Distributed Predictive Control Method, Equipment and Medium for Multi-Component Rare Earth Extraction Process

Abstract

The present application discloses a distributed predictive control method, device and medium for multi-component rare earth extraction process, which relates to the technical field of rare earth extraction. The method includes: First, convert the non-linear dynamic model of the rare earth extraction process into a nominal system state space model considering the state coupling of the upstream subsystem. Second, design a distributed controller for the nominal system with input and output constraints to achieve stable tracking control. Then, considering the uncertainty of the model and parameters as well as external disturbances, use a disturbance observer to estimate the lumped disturbance received by each subsystem, and design a feedforward disturbance compensation controller based on the disturbance estimation value to achieve compensation for the lumped disturbance. Finally, combine the distributed model predictive controller of the nominal system with the disturbance compensation controller to design a composite controller, which realizes that the multi-component rare earth extraction process can still make the output track the set value under the conditions of model mismatch and external disturbances, so as to achieve robust and stable control.

Description

Technical Field

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

[0002] As an important part of the midstream of the rare earth industrial chain, the rare earth separation industry undertakes the task of separating and purifying mixed rare earth raw materials into single rare earth elements. The current rare earth control system still relies on manual flow setting, and the fluctuation range of process control is relatively large, affecting the quality and stability of products.

[0003] Currently, in the control of rare earth extraction processes, most studies focus on the control problems of binary rare earth extraction processes, and there are few research literatures on the control of multi-component rare earth extraction processes. However, enterprises generally implement a multi-component rare earth extraction production mode. The multi-component rare earth extraction and separation process is composed of multiple interconnected subsystems in series, with the characteristics of multi-variables, multi-couplings, and multi-constraints. Each subsystem is controlled by a separate local controller. Distributed model predictive control has been increasingly applied and concerned in industrial and academic fields due to its advantages such as flexibility, easy implementation, and strong fault tolerance. Although there have been many theoretical and application achievements in distributed predictive control, its application in rare earth extraction processes is still very few.

[0004] In addition, due to the complex and changeable nature of the rare earth extraction and separation process, and the strong uncertainty of the interference changes in the external environment, it is necessary to study the control problem of multi-component rare earth extraction in the presence of disturbances. Once internal uncertainty disturbances and external disturbances occur in the actual system, it is very 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 the present application is to provide a distributed predictive control method, device and medium for a multi-component rare earth extraction process, which can achieve the tracking of the set value of the component content in the multi-component rare earth extraction process under uncertain environmental factors, and at the same time ensure the stable operation of the system.

[0006] To achieve the above purpose, the present application provides the following solutions:

[0007] In a first aspect, the present application provides a distributed predictive control method for a multi-component rare earth extraction process, including:

[0008] Construct a non-linear dynamic model for each sub-process in the multi-component rare earth cascade extraction process according to the mechanism of the rare earth extraction process;

[0009] Construct a state - space model for each sub - process based on the lumped disturbance and the non - linear dynamic model. Among them, 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. The lumped disturbance includes the uncertain term and the external disturbance term in each non - linear dynamic model;

[0010] Construct a discrete nominal system model for each sub - process according to the state - space model;

[0011] For each sub - process, establish an objective function including input - output constraints, and design a distributed predictive controller for each nominal system according to the objective function and the corresponding discrete nominal system model;

[0012] Design a disturbance observer for each sub - process, and use each disturbance observer to estimate the lumped disturbance of the corresponding discrete nominal system model to obtain a disturbance estimate value. Among them, the disturbance observer includes an upstream sub - process state coupling term;

[0013] Determine a compensation control quantity according to the disturbance estimate value, and design a feed - forward compensation controller according to the compensation control quantity;

[0014] Design a composite controller for each sub - process according to the distributed predictive controller and the corresponding feed - forward compensation controller. The composite controller is used to control the output of the corresponding sub - process to converge to the target value.

[0015] In a second aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor. 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.

[0016] In a third aspect, the present application provides a computer - readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the distributed predictive control method for the multi - component rare - earth extraction process described in the first aspect above.

[0017] According to the specific embodiments provided by the present application, the present application has the following technical effects:

[0018] 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 the lumped disturbance according to the disturbance observer designed for each sub-process, designing a feed-forward compensation controller according to the disturbance estimation value, and designing a composite controller for each sub-process in combination with the distributed predictive controller, which can track the set value of the component content in the multi-component rare earth extraction process under uncertain environmental factors, and also ensure the stable operation of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained according to these drawings.

[0020] Figure 1 It is a schematic flow chart of a distributed predictive control method for a multi-component rare earth extraction process in Embodiment 1 of the present application;

[0021] Figure 2 It is a schematic diagram of the tracking curve of the component content at the aqueous phase outlet of the first-stage sub-process in Embodiment 1 of the present application;

[0022] Figure 3 It is a schematic diagram of the tracking curve of the component content at the organic phase outlet of the first-stage sub-process in Embodiment 1 of the present application;

[0023] Figure 4 It is a schematic diagram of the tracking curve of the component content at the aqueous phase outlet of the second-stage sub-process in Embodiment 1 of the present application;

[0024] Figure 5 It is a schematic diagram of the tracking curve of the component content at the organic phase outlet of the second-stage sub-process in Embodiment 1 of the present application;

[0025] Figure 6 It is a schematic diagram of the tracking curve of the component content at the aqueous phase outlet of the third-stage sub-process in Embodiment 1 of the present application;

[0026] Figure 7 It is a schematic diagram of the tracking curve of the component content at the organic phase outlet of the third-stage sub-process in Embodiment 1 of the present application;

[0027] Figure 8 It is a schematic diagram of the tracking curve of the component content at the aqueous phase outlet of the first-stage sub-process with different set values in Embodiment 1 of the present application;

[0028] Figure 9Schematic diagram of the tracking curve of the organic phase outlet component content with different set values in the first-stage sub-process in Embodiment 1 of this application;

[0029] Figure 10 Schematic diagram of the tracking curve of the aqueous phase outlet component content with different set values in the second-stage sub-process in Embodiment 1 of this application;

[0030] Figure 11 Schematic diagram of the tracking curve of the organic phase outlet component content with different set values in the second-stage sub-process in Embodiment 1 of this application;

[0031] Figure 12 Schematic diagram of the tracking curve of the aqueous phase outlet component content with different set values in the third-stage sub-process in Embodiment 1 of this application;

[0032] Figure 13 Schematic diagram of the tracking curve of the organic phase outlet component content with different set values in the third-stage sub-process in Embodiment 1 of this application;

[0033] Figure 14 Schematic diagram of the structure of a computer device provided in Embodiment 2 of this application. Detailed implementation manners

[0034] Next, the technical solutions in the embodiments of this application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this application.

[0035] To make the above objects, features, and advantages of this application more obvious and understandable, the following further describes this application in detail in conjunction with the accompanying drawings and specific implementation manners.

[0036] Embodiment 1

[0037] 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 also achieve the tracking of the component content set value under uncertain environmental factors, and at the same time ensure the stable operation of the system. As Figure 1 shown, this distributed predictive control method for the multi-component rare earth extraction process includes:

[0038] S1: According to the mechanism of the rare earth extraction process, construct a non-linear dynamic model of each sub-process system of the multi-component rare earth cascade extraction process;

[0039] S2: Considering the uncertainties of the rare earth extraction process parameters and external disturbances, the sum of the model uncertainty term and the external disturbance term of each non-linear dynamic model is regarded as a lumped disturbance, and based on this, the state space model of each sub-process is constructed. The state space model of each sub-process includes the state coupling term of the upstream sub-process, where the state coupling term of the upstream sub-process refers to the coupling relationship between the state of each sub-process and the state of the upstream sub-process;

[0040] 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;

[0041] S4: An objective function for the rare earth extraction process with input and output constraints is established, and a distributed predictive controller for each nominal system based on the multi-component rare earth extraction process is designed according to the objective function and the corresponding discrete nominal system model;

[0042] S5: A disturbance observer with a state coupling term is designed for each sub-process to estimate the model uncertainty and external disturbances (i.e., the model uncertainty term and the external disturbance term, which is also the lumped disturbance), and a disturbance estimation value is obtained, thereby improving the anti-interference ability of the system;

[0043] S6: The disturbance estimation value is used to design a compensation control quantity, and a feed-forward compensation controller is introduced. The distributed model predictive controller of each nominal system is combined 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 the vicinity of the output value of the nominal system.

[0044] Specifically, in S1, according to the mechanism of the rare earth extraction process, the non-linear dynamic model of the th sub-process of the rare earth extraction process can be obtained, and the expression is as follows:

[0045] ;

[0046] ;

[0047] ;

[0048] ; ;

[0049] Among them, represents derivative; represents the concentration of the difficult-to-extract component element in the aqueous phase of each extraction tank in the th sub-process, , represents the The concentration of the difficult-to-extract component element in the aqueous phase of the first extraction tank in the th extraction tank in the concentration of rare earth elements present in the extractant in the feedstock composition of the concentration of rare earth elements present in the scrub solution in the feed flow rate value of the scrub solution flow rate value of the represents the uncertainty term of the aqueous phase model; derivative of concentration of the easy-to-extract component element in the organic phase of each extraction tank in the concentration of the easy-to-extract component element in the organic phase of the first extraction tank in the th extraction tank in the extractant flow rate value of the represents the uncertainty term of the organic phase model; aqueous phase outlet product composition content of the output matrix of the organic phase outlet product composition content of the output matrix of the volume retention in the first extraction tank in the aqueous phase of the ​​​​​​​​​​​​​​​​​​​​​​​​​​​Volume holdup in the first-stage extraction tank; Denote the -th sub-process's volume holdup in the first-stage extraction tank of the organic phase; Denote the -th sub-process's volume holdup in the -th stage extraction tank of the organic phase; , Denote the initial value of the feed flow rate of the -th sub-process, Denote the initial value of the detergent flow rate of the -th sub-process; ; , Denote the initial value of the extractant flow rate of the -th sub-process.

[0050] In S2, considering external disturbances, the sum of the model uncertainty term and the external disturbance term is regarded as the lumped disturbance , and the expression is:

[0051] ;

[0052] where, Denote the total uncertainty term, Denote the external disturbance.

[0053] Therefore, considering the lumped disturbance, the state-space model of the rare-earth extraction process is as follows:

[0054] ;

[0055] where, Denote the derivative of ; , , Denote the concentration (i.e., the state) of the nominal system of the -th sub-process at time i , Denote the output of the nominal system of the -th sub-process at time (i.e., the component content at both ends of the outlet of the nominal system of the -th sub-process at time ); , , ; is the coefficient matrix of the corresponding dimension in the -th sub-process; , Denote the output matrix of the corresponding dimension in the aqueous phase of the -th sub-process, Denote the The output matrix of the sub-process in the corresponding dimension in the organic phase.

[0056] Furthermore, the above formula can be discretized to obtain a discrete-time state-space model:

[0057] ;

[0058] where denotes the state of the nominal system of the th sub-process at time ; i denotes the state matrix of the th sub-process; i denotes the input matrix of the th sub-process; i denotes the coefficient matrix of the state coupling term with the upstream sub-process t is the continuous time, k is the discrete time.

[0059] In S3, considering that each sub-process is only affected by the upstream sub-process, the discrete nominal dynamic model of the i th sub-process is as follows:

[0060] ;

[0061] where , , and are respectively the state, control input, and system output of the nominal system of the th sub-process at time , is the rare earth element concentration of the -1th sub-process at time

[0062] In S4, based on the idea of rolling optimization, the objective function of the i th sub-process is as follows:

[0063] ;

[0064] subject to: ;

[0065] ;

[0066] where denotes k the objective function of the i th sub-process at time and are respectively thei The prediction horizon and control horizon of the sub-process. and denote the weight matrix of the i -th sub-process corresponding dimension. and respectively denote the predicted k at time -th sub-process predicted output and the target set value at time -th. denotes the predicted k at time control input vector at time and respectively denote the upper and lower limits of the control input quantity , and respectively denote the upper and lower limits of the output variable .

[0067] The problem of controlling the component content in the rare earth extraction and separation process is transformed into the following optimization problem:

[0068] ;

[0069] Subject to

[0070] ;

[0071] ;

[0072] where

[0073] , ,

[0074] ;

[0075] The output prediction equation is:

[0076] ;

[0077] where , , ;

[0078] Based on the above analysis, the optimization problem of the constrained model predictive control for the -th sub-process can be transformed into the following constrained QP problem:

[0079] ;

[0080] Subject to: , ;

[0081] Among them , , , .

[0082] By solving the above optimization problem, the optimal control sequence of the nominal system part of the th sub-process at time is as follows:

[0083] ;

[0084] In S5, a disturbance observer with state coupling terms is designed as follows:

[0085] ;

[0086] Among them, is an intermediate variable, is the observer gain, is the estimated value of the unknown lumped disturbance .

[0087] Define the estimation error as , and the calculation formula of the estimation error is as follows:

[0088] ;

[0089] Among them , the spectral radius of the matrix P i needs to satisfy , represents the spectral radius.

[0090] In S6, combined with the proposed disturbance observer, the composite controller is designed as follows:

[0091] ;

[0092] Among them, is the first element in the optimal control sequence obtained by the nominal MPC , is the disturbance compensation gain, satisfying .

[0093] The simulation example of this embodiment is as follows:

[0094] Taking the extraction production process of LaCePrNd four-component rare earth in a certain rare earth production enterprise as the object for simulation, the extraction results of the four-component rare earth extraction process are as Figures 2 - 13 shown, taking Figure 2For example, the unit of the abscissa of this figure is the time step (i.e., the time unit), and the unit of the ordinate is the percentage. Figures 3 - 13 The units of the abscissa and ordinate of Figure 2 are the same as those of

[0095] The continuously bounded disturbance signals added to each subsystem are shown as follows:

[0096] ;

[0097] ;

[0098] ;

[0099] The parameter settings of the disturbance observer are as follows:

[0100] ,

[0101] ,

[0102] .

[0103] Simulation equipment and platform:

[0104] The computer CPU is AMD Ryzen 7 5800H with Radeon Graphics, 3.20 GHz, Windows 11 Home Chinese Edition, and the simulation platform is MATLAB R2021a.

[0105] Compared with the existing control technologies, the advantages of the distributed predictive control method for the multi-component rare earth extraction process provided in this embodiment are as follows:

[0106] 1. In the design of the controller in this embodiment, the constraint conditions of the extractant flow rate, feed liquid flow rate, detergent flow rate, and the component content of the outlet product are considered, so that the input flow rate and output component content of each sub-process always meet the physical constraints during the entire multi-component rare earth extraction process. The composite control method proposed in this embodiment can ensure that the component content of the outlet product of each sub-process converges to the vicinity of the nominal value, and solves the problem that the multi-component rare earth extraction process can effectively track the target set value under the physical constraint conditions.

[0107] 2. Based on the characteristic that each sub-process in the multi-component rare earth extraction is only related to the outlet product of its upstream sub-process, this embodiment designs a prediction model including the coupling term of the upstream sub-process state, effectively reducing the communication burden between the controllers and improving the integrity of the rare earth extraction process control at the same time.

[0108] 3. In this embodiment, a disturbance observer considering the coupling term of the upstream sub-process state is designed for each sub-process to estimate model uncertainties and external disturbances, effectively dealing with the influence of internal and external disturbances on the rare earth extraction process control system, and ensuring the stability and robustness of the control system.

[0109] 4. The composite control strategy proposed in this embodiment consists of distributed predictive control based on the nominal system model and a feed-forward compensation controller based on the 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, and at the same time has strong anti-interference ability. The proposed control method has high practical application value.

[0110] Embodiment 2

[0111] This embodiment provides a computer device, which can be a server or a terminal, and its internal structure diagram can be as shown in Figure 14 The figure. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated 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 external devices. The communication interface of the computer device is used to communicate with external terminals through a network connection. When the computer program is executed by the processor, it realizes the distributed predictive control method for the multi-component rare earth extraction process in Embodiment 1.

[0112] Those skilled in the art can understand that Figure 14 the structure shown in is only a block diagram of some structures 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 some components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor, and a computer program is stored in the memory. When the processor executes the computer program, the steps in the above method embodiments are realized.

[0113] Embodiment 3

[0114] 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.

[0115] Embodiment 4

[0116] 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.

[0117] 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 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 need to comply with relevant regulations.

[0118] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. 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 methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memories. Non-volatile memories 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 memories can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0119] In each of the embodiments provided in this application, the databases involved 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., and is not limited thereto. In each of the embodiments provided in this application, the processor may be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., and is not limited thereto.

[0120] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, 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, it should be considered as the scope described in this specification.

[0121] Specific examples are used in this article to elaborate on the principles and implementation manners of this application. The description of the above embodiments is only used to help understand the method and its core idea of this application; at the same time, for those of ordinary skill in the art, according to the idea of this application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to 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; The state space model of each sub-process is constructed 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, 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, and the lumped disturbance includes the uncertainty term and the external disturbance term in each nonlinear dynamic model; 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 Sub-process liquid components; , Indicates The concentration of rare earth elements in the detergents of each process; 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; A disturbance observer is designed for each sub-process, and each disturbance observer is used to estimate the lumped disturbance of the corresponding discrete nominal system model to obtain a disturbance estimation value, wherein the disturbance observer includes an upstream sub-process state coupling term; 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 each sub-process nominal system; 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 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 .

3. 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 .

4. 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 each sub-process nominal system; 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.

5. 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.

6. 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.

7. 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 6.

8. 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 6 is implemented.

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