow rate WL with two inputs and two outputs taking the optimal operating point into consideration. Graph 828a shows the trends in the optimal operating point.

[0048] The vertical axis of graphs 829 to 832 represents heater heat input Qin, and the horizontal axis represents time. Graphs 829a to 832a represent target values ​​of heater heat input Qin (target values ​​when the exhaust gas flow rate is 60%), and graph 832c shows the transition of the optimal operating point.

[0049] The vertical axis of graphs 833 to 836 represents the CO2 capture rate, and the horizontal axis represents time. Graphs 833b to 836b respectively show (1) the change in CO2 capture rate under conventional control, (2) the change in CO2 capture rate with one input and one output, (3) the change in CO2 capture rate with two inputs and two outputs, and (4) the change in CO2 capture rate with two inputs and two outputs taking the optimal operating point into consideration. (4) The case of two inputs and two outputs taking the optimal operating point into consideration shows the smallest transient change.

[0050] Summarizing the above results, the following findings (a) to (d) can be obtained. (a) By setting the initial value of the operating point near the optimal operating point, the heater heat input Qin per unit CO2 flow rate can be reduced. (b) To suppress CO2 concentration overshoot, two-input, two-output control (3) or (4) is necessary. (c) From the perspective of controllability and economy, (4) two-input, two-output control that takes the optimal operating point into consideration is optimal. (d) Even with control (2) to (3), controllability and economy can be improved compared to conventional control (1). Furthermore, by adjusting the weighting coefficients (Q, R) in equation (1) in Figure 5, it is possible to control according to the plant operation objectives, such as prioritizing economy or controllability.

[0051] (operation) Next, the operation of the control device 10 will be described. Fig. 11 is a flowchart showing an example of control of a CO2 recovery device according to an embodiment. It is assumed that information on which control method to adopt, (2) one input, one output, (3) two inputs, two outputs, or (4) two inputs, two outputs taking into account the optimal operating point, is input in advance to the control device 10 and set in the memory unit 14. First, the data acquisition unit 11 acquires the flow rate of the flue gas, the flue gas temperature, and the CO2 concentration in the flue gas measured by the flue gas measuring instrument 101, the CO2 concentration measured by the CO2 concentration meter 102, and the gas flow rate and concentration of CO2 gas measured by the CO2 gas measuring instrument 103 (step S1).

[0052] Next, the target value setting unit 121 sets target values ​​(reference values) (step S2). The target value setting unit 121 sets target values ​​of the CO2 concentration and / or the CO2 capture rate according to the exhaust gas flow rate and the exhaust gas temperature. For example, the memory unit 14 may have registered therein a setting table in which target values ​​of the CO2 concentration and target values ​​of the CO2 capture rate are set in association with the exhaust gas flow rate and the exhaust gas temperature, and the target value setting unit 121 may set the target values ​​of the CO2 concentration and the CO2 capture rate based on this setting table and the exhaust gas flow rate and the exhaust gas temperature acquired in step S1. Furthermore, when (4) two-input two-output control is performed taking into account the optimal operating point, the target value setting unit 121 sets the optimal operating point according to the exhaust gas flow rate (or the exhaust gas flow rate and the exhaust gas temperature). For example, the memory unit 14 stores data on operating points at various exhaust gas flow rates, as shown in Fig. 6, for each target value of the CO2 concentration and the CO2 capture rate, and the target value setting unit 121 refers to the data corresponding to the target values ​​of the CO2 concentration and the CO2 capture rate to read the optimal operating point corresponding to the exhaust gas flow rate acquired in step S1. The target value setting unit 121 sets the read optimal operating points (lean absorbent flow rate WL and heater heat input Qin) as target values ​​of the manipulated variables.

[0053] Next, the optimization calculation unit 123 sets an evaluation function (step S3). The optimization calculation unit 123 calculates x and x in equation (1) of FIG. ref, u are set to values ​​corresponding to one of the control methods: (2) one input, one output; (3) two inputs, two outputs; or (4) two inputs, two outputs taking into account the optimal operating point; and the weighting coefficients Sf, Q, and R are set to predetermined values.

[0054] Next, the optimization calculation unit 123 executes an optimization calculation (step S4). Using the prediction model 122, the optimization calculation unit 123 calculates manipulated variables that can achieve the target values ​​of the CO2 capture rate (the flow rate of CO2 discharged from the regeneration tower 3) and the CO2 concentration (or, in addition, that can realize operation at the optimal operating point) through the process described with reference to FIG. 3. The optimization calculation can use the C / GMRES method. This makes it possible to calculate manipulated variables that minimize the value of the evaluation function set in step S3 in a relatively short calculation time. The manipulated variables are the lean absorbent flow rate WL and the heater heat input Qin. The optimization calculation unit 123 outputs the manipulated variables calculated by the optimization calculation to the control unit 13.

[0055] Next, the control unit 13 controls the actual machine using the manipulated variable calculated by the optimization calculation (step S5). For example, the control unit 13 calculates the aperture of the lean absorbent control valve 62 based on the lean absorbent flow rate WL calculated in step S4, and controls the lean absorbent control valve 62 with the calculated aperture. Also, for example, the control unit 13 calculates the output of the electric heater and the aperture of a valve provided in a pipe that supplies saturated steam to the regenerative heater 33 based on the heater heat input Qin calculated in step S4, and controls the electric heater with the calculated output or the valve with the calculated aperture.

[0056] (effect) As described above, the nonlinear model predictive control (NMPC) of this embodiment enables the CO2 capture system to be operated economically while controlling the control variables of the CO2 capture system to target values. In particular, by providing an optimal operating point as a reference and operating the system in accordance with the reference, it is possible to achieve both controllability and economic efficiency. Furthermore, interference between the control that optimizes the CO2 capture rate and the control that optimizes the CO2 concentration can be adjusted using the weight R of the controlled variables, so there is no need to worry about interference. Furthermore, by reducing the order of the prediction model and using optimization calculations using the C / GMRES method, it is possible to shorten the update period to, for example, one second and the prediction time to several minutes (e.g., five minutes), thereby enabling real-time control of the CO2 capture system. This, for example, can improve controllability during transient changes in the plant.

[0057] 12 is a diagram showing an example of the hardware configuration of a control device according to each embodiment. A computer 900 includes a CPU 901, a main storage device 902, an auxiliary storage device 903, an input / output interface 904, and a communication interface 905. The above-described control device 10 is implemented in the computer 900. The above-described functions are stored in the auxiliary storage device 903 in the form of a program. The CPU 901 reads the program from the auxiliary storage device 903, loads it into the main storage device 902, and executes the above-described processing in accordance with the program. The CPU 901 also allocates a storage area in the main storage device 902 in accordance with the program. The CPU 901 also allocates a storage area in the auxiliary storage device 903 for storing data being processed in accordance with the program.

[0058] A program for implementing all or part of the functions of the control device 10 may be recorded on a computer-readable recording medium, and the program may be loaded into a computer system and executed to perform processing by each functional unit. The term "computer system" as used herein includes hardware such as an OS and peripheral devices. Furthermore, if a WWW system is used, the term "computer system" also includes a homepage provision environment (or display environment). Furthermore, the term "computer-readable recording medium" refers to portable media such as CDs, DVDs, and USBs, as well as storage devices such as hard disks built into the computer system. Furthermore, if the program is distributed to the computer 900 via a communication line, the computer 900 that receives the program may load the program into the main storage device 902 and execute the above-described processing. Furthermore, the program may be for implementing part of the above-described functions, or may be capable of implementing the above-described functions in combination with a program already stored in the computer system.

[0059] In other embodiments, the control device 10 may include a custom LSI (Large Scale Integrated Circuit) such as a PLD (Programmable Logic Device) in addition to or instead of the above configuration. Examples of PLDs include PAL (Programmable Array Logic), GAL (Generic Array Logic), CPLD (Complex Programmable Logic Device), and FPGA (Field Programmable Gate Array). In this case, some or all of the functions realized by the processor may be realized by the integrated circuit.

[0060] As described above, several embodiments according to the present disclosure have been described, but all of these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their modifications are included in the scope of the invention and its equivalents as defined in the claims, as well as in the scope and spirit of the invention.

[0061] <Additional Notes> The control device, CO2 recovery device, control method, and program described in each embodiment can be understood, for example, as follows.

[0062] (1) A control device according to a first aspect includes a data acquisition unit that acquires operating data (such as exhaust gas flow rate) of a CO2 recovery device, a target value setting unit that sets target values ​​for parameters to be controlled based on the operating data, a prediction model that simulates the operation and state of the CO2 recovery device, an optimization calculation unit that calculates the operating variables that achieve the following objectives: predicting, based on the prediction model, the predicted values ​​of the parameters when a predetermined operating variable is applied to the CO2 recovery device, while minimizing the deviation between the predicted value of the parameters and the target value; and optimizing the predicted value based on the prediction model of the amount of heat to be supplied to a regenerative heater provided in a regenerator of the CO2 recovery device; and a control unit that controls the CO2 recovery device based on the calculated operating variables. This makes it possible to control the CO2 capture device in a way that takes economic efficiency into consideration while controlling the control amount to a target value.

[0063] (2) A control device according to a second aspect is the control device of (1), which performs the optimization calculation using the C / GMRES method. This enables high-speed optimization calculations and real-time control of the CO2 capture device.

[0064] (3) A control device according to a third aspect is a control device according to (1) to (2), wherein the optimization calculation unit performs the optimization calculation with the objectives of minimizing the deviation between the predicted value and the target value of the CO2 flow rate discharged from the regeneration tower and minimizing the heat quantity. This makes it possible to realize control that minimizes the heater heat input Qin while bringing the CO2 flow rate close to the target value.

[0065] (4) A control device according to a fourth aspect is a control device according to (1) to (2), wherein the optimization calculation unit performs the optimization calculation with the objectives of minimizing the deviation between the predicted value and the target value of the CO2 flow rate discharged from the regeneration tower, minimizing the deviation between the predicted value and the target value of the CO2 concentration discharged from the absorption tower of the CO2 recovery device, and minimizing the heat quantity. This makes it possible to realize control that minimizes the heater heat input Qin while bringing the CO2 concentration and CO2 capture rate closer to the target values.

[0066] (5) A control device according to a fifth aspect is the control device of (1) to (2), wherein the optimization calculation unit performs the optimization calculations with the objectives of minimizing the deviation between the predicted value and the target value of the flow rate of CO2 discharged from the regeneration tower, minimizing the deviation between the predicted value and the target value of the concentration of CO2 discharged from the absorption tower of the CO2 recovery device, minimizing the deviation between the predicted value and the target value of the flow rate of lean absorption liquid supplied from the regeneration tower to the absorption tower, and minimizing the deviation between the predicted value and the target value of the heat quantity. This makes it possible to achieve both economical and controllable control.

[0067] (6) A CO2 recovery apparatus according to a sixth aspect includes an absorption tower, a regeneration tower, a regeneration heater provided in the regeneration tower, a pipe for delivering lean absorption liquid from the regeneration tower to the absorption tower, a valve provided in the pipe, and the control device described in (1) to (5).

[0068] (7) A control method according to a seventh aspect acquires operating data of a CO2 recovery device, sets target values ​​for parameters to be controlled based on the operating data, predicts the predicted values ​​of the parameters when a predetermined operating variable is applied to the CO2 recovery device based on a prediction model that simulates the operation and state of the CO2 recovery device, and minimizes the deviation between the predicted value of the parameters and the target value, and optimizes the predicted value based on the prediction model of the amount of heat to be supplied to a regenerative heater provided in a regenerator of the CO2 recovery device, calculates the operating variable that achieves the above-mentioned objective by optimization calculation, and controls the CO2 recovery device based on the calculated operating variable.

[0069] (8) A program according to an eighth aspect causes a computer to acquire operating data of a CO2 recovery device, set target values ​​for parameters to be controlled based on the operating data, predict a predicted value of the parameter when a predetermined operating variable is applied to the CO2 recovery device based on a prediction model that simulates the operation and state of the CO2 recovery device, while minimizing the deviation between the predicted value of the parameter and the target value, and optimizing a predicted value based on the prediction model of the amount of heat to be supplied to a regenerative heater provided in a regenerator of the CO2 recovery device, with the objective of calculating the operating variable that achieves the above objective through optimization calculation, and controlling the CO2 recovery device based on the calculated operating variable. [Explanation of symbols]

[0070] 2. Absorption tower 3...Regeneration Tower 4...Heat exchanger 10. Control device 11. Data acquisition section 12...Operation amount calculation section 121 Target value setting unit 122···Prediction Model 123...Optimization calculation section 13 Control section 14...Storage section 21 Pump 31. Circulation line 33...Regenerative heater 51, 52, 53, 54 Piping 61. Rich absorbent liquid control valve 62 Lean absorbent liquid control valve 71, 72...flow meter 100...CO2 capture device 101 Exhaust gas measuring instrument 102...CO2 concentration meter 103 CO2 Gas Meter 900···Computer 901 CPU 902...Main memory 903...Auxiliary storage device 904 Input / Output Interface 905···Communication Interface

Claims

1. CO ２ a data acquisition unit that acquires operation data of the recovery device; a target value setting unit that sets a target value of a parameter of a controlled object based on the operating data; The CO ２ a predictive model for predicting the state of the recovery device; The predetermined operation amount is ２ a prediction model for predicting a predicted value of the parameter when the parameter is applied to the recovery device, and minimizing a deviation between the predicted value of the parameter and the target value; ２ an optimization calculation unit that calculates the manipulated variable by optimization calculation when the objective is to optimize a predicted value based on the prediction model of the amount of heat supplied to a regenerative heater provided in a regenerator of a recovery device; Based on the calculated manipulated variable, ２ a control unit that controls the recovery device; A control device comprising:

2. The optimization calculation is performed using the C / GMRES method. The control device according to claim 1 .

3. The optimization calculation unit calculates the CO discharged from the regeneration tower. ２ performing the optimization calculation with the objectives of minimizing the deviation between the predicted value and the target value of the flow rate and minimizing the amount of heat; The control device according to claim 1 or 2.

4. The optimization calculation unit calculates the CO discharged from the regeneration tower. ２ Minimizing the deviation between the predicted value and the target value of the flow rate; ２ CO emitted from the absorption tower of the recovery unit ２ performing the optimization calculation with the objectives of minimizing the deviation between the predicted value and the target value of the concentration of The control device according to claim 1 or 2.

5. The optimization calculation unit calculates the CO discharged from the regeneration tower. ２ Minimizing the deviation between the predicted value and the target value of the flow rate; ２ CO emitted from the absorption tower of the recovery unit ２ the optimization calculation is performed with the objectives of minimizing the deviation between the predicted value and the target value of the concentration of the lean absorbent, minimizing the deviation between the predicted value and the target value of the flow rate of the lean absorbent supplied from the regeneration tower to the absorption tower, and minimizing the deviation between the predicted value and the target value of the amount of heat; The control device according to claim 1 or 2.

6. an absorption tower; a regeneration tower; a regeneration heater provided in the regeneration tower; a pipe for delivering lean absorption liquid from the regeneration tower to the absorption tower; and a valve provided in the pipe. The control device according to claim 1 or 2; CO equipped with ２ Recovery device.

7. CO ２ Acquire operation data of the recovery device, setting a target value of a parameter to be controlled based on the operating data; The predetermined operation amount is ２ The predicted values ​​of the parameters when applied to the CO recovery device are ２ minimizing the deviation between the predicted value of the parameter and the target value while predicting the state of the recovery device based on a prediction model; ２ Optimizing a predicted value based on the prediction model of the amount of heat supplied to a regenerative heater provided in a regenerator of a recovery device, the manipulated variable that achieves the purpose is calculated by optimization calculation; Based on the calculated manipulated variable, ２ Controlling the recovery device; Control method.

8. On the computer, CO ２ Acquire operation data of the recovery device, setting a target value of a parameter to be controlled based on the operating data; The predetermined operation amount is ２ The predicted values ​​of the parameters when applied to the CO recovery device are ２ minimizing the deviation between the predicted value of the parameter and the target value while predicting the state of the recovery device based on a prediction model; ２ Optimizing a predicted value based on the prediction model of the amount of heat supplied to a regenerative heater provided in a regenerator of a recovery device, the manipulated variable that achieves the purpose is calculated by optimization calculation; Based on the calculated manipulated variable, ２ a process for controlling the recovery device; A program that executes the following.

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