Intelligent feeding system for rare earth metal electrolysis and method of use thereof

Abstract

The present application relates to the technical field of rare earth electrolysis, and discloses an intelligent feeding system for rare earth metal electrolysis and a use method thereof, which comprises an intelligent feeding algorithm module, a controller and a rare earth blanking machine, the controller is used for receiving parameters output by the intelligent feeding algorithm module, and the controller is also used for sending blanking instructions to the rare earth blanking machine; the intelligent feeding algorithm module comprises an electrolysis process data acquisition module, an electrolytic cell state evaluation module, an electrolysis parameter prediction module and an electrolysis prediction parameter optimization module. According to the present application, the state of the electrolytic cell is first accurately evaluated by the intelligent feeding algorithm module, and then the accurate prediction of the process parameters is realized under the condition that the current efficiency is optimal, finally, the electrolysis process parameters are optimized by combining the evaluation result of the cell state and the prediction result of the electrolysis process parameters, the optimal solution parameters are output, the controller responds and issues instructions to control the blanking rate of the rare earth blanking machine, and the content of the rare earth elements in the molten electrolyte is effectively controlled.

Description

Technical Field

[0001] This invention relates to the field of rare earth electrolysis technology, specifically to an intelligent feeding system for rare earth metal electrolysis and its usage method. Background Technology

[0002] Rare earth elements are a collective term for the lanthanide series elements (15 metals with atomic numbers 57-71) and their related elements scandium and yttrium in the periodic table. They comprise 17 chemically similar metallic components. Due to their unique photoelectric, electromagnetic, and catalytic properties, these elements have become key materials in modern industry. In the process of producing metallic neodymium through molten salt electrolysis of praseodymium and neodymium oxides, numerous complex physical and electrochemical changes occur within the electrolytic cell, mainly including redox reactions and electrochemical reactions. Among these, the electrolysis temperature is one of the key factors determining the performance of the electrolytic cell, and it is closely related to the amount and concentration of rare earth oxides fed into the cell. To effectively utilize the performance of the electrolytic cell, it is necessary not only to control the amount of rare earth oxides fed into the cell but also to adjust the feeding rate in real time to ensure effective control of the rare earth element content in the molten electrolyte, thereby ensuring that the electrolytic cell remains in a stable state.

[0003] Chinese Patent Publication No. CN113604842A discloses an intelligent feeding system for rare earth metal electrolysis and its usage method, comprising a material tank, a quantitative feeding tank I, a mixing tank, a vacuum suction device, a temporary storage tank, and a quantitative feeding tank II arranged sequentially, with transparent material pipes connecting each device; it also includes a deblocking device and a control system; the deblocking device includes a support plate, a track, a photoelectric sensor, and a moving vibration device; the support plate and track are arranged along the transparent material pipe; a photoelectric sensor is installed on the outside of the transparent material pipe; the moving vibration device is movably mounted on the track; the mixing tank contains stirring rod I and stirring rod II; stirring rod I and stirring rod II are respectively connected to stirring motor I and stirring motor II, and each is equipped with a speed sensor; the quantitative feeding tank I and quantitative feeding tank II are equipped with weight sensors.

[0004] Although the above-mentioned device can realize the function of automatic quantitative feeding in the rare earth metal electrolysis process, it cannot control the feeding rate of rare earth oxides in real time to adjust the temperature of the electrolytic cell when the temperature of the electrolytic cell rises or falls too quickly. Therefore, the market urgently needs an intelligent feeding system for rare earth metal electrolysis and its usage method to solve the above problems. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent feeding system for rare earth metal electrolysis and its usage method, in order to solve the problem mentioned in the background art that although the current intelligent feeding system can realize the function of automatic quantitative feeding in the rare earth metal electrolysis process, it cannot control the feeding rate of rare earth oxides in real time to adjust the temperature of the electrolytic cell when the temperature of the electrolytic cell rises or falls too quickly.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an intelligent feeding system for rare earth metal electrolysis, comprising...

[0007] The system includes an intelligent feeding algorithm module, a controller, and a rare earth feeding machine. The controller is used to receive parameters output by the intelligent feeding algorithm module and to send feeding instructions to the rare earth feeding machine.

[0008] The intelligent feeding algorithm module includes an electrolysis process data acquisition module, an electrolytic cell status evaluation module, an electrolysis parameter prediction module, and an electrolysis prediction parameter optimization module.

[0009] The electrolysis prediction parameter optimization module is used to calculate and output the optimal feeding rate parameter when the electrolytic cell is in a steady state, using the output parameters of the electrolytic cell state evaluation module and the electrolysis parameter prediction module as constraints. The electrolysis process data acquisition module is used to acquire rare earth oxide feeding rate parameters, oxide concentration parameters, electrolytic cell voltage parameters, electrolysis temperature parameters, and electrolysis current parameters through sensors.

[0010] After obtaining the rare earth electrolysis process parameters through the electrolysis process data acquisition module, the intelligent feeding algorithm module calculates the optimal feeding rate parameters and transmits them to the controller while ensuring that the electrolysis cell is in a stable state. The controller then issues instructions to adjust the feeding rate of the feeding machine.

[0011] As a further preferred embodiment of this technical solution: the electrolytic cell status assessment module is used to classify and determine the cell status according to the process parameters obtained by the electrolytic process data acquisition module. The electrolytic cell status assessment module includes an electrolytic process data receiving unit, an electrolytic cell status preset assessment and classification unit, and an electrolytic cell status determination and output unit.

[0012] Among them, the rare earth oxide feed rate parameter, oxide concentration parameter, electrolytic cell voltage parameter, electrolysis temperature parameter, and electrolysis current parameter are classified as input variables of the electrolytic cell status evaluation module by the electrolysis process data acquisition module. The electrolysis process data receiving unit is used to receive input variables. The data is then transmitted to the electrolytic cell state preset evaluation and classification unit, which processes the input variables. The electrolytic cell state determination output unit classifies and determines the state of the electrolytic cell, and outputs the determination result of the electrolytic cell state. .

[0013] As a further preferred embodiment of this technical solution: the electrolysis parameter prediction module is used to predict the process parameters in the rare earth electrolysis process based on the time series characteristics of the electrolysis data. The electrolysis parameter prediction module includes an electrolysis process data receiving unit, a process data feature extraction unit, and an electrolysis data prediction unit.

[0014] Among them, the rare earth oxide feeding rate parameter, oxide concentration parameter, electrolytic cell voltage parameter, electrolysis temperature parameter, and electrolysis current parameter are classified by the electrolysis process data acquisition module as input variables of the electrolysis parameter prediction module. The input variables The process data is received by the electrolysis process data receiving unit and transmitted to the process data feature extraction unit. The process data feature extraction unit is used to extract the feature information between process parameter data during electrolysis and retain the time series features of the data. The electrolysis data prediction unit is used to receive the data processed by the process data feature extraction unit and output the predicted electrolysis process parameters after calculation with the goal of optimizing current efficiency.

[0015] As a further preferred embodiment of this technical solution: the electrolysis prediction parameter optimization module is used to construct a target optimization function based on the judgment result of the electrolytic cell state evaluation module and the optimal solution of the electrolysis parameter prediction module. The electrolysis prediction parameter optimization module includes an algorithm optimization unit, which performs optimization search based on multiple sets of output parameters of the target optimization function and the high interdependence between the output parameters, and outputs the optimal solution process parameters.

[0016] The formula for the objective optimization function is as follows:

[0017] (1)

[0018] In its formula (1), Let K be a function of the electrolysis data prediction unit, K be a constant, and C be the discrimination result output by the electrolytic cell state determination output unit. F(x) lies in the interval (0, 1), and Ω is The feasible solution space, and These are the input variables for the electrolytic cell state assessment module and the electrolysis parameter prediction module.

[0019] As a further preferred embodiment of this technical solution: the algorithm optimization unit outputs the optimal solution process parameters, and the controller reads the optimal solution process parameters output by the algorithm optimization unit and responds to the output mapping parameter command to control the feeding rate of the rare earth feeding machine.

[0020] As a further preferred embodiment of this technical solution: the rare earth feeding machine includes a weighing and feeding unit and a feeding speed regulation unit, wherein the weighing and feeding unit is connected to the feeding speed regulation unit.

[0021] As a further preferred embodiment of this technical solution: the feeding speed control unit includes a feeding groove. After the weighing and feeding unit quantitatively weighs and feeds the rare earth oxides into the feeding speed control unit, the rotation speed of the feeding groove in the feeding speed control unit controls the feeding rate of the rare earth oxides.

[0022] As a further preferred embodiment of this technical solution: the weighing and feeding unit includes a quantitative dispensing tank, a tank support frame, and a feeding baffle. The quantitative dispensing tank is embedded in the tank support frame. Weight sensors are connected to the four corners of the upper end of the quantitative dispensing tank. The weight sensors are used to weigh the rare earth oxides in the quantitative dispensing tank. The weight sensors are threadedly connected to the tank support frame. A feeding trough is opened at the lower part of the quantitative dispensing tank. The feeding baffle is used to control the opening and closing of the feeding trough. The tank support frame is fixedly connected to the feeding speed control unit.

[0023] As a further preferred embodiment of this technical solution: the feeding speed control unit further includes a temporary storage tank, a feeding speed control fixing frame, and a feeding speed control drive component. The temporary storage tank has an internal cavity, which is a conical structure. The feeding speed control fixing frame is connected to the temporary storage tank. Both ends of the feeding groove are connected to connecting shafts. The connecting shafts and the feeding groove are rotatably connected to the feeding speed control fixing frame. The feeding groove is located at the lower end of the internal cavity of the storage tank. One end of one set of connecting shafts away from the feeding groove is fixedly connected to the output end of the feeding speed control drive component. The feeding speed control drive component is bolted to the feeding speed control fixing frame. The tank support frame is fixedly connected to the temporary storage tank.

[0024] As a further preferred embodiment of this technical solution, the material supply method includes:

[0025] A1: Obtain electrolysis process data, including rare earth oxide feed rate parameters, oxide concentration parameters, electrolytic cell voltage parameters, electrolysis temperature parameters, and electrolysis current parameters;

[0026] A2: Based on the electrolysis process data obtained in A1, perform algorithm optimization and output the optimal solution parameters. The optimal solution parameters are the process parameters output by the intelligent feeding algorithm module after optimization.

[0027] A3: Responds to the optimal solution parameters and outputs mapping parameter instructions to control the rotation speed of the loading and unloading speed adjustment drive of the rare earth feeding machine.

[0028] Compared with the prior art, the beneficial effects of the present invention are:

[0029] 1. In this invention, the intelligent feeding algorithm module first accurately evaluates the state of the electrolytic cell, and based on this, accurately predicts the process parameters under the condition of optimal current efficiency. Finally, combining the cell state evaluation results and the electrolytic process parameter prediction results, the electrolytic process parameters are optimized and the optimal solution parameters are output. The controller responds and issues instructions to control the feeding rate of the rare earth feeding machine. This overcomes the problem that the existing rare earth feeding system cannot control the feeding rate of rare earth oxides in real time when the temperature of the electrolytic cell rises or falls too quickly. It also ensures that the electrolytic cell remains in a stable state during the rare earth electrolysis process, and at the same time achieves the energy-saving and consumption-reducing goals of the rare earth electrolysis industry, thereby improving the economic benefits of rare earth electrolysis enterprises.

[0030] 2. In this invention, the intelligent feeding algorithm module constructs a target optimization function with the judgment result of the electrolytic cell state evaluation module and the optimal solution of the electrolysis parameter prediction module as the target. Its algorithm optimization unit optimizes and searches the output parameters of the target optimization function and the high interdependence between each output parameter, and then outputs the optimal solution process parameters, so that the intelligent feeding system not only takes into account the state of the electrolytic cell but also ensures the current efficiency.

[0031] 3. The weighing and feeding unit in this invention has an automatic quantitative feeding function, and the inner cavity of the quantitative feeding tank is a conical structure, which effectively prevents the accumulation of material at the bottom of the quantitative feeding tank. The inner cavity of the temporary storage tank also has the same structure and the same function. Furthermore, the feeding groove is set at the bottom of the inner cavity of the storage tank, which further ensures the smooth feeding of rare earth oxides. Attached Figure Description

[0032] Figure 1 This is a schematic diagram of the architecture of an intelligent feeding system for rare earth metal electrolysis and its usage method according to the present invention.

[0033] Figure 2 This is a flowchart illustrating the operation of the intelligent algorithm module in the intelligent feeding system for rare earth metal electrolysis and its usage method of the present invention.

[0034] Figure 3 This is a flowchart of the operation of the electrolytic cell status assessment module in the intelligent feeding system and its usage method for rare earth metal electrolysis of the present invention.

[0035] Figure 4 This is a flowchart illustrating the operation of the electrolysis parameter prediction module in the intelligent feeding system for rare earth metal electrolysis and its usage method of the present invention.

[0036] Figure 5 This is a flowchart of the operation of the electrolysis prediction parameter optimization module in the intelligent feeding system for rare earth metal electrolysis and its usage method of the present invention.

[0037] Figure 6This is a three-dimensional structural diagram of the rare earth feeding machine in the intelligent feeding system for rare earth metal electrolysis and its usage method of the present invention. Figure 1 ;

[0038] Figure 7 This is a front view of the rare earth feeder in the intelligent feeding system for rare earth metal electrolysis and its usage method of the present invention.

[0039] Figure 8 for Figure 7 A schematic diagram of the cross-sectional structure at point AA along the middle edge;

[0040] Figure 9 for Figure 8 Enlarged structural diagram at point B;

[0041] Figure 10 This is a three-dimensional structural diagram of the rare earth feeding machine in the intelligent feeding system for rare earth metal electrolysis and its usage method of the present invention. Figure 2 ;

[0042] Figure 11 for Figure 10 Enlarged diagram of point C in the middle.

[0043] In the diagram: 10. Weighing and feeding unit; 101. Quantitative dispensing tank; 102. Weight sensor; 103. Tank support frame; 104. Feeding baffle; 105. Baffle drive component; 20. Feeding speed control unit; 201. Temporary storage tank; 202. Storage tank inner cavity; 203. Feeding speed control fixing frame; 204. Feeding groove; 205. Connecting shaft; 206. Feeding speed control drive component; 30. Transparent material tube. Detailed Implementation

[0044] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0045] In the description of this invention, it should be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0046] Example

[0047] As mentioned in the background section, in order to control the amount of rare earth oxides fed into the feed, the relevant technology employs a method of installing a weight sensor inside the quantitative feeding tank.

[0048] Specifically, a rare earth metal electrolysis intelligent feeding system and its usage method include a material tank, a quantitative feeding tank I, a mixing tank, a vacuum suction device, a temporary storage tank, and a quantitative feeding tank II arranged sequentially, with transparent material pipes connected to each device; it also includes a deblocking device and a control system; the deblocking device includes a support plate, a track, a photoelectric sensor, and a moving vibration device; the support plate and track are arranged along the transparent material pipe; a photoelectric sensor is installed on the outside of the transparent material pipe; the moving vibration device is movably installed on the track; the mixing tank is equipped with stirring rod I and stirring rod II; stirring rod I and stirring rod II are respectively connected to stirring motor I and stirring motor II, and each is equipped with a speed sensor; the quantitative feeding tank I and quantitative feeding tank II are equipped with weight sensors.

[0049] However, although the above-mentioned device can realize the function of automatic quantitative feeding in the rare earth metal electrolysis process, it cannot control the feeding rate of rare earth oxides in real time when the temperature of the electrolytic cell rises or falls too quickly, so it cannot effectively adjust the temperature of the electrolytic cell in real time.

[0050] To address the issue of controlling the electrolytic cell temperature by real-time control of the feeding rate of rare earth oxides when the temperature rises or falls too rapidly, this application presents an intelligent feeding system for rare earth metal electrolysis and its usage method. Figures 1-11 As shown, it includes an intelligent feeding algorithm module, a controller, and a rare earth feeding machine. The controller is used to receive parameters output by the intelligent feeding algorithm module and also to send feeding instructions to the rare earth feeding machine.

[0051] like Figure 2 As shown, the intelligent feeding algorithm module includes an electrolysis process data acquisition module, an electrolyzer status assessment module, an electrolysis parameter prediction module, and an electrolysis prediction parameter optimization module;

[0052] like Figure 5 As shown, the electrolysis prediction parameter optimization module is used to calculate and output the optimal feeding rate parameter when the electrolytic cell is in a steady state, using the output parameters of the electrolytic cell state evaluation module and the electrolysis parameter prediction module as constraints. The electrolysis process data acquisition module is used to acquire rare earth oxide feeding rate parameters, oxide concentration parameters, electrolytic cell voltage parameters, electrolysis temperature parameters, and electrolysis current parameters through sensors.

[0053] Optionally, the sensors include speed sensors, high-temperature resistant probes, temperature sensors, voltage sensors, and current sensors.

[0054] After obtaining the rare earth electrolysis process parameters through the electrolysis process data acquisition module, the intelligent feeding algorithm module calculates the optimal feeding rate parameters and transmits them to the controller while ensuring that the electrolysis cell is in a stable state. The controller then issues instructions to adjust the feeding rate of the feeding machine.

[0055] In this way, the intelligent feeding algorithm module can first accurately assess the state of the electrolytic cell, and based on this, accurately predict the process parameters under the condition of better current efficiency. Finally, by combining the cell state assessment results and the electrolytic process parameter prediction results, the electrolytic process parameters are optimized and the optimal solution parameters are output. The controller responds to the optimal solution parameters and issues instructions to control the feeding rate of the rare earth feeding machine.

[0056] Furthermore, the intelligent feeding algorithm module constructs a target optimization function based on the judgment results of the electrolytic cell condition assessment module and the optimal solution of the electrolysis parameter prediction module. Its algorithm optimization unit optimizes and searches the output parameters of the target optimization function and the high interdependence between the output parameters, thereby outputting the optimal solution process parameters. This ensures that the intelligent feeding system not only takes into account the electrolytic cell condition but also guarantees current efficiency.

[0057] In addition, the weighing and feeding unit 10 has an automatic quantitative feeding function, and the inner cavity of the quantitative feeding tank 101 is a conical structure, which effectively prevents the accumulation of material at the bottom of the quantitative feeding tank 101. The inner cavity 202 of the temporary storage tank 201 also has the same structure and the same function. Furthermore, the feeding groove 204 is set at the bottom of the inner cavity 202 of the storage tank, which further ensures the smooth feeding of rare earth oxides.

[0058] The intelligent feeding system and its usage method for rare earth metal electrolysis in this application embodiment can be applied to the process of producing elemental neodymium metal by molten salt electrolysis of praseodymium and neodymium oxides. Specifically, the rare earth oxides are not limited to lanthanum, cerium, praseodymium, neodymium, etc.

[0059] In this embodiment, as Figure 3 As shown, the electrolytic cell status assessment module is used to classify and determine the cell status based on the process parameters obtained by the electrolysis process data acquisition module. The electrolytic cell status assessment module includes an electrolysis process data receiving unit, an electrolytic cell status preset assessment classification unit, and an electrolytic cell status determination output unit. The electrolytic cell status assessment module can accurately classify and determine the status of the electrolytic cell, providing accurate input variables for the next electrolysis prediction parameter optimization module.

[0060] Among them, the rare earth oxide feeding rate parameter, oxide concentration parameter, electrolytic cell voltage parameter, electrolysis temperature parameter, and electrolysis current parameter are classified as input variables of the electrolytic cell state assessment module by the electrolysis process data acquisition module. The electrolysis process data receiving unit is used to receive the input variables and transmit them to the electrolytic cell state preset assessment classification unit. The electrolytic cell state preset assessment classification unit classifies and determines the input variables, and the electrolytic cell state determination output unit outputs the determination result of the electrolytic cell state.

[0061] Specifically, the electrolytic cell is most stable when the temperature is between 1062 and 1072 degrees Celsius. When the temperature is above 1072 degrees Celsius, the electrolytic cell is in a hot cell state, and when the temperature is below 1062 degrees Celsius, the electrolytic cell is in a cold cell state. As long as the electrolytic cell is in a cell state, the electrolysis efficiency is poor.

[0062] In this embodiment, as Figure 4 As shown, the electrolysis parameter prediction module is used to predict the process parameters in the rare earth electrolysis process based on the time series characteristics of the electrolysis data. The electrolysis parameter prediction module includes an electrolysis process data receiving unit, a process data feature extraction unit, and an electrolysis data prediction unit.

[0063] Among them, the rare earth oxide feeding rate parameter, oxide concentration parameter, electrolytic cell voltage parameter, electrolysis temperature parameter, and electrolysis current parameter are classified as input variables of the electrolysis parameter prediction module by the electrolysis process data acquisition module. Input variables The process data is received by the electrolysis process data receiving unit and transmitted to the process data feature extraction unit. The process data feature extraction unit is used to extract the feature information between process parameter data during electrolysis and retain the time series features of the data. The electrolysis data prediction unit is used to receive the data processed by the process data feature extraction unit. The electrolysis data prediction unit calculates the optimal current efficiency and outputs the predicted electrolysis process parameters. The electrolysis parameter prediction module can also effectively predict the current efficiency in the rare earth electrolysis production process.

[0064] In this embodiment, as Figure 5 As shown, the electrolysis prediction parameter optimization module is used to construct a target optimization function based on the judgment result of the electrolytic cell state evaluation module and the optimal solution of the electrolysis parameter prediction module. That is, based on the electrolytic cell state being in a steady state and the current efficiency being in a high efficiency range, the electrolysis prediction parameter optimization module includes an algorithm optimization unit. The algorithm optimization unit performs optimization search based on multiple sets of output parameters of the target optimization function and the high interdependence between each output parameter, and outputs the optimal solution process parameters.

[0065] The formula for the objective optimization function is:

[0066] (1)

[0067] In its formula (1), Let K be a function of the electrolysis data prediction unit, K be a constant, and C be the discrimination result output by the electrolytic cell state determination output unit. F(x) lies in the interval (0, 1), and Ω is The feasible solution space, and These are the input variables for the electrolyzer state assessment module and the electrolysis parameter prediction module.

[0068] In this embodiment, specifically: during the rare earth electrolysis process, there is a high degree of interdependence among various parameters. This intelligent feeding algorithm module uses the algorithm optimization unit to determine the optimal process parameters and uses them as the final decision basis.

[0069] In this embodiment, specifically: the algorithm optimization unit outputs the optimal solution process parameters, the controller reads the optimal solution process parameters output by the algorithm optimization unit and responds to the output mapping parameter command to control the feeding rate of the rare earth feeding machine.

[0070] In this embodiment, as Figure 6 As shown, the rare earth feeding machine includes a weighing and feeding unit 10 and a feeding speed regulation unit 20, with the weighing and feeding unit 10 connected to the feeding speed regulation unit 20.

[0071] In this embodiment, specifically: the feeding speed control unit 20 includes a feeding groove 204. After the weighing and feeding unit 10 quantitatively weighs and feeds the rare earth oxides into the feeding speed control unit 20, the rotation speed of the feeding groove 204 in the feeding speed control unit 20 controls the feeding speed of the rare earth oxides.

[0072] In this embodiment, refer to Figure 8 and Figure 11 The weighing and feeding unit 10 includes a quantitative dispensing tank 101, a tank support frame 103, and a feeding baffle 104. The quantitative dispensing tank 101 is embedded in the tank support frame 103. Weight sensors 102 are connected to the four corners of the upper end of the quantitative dispensing tank 101. The weight sensors 102 are used to weigh the rare earth oxides in the quantitative dispensing tank 101. The weight sensors 102 are threadedly connected to the tank support frame 103. A feeding trough is opened at the lower part of the quantitative dispensing tank 101. The feeding baffle 104 is used to control the opening and closing of the feeding trough. The feeding baffle 104 is connected to the quantitative dispensing tank 101 through a rotating shaft. The tank support frame 103 is fixedly connected to the feeding speed control unit 20.

[0073] Reference Figure 8 and Figure 11The weighing and feeding unit 10 also includes a baffle drive 105, which is driven by the controller. The feeding baffle 104 has the function of opening and closing the feeding of rare earth oxides in the quantitative feeding tank 101.

[0074] Specifically, the controller can be a programmable logic controller (PLC), which adjusts the motor speed according to the given input signal and set parameters. In this embodiment, the PLC receives the output signal from the intelligent feeding algorithm module and issues instructions to adjust the speed of the feeding speed control drive 206 according to the preset parameters.

[0075] In some embodiments, refer to Figure 11 The discharge baffle 104 is hinged to the quantitative dispensing tank 101 to realize the function of closing and opening the quantitative dispensing tank 101 by the discharge baffle 104.

[0076] In this embodiment, reference Figure 9 Specifically: the feeding speed control unit 20 also includes a temporary storage tank 201, a feeding speed control fixing frame 203 and a feeding speed control drive component 206. The temporary storage tank 201 has an inner cavity 202 inside, which is a conical structure to facilitate the smooth feeding of rare earth oxides.

[0077] refer to Figure 9 and Figure 11 The feeding speed regulating fixing frame 203 is connected to the temporary storage tank 201. Both ends of the feeding groove 204 are connected to the connecting shaft 205. The connecting shaft 205 and the feeding groove 204 are rotatably connected in the feeding speed regulating fixing frame 203. The feeding groove 204 is located at the lower end of the inner cavity 202 of the storage tank. One end of a set of connecting shafts 205 away from the feeding groove 204 is fixedly connected to the output end of the feeding speed regulating drive 206. The feeding speed regulating drive 206 is bolted to the feeding speed regulating fixing frame 203. The tank support frame 103 is fixedly connected to the temporary storage tank 201.

[0078] refer to Figure 9 Specifically, the feeding groove 204 is used for feeding rare earth oxides, and can be a groove structure or a shovel structure.

[0079] In this embodiment, reference Figure 6 The rare earth feeding machine also includes a transparent material pipe 30, one end of which is connected to a temporary storage tank 201, and the other end of which is connected to the feed inlet of the electrolytic cell.

[0080] In this embodiment, the specific method of feeding and using materials includes:

[0081] A1: Obtain electrolysis process data, including rare earth oxide feed rate parameters, oxide concentration parameters, electrolytic cell voltage parameters, electrolysis temperature parameters, and electrolysis current parameters;

[0082] A2: Based on the electrolysis process data obtained in A1, perform algorithm optimization and output the optimal solution parameters. The optimal solution parameters are the process parameters output by the intelligent feeding algorithm module after optimization.

[0083] A3: Respond to the optimal solution parameters and output mapping parameter instructions to control the rotation speed of the rare earth feeding machine's loading and unloading speed control drive 206.

[0084] To better understand the working process of the intelligent feeding system for rare earth metal electrolysis and its usage method according to embodiments of this application, please refer to... Figures 1-11 The following is a detailed description of a specific embodiment:

[0085] During initial operation, the electrolysis process data acquisition module acquires rare earth oxide feeding rate parameters, oxide concentration parameters, electrolysis cell voltage parameters, electrolysis temperature parameters, and electrolysis current parameters through sensors. The electrolysis process data receiving unit then receives and processes the acquired process parameters and cleans the process parameter data. At this time, the electrolysis cell status preset evaluation and classification unit classifies and judges the cleaned process parameter data, and the electrolysis cell status judgment output unit outputs the judgment result of the electrolysis cell status.

[0086] The electrolysis process data acquisition module acquires rare earth oxide feeding rate parameters, oxide concentration parameters, electrolytic cell voltage parameters, electrolysis temperature parameters, and electrolysis current parameters through sensors. The electrolysis process data receiving unit then receives the acquired process parameters and cleans the process parameter data. The process data feature extraction unit then normalizes the cleaned process parameter data, extracts the feature information between the data, and retains the time series features of the data. Finally, the electrolysis data prediction unit receives the output data from the process data feature extraction unit and outputs the corresponding process prediction data under the condition of optimal current efficiency in the rare earth electrolysis production process.

[0087] Then, the electrolysis prediction parameter optimization module constructs the objective optimization function and receives the discrimination result data of the electrolytic cell state output unit and the process prediction data output by the electrolysis data prediction unit. The objective optimization function then outputs multiple sets of process parameters under the dual constraints of the electrolytic cell state evaluation result being in a steady state and the optimal electrolysis current efficiency. Finally, the algorithm optimization unit outputs the optimal solution parameters.

[0088] Finally, the controller responds to the optimal solution parameters and sends control commands to the feeding speed control drive 206 to adjust the rotation speed of the feeding chute 204 in real time, thereby controlling the feeding rate of rare earth oxides and adjusting the temperature of the electrolytic cell.

[0089] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An intelligent feeding system for rare earth metal electrolysis, characterized in that: include The system includes an intelligent feeding algorithm module, a controller, and a rare earth feeding machine. The controller is used to receive parameters output by the intelligent feeding algorithm module and to send feeding instructions to the rare earth feeding machine. The intelligent feeding algorithm module includes an electrolysis process data acquisition module, an electrolytic cell status evaluation module, an electrolysis parameter prediction module, and an electrolysis prediction parameter optimization module. The electrolysis prediction parameter optimization module is used to calculate and output the optimal feeding rate parameter when the electrolytic cell is in a steady state, using the output parameters of the electrolytic cell state evaluation module and the electrolysis parameter prediction module as constraints. The electrolysis process data acquisition module is used to acquire rare earth oxide feeding rate parameters, oxide concentration parameters, electrolytic cell voltage parameters, electrolysis temperature parameters, and electrolysis current parameters through sensors. After obtaining the rare earth electrolysis process parameters through the electrolysis process data acquisition module, the intelligent feeding algorithm module calculates the optimal feeding rate parameters and transmits them to the controller while ensuring that the electrolysis cell is in a stable state. The controller then issues instructions to adjust the feeding rate of the feeding machine. The electrolytic cell status assessment module is used to classify and determine the cell status based on the process parameters obtained by the electrolysis process data acquisition module. The electrolytic cell status assessment module includes an electrolysis process data receiving unit, an electrolytic cell status preset assessment and classification unit, and an electrolytic cell status determination and output unit. Among them, the rare earth oxide feed rate parameter, oxide concentration parameter, electrolytic cell voltage parameter, electrolysis temperature parameter, and electrolysis current parameter are classified as input variables of the electrolytic cell status evaluation module by the electrolysis process data acquisition module. The electrolysis process data receiving unit is used to receive input variables. The data is then transmitted to the electrolytic cell state preset evaluation and classification unit, which processes the input variables. The electrolytic cell state determination output unit classifies and determines the state of the electrolytic cell, and outputs the determination result of the electrolytic cell state. ; The electrolysis parameter prediction module is used to predict process parameters in the rare earth electrolysis process based on the time series characteristics of electrolysis data. The electrolysis parameter prediction module includes an electrolysis process data receiving unit, a process data feature extraction unit, and an electrolysis data prediction unit. Among them, the rare earth oxide feeding rate parameter, oxide concentration parameter, electrolytic cell voltage parameter, electrolysis temperature parameter, and electrolysis current parameter are classified by the electrolysis process data acquisition module as input variables of the electrolysis parameter prediction module. The input variables The process data is received by the electrolysis process data receiving unit and transmitted to the process data feature extraction unit. The process data feature extraction unit is used to extract the feature information between process parameter data during electrolysis and retain the time series features of the data. The electrolysis data prediction unit is used to receive the data processed by the process data feature extraction unit and output the predicted electrolysis process parameters after calculation with the goal of optimizing current efficiency. The electrolysis prediction parameter optimization module is used to construct a target optimization function based on the judgment result of the electrolytic cell state evaluation module and the optimal solution of the electrolysis parameter prediction module. The electrolysis prediction parameter optimization module includes an algorithm optimization unit. The algorithm optimization unit performs optimization search based on multiple sets of output parameters of the target optimization function and the high interdependence between each output parameter, and outputs the optimal solution process parameters. The formula for the objective optimization function is as follows: （1） In its formula (1), Let K be a function of the electrolysis data prediction unit, K be a constant, and C be the discrimination result output by the electrolytic cell state determination output unit. F(x) lies in the interval (0, 1), and Ω is The feasible solution space, and These are the input variables for the electrolytic cell state assessment module and the electrolysis parameter prediction module.

2. The intelligent feeding system for rare earth metal electrolysis according to claim 1, characterized in that: The algorithm optimization unit outputs the optimal solution process parameters, and the controller reads the optimal solution process parameters output by the algorithm optimization unit and responds to the output mapping parameter command to control the feeding rate of the rare earth feeding machine.

3. The intelligent feeding system for rare earth metal electrolysis according to claim 1, characterized in that: The rare earth feeding machine includes a weighing and feeding unit and a feeding speed regulation unit, and the weighing and feeding unit is connected to the feeding speed regulation unit.

4. The intelligent feeding system for rare earth metal electrolysis according to claim 3, characterized in that: The feeding speed control unit includes a feeding chute. After the weighing and feeding unit quantitatively weighs and feeds the rare earth oxides into the feeding speed control unit, the rotation speed of the feeding chute in the feeding speed control unit controls the feeding rate of the rare earth oxides.

5. The intelligent feeding system for rare earth metal electrolysis according to claim 4, characterized in that: The weighing and feeding unit includes a quantitative dispensing tank, a tank support frame, and a feeding baffle. The quantitative dispensing tank is embedded in the tank support frame. Weight sensors are connected to the four corners of the upper end of the quantitative dispensing tank. The weight sensors are used to weigh the rare earth oxides in the quantitative dispensing tank. The weight sensors are threadedly connected to the tank support frame. A feeding trough is opened at the lower part of the quantitative dispensing tank. The feeding baffle is used to control the opening and closing of the feeding trough. The tank support frame is fixedly connected to the feeding speed control unit.

6. The intelligent feeding system for rare earth metal electrolysis according to claim 5, characterized in that: The feeding speed control unit also includes a temporary storage tank, a feeding speed control fixing frame, and a feeding speed control drive. The temporary storage tank has an internal cavity, which is a conical structure. The feeding speed control fixing frame is connected to the temporary storage tank. Both ends of the feeding groove are connected to connecting shafts. The connecting shafts and the feeding groove are rotatably connected to the feeding speed control fixing frame. The feeding groove is located at the lower end of the internal cavity of the storage tank. One end of the connecting shaft away from the feeding groove is fixedly connected to the output end of the feeding speed control drive. The feeding speed control drive is bolted to the feeding speed control fixing frame. The tank support frame is fixedly connected to the temporary storage tank.

7. A method for feeding rare earth metals into an electrolytic reactor, characterized in that, The intelligent feeding system for rare earth metal electrolysis according to any one of claims 1-6, wherein the feeding method includes: A1: Obtain electrolysis process data, including rare earth oxide feed rate parameters, oxide concentration parameters, electrolytic cell voltage parameters, electrolysis temperature parameters, and electrolysis current parameters; A2: Based on the electrolysis process data obtained in A1, perform algorithm optimization and output the optimal solution parameters. The optimal solution parameters are the process parameters output by the intelligent feeding algorithm module after optimization. A3: Responds to the optimal solution parameters and outputs mapping parameter instructions to control the rotation speed of the loading and unloading speed adjustment drive of the rare earth feeding machine.

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