High-order model control method based on linearization model and GA-ADRC

Abstract

The invention discloses a high-order model control method based on a linearization model and GA-ADRC, and relates to the field of heat exchanger control simulation, and the method comprises the steps: constructing a single-shell-pass single-tube-pass heat exchanger model which comprises a shell and a heat exchange space, the heat exchange space is divided into a shell pass region and a tube pass region, and the shell pass region is internally provided with a plurality of baffle plates; carrying out heat balance analysis to obtain a dynamic equation of each heat exchange link of the heat exchanger, carrying out discretization processing, and carrying out linearization processing on the mass flow rate and the temperature of the shell pass hot fluid to realize decoupling; and an ESO state observer is arranged to estimate the state variable of the heat exchanger in real time, and the single-shell-side and single-tube-side heat exchanger model is controlled through a GA-ADRC-based nonlinear model control method. According to the method, the complex heat transfer problem can be linearized, a GA-ADRC controller independent of an accurate model is designed, the actual nonlinear model can be successfully adapted, the adjustment time is shortened, and the control precision and the anti-interference capability are effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of heat exchanger control simulation, and in particular to a high-order model control method based on a linearized model and GA-ADRC. Background Art

[0002] The primary function of a heat exchanger is to exchange heat. The working fluid is typically a liquid, gas, or a combination of both. During operation, heat is transferred from one working fluid to another through conduction, convection, and radiation, thereby heating or cooling the working fluid. Heat exchangers are widely used in industrial applications. They are not only used to heat or cool fluids, but also to recover waste heat from process flows, improving energy efficiency. Furthermore, heat exchangers are used in applications requiring precise temperature control, such as regulating reactor temperature in chemical production.

[0003] In the existing technology, most heat exchangers are controlled through PID, which only performs a simple identification of the heat exchange mechanism and generally does not establish a model. As a result, there are problems such as inaccurate control, delayed response, and unstable operating conditions. This leads to frequent adjustment of the operating mechanism, uneven output temperature of the heat exchanger, and increased energy consumption. Summary of the Invention

[0004] The problem to be solved by the present invention is to provide a high-order model control method based on linearization model and GA-ADRC, which is used to improve the control accuracy of heat exchanger control system and realize rapid regulation of heat exchanger outlet water temperature.

[0005] The present invention adopts the following technical solution: a high-order model control method based on a linearized model and GA-ADRC, comprising the following steps:

[0006] Step 1: Construct a single-shell-pass and single-tube-pass heat exchanger model: including a shell and a heat exchange space, wherein the heat exchange space is divided into a shell-side region and a tube-side region, wherein the internal fluids are respectively a shell-side hot fluid and a tube-side cold fluid, and a plurality of baffles are also provided inside the shell-side region;

[0007] Step 2: Linearize the single-shell and single-tube heat exchanger model: Perform heat balance analysis on the shell-side hot fluid, tube-side cold fluid, and tube outer wall by taking micro-elements to obtain the dynamic equations of each heat exchange link of the heat exchanger and discretize them; linearize the mass flow rate and temperature of the shell-side hot fluid to achieve decoupling;

[0008] Step 3. Control the single-shell and single-tube heat exchanger model using a nonlinear model control method based on GA-ADRC: set the ESO extended state observer to estimate the state variables of the heat exchanger in real time, and construct an ADRC controller based on the heat exchanger output temperature signal as negative feedback; use the genetic algorithm to adjust the ADRC controller parameters according to the heat exchanger output response, output a control signal, and regulate the water temperature at the heat exchanger outlet.

[0009] Preferably, in step 1, the shell is used to limit the flow of fluid in the heat exchange space, the tube-side area is a plurality of heat exchange pipes that transversely pass through both ends of the shell, and the shell-side area is the heat exchange space inside the shell excluding the heat exchange pipes;

[0010] Assume that in the single-shell and single-tube heat exchanger model, the fluid flow in the heat exchange space is in a plug flow state, the shell-side hot fluid and the tube-side cold fluid belong to a distributed parameter system, there is no axial mixing, and the temperature of each point on the same cross section in the heat exchange space is the same. There is no radial heat transfer on the tube wall, and the tube wall temperature is the same everywhere.

[0011] Preferably, in step 2, a heat balance analysis is performed on the shell-side thermal fluid using a microelement as follows:

[0012] The rate of change of heat storage in the shell-side thermal fluid element is the heat of the shell-side thermal fluid entering the element, minus the heat of the shell-side thermal fluid leaving the element, and then minus the heat of the shell-side thermal fluid element transferred to the outer wall of the heat exchange tube, which can be expressed as:

[0013] [Change rate of heat storage within one microelement of fluid]

[0014] = [heat of fluid 1 entering the microelement] - [heat of fluid 1 leaving the microelement] - [heat of fluid 1 transferred to the outer wall of the heat exchange tube]

[0015] Right now:

[0016]

[0017] The shell-side thermal fluid dynamic equation is obtained, which is expressed as:

[0018]

[0019] Similarly, the dynamic equation of the cold fluid in the tube is obtained, which is expressed as:

[0020]

[0021] Preferably, the heat transfer coefficient is expressed as:

[0022]

[0023] Where:

[0024]

[0025] Among them, dx is the infinitesimal element to be analyzed, d o ,d i is the outer diameter of the heat exchange tube, r1, r2 are the inner and outer radii of the heat exchange tube, m; c p ,c p1 ,c p2 is the specific heat capacity of the heat exchange tube, shell-side hot fluid, and tube-side cold fluid, J / (kg·K); K o ,K i is the shell-side and tube-side heat transfer coefficient, W / (m 2 ·K); m1, m2 are the mass flow rates of the shell-side hot fluid and tube-side cold fluid, kg / s; M w , M1, M2 are the mass per unit length of heat exchange tube, shell side hot fluid, and tube side cold fluid, in kg / m; n is the number of heat exchange tubes; R o ,R i is the shell-side and tube-side structural thermal resistance, m 2 K / W; T1,T wo ,T wi , T2 is the temperature of the shell-side hot fluid, the outer wall of the tube, the inner wall of the tube, and the tube-side cold fluid, K; λ, λ1, λ2 are the thermal conductivities of the heat exchange tube, the shell-side hot fluid, and the tube-side cold fluid, W / (m·K); T(x, t) is the temperature of the fluid element at position x at time t.

[0026] Preferably, a microelement is taken from the outer wall of the tube to perform a heat balance analysis, and the method is as follows:

[0027] The rate of change of heat storage in the microelement of the outer wall of the tube is the heat transferred from the shell-side thermal fluid to the microelement of the outer wall of the tube minus the heat transferred from the outer wall of the tube to the inner wall of the tube, which can be expressed as:

[0028] [Change rate of heat storage in the microelement of the outer wall of the tube]

[0029] = [heat transferred from the heat fluid 1 to the outer wall of the tube] - [heat transferred from the outer wall of the tube to the inner wall of the tube]

[0030] Right now:

[0031]

[0032] The dynamic equation of the outer wall of the tube is obtained, which is expressed as:

[0033]

[0034] Similarly, the dynamic equation of the inner wall of the tube is obtained, which is expressed as:

[0035]

[0036] Among them, c p is the specific heat capacity of the heat exchange tube, M wis the mass per unit length of the heat exchange tube, λ is the thermal conductivity of the heat exchange tube, and r1 and r2 are the inner and outer radii of the heat exchange tube.

[0037] Preferably, the dynamic equations of each heat exchange link of the heat exchanger, i.e., the nonlinear model of the heat exchanger, are expressed as:

[0038]

[0039] Faced with the partial differential equations of the heat exchanger nonlinear model (9), x is discretized and the heat exchanger is divided into N sections according to the position of the baffles. The following is obtained by discretizing x:

[0040]

[0041] The dynamic equations of each heat exchange link of the heat exchanger are discretized to obtain the shell-side discretization equation, which is expressed as:

[0042]

[0043] The discretization equation of the outer wall of the tube is expressed as:

[0044]

[0045] The discretization equation of the inner wall of the tube is expressed as:

[0046]

[0047] The pipe-pass discretization equation is expressed as:

[0048]

[0049] Among them, the subscript i represents the endpoint of each segment, i∈[0,N], and Δx represents the length of each segment, that is, the baffle spacing B.

[0050] Furthermore, the shell-side discretization equation (11) cannot be applied to the superposition principle, and is therefore nonlinear. Directly solving the nonlinear differential equation is difficult, and it is necessary to linearize m1 and T1 to achieve decoupling. Select a stable point and perform a Taylor first-order expansion of the discretization equation at that point, writing it in the form of an incremental equation.

[0051] Preferably, the mass flow rate m1 and temperature T1 of the shell-side thermal fluid are linearized to achieve decoupling, as follows:

[0052] Shell-side heat transfer coefficient K o Perform linearization:

[0053]

[0054] Pick

[0055]

[0056] Arranging the shell-side discretization equation (11), we obtain:

[0057]

[0058] Arrange the discretization equation (12) of the outer wall of the tube and obtain:

[0059]

[0060] Equations (17), (18), (11), and (12) are expressed in state space form as follows:

[0061]

[0062] Among them, U is the mass flow rate m1 of the shell-side thermal fluid, Y is T 2,out .

[0063] Preferably, in the A matrix:

[0064]

[0065] The remaining elements are zero.

[0066] In the B matrix:

[0067]

[0068] The remaining elements are zero.

[0069] In the C matrix: C 1,N =1, C 2,3N+1 =1, and the rest of the elements are zero.

[0070] Preferably, in step 3, the ESO extended state observer is expressed as follows:

[0071]

[0072] Use z1 to design the state feedback control law, which is expressed as follows:

[0073]

[0074] Equations (22) and (22) are the ADRC controller (active disturbance rejection controller).

[0075] Using the classic PID controller as the control group, the expression is as follows:

[0076]

[0077] Among them, K p , KI , K D , K N They are proportional, integral, differential, and filter coefficients,

[0078] They represent the estimated derivatives of the system state 1.2,

[0079] Same as above; β1 and β2 represent ESO coefficients respectively, b0 represents the high-frequency gain estimation value, u0 and u represent the original control signal and the control signal respectively, r represents the set value signal, and s represents the Laplace operator.

[0080] Preferably, in step 3, the genetic algorithm parameters include: population size, number of iterations and b0,w o ,w c Constraint range; where b0,w o ,w c denote controller parameters, observer bandwidth, and controller bandwidth respectively;

[0081] The ADRC controller parameters are adjusted using a genetic algorithm based on the heat exchanger output response as follows:

[0082] Step 3.1: Based on the actual working conditions, collect b0,w o ,w c Genetic coding to generate the initial population;

[0083] Step 3.2, conduct simulation, based on the total deviation, individual absolute error integral IAE and maximum sensitivity M S Constrained design method, constructing the objective function, obtaining the individual absolute error integral IAE and sensitivity M S Evaluate the results and determine whether to terminate the iteration based on the preset termination conditions;

[0084] Step 3.3: If the iteration is not terminated, select high-quality genes, perform cross-compilation, generate a new population, and return to step 3.2 for simulation;

[0085] Step 3.4: If the iteration is terminated, output the optimal solution.

[0086] Preferably, the single-shell-pass and single-tube-pass heat exchanger model further includes: an electric heating device, a hot-end water pump, and a water pipeline;

[0087] The electric heating device is used to generate high-temperature fluid and includes a hot fluid output port;

[0088] The hot-end water pump has one end of its water inlet pipe connected to the hot fluid output outlet and the other end connected to the shell-side hot fluid inlet;

[0089] One end of the water pipeline is connected to the cold fluid outlet of the pipe.

[0090] Preferably, the single-shell-pass and single-tube-pass heat exchanger model further includes: a check valve, a relief valve, and a pressure relief valve;

[0091] The check valve is arranged on one end of the water inlet pipe close to the hot fluid output outlet;

[0092] The overflow valve is arranged on the water inlet pipeline, between the check valve and the shell-side hot fluid inlet;

[0093] The pressure relief valve is arranged on the water supply pipeline after the cold fluid outlet

[0094] Preferably, the present invention is based on a high-order model control method of a linearized model and GA-ADRC, including: a single-shell-pass and single-tube-pass heat exchanger model, an active disturbance rejection controller, and a heat exchanger nonlinear model.

[0095] Compared with the prior art, the present invention adopts the above technical solution and has the following technical effects:

[0096] The high-order model control method of the present invention constructs a linearized heat exchanger model, optimizes controller parameters using a genetic algorithm, and applies the controller to an actual nonlinear model. This method can improve the control accuracy of the control system, achieve rapid regulation of the water temperature at the heat exchanger outlet, reduce the adjustment frequency of the actuator, lower energy consumption, and extend the service life of the mechanical device. BRIEF DESCRIPTION OF THE DRAWINGS

[0097] Figure 1 This is a schematic structural diagram of a single-shell-pass and single-tube-pass countercurrent heat exchanger according to an embodiment of the present invention;

[0098] Figure 2 Schematic diagram of a GA-ADRC and PID comparison simulation platform according to an embodiment of the present invention;

[0099] Figure 3 Schematic diagram of a GA-ADRC parameter training platform based on a linearized model according to an embodiment of the present invention;

[0100] Figure 4 Schematic diagram of the GA-ADRC parameter search process according to an embodiment of the present invention;

[0101] Figure 5 Schematic diagram of the structure of an ADRC feedback controller according to an embodiment of the present invention;

[0102] Figure 6 Schematic diagram of the structure of the ESO extended state observer according to an embodiment of the present invention;

[0103] Figure 7 A schematic diagram of the Pareto front for target optimization according to an embodiment of the present invention;

[0104] Figure 8 Schematic diagram of the performance of the ESO extended state observer according to an embodiment of the present invention;

[0105] Figure 9 This is a schematic diagram of simulation results comparing set value tracking conditions according to an embodiment of the present invention;

[0106] Figure 10 Schematic diagram of simulation results comparing the immunity to external disturbances of an embodiment of the present invention;

[0107] Figure 11 This is a comparison of simulation results of the robustness of random perturbations of model parameters in an embodiment of the present invention. DETAILED DESCRIPTION

[0108] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0109] At the same time, the step numbers in the embodiments of the present invention are set only for the convenience of explanation and description, and no limitation is placed on the order of the steps. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0110] In one embodiment of the present invention, a single shell and single tube heat exchanger model, such as Figure 1 As shown, it includes: a shell and a heat exchange space, the shell is used to limit the flow heat exchange space, and the heat exchange space includes a shell-side area and a tube-side area.

[0111] The tube-side area is a plurality of pipes that pass through both ends of the shell transversely, and the shell-side area is the internal space of the shell excluding the pipe portion; a plurality of baffles are arranged inside the shell-side area.

[0112] In this embodiment, the single shell and tube heat exchanger model is controlled by a nonlinear model control method based on GA-ADRC. The GA-ADRC controller system is as follows: Figure 2 As shown in the figure, the GA algorithm outputs the optimal ADRC parameters. The ADRC controller outputs the control signal according to the set value signal and the output response to complete the control of the cold fluid outlet temperature of the heat exchanger. At the same time, PID is applied to the same model and scenario to compare its control effect.

[0113] Specifically, the nonlinear model control method of the GA-ADRC is implemented by first setting an ESO extended state observer to estimate the state variables of the heat exchanger in real time, and designing the ADRC controller based on negative feedback of the output temperature.

[0114] Then, the ADRC controller parameters are adjusted according to the heat exchanger output response through the genetic (GA) algorithm. Through the nonlinear model control method of GA-ADRC, the control signal is output according to the set signal and the heat exchanger output temperature signal so that the heat exchanger output reaches the set temperature.

[0115] Among them, the schematic diagram of the GA-ADRC parameter training platform based on the linearized model is as follows: Figure 3 As shown; GA-ADRC parameter search process, as shown Figure 4 shown.

[0116] Specifically, Figure 4 In the case of the total deviation IAE and the maximum sensitivity M S Constrained design method, the objective function constructed is:

[0117]

[0118] Among them, y(t) and r(t) represent the output response and set value signals respectively, G c (s), G p (s) represent the controller transfer function and the controlled object transfer function, S(s) represents the sensitivity function, and the individual absolute error integral IAE and sensitivity M are obtained. S Evaluation results.

[0119] In this embodiment, the GA algorithm is set according to the parameters shown in Table 1 below:

[0120] Table 1 Genetic algorithm parameters

[0121]

[0122] Furthermore, the ADRC feedback controller structure diagram of this embodiment is as follows: Figure 5 As shown in the simulink modular implementation corresponding to formula (23), the ESO extended state observer structure diagram is as follows: Figure 6 As shown, the simulink modular implementation of formula (22) is corresponding.

[0123] IAE and M S The Pareto frontier of two-objective optimization is as follows Figure 7 As shown, the appropriate IAE and M S , select controller parameter b0 = 0.008, observer bandwidth w o =3, controller bandwidth wc =0.1.

[0124] The performance of the ESO extended state observer in this embodiment is as follows: Figure 8 As shown in the figure, it can be seen that the ESO extended state observer has a small real-time estimation deviation for the total disturbance f and the output value y, and has a good tracking effect.

[0125] Furthermore, in this embodiment, the set value tracking working condition is compared with the simulation results, such as Figure 9 As shown in the nonlinear model, the ADRC control system still shows a good control effect; the external disturbance immunity comparison simulation, such as Figure 10 As shown,

[0126] The results show that the GA-ADRC-based nonlinear model control method of the present invention exhibits a stronger control effect in the control of nonlinear models, with the total error IAE significantly smaller than that of the PID control system, and even smaller than the total error of the linear model itself. This shows that the ESO extended state observer module achieves strong error tracking performance in the face of external disturbances, and can also observe and compensate for internal disturbances caused by the model's own uncertainty. Therefore, the GA-ADRC controller parameters adjusted based on the linearized model can be well applied to the control scenarios of nonlinear models.

[0127] Furthermore, the robustness of the model parameters of the present invention is compared with the simulation results of random perturbations, such as Figure 11 As shown in the figure, the results show that when the model parameters of the controlled object are perturbed within ±5%, the overshoot and IAE fluctuation range of the GA-ADRC are smaller than those of the PID controller. This fully demonstrates that the GA-ADRC controller of the present invention can comprehensively observe the unmodeled dynamics of the system and has a high tolerance for model accuracy.

[0128] In summary, the present invention includes a novel nonlinear model control scheme based on a linearized heat exchanger model and GA-ADRC, that is, a novel complex high-order model control method based on a linearized model and GA-ADRC. This enables a shell-and-tube heat exchanger equipped with GA-ADRC to have a faster output temperature response than a traditional PID controller system, thereby facilitating uniform heat exchange between the hot and cold ends of the shell and reducing the temperature gradient and pressure non-uniformity of the shell. At the same time, the system reduces the adjustment frequency of the actuator, saves energy consumption, and extends the service life of the mechanical structure.

[0129] According to the high-order model control method based on the linearized model and GA-ADRC of the present invention, a high-order nonlinear model is obtained through distributed modeling and discretization, and Taylor expansion is performed at the equilibrium point to obtain a linearized model with variable decoupling. The GA algorithm trained based on the model can be used for real-time parameter adjustment of the ADRC controller, and can be applied to the control problems of nonlinear models to achieve fast response and high stability, thereby reducing the adjustment amplitude and frequency of the action mechanism and improving the operating efficiency and service life of the insulation device.

[0130] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include one or more of such features. "Multiple" means two or more, unless otherwise specifically defined.

[0131] In the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," "connect," "fixed," etc. should be understood broadly. For example, they may refer to fixed connection, detachable connection, or integration; mechanical connection or electrical connection; direct connection or indirect connection through an intermediate medium; internal communication between two components or interaction between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0132] In the description of this specification, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0133] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention pertain.

[0134] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, it can be implemented using techniques known in the art or a combination thereof, such as: a discrete logic circuit having logic gate circuits for implementing logical functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0135] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0136] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing module, or each unit may exist physically separately, or two or more units may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or in the form of software functional modules. If the integrated modules are implemented in the form of software functional modules and sold or used as independent products, they may also be stored in a computer-readable storage medium.

[0137] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A high-order model control method based on linearized model and GA-ADRC, characterized in that: The steps include: Step 1: Construct a single-shell-pass and single-tube-pass heat exchanger model: including a shell and a heat exchange space, wherein the heat exchange space is divided into a shell-side region and a tube-side region, wherein the internal fluids are respectively a shell-side hot fluid and a tube-side cold fluid, and a plurality of baffles are also provided inside the shell-side region; Step 2: Linearize the single-shell and single-tube heat exchanger model: Perform heat balance analysis on the shell-side hot fluid, tube-side cold fluid, and tube outer wall by taking micro-elements to obtain the dynamic equations of each heat exchange link of the heat exchanger and discretize them; linearize the mass flow rate and temperature of the shell-side hot fluid to achieve decoupling; Step 3. Control the single-shell and single-tube heat exchanger model using a nonlinear model control method based on GA-ADRC: set the ESO extended state observer to estimate the state variables of the heat exchanger in real time, and construct an ADRC controller based on negative feedback according to the heat exchanger output temperature signal; use the genetic algorithm to adjust the ADRC controller parameters according to the heat exchanger output response, output the control signal, and regulate the water temperature at the heat exchanger outlet.

2. The high-order model control method based on linearized model and GA-ADRC according to claim 1 is characterized in that: The shell is used to limit the flow of fluid in the heat exchange space. The tube-side area is a plurality of heat exchange pipes that pass through both ends of the shell horizontally. The shell-side area is the heat exchange space inside the shell excluding the heat exchange pipes. Assume that in the single-shell and single-tube heat exchanger model, the fluid flow in the heat exchange space is in a plug flow state, the shell-side hot fluid and the tube-side cold fluid belong to a distributed parameter system, there is no axial mixing, and the temperature of each point on the same cross section in the heat exchange space is the same. There is no radial heat transfer on the tube wall, and the tube wall temperature is the same everywhere.

3. The high-order model control method based on linearized model and GA-ADRC according to claim 2, characterized in that: Take microelement of shell-side thermal fluid for heat balance analysis, the method is as follows: The rate of change of heat storage in the shell-side thermal fluid element is the heat of the shell-side thermal fluid entering the element, minus the heat of the shell-side thermal fluid leaving the element, and then minus the heat of the shell-side thermal fluid element transferred to the outer wall of the heat exchange tube, which can be expressed as: The shell-side thermal fluid dynamic equation is obtained, which is expressed as: Similarly, the dynamic equation of the cold fluid in the tube is obtained, which is expressed as: Among them, dx is the infinitesimal element to be analyzed, d o ,d i is the outer diameter and inner diameter of the heat exchange tube, c p1 ,c p2 is the specific heat capacity of the shell-side hot fluid and the tube-side cold fluid, K o ,K i is the shell-side and tube-side heat transfer coefficient, m1, m2 are the mass flow rates of the shell-side hot fluid and tube-side cold fluid, M1, M2 are the mass per unit length of the shell-side hot fluid and tube-side cold fluid, n is the number of heat exchange tubes; T1, T wo ,T wi , T2 is the temperature of the shell-side hot fluid, the outer wall of the tube, the inner wall of the tube, and the cold fluid in the tube side, and T(x,t) is the temperature of the fluid element at position x at time t.

4. The high-order model control method based on linearized model and GA-ADRC according to claim 3 is characterized in that: Take a microelement of the outer wall of the tube for heat balance analysis, the method is as follows: The rate of change of heat storage in the microelement of the outer wall of the tube is the heat transferred from the shell-side thermal fluid to the microelement of the outer wall of the tube minus the heat transferred from the outer wall of the tube to the inner wall of the tube, which can be expressed as: The dynamic equation of the outer wall of the tube is obtained, which is expressed as: Similarly, the dynamic equation of the inner wall of the tube is obtained, which is expressed as: Among them, c p is the specific heat capacity of the heat exchange tube, M w is the mass per unit length of the heat exchange tube, λ is the thermal conductivity of the heat exchange tube, and r1 and r2 are the inner and outer radii of the heat exchange tube.

5. The high-order model control method based on linearized model and GA-ADRC according to claim 4 is characterized in that: The dynamic equations of each heat exchange link of the heat exchanger are expressed as: The heat exchanger is divided into N sections according to the position of the baffles, and x is discretized to obtain: The dynamic equations of each heat exchange link of the heat exchanger are discretized to obtain the shell-side discretization equation, which is expressed as: The discretization equation of the outer wall of the tube is expressed as: The discretization equation of the inner wall of the tube is expressed as: The pipe-pass discretization equation is expressed as: Wherein, the subscript i represents the endpoint of each segment, i∈[0,N], and Δx represents the length of each segment.

6. The high-order model control method based on linearized model and GA-ADRC according to claim 5, characterized in that: The mass flow rate m1 and temperature T1 of the shell-side thermal fluid are linearized to achieve decoupling as follows: Shell-side heat transfer coefficient K o Perform linearization: Pick Arranging the shell-side discretization equation (9), we obtain: Arranging the discretization equation (10) of the outer wall of the tube, we can obtain: Equations (15)(16)(11)(12) can be expressed in state space form as follows: Among them, U is the mass flow rate m1 of the shell-side thermal fluid, Y is T 2,out , used to represent the hot fluid flow rate and the cold fluid outlet temperature, and matrices A, B, and C represent the state space coefficients of the model respectively.

7. The high-order model control method based on linearized model and GA-ADRC according to claim 1, characterized in that: The ESO extended state observer is expressed as follows: The feedback control law uses z1 as state feedback and is expressed as follows: Using the classic PID controller as the control group, the expression is as follows: Among them, K p , K I , K D , K N They are proportional, integral, differential, and filter coefficients, represents the derivative of the estimated value of the system state, Same as above; β1 and β2 represent ESO coefficients respectively, b0 represents the high-frequency gain estimation value, u0 and u represent the original control signal and the control signal respectively, r represents the set value signal, and s represents the Laplace operator.

8. The high-order model control method based on linearized model and GA-ADRC according to claim 1, characterized in that: In step 3, the ADRC controller parameters include b0, β1, β2, k p ; The genetic algorithm parameters include: population size, number of iterations and b0,w o ,w c Constraint range; b0,w o ,w c denote controller parameters, observer bandwidth, and controller bandwidth respectively; The ADRC controller parameters are adjusted using a genetic algorithm based on the heat exchanger output response as follows: Step 3.1: Based on the actual working conditions, collect b0,w o ,w c Genetic coding to generate the initial population; Step 3.2, conduct simulation, based on the total deviation, individual absolute error integral IAE and maximum sensitivity M S Constraint design method, constructs the objective function, obtains the fitness value evaluation result of each individual, and determines whether to terminate the iteration according to the preset termination condition; Step 3.3: If the iteration is not terminated, perform non-dominated sorting and crowding distance calculation, select high-quality individuals for crossover mutation, generate a new generation of population, and return to step 3.2 to continue evaluation; Step 3.4: If the iteration is terminated, output the optimal solution set on the Pareto front.

9. The high-order model control method based on linearized model and GA-ADRC according to claim 1, characterized in that: The single-shell-pass and single-tube-pass heat exchanger model also includes: an electric heating device, a hot-end water pump, and a water pipeline; The electric heating device is used to generate high-temperature fluid and includes a hot fluid output port; The hot-end water pump has one end of its water inlet pipe connected to the hot fluid output outlet and the other end connected to the shell-side hot fluid inlet; One end of the water pipe is connected to the cold fluid outlet of the pipe, and the other end is connected to the water supply side inlet of the user.

10. The high-order model control method based on linearized model and GA-ADRC according to claim 9, characterized in that: The single-shell-pass and single-tube-pass heat exchanger model further includes: a check valve, a relief valve, and a pressure relief valve; The check valve is arranged on one end of the water inlet pipe close to the hot fluid output outlet; The overflow valve is arranged on the water inlet pipeline, between the check valve and the shell-side hot fluid inlet; The pressure relief valve is arranged on the water supply pipeline after the cold fluid outlet.

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