Multi-component rare earth extraction process distributed predictive control method, equipment and medium

Abstract

The invention discloses a multi-component rare earth extraction process distributed predictive control method and device and a medium, and relates to the technical field of rare earth extraction.The method comprises the steps that firstly, a rare earth extraction process nonlinear dynamic model is converted into a nominal system state space model considering upstream subsystem state coupling; secondly, designing a nominal system distributed controller with input and output constraints to realize stable tracking control; and then, considering the uncertainty of the model and parameters and external disturbance, estimating the lumped disturbance suffered by each subsystem by using a disturbance observer, and designing a feedforward disturbance compensation controller based on a disturbance estimation value to realize the compensation of the lumped disturbance. And finally, combining a nominal system distributed model prediction controller with an interference compensation controller, designing a composite controller, and realizing that the output can still track a set value under the conditions of model mismatch and external disturbance in the multi-component rare earth extraction process, thereby realizing robustness and stability control.

Description

Technical Field

[0001] The present application relates to the technical field of rare earth extraction, and in particular to a distributed predictive control method, equipment and medium for a multi-component rare earth extraction process. Background Art

[0002] As an important link in the midstream of the rare earth industry chain, the rare earth separation industry is responsible for separating and purifying mixed rare earth raw materials into single rare earth elements. The current rare earth control system still relies on manual given flow, and the fluctuation range of process control is large, which affects the quality and stability of the product.

[0003] At present, in the rare earth extraction process control, most of the research is on the control of the two-component rare earth extraction process. There are few research papers on the control of the multi-component rare earth extraction process, but enterprises generally implement a multi-component rare earth extraction production model. The multi-component rare earth extraction separation process is composed of multiple interrelated subsystems in series, with the characteristics of multi-variables, multi-coupling, and multi-constraints. Each subsystem is controlled by a separate local controller. Distributed model predictive control has been increasingly used and paid attention to in industry and academia due to its advantages such as flexibility, easy implementation, and strong fault tolerance. Although distributed predictive control has many theoretical and application results, it is still rarely used in the rare earth extraction process.

[0004] In addition, since the rare earth extraction and separation process is complex and changeable, and the interference changes of the external environment have strong uncertainty, it is necessary to study the multi-component rare earth extraction control problem in the presence of disturbances. In the actual system, once internal uncertainty disturbances and external disturbances occur, it is difficult for the control system to ensure that the component content tracks the target set value. Therefore, in the actual multi-component rare earth extraction and separation process, how to ensure that the component content of each subsystem can track the target set value in the presence of disturbances is still a challenging topic. Summary of the invention

[0005] The purpose of this application is to provide a distributed predictive control method, equipment and medium for a multi-component rare earth extraction process, which can track the set values ​​of the component contents in the multi-component rare earth extraction process under uncertain environmental factors, while also ensuring the stable operation of the system.

[0006] To achieve the above objectives, this application provides the following solutions: In a first aspect, the present application provides a distributed predictive control method for a multi-component rare earth extraction process, comprising: According to the mechanism of rare earth extraction process, a nonlinear dynamic model of each sub-process in the multi-component rare earth cascade extraction process is constructed; Constructing a state space model of each sub-process according to the lumped disturbance and the nonlinear dynamic model, wherein the state space model of each sub-process includes an upstream sub-process state coupling term, the upstream sub-process state coupling term refers to the coupling relationship between the state of each sub-process and the state of the upstream sub-process, and the lumped disturbance includes the uncertainty term and the external disturbance term in each nonlinear dynamic model; constructing a discrete nominal system model for each sub-process according to the state space model; For each sub-process, an objective function including input and output constraints is established, and a distributed predictive controller of each nominal system is designed according to the objective function and the corresponding discrete nominal system model; Designing a disturbance observer for each sub-process, and using each of the disturbance observers to estimate the lumped disturbance of the corresponding discrete nominal system model to obtain a disturbance estimate, wherein the disturbance observer includes an upstream sub-process state coupling term; Determining a compensation control amount according to the disturbance estimation value, and designing a feedforward compensation controller according to the compensation control amount; A composite controller for each sub-process is designed based on the distributed prediction controller and the corresponding feedforward compensation controller, and the composite controller is used to control the output of the corresponding sub-process to converge to a target value.

[0007] In a second aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the distributed predictive control method for the multi-component rare earth extraction process described in the first aspect above.

[0008] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the distributed predictive control method for a multi-component rare earth extraction process as described in the first aspect above.

[0009] According to the specific embodiments provided in this application, this application has the following technical effects: The present application provides a distributed predictive control method, device and medium for a multi-component rare earth extraction process. The method includes constructing a discrete nominal system model for each sub-process, and designing a distributed predictive controller for the nominal system of each sub-process in combination with an objective function; estimating a lumped disturbance according to a disturbance observer designed for each sub-process, designing a feedforward compensation controller according to the disturbance estimation value, and designing a composite controller for each sub-process in combination with the distributed predictive controller. The method can track the set values ​​of the component contents in the multi-component rare earth extraction process under uncertain environmental factors, while also ensuring the stable operation of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0011] Figure 1 This is a schematic flow chart of a distributed predictive control method for a multi-component rare earth extraction process in Example 1 of the present application; Figure 2 This is a schematic diagram of a tracking curve of component content at the outlet of the water phase of the first-stage sub-process in Example 1 of the present application; Figure 3 This is a schematic diagram of the tracking curve of the component content of the organic phase outlet of the first-stage sub-process in Example 1 of the present application; Figure 4 This is a schematic diagram of a tracking curve of component content at the outlet of the water phase of the second-stage sub-process in Example 1 of the present application; Figure 5 This is a schematic diagram of the tracking curve of the component content of the organic phase outlet of the second-stage sub-process in Example 1 of the present application; Figure 6 This is a schematic diagram of a tracking curve of component content at the outlet of the water phase of the third-stage sub-process in Example 1 of the present application; Figure 7 This is a schematic diagram of the tracking curve of the component content of the organic phase outlet of the third-stage sub-process in Example 1 of the present application; Figure 8 This is a schematic diagram of the tracking curve of the component content of the water phase outlet at different set values ​​of the first-stage sub-process in Example 1 of the present application; Fig. 9 This is a schematic diagram of tracking curves of component contents at the outlet of the organic phase at different set values ​​of the first-stage sub-process in Example 1 of the present application; Fig.10 This is a schematic diagram of the tracking curve of the component content of the water phase outlet at different set values ​​of the second-stage sub-process in Example 1 of the present application; Fig.11 This is a schematic diagram of tracking curves of component contents at the outlet of the organic phase at different set values ​​of the second-stage sub-process in Example 1 of the present application; Fig.12 This is a schematic diagram of the tracking curve of the component content of the water phase outlet at different set values ​​of the third-level sub-process in Example 1 of the present application; Fig.13 This is a schematic diagram of tracking curves of component contents at the outlet of the organic phase at different set values ​​of the third-stage sub-process in Example 1 of the present application; Fig.14 A schematic diagram of the structure of a computer device provided in Example 2 of the present application. DETAILED DESCRIPTION

[0012] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0013] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0014] Example 1 For the multi-component rare earth extraction and separation process affected by physical constraints and unknown disturbances, this embodiment provides a distributed predictive control method for the multi-component rare earth extraction process based on a disturbance observer, which can track the set value of the component content under uncertain environmental factors and ensure the stable operation of the system. Figure 1 As shown, the distributed predictive control method for the multi-component rare earth extraction process includes: S1: According to the mechanism of rare earth extraction process, a nonlinear dynamic model of each sub-process system of multi-component rare earth cascade extraction process is constructed; S2: Considering the uncertainty of rare earth extraction process parameters and external disturbances, the sum of the model uncertainty term and the external disturbance term of each nonlinear dynamic model is regarded as a lumped disturbance, so as to construct a state space model of each sub-process, and the state space model of each sub-process includes an upstream sub-process state coupling term, and the upstream sub-process state coupling term refers to the coupling relationship between the state of each sub-process and the state of the upstream sub-process; S3: Considering that each sub-process is only affected by the upstream sub-process, a discrete nominal system model of each sub-process considering the state coupling term of the upstream sub-process is constructed according to the state space model; S4: Establish the objective function of the rare earth extraction process with input and output constraints, and design a distributed predictive controller based on each nominal system of the multi-component rare earth extraction process according to the objective function and the corresponding discrete nominal system model; S5: Design a disturbance observer with state coupling term for each sub-process, estimate the model uncertainty and external disturbance (i.e., model uncertainty term and external disturbance term, i.e., lumped disturbance), and obtain the disturbance estimation value, so as to improve the anti-disturbance ability of the system; S6: Use the disturbance estimate to design the compensation control quantity, and introduce a feedforward compensation controller. Combine the distributed model predictive controller of each nominal system with the corresponding disturbance compensation controller to design a composite controller so that the output of the system converges to the target value. Preferably, the output of the system converges to near the output value of the nominal system.

[0015] Specifically, in S1, according to the rare earth extraction process mechanism, the first The nonlinear dynamic model of the rare earth extraction process sub-process is expressed as follows: ; ; ; ; ; in, express The derivative of Indicates The concentration of difficult-to-extract components in the aqueous phase of each extraction tank in each sub-process, , Indicates The concentration of the difficult-to-extract components in the aqueous phase of the first-stage extraction tank in each sub-process, Indicates The difficult-to-extract components in each sub-process are The concentration in the aqueous phase of the secondary extraction tank; , Indicates The concentration of rare earth elements in the extractant in each sub-process; , Indicates Each sub-process liquid component; , Indicates The concentration of rare earth elements in the detergents of each process; express Moment Feed flow value of each sub-process; express Moment Detergent flow value for each sub-process; represents the uncertainty of the water phase model; express The derivative of Indicates The concentration of easily extractable component elements in the organic phase of each extraction tank in each sub-process, , Indicates The concentration of easily extractable component elements in the organic phase of the first stage extraction tank in each sub-process, Indicates The easily extractable component elements in each sub-process are The concentration in the organic phase of the secondary extraction tank; express Moment Extractant flow rate value of each sub-process; represents the uncertainty of the organic phase model; Indicates Component content of the aqueous phase outlet product of each sub-process; Indicates The output matrix of the corresponding dimensions of each sub-process in the water phase; Indicates The component content of the organic phase outlet product of each sub-process; Indicates Output matrix of corresponding dimensions of each sub-process in the organic phase; Indicates Volume hold-up of each sub-process in the first stage extraction tank in the aqueous phase; Indicates The sub-process is in the water phase Volume hold-up in the secondary extraction tank; Indicates Volume hold-up of each sub-process in the first stage extraction tank in the organic phase; Indicates The sub-process is in the organic phase Volume hold-up in the secondary extraction tank; , Indicates The initial value of the feed flow rate of each sub-process, Indicates Initial value of detergent flow rate for each sub-process; ; , Indicates Initial value of the extractant flow rate for each sub-process.

[0016] In S2, external disturbance is considered, and the sum of model uncertainty term and external disturbance term is expressed as lumped disturbance , the expression is: ; in, represents the total uncertainty term, Represents an external disturbance.

[0017] Therefore, considering the lumped disturbance, the state space model of the rare earth extraction process is as follows: ; in, express The derivative of , , express Moment i The concentration (i.e. state) of the nominal system of each sub-process, express Moment The output of the nominal system of each subprocess (i.e. Moment The component content at the outlets of each sub-process nominal system); , , ; For the The coefficient matrix of the corresponding dimension in each sub-process; , Indicates The output matrix of the corresponding dimension of each sub-process in the water phase, Indicates The output matrix of each sub-process in the organic phase with corresponding dimensions.

[0018] Furthermore, the above formula can be discretized to obtain a discrete-time state space model: ; in, express Moment The status of each sub-process nominal system; Indicates i Sub-process state matrix; Indicates i Sub-process input matrix; Indicates the upstream sub-process i -1 state coupling term coefficient matrix; t is continuous time, k is discrete time.

[0019] In S3, considering that each sub-process is only affected by the upstream sub-process, i The discrete nominal dynamic model of the sub-process is as follows: ; in, , , and They are Moment The states, control inputs and system outputs of each sub-process nominal system. , yes Moment -1 Sub-process rare earth element concentration.

[0020] In S4, based on the idea of ​​rolling optimization, i The objective function of each sub-process is as follows: ; satisfy: ; ; in, express k Moment i The objective function of each sub-process; and Respectively i The prediction time domain and control time domain of each sub-process. and Indicates i The weight matrix of the corresponding dimension of each sub-process. and Respectively k Predicted at all times Moment The predicted output and target setpoints for each sub-process. express k Predicted at all times The control input vector at time , and Respectively represent the control input The upper and lower limits of and Represents the output variables The upper and lower limits of .

[0021] The problem of controlling the component content in the rare earth extraction and separation process is transformed into the following optimization problem: ; satisfy ; ; in, , , ; The output prediction equation is: ; in , , ; Based on the above analysis, The optimization problem of the constrained model predictive control of each sub-process can be transformed into the following constrained QP problem: ; satisfy: , ; in , , , .

[0022] By solving the above optimization problem, we can get The nominal system part of each sub-process is The optimal control sequence at time is: ; In S5, the following disturbance observer with state coupling term is designed: ; in, is an intermediate variable, is the observer gain, is the unknown lumped disturbance The estimated value of .

[0023] The estimation error is defined as , the estimation error calculation formula is as follows: ; in ,matrix P i The spectral radius needs to satisfy , Represents the spectral radius.

[0024] In S6, combined with the proposed disturbance observer, the design of the composite controller is as follows: ; in, is the first element in the optimal control sequence obtained by nominal MPC , is the disturbance compensation gain, satisfying .

[0025] The simulation example of this embodiment is as follows: The LaCePrNd four-component rare earth extraction production process of a rare earth production enterprise was simulated. The extraction results of the four-component rare earth extraction process are as follows: Figure 2-Figure 13 As shown, Figure 2For example, the horizontal axis of the figure is the time step (i.e. time unit), and the vertical axis is the percentage. Figure 3-Figure 13 The units of the horizontal and vertical coordinates are Figure 2 same.

[0026] The continuous and bounded disturbance signals added to each subsystem are shown as follows: ; ; ; The parameters of the disturbance observer are set as follows: , , .

[0027] Simulation devices and platforms: The computer CPU is AMD Ryzen 7 5800H with Radeon Graphics, 3.20GHz, Windows 11 Home Chinese version, and the simulation platform is MATLAB R2021a.

[0028] Compared with the existing control technology, the distributed predictive control method for multi-component rare earth extraction process provided in this embodiment has the following advantages: 1. This embodiment takes into account the constraints of the extractant flow rate, liquid flow rate, detergent flow rate and the content of the component of the outlet product in the design of the controller, so that the input flow rate and output component content of each sub-process always meet the physical constraints in the entire multi-component rare earth extraction process. The composite control method proposed in this embodiment can ensure that the content of the component of the outlet product of each sub-process converges to the nominal value, solving the problem that the multi-component rare earth extraction process can effectively track the target set value under physical constraints.

[0029] 2. Based on the characteristic that each sub-process in multi-component rare earth extraction is only associated with the export product of its upstream sub-process, this embodiment designs a prediction model including upstream sub-process state coupling terms, which effectively reduces the communication burden between controllers and improves the integrity of rare earth extraction process control.

[0030] 3. This embodiment designs a disturbance observer for each sub-process that takes into account the upstream sub-process state coupling term, estimates the model uncertainty and external disturbances, effectively handles the impact of internal and external disturbances on the rare earth extraction process control system, and ensures the stability and robustness of the control system.

[0031] 4. The composite control strategy proposed in this embodiment consists of a distributed predictive control based on a nominal system model and a feedforward compensation controller based on a disturbance observer. Under the proposed control method, the fluctuation amplitude of the component content tracking error is very small, which can ensure the stable control of the multi-component rare earth extraction process to a certain extent. At the same time, it has strong anti-interference ability. The proposed control method has high practical application value.

[0032] Example 2 This embodiment provides a computer device, which may be a server or a terminal. Its internal structure diagram may be as follows: Fig.14 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data related to the distributed predictive control method for the multi-component rare earth extraction process. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the distributed predictive control method for the multi-component rare earth extraction process in Example 1 is implemented.

[0033] Those skilled in the art will understand that Fig.14 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0034] Example 3 This embodiment provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the distributed predictive control method for the multi-component rare earth extraction process in Embodiment 1.

[0035] Example 4 This embodiment provides a computer program product, including a computer program, which, when executed by a processor, implements the distributed predictive control method for the multi-component rare earth extraction process in Embodiment 1.

[0036] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0037] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0038] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0039] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0040] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A distributed predictive control method for a multi-component rare earth extraction process, characterized in that: The distributed predictive control method for the multi-component rare earth extraction process comprises: According to the mechanism of rare earth extraction process, a nonlinear dynamic model of each sub-process in the multi-component rare earth cascade extraction process is constructed; Constructing a state space model of each sub-process according to the lumped disturbance and the nonlinear dynamic model, wherein the state space model of each sub-process includes an upstream sub-process state coupling term, the upstream sub-process state coupling term refers to the coupling relationship between the state of each sub-process and the state of the upstream sub-process, and the lumped disturbance includes the uncertainty term and the external disturbance term in each nonlinear dynamic model; constructing a discrete nominal system model for each sub-process according to the state space model; For each sub-process, an objective function including input and output constraints is established, and a distributed predictive controller of each nominal system is designed according to the objective function and the corresponding discrete nominal system model; Designing a disturbance observer for each sub-process, and using each of the disturbance observers to estimate the lumped disturbance of the corresponding discrete nominal system model to obtain a disturbance estimate, wherein the disturbance observer includes an upstream sub-process state coupling term; Determining a compensation control amount according to the disturbance estimation value, and designing a feedforward compensation controller according to the compensation control amount; A composite controller for each sub-process is designed based on the distributed prediction controller and the corresponding feedforward compensation controller, and the composite controller is used to control the output of the corresponding sub-process to converge to a target value.

2. The distributed predictive control method for multi-component rare earth extraction process according to claim 1, characterized in that: The expression of the disturbance observer is: ； in, express Moment Intermediate variables of each sub-process; Indicates Observer gain of each sub-process; Indicates The coefficient matrix of the corresponding dimension of each sub-process; express Moment Unknown lumped disturbance in each subprocess An estimated value of Indicates i The state matrix of the nominal system of each sub-process; Indicates i Input matrix of the nominal system of each sub-process; Indicates the upstream sub-process i -1 state coupling term coefficient matrix; and Respectively Moment The status and control inputs of the sub-process nominal system; express Moment The rare earth element concentration of each sub-process; express Moment Unknown lumped disturbance in each subprocess An estimated value of express Moment The status of the sub-process nominal system.

3. The distributed predictive control method for multi-component rare earth extraction process according to claim 1, characterized in that: The expression of the composite controller is: ； in, express k Moment The first element of the optimal control sequence obtained by the sub-process nominal system ; represents the disturbance compensation gain; express Moment Unknown lumped disturbance in each subprocess The estimated value of .

4. The distributed predictive control method for multi-component rare earth extraction process according to claim 1, characterized in that: The expression of the objective function is: ； satisfy: ; ； in, express k Moment i The objective function of each sub-process; and Respectively represent i Prediction time domain and control time domain of each sub-process; and Respectively k Predicted at all times Moment Predicted output and target setpoints for each sub-process; and Respectively represent the weight matrices of corresponding dimensions; express k Predicted at all times Moment The control input vector of each sub-process; and Represents the operation variables The upper and lower limits of and Represents the output variables The upper and lower limits of .

5. The distributed predictive control method for multi-component rare earth extraction process according to claim 1, characterized in that: The expression of the distributed predictive controller is: ； ； ； ； ； ； ； ； ； ； in, express k Moment i The control input vector of each sub-process; and Represent the control input variables The upper and lower limits of express k Predicted at all times Moment The control input vector of each sub-process; Indicates i The state matrix of the nominal system of each sub-process; Indicates i Input matrix of the nominal system of each sub-process; Indicates i Output matrix of the nominal system of each sub-process; Indicates the maximum value of the output variable; express Moment i The status of each sub-process nominal system; express k Moment The aggregate disturbance of each sub-process; express Moment The rare earth element concentration of each sub-process; represents the observer gain; Indicates the minimum value of the output variable; and Respectively represent the weight matrices of corresponding dimensions; express k +1 moment target setting value; and Respectively represent i The prediction time domain and control time domain of each sub-process.

6. The distributed predictive control method for multi-component rare earth extraction process according to claim 1, characterized in that: The expression of the nonlinear dynamic model is: ； ； ； ； ； in, express The derivative of Indicates The concentration of difficult-to-extract components in the aqueous phase of each extraction tank in each sub-process, , Indicates The concentration of the difficult-to-extract components in the aqueous phase of the first-stage extraction tank in each sub-process, Indicates The difficult-to-extract components in each sub-process are The concentration in the aqueous phase of the secondary extraction tank; , Indicates The liquid components of each sub-process; , Indicates The concentration of rare earth elements in the detergents of each process; express Moment Feed flow value of each sub-process; express Moment Detergent flow value for each sub-process; represents the uncertainty of the water phase model; express The derivative of Indicates The concentration of easily extractable component elements in the organic phase of each extraction tank in each sub-process, , Indicates The concentration of easily extractable component elements in the organic phase of the first stage extraction tank in each sub-process, Indicates The easily extractable component elements in each sub-process are The concentration in the organic phase of the secondary extraction tank; , Indicates The concentration of rare earth elements in the extractant in each sub-process; express Moment Extractant flow rate value of each sub-process; represents the uncertainty of the organic phase model; Indicates Component content of the aqueous phase outlet product of each sub-process; Indicates The output matrix of the corresponding dimensions of each sub-process in the water phase; Indicates The component content of the organic phase outlet product of each sub-process; Indicates Output matrix of corresponding dimensions of each sub-process in the organic phase; Indicates Volume hold-up of each sub-process in the first stage extraction tank in the aqueous phase; Indicates The sub-process is in the water phase Volume hold-up in the secondary extraction tank; Indicates Volume hold-up of each sub-process in the first stage extraction tank in the organic phase; Indicates The sub-process is in the organic phase Volume hold-up in the secondary extraction tank; , Indicates The initial value of the feed flow rate of each sub-process, Indicates Initial value of detergent flow rate for each sub-process; ; , Indicates Initial value of the extractant flow rate for each sub-process.

7. The distributed predictive control method for multi-component rare earth extraction process according to claim 1, characterized in that: The expression of the state space model is: ； ； ； ； ； ； ； ； in, express The derivative of express Moment i The concentration of each sub-process nominal system; , Indicates The concentration of difficult-to-extract components in the aqueous phase of each extraction tank in each sub-process, , Indicates The concentration of the difficult-to-extract components in the aqueous phase of the first-stage extraction tank in each sub-process, Indicates The difficult-to-extract components in each sub-process are The concentration in the aqueous phase of the secondary extraction tank, Indicates The concentration of easily extractable component elements in the organic phase of each extraction tank in each sub-process, , Indicates The concentration of easily extractable component elements in the organic phase of the first stage extraction tank in each sub-process, Indicates The easily extractable component elements in each sub-process are The concentration in the organic phase of the secondary extraction tank; express Moment The output of the nominal system of each subprocess, , Indicates The content of components in the aqueous phase outlet product of each sub-process, Indicates The component content of the organic phase outlet product of each sub-process; express Moment Control inputs of the nominal system of each sub-process; Indicates the upstream sub-process i -1 state coupling term coefficient matrix; express Moment The rare earth element concentration of each sub-process; Indicates The coefficient matrix of the corresponding dimension in each sub-process; express t Moment i The aggregate disturbance of each sub-process; , Indicates The output matrix of the corresponding dimension of each sub-process in the water phase, Indicates Output matrix of corresponding dimensions of each sub-process in the organic phase; Indicates Volume hold-up of each sub-process in the first stage extraction tank in the aqueous phase; Indicates The sub-process is in the water phase Volume hold-up in the secondary extraction tank; Indicates Volume hold-up of each sub-process in the first stage extraction tank in the organic phase; Indicates The sub-process is in the organic phase Volume hold-up in the secondary extraction tank; , Indicates The initial value of the feed flow rate of each sub-process, Indicates Initial value of detergent flow rate for each sub-process; ; , Indicates Initial value of the extractant flow rate of each sub-process; express Moment Feed flow value of each sub-process; express Moment Detergent flow value for each sub-process; express Moment Extractant flow rate value of each sub-process; , Indicates The concentration of rare earth elements in the extractant in each sub-process; , Indicates Each sub-process liquid component; , Indicates The concentration of rare earth elements in individual process detergents.

8. The distributed predictive control method for multi-component rare earth extraction process according to claim 1, characterized in that: The expression of the discrete nominal system model is: ； in, express Moment The status of each sub-process nominal system; Indicates i Sub-process state matrix; Indicates i Sub-process input matrix; Indicates the upstream sub-process i -1 state coupling term coefficient matrix; and Respectively Moment The status and control inputs of the sub-process nominal system; express Moment The rare earth element concentration of each sub-process; express k Moment i The output of the nominal system of each subprocess, , Indicates The content of components in the aqueous phase outlet product of each sub-process, Indicates The component content of the organic phase outlet product of each sub-process; , Indicates The output matrix of the corresponding dimension of each sub-process in the water phase, Indicates The output matrix of each sub-process in the organic phase with corresponding dimensions.

9. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the distributed predictive control method for a multi-component rare earth extraction process according to any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the distributed predictive control method for a multi-component rare earth extraction process according to any one of claims 1 to 8 is implemented.

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