Ionic rare earth ore leaching method, device and storage medium based on digital twin

Abstract

The present invention discloses a method, device and storage medium for leaching ionic rare earth ores based on digital twins, which relates to the technical field of mine leaching and can improve the leaching rate and leaching efficiency of rare earth elements. It includes: constructing a preset digital twin coupling model of rare earth ores, including a preset ionic rare earth element leaching model and a preset hydrometallurgical treatment model; obtaining the leaching agent property information of the leaching agent, the rare earth ore property information corresponding to the target ionic rare earth ore, and the operating parameters under the in-situ leaching process; inputting the leaching agent property information, the rare earth ore property information and the operating parameters into the preset ionic rare earth element leaching model to obtain the overall seepage field distribution data and concentration field distribution data of the rare earth mine; inputting the extracted seepage field distribution data and concentration field distribution data at the liquid collection tunnel as initial parameters into the preset hydrometallurgical treatment model to obtain the predicted process operation parameter information of the hydrometallurgical treatment process; based on the predicted process operation parameter information, hydrometallurgical treatment is performed on the leaching mother liquor to obtain ionic rare earth elements.

Description

Technical Field

[0001] The present invention relates to the field of mine leaching technology, and in particular to an ionic rare earth ore leaching method, device and storage medium based on digital twins. Background Art

[0002] Due to strong weathering during the mineralization process of rare earth ore, rare earth elements migrate and enrich in the form of ions, mainly attached to the surface of clay mineral particles in the form of hydrated cations or hydroxyl water and cations, and distributed in layers or lenses in the weathering crust of granite or volcanic rocks. Rare earth elements are widely used, based on this, it is necessary to leach the rare earth elements in rare earth ore.

[0003] At present, the leaching process of rare earth ores is usually carried out through the experience accumulated by on-site engineering personnel. However, this leaching method is greatly affected by human subjective factors, which will lead to a low leaching rate of rare earth elements in rare earth ores. At the same time, this manual leaching method will lead to a low leaching efficiency of rare earth elements in rare earth ores. Summary of the invention

[0004] The present invention provides an ionic rare earth ore leaching method, device and storage medium based on digital twin, which are mainly capable of improving the leaching rate of rare earth elements in rare earth ore and improving the leaching efficiency of rare earth elements in rare earth ore.

[0005] According to a first aspect of the present invention, there is provided an ionic rare earth ore leaching method based on digital twin, comprising:

[0006] Based on the actual leaching property data corresponding to the target ionic rare earth ore, a preset rare earth ore digital twin coupling model is constructed, wherein the preset rare earth ore digital twin coupling model includes a preset ionic rare earth element leaching model and a preset hydrometallurgical treatment model;

[0007] In response to a leaching signal corresponding to a target ionic rare earth ore, obtaining leaching agent property information of a leaching agent used for leaching the target ionic rare earth ore, rare earth ore property information corresponding to the target ionic rare earth ore, and operating parameters of an in-situ leaching process for the target ionic rare earth ore;

[0008] Inputting the leaching agent property information, the rare earth ore property information and the operating condition parameters into the preset ionic rare earth element leaching model to analyze the leaching process, and obtaining the seepage field distribution data and concentration field distribution data of the in-situ leaching process for the target ionic rare earth ore;

[0009] The seepage field distribution data and the data corresponding to the liquid collecting tunnel in the concentration field distribution data are input as initial parameters into the preset hydrometallurgical treatment model for analysis and processing, so as to obtain the predicted process operation parameter information for the hydrometallurgical treatment process of the leaching mother liquor, wherein the leaching mother liquor is generated in the in-situ leaching process of the target ionic rare earth ore;

[0010] Based on the predicted process operation parameter information, the leaching mother liquor is subjected to hydrometallurgical treatment to obtain the ionic rare earth elements leached from the target ionic rare earth ore.

[0011] Optionally, the preset ionic rare earth element leaching model includes a saturated seepage module, a solute transport module, and an ion exchange reaction module; the leaching agent property information, the rare earth ore property information, and the operating condition parameters are input into the preset ionic rare earth element leaching model to analyze the leaching process, and the seepage field distribution data and concentration field distribution data of the in-situ leaching process for the target ionic rare earth ore are obtained, including:

[0012] Inputting the leaching agent property information, the rare earth ore property information and the operating condition parameters into the saturated seepage module for seepage analysis, and obtaining seepage field distribution data for the in-situ leaching process of the target ionic rare earth ore, wherein the seepage field distribution data includes the head distribution data of groundwater in the target ionic rare earth ore, and the seepage velocity distribution data of the leaching agent in the target ionic rare earth ore;

[0013] Inputting the seepage velocity distribution data into the solute transport module for convection-diffusion analysis to obtain the initial concentration field distribution data of the target ionic rare earth ore before the ion exchange reaction occurs;

[0014] The initial concentration field distribution data is input into the ion exchange reaction module for ion exchange to obtain concentration field distribution data for an in-situ leaching process of the target ionic rare earth ore.

[0015] Optionally, the seepage field distribution data and the data corresponding to the liquid collecting channel in the concentration field distribution data are input as initial parameters into the preset hydrometallurgical treatment model for analysis and processing to obtain predicted process operation parameter information for the hydrometallurgical treatment process of the leaching mother liquor, including:

[0016] Based on the data corresponding to the liquid collecting tunnel extracted from the seepage field distribution data and the concentration field distribution data, determining the flow data, ammonium ion concentration data, and rare earth ion concentration data of the leaching mother solution during the in-situ leaching of the target ionic rare earth ore;

[0017] The flow data, ammonium ion concentration data, and rare earth ion concentration data of the leaching mother liquor are input into the preset hydrometallurgical treatment model for analysis and processing to obtain predicted process operation parameter information for the hydrometallurgical treatment process of the leaching mother liquor.

[0018] Optionally, the determining of the flow data of the leaching mother solution, the ammonium ion concentration data, and the rare earth ion concentration data in the in-situ leaching process of the target ionic rare earth ore based on the data corresponding to the liquid collecting tunnel extracted from the seepage field distribution data and the concentration field distribution data includes:

[0019] In the target ionic rare earth ore, based on the structural data of the liquid collecting tunnel, a leaching surface corresponding to the leaching mother solution is determined, and a leaching surface area corresponding to the leaching surface is determined;

[0020] Determining the seepage surface velocity data of the leaching surface based on the seepage velocity distribution data in the seepage field distribution data;

[0021] Multiplying the leaching surface area by the seepage surface velocity data to obtain flow data of leaching mother liquor during in-situ leaching of the target ionic rare earth ore;

[0022] Based on the concentration field distribution data and the structure data of the liquid collecting tunnel, the ammonium ion concentration data and the rare earth ion concentration data of the leaching mother liquor during the in-situ leaching of the target ionic rare earth ore are determined.

[0023] Optionally, the hydrometallurgical treatment process includes: an impurity removal process, a precipitation process, and a liquid preparation process. The flow data, ammonium ion concentration data, and rare earth ion concentration data of the leaching mother liquor are input into the preset hydrometallurgical treatment model for analysis and processing to obtain predicted process operation parameter information for the hydrometallurgical treatment process of the leaching mother liquor, including:

[0024] The flow rate data, ammonium ion concentration data, and rare earth ion concentration data of the leaching mother liquor are input as initial parameters into the preset hydrometallurgical treatment model for analysis and processing to obtain the predicted impurity removal process operation parameter information for the impurity removal process of the leaching mother liquor;

[0025] Determine the predicted precipitation process operating parameter information for the precipitation process of the leaching mother liquor after the impurity removal treatment based on the flow data, ammonium ion concentration data, rare earth ion concentration data and the predicted impurity removal process operating parameters of the leaching mother liquor;

[0026] Based on the flow data, ammonium ion concentration data, rare earth ion concentration data of the leaching mother liquor and the predicted precipitation process operation parameter information, the predicted liquid preparation process operation parameter information for the liquid preparation process of the leaching mother liquor after precipitation treatment is determined.

[0027] Optionally, the predicted process operation parameter information includes: predicted impurity removal process operation parameter information, predicted precipitation process operation parameter information, and predicted liquid preparation process operation parameter information; based on the predicted process operation parameter information, the leaching mother liquor is subjected to hydrometallurgical treatment to obtain the ionic rare earth elements leached from the target ionic rare earth ore, including:

[0028] Based on the predicted impurity removal process operation parameter information, the leaching mother liquor is subjected to impurity removal treatment to obtain the impurity-removed leaching mother liquor;

[0029] Based on the predicted precipitation process operation parameter information, the leaching mother liquor after the impurity removal treatment is subjected to precipitation treatment to obtain a supernatant and a precipitate, and ammonium ions are separated from the supernatant, and the precipitate is determined to be an ionic rare earth element;

[0030] Based on the predicted liquid preparation process operation parameter information, the ammonium ions are prepared to obtain a recycling leaching agent, so that the target ionic rare earth ore can be cyclically leached using the recycling leaching agent to obtain ionic rare earth elements cyclically leached in the target ionic rare earth ore.

[0031] Optionally, before inputting the seepage field distribution data and the data corresponding to the liquid collecting channel in the concentration field distribution data as initial parameters into the preset hydrometallurgical treatment model for analysis and processing to obtain predicted process operation parameter information for the hydrometallurgical treatment process of the leaching mother liquor, the method further includes:

[0032] Acquire sample production data, sample sensor data, and sample water-metallurgical equipment data, wherein the sample production data includes sample seepage field distribution data and the concentration field distribution data, and the sample water-metallurgical equipment data includes actual process operation parameters;

[0033] Based on the preset mechanism model, the data model coupling iterative algorithm continuously accumulates sample production data, sample sensor data, and sample water metallurgical equipment data of the sample water metallurgical treatment process, and digitally models all elements and processes of the sample water metallurgical process to obtain the preset water metallurgical treatment model.

[0034] Optionally, the method further comprises:

[0035] Based on the element balance algorithm, the flow direction of the key elements generated by the preset hydrometallurgical treatment model during the hydrometallurgical treatment process is tracked to obtain the element flow direction tracking result;

[0036] Based on the element flow tracking results, the next leaching process corresponding to the preset ionic rare earth element leaching model is determined to achieve two-way coupling between the preset ionic rare earth element leaching model and the preset hydrometallurgical treatment model, and the preset rare earth mine digital twin coupling model of the whole process is corrected according to the ammonia nitrogen analysis data of the leaching mother liquor, and data analysis is performed on the mother liquor collection situation using the liquid collection tunnel and the ammonia nitrogen loss of the leaching agent during the in-situ leaching process.

[0037] Optionally, after the leaching mother liquor is subjected to hydrometallurgical treatment based on the predicted process operation parameter information to obtain the ionic rare earth elements leached from the target ionic rare earth ore, the method further comprises:

[0038] Determining the leaching rate of the ionic rare earth elements in the in-situ leaching process and the recovery rate of the ionic rare earth elements in the hydrometallurgical treatment process;

[0039] The leaching rate of the ionic rare earth elements and the recovery rate of the ionic rare earth elements are sent to the display terminal of the preset rare earth mine digital twin coupling model for display.

[0040] According to a second aspect of the present invention, there is provided an ionic rare earth ore leaching device based on digital twin, comprising:

[0041] A construction unit is used to construct a preset rare earth mine digital twin coupling model based on actual leaching property data corresponding to the target ionic rare earth mine, wherein the preset rare earth mine digital twin coupling model includes a preset ionic rare earth element leaching model and a preset hydrometallurgical treatment model;

[0042] An acquisition unit, configured to obtain, in response to a leaching signal corresponding to a target ionic rare earth ore, property information of a leaching agent used for leaching the target ionic rare earth ore, property information of a rare earth ore corresponding to the target ionic rare earth ore, and operating parameters of an in-situ leaching process for the target ionic rare earth ore;

[0043] A leaching analysis unit, for inputting the leaching agent property information, the rare earth ore property information and the operating condition parameters into the preset ionic rare earth element leaching model to perform leaching process analysis, and obtain seepage field distribution data and concentration field distribution data for the in-situ leaching process of the target ionic rare earth ore;

[0044] A parameter prediction unit, used to input the seepage field distribution data and the data corresponding to the liquid collecting tunnel in the concentration field distribution data as initial parameters into the preset hydrometallurgical treatment model for analysis and processing, and obtain predicted process operation parameter information for the hydrometallurgical treatment process of the leaching mother liquor, wherein the leaching mother liquor is generated in the in-situ leaching process of the target ionic rare earth ore;

[0045] A hydrometallurgical treatment unit is used to perform hydrometallurgical treatment on the leaching mother liquor based on the predicted process operation parameter information to obtain the ionic rare earth elements leached from the target ionic rare earth ore.

[0046] According to a third aspect of the present invention, there is provided a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned digital twin-based ionic rare earth leaching method.

[0047] According to a fourth aspect of the present invention, there is provided a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned digital twin-based ionic rare earth leaching method when executing the program.

[0048] According to a digital twin-based ionic rare earth ore leaching method, device and storage medium provided by the present invention, compared with the current method of leaching rare earth ores through the experience accumulated by on-site engineering personnel, the present invention constructs a preset rare earth ore digital twin coupling model based on the actual leaching attribute data corresponding to the target ionic rare earth ore, wherein the preset rare earth ore digital twin coupling model includes a preset ionic rare earth element leaching model and a preset hydrometallurgical treatment model; and in response to a leaching signal corresponding to the target ionic rare earth ore, obtains the leaching agent attribute information of the leaching agent used for leaching the target ionic rare earth ore, the rare earth ore attribute information corresponding to the target ionic rare earth ore, and the operating parameters of the in-situ leaching process for the target ionic rare earth ore; then the leaching agent attribute information is used to obtain the target ionic rare earth ore. , the rare earth ore property information and the operating parameters are input into the preset ionic rare earth element leaching model for leaching process analysis to obtain seepage field distribution data and concentration field distribution data for the in-situ leaching process of the target ionic rare earth ore; at the same time, the data corresponding to the liquid collecting channel in the seepage field distribution data and the concentration field distribution data are input into the preset hydrometallurgical treatment model as initial parameters for analysis and processing to obtain predicted process operation parameter information for the hydrometallurgical treatment process of the leaching mother liquor, wherein the leaching mother liquor is generated in the in-situ leaching process of the target ionic rare earth ore; finally, based on the predicted process operation parameter information, the leaching mother liquor is hydrometallurgically treated to obtain the ionic rare earth elements leached from the target ionic rare earth ore. Therefore, by constructing a preset rare earth mine digital twin coupling model to digitally present the entire mining process of the rare earth mine, the actual leaching process can be observed in real time, and the actual leaching data at each moment and each process can be collected in real time. The actual leaching data is analyzed by using a preset ionic rare earth element leaching model to obtain the seepage field distribution data and concentration field distribution data of the leaching process. At the same time, the seepage field distribution data and the concentration field distribution data obtained by analyzing the preset ionic rare earth element leaching model are input as initial parameters into the preset hydrometallurgical treatment model for analysis to obtain the predicted process operation parameter information of the hydrometallurgical treatment process. Finally, the leaching mother liquor is hydrometallurgically treated according to the predicted process operation parameter information to obtain the ionic rare earth elements leached from the rare earth ore, thereby improving the acquisition rate and leaching efficiency of rare earth elements in the rare earth ore. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0050] Figure 1A flow chart of a method for leaching an ionic rare earth ore based on digital twins provided in an embodiment of the present invention is shown;

[0051] Figure 2 Another flow chart of an ionic rare earth ore leaching method based on digital twins provided in an embodiment of the present invention is shown;

[0052] Figure 3 A schematic structural diagram of an ionic rare earth ore leaching device based on digital twins provided in an embodiment of the present invention is shown;

[0053] Figure 4 A schematic structural diagram of another ionic rare earth ore leaching device based on digital twin provided in an embodiment of the present invention is shown;

[0054] Figure 5 A schematic diagram of the physical structure of a computer device provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0055] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other without conflict.

[0056] At present, the leaching method of rare earth ore is carried out through the experience accumulated by on-site engineering personnel. However, this leaching method will lead to a low leaching rate of rare earth elements in rare earth ore. At the same time, this artificial leaching method will lead to a low leaching efficiency of rare earth elements in rare earth ore.

[0057] In order to solve the above problems, the embodiment of the present invention provides an ionic rare earth ore leaching method based on digital twin, such as Figure 1 As shown, the method includes:

[0058] 101. Based on the actual leaching property data corresponding to the target ionic rare earth ore, a preset rare earth ore digital twin coupling model is constructed, wherein the preset rare earth ore digital twin coupling model includes a preset ionic rare earth element leaching model and a preset hydrometallurgical treatment model.

[0059] Among them, the actual leaching attribute data refers to the data corresponding to each process of leaching. The actual leaching attribute data can realize the digital simulation of the entire leaching process. The actual leaching attribute data include: three-dimensional data of the ore body corresponding to the target ionic rare earth ore, three-dimensional data of the impurity transfer pool, impurity removal pool, sedimentation transfer pool, sedimentation pool, incubation pool, filter press, liquid preparation transfer pool, liquid preparation pool, etc., and working parameter data in the actual leaching process (including the tools used in the leaching process, the corresponding usage parameters and three-dimensional data of each tool, the selection of the injection system, the selection of the liquid collection system, the initial grade distribution, the initial groundwater level distribution and other boundary conditions); the preset rare earth mine digital twin coupling model is a model that presents the leaching physical objects and processes in a digital space in a digital way, simulating the leaching method and characteristics in a real environment.

[0060] For the embodiment of the present invention, after obtaining the actual leaching property data corresponding to the target ionic rare earth ore, the three-dimensional ore body data in the actual leaching property data is first used to construct a three-dimensional twin ore body model of the in-situ leaching process of the target ionic rare earth ore, wherein the three-dimensional ore body data is obtained based on the geological exploration data of the actual leaching mining area, and the three-dimensional twin ore body model includes a residual slope accumulation layer, a fully weathered layer, a semi-weathered layer, a slightly weathered layer, etc., as well as an independent liquid collection tunnel model, etc., and a preset ionic rare earth element leaching model is constructed according to the leaching process data in the actual leaching property data (such as the working parameter data of the leaching process, the various equipment used in the leaching process and its corresponding equipment parameters and other data related to the leaching process). At the same time, using the actual hydrometallurgical process, an impurity removal transfer pool, an impurity removal pool, a sedimentation transfer pool, a sedimentation pool, an incubation pool, a filter press, The intelligent body model of the main hydrometallurgical links such as the liquid transfer pool and the liquid distribution pool, that is, the preset hydrometallurgical treatment model, is composed of the above-mentioned preset ionic rare earth element leaching model, the preset hydrometallurgical treatment model, etc., which constitutes the preset rare earth mine digital twin coupling model. Then, the preset rare earth mine digital twin coupling model is used to digitally present the entire mining process of the rare earth mine, so that the actual leaching and hydrometallurgical production process can be observed in real time, and the actual leaching and hydrometallurgical production data at each time and each process can be collected in real time. By digitally expressing and modeling the leaching and hydrometallurgical production process, guidance and predictive analysis can be provided for the actual leaching and hydrometallurgical production process, and then in the actual leaching and hydrometallurgical production process, accidents such as ore body slope collapse and leaching liquid leakage can be effectively prevented, and ecological damage around the mine can be reduced, so as to achieve sustainable mining of minerals and sustainable ecological development. The embodiment of the present invention is mainly applicable to the scene of leaching and hydrometallurgical production of rare earth elements in rare earth mines. The execution subject of the embodiment of the present invention is a device or equipment capable of leaching and hydrometallurgical production of rare earth elements in rare earth mines, which can be specifically set on the server side.

[0061] 102. In response to a leaching signal corresponding to a target ionic rare earth ore, obtain leaching agent property information of a leaching agent used for leaching the target ionic rare earth ore, rare earth ore property information corresponding to the target ionic rare earth ore, and operating parameters of an in-situ leaching process for the target ionic rare earth ore.

[0062] Among them, the leaching agent attribute information includes the type, specification, concentration and other information of the leaching agent. For example, the leaching agent can be ammonium salt (ammonium sulfate), magnesium salt and calcium salt; the rare earth ore attribute information includes the three-dimensional information of the rare earth ore, the permeability coefficient of the rare earth ore along the x, y and z directions, the unit water storage coefficient of the ore body, the hydrodynamic diffusion coefficient of the ore body, the porosity of the ore body, the concentration values ​​of different substances in the ore body, etc. The operating parameters include: the coordinate information of the leaching process (with x, y and z as the three-dimensional axis coordinates), the leaching time information, etc.

[0063] For the embodiment of the present invention, when the leaching agent is injected into the injection hole of the target ionic rare earth ore, the leaching signal is triggered. When the leaching signal is received, the leaching agent property information of the leaching agent used for leaching the target ionic rare earth ore, the rare earth ore property information corresponding to the target ionic rare earth ore, and the operating parameters of the in-situ leaching process for the target ionic rare earth ore are collected. Then, the leaching agent property information, the rare earth ore property information and the operating parameters are input into the preset ionic rare earth element leaching model to analyze the leaching process, and the seepage field distribution data and the concentration field distribution data of the in-situ leaching process for the target ionic rare earth ore are obtained. According to the data corresponding to the liquid collecting tunnel in the seepage field distribution data and the concentration field distribution data, the predicted process operation parameter information of the hydrometallurgical treatment process of the leaching mother liquor is determined. Finally, the inlet and outlet mother liquors are hydrometallurgically treated using the predicted process operation parameter information to obtain rare earth elements. The actual leaching data is analyzed using a preset ionic rare earth element leaching model to obtain flow data, ammonium ion concentration data, and rare earth ion concentration data of the leaching mother liquor. At the same time, the flow data, ammonium ion concentration data, and rare earth ion concentration data of the leaching mother liquor obtained by analyzing the preset ionic rare earth element leaching model are input as initial parameters into the preset hydrometallurgical treatment model for analysis through data coupling to obtain the predicted process operation parameter information of the hydrometallurgical treatment process. Finally, the leaching mother liquor is hydrometallurgically treated according to the predicted process operation parameter information to obtain the ionic rare earth elements leached from the rare earth ore, thereby improving the acquisition rate and leaching efficiency of rare earth elements in the rare earth ore.

[0064] 103. Input the leaching agent property information, rare earth ore property information and operating parameters into a preset ionic rare earth element leaching model to analyze the leaching process, and obtain the seepage field distribution data and concentration field distribution data of the in-situ leaching process for the target ionic rare earth ore.

[0065] Among them, the seepage field distribution data refers to the head distribution data and seepage velocity distribution data of the seepage of the leaching agent; the concentration field distribution data refers to the concentration field distribution data that describes the solute migration process and ion exchange process during the leaching process, that is, the concentration field data of ammonium ions and rare earth ions in the ore body and the leaching mother liquor.

[0066] For the embodiment of the present invention, a mathematical mechanism twin model of the in-situ leaching process of ionic rare earth ores is constructed. The in-situ leaching process of the target ionic rare earth ores includes: the seepage process of the leaching agent in the porous medium ore body, the solute migration process, and the physical-chemical process of the chemical reaction, wherein the essence of the chemical reaction is the reversible ion exchange process between the solid phase and the liquid phase. Therefore, the working process of the preset ionic rare earth element leaching model constructed includes: the saturated seepage process of the leaching agent, the convection diffusion process of the solute ions, and the ion exchange reaction process of the solute ions. The embodiment of the present invention performs twin mapping on various processes and parameters of leaching, including: boundary conditions such as the selection of the injection system, the selection of the liquid collection system, the initial grade distribution, the initial groundwater level distribution, and related parameters, including: twin mapping of physical parameters such as the concentration of the leaching agent and the permeability coefficient of the formation, and develops an autonomous solution program through the data of the twin mapping, and then performs the solution to obtain data information of the in-situ leaching process of the ionic rare earth ore to achieve digital simulation. The solution program is developed by programming using the finite element method, and then the solution program in the preset ionic rare earth element leaching model can be used to digitally express and model the groundwater seepage field, ion exchange reaction field, and solute migration field in the in-situ leaching process of the ionic rare earth ore, and can provide guidance and predictive analysis for the actual leaching process, and then effectively prevent accidents such as ore body slope collapse and leaching liquid leakage in the actual generation process, reduce ecological damage around the mine, and achieve sustainable mining of minerals and sustainable ecological development. Furthermore, a preset ionic rare earth element leaching model is used to analyze the logical relationship between the three processes of seepage, solute transport and ion exchange reaction. The saturated seepage mathematical equation, the convection-diffusion mathematical equation and the equilibrium ion exchange reaction equation are combined to solve the mechanism model, and the seepage field distribution data and concentration field distribution data of the in-situ leaching process of the target ionic rare earth ore are obtained. Then, the data corresponding to the liquid collecting channel in the seepage field distribution data and the concentration field distribution data are input as initial parameters into the preset hydrometallurgical treatment model for analysis, and the predicted process operation parameter information of the hydrometallurgical treatment process is obtained. Finally, the leaching mother liquor is hydrometallurgically treated according to the predicted process operation parameter information to obtain the ionic rare earth elements leached from the rare earth ore, thereby improving the recovery rate and leaching efficiency of rare earth elements in the rare earth ore.

[0067] 104. The data corresponding to the liquid collecting tunnel in the seepage field distribution data and the concentration field distribution data are input as initial parameters into the preset hydrometallurgical treatment model for analysis and processing to obtain the predicted process operation parameter information for the hydrometallurgical treatment process of the leaching mother liquor, wherein the leaching mother liquor is produced in the in-situ leaching process of the target ionic rare earth ore.

[0068] Among them, the leaching mother liquor refers to the liquid obtained by exchanging and leaching the rare earth elements in the ion adsorption state from the target ionic rare earth ore body with the leaching agent; the preset hydrometallurgical treatment model is a hydrometallurgical treatment full-process data model established by digitizing the relevant hydrometallurgical production processes to realize dynamic simulation based on material flow and information flow. The core process flow of the preset hydrometallurgical treatment model is: impurity removal, precipitation, etc. The preset hydrometallurgical treatment model mainly includes: equipment model unit, operating parameter data mapping unit, hydrometallurgical process flow logic unit, data model unit, data information unit, model display unit. The equipment model unit is an equipment intelligent body model constructed based on the properties of the equipment used in each process of the hydrometallurgical process, including the impurity removal tank, sedimentation tank, and liquid preparation tank. The operating parameter data mapping unit is based on the constructed equipment intelligent body model to perform data mapping of the operating parameters of the hydrometallurgical treatment process, including: mother liquor flow rate of the leaching mother liquor, rare earth ion The hydrometallurgical process logic unit is based on the actual hydrometallurgical treatment process. For the core procedures, the corresponding process logic is constructed, such as mother liquor-removal of impurities-precipitation-incubation-liquid preparation. The data model unit is based on the constructed equipment intelligent body and process logic. A digital process model of the entire ionic rare earth mine hydrometallurgical production process from leaching mother liquor input to rare earth product output is established, and multiple factors such as material flow and information flow are digitally coupled and connected. The data information unit and the model display unit can quantitatively analyze the flow of key elements and substances, such as ammonium ions and rare earth ions, through the hydrometallurgical process data model, to understand and master the flow direction and flow of substances in the complete hydrometallurgical production process, and improve resource utilization efficiency and life efficiency. The distribution of ammonium ion flow generated by the data information unit provides data support for the model coupling from hydrometallurgical treatment to in-situ leaching, and ultimately realizes dynamic spatiotemporal monitoring of hydrometallurgy, integrates the main production processes such as in-situ leaching, impurity removal, precipitation, and liquid preparation, and constructs a full-process digital twin mine coupling model and system.

[0069] The predicted process operation parameter information includes: the liquid level value, pH value, amount of ammonium carbonate added in the impurity removal process, the liquid level value, pH value, amount of ammonium carbonate added in the precipitation process, the liquid level value, pH value, amount of ammonium carbonate added in the incubation process, and the liquid level value, pH value, amount of ammonium sulfate added in the liquid preparation process.

[0070] For the embodiments of the present invention, in order to improve the prediction accuracy of the preset hydrometallurgical treatment model, it is first necessary to construct the preset hydrometallurgical treatment model. Based on this, the method includes: obtaining sample production data, sample sensor data, and sample hydrometallurgical equipment data, wherein the sample production data includes sample seepage field distribution data and the concentration field distribution data, and the sample hydrometallurgical equipment data includes actual process operation parameters; based on the preset mechanism model, the data model is coupled with an iterative algorithm to continuously accumulate the sample production data, sample sensor data, and sample hydrometallurgical equipment data of the sample hydrometallurgical treatment process, and digital modeling is performed on all elements and the entire process of the sample hydrometallurgical process to obtain the preset hydrometallurgical treatment model.

[0071] Specifically, a preset mechanism model is constructed in advance, and sample production data, sample sensor data, and sample water metallurgical equipment data are obtained, wherein the sample production data and the sample sensor include sample seepage field distribution data and concentration field distribution data, and the sample water metallurgical equipment data include actual process operation parameters. First, the sample seepage field distribution data and the concentration field distribution data and other data are input into the preset mechanism model to obtain predicted process parameters, and then the loss function is determined according to the predicted process parameters and the actual process operation parameters. Finally, according to the loss function, the model parameters in the preset mechanism model are iteratively trained until the preset mechanism model with the prediction accuracy meeting the requirements is obtained. At this time, the preset mechanism model with the prediction accuracy meeting the requirements is determined as the preset water metallurgical treatment model.

[0072] For the embodiment of the present invention, after obtaining the seepage field distribution data and the concentration field distribution data, the data corresponding to the liquid collecting tunnel can be directly extracted from the seepage field distribution data and the concentration field distribution data, and the seepage field distribution data and the concentration field distribution data are converted into the flow data, ammonium ion concentration data and rare earth ion concentration data of the leaching mother liquor according to the physical model of the liquid collecting tunnel and the actual leaching arrangement through data coupling, and the flow data, ammonium ion concentration data and rare earth ion concentration data are passed as initial parameters to the preset hydrometallurgical treatment model, so as to provide a method for the process from in situ leaching to hydrometallurgy. The model coupling of the treatment provides data support, and then the flow data, ammonium ion concentration data and rare earth ion concentration data are analyzed by using the preset hydrometallurgical treatment model, so as to obtain the predicted process operation parameter information of the hydrometallurgical treatment process for the leaching mother liquor, and finally the predicted process operation parameter information is used to carry out hydrometallurgical treatment on the leaching mother liquor to obtain the ionic rare earth elements leached in the target ionic rare earth ore. Therefore, through the two-way coupling of the preset ionic rare earth element leaching model and the preset hydrometallurgical treatment model, the leaching rate and leaching efficiency of the ionic rare earth elements in the rare earth ore can be improved.

[0073] 105. Based on the predicted process operation parameter information, the leaching mother liquor is subjected to hydrometallurgical treatment to obtain the ionic rare earth elements leached from the target ionic rare earth ore.

[0074] According to the embodiment of the present invention, after obtaining the predicted process operation parameter information of the hydrometallurgical treatment process of the leaching mother liquor, the leaching mother liquor is subjected to hydrometallurgical treatment such as impurity removal, precipitation, incubation, and liquid preparation based on the predicted process operation parameter information to finally obtain ionic rare earth elements. Therefore, by constructing a preset rare earth mine digital twin coupling model to digitally present the entire mining process of the rare earth mine, the actual leaching and hydrometallurgical production processes can be observed in real time, and the actual leaching data and hydrometallurgical production data at each time and each process can be collected in real time. By using the actual leaching and hydrometallurgical production data, the actual leaching data is analyzed by a preset ionic rare earth element leaching model to obtain the seepage field distribution data and concentration field distribution data of the leaching mother liquor. At the same time, the seepage field distribution data and concentration field distribution data of the leaching mother liquor obtained by analyzing the preset ionic rare earth element leaching model are input as initial parameters into the preset hydrometallurgical treatment model for analysis through data coupling to obtain the predicted process operation parameter information of the hydrometallurgical treatment process. Finally, the leaching mother liquor is hydrometallurgically treated according to the predicted process operation parameter information to obtain the ionic rare earth elements leached from the rare earth ore, thereby improving the acquisition rate and leaching efficiency of rare earth elements in the rare earth ore.

[0075] Furthermore, multiple elements are involved in the hydrometallurgical treatment process. In order to track the flow of multiple elements, the method includes: based on the element balance algorithm, tracking the flow of key elements generated by the preset hydrometallurgical treatment model during the hydrometallurgical treatment process to obtain element flow tracking results; based on the element flow tracking results, determining the next leaching process corresponding to the preset ionic rare earth element leaching model, so as to achieve two-way coupling between the preset ionic rare earth element leaching model and the preset hydrometallurgical treatment model, and correcting the digital twin coupling model of the preset rare earth mine in the whole process according to the ammonia nitrogen analysis data of the leaching mother liquor, and performing data analysis on the collection of the mother liquor using the liquid collection tunnel and the loss of ammonia and nitrogen of the leaching agent during the in-situ leaching process.

[0076] Specifically, the hydrometallurgical process will produce rare earth ions, ammonium ions, impurity ions, etc. Among them, rare earth ions and ammonium ions are key elements, and their flow direction and flow rate must be tracked, because ammonium ions can be converted into new leaching agents through liquid preparation during the hydrometallurgical process, and the new leaching agents can be returned to the original leaching injection system for recycling, realizing the model coupling from hydrometallurgical treatment to in-situ leaching. At the same time, the digital twin coupling model of rare earth ore preset in the whole process is corrected according to the ammonia nitrogen analysis data of the leaching mother liquor, and data analysis is conducted on the mother liquor collection using the liquid collection tunnel and the ammonia nitrogen loss of the leaching agent during the in-situ leaching process.

[0077] Furthermore, in order to enable the staff to observe the leaching situation in real time, the embodiment of the present invention also includes: determining the leaching rate of ionic rare earth elements in the in-situ leaching process and the recovery rate of ionic rare earth elements in the hydrometallurgical treatment process; sending the leaching rate of ionic rare earth elements and the recovery rate of ionic rare earth elements to the display terminal of the preset rare earth mine digital twin coupling model for display.

[0078] Specifically, during the leaching process, the leaching rate of ionic rare earth elements is calculated in real time, and during the hydrometallurgical treatment process, the recovery rate of ionic rare earth elements is recorded in real time. The leaching rate of ionic rare earth elements and the recovery rate of ionic rare earth elements are sent to the display terminal of the preset rare earth mine digital twin coupling model for display, so that the staff can understand the leaching effect in real time and make corresponding adjustments to the leaching process when necessary.

[0079] The embodiment of the present invention establishes a preset rare earth mine digital twin coupling model, that is, a mechanism model of the entire process of in-situ leaching of ionic rare earth mines, which can realize dynamic visual simulation, management and control of the entire process of in-situ leaching of ionic rare earth mines, and perform simulation calculations of the leaching process. At the same time, by guiding the layout of the injection system and the liquid collection system, the penetration range of the leaching agent is stabilized and reasonable, the injection blind area is reduced, the rare earth resources are effectively utilized, and the comprehensive resource recovery rate is improved, thereby improving the acquisition rate of rare earth elements in rare earth mines and the leaching efficiency.

[0080] The embodiment of the present invention realizes digital modeling of the entire process of hydrometallurgical treatment of leaching mother liquor by constructing a preset hydrometallurgical treatment model, solves the problem of isolated management of each process in the existing hydrometallurgical process, and thus can improve the efficiency and collaboration of hydrometallurgical treatment.

[0081] The embodiment of the present invention solves the mathematical solution of the mechanism model of the whole process of in-situ leaching, forms an independently written mathematical solution program, thereby realizing the digital simulation of the in-situ leaching process of ionic rare earth ores, and can provide guiding opinions and predictive analysis for the actual leaching process.

[0082] The embodiment of the present invention tracks the flow of elements in the entire process and arranges a suitable liquid collection system at a suitable position to reduce the unorganized leakage of the leaching mother liquor, improve the resource recovery rate, and reduce pollution to the mining environment.

[0083] According to a digital twin-based ionic rare earth ore leaching method provided by the present invention, compared with the current method of leaching rare earth ores through the experience accumulated by on-site engineering personnel, the present invention constructs a preset rare earth ore digital twin coupling model based on the actual leaching property data corresponding to the target ionic rare earth ore, wherein the preset rare earth ore digital twin coupling model includes a preset ionic rare earth element leaching model and a preset hydrometallurgical treatment model; and in response to the leaching signal corresponding to the target ionic rare earth ore, obtains the leaching agent property information of the leaching agent used for leaching the target ionic rare earth ore, the rare earth ore property information corresponding to the target ionic rare earth ore, and the operating parameters of the in-situ leaching process for the target ionic rare earth ore; then the leaching agent property information, the rare earth ore property information, the rare earth ore property information, the operating parameters of the in-situ leaching process for the target ionic rare earth ore, and the operating parameters of the in-situ leaching process for the target ionic rare earth ore. The soil ore property information and the operating parameters are input into the preset ionic rare earth element leaching model for leaching process analysis to obtain the seepage field distribution data and concentration field distribution data for the in-situ leaching process of the target ionic rare earth ore; at the same time, the data corresponding to the liquid collecting channel in the seepage field distribution data and the concentration field distribution data are input into the preset hydrometallurgical treatment model as initial parameters for analysis and processing to obtain the predicted process operation parameter information for the hydrometallurgical treatment process of the leaching mother liquor, wherein the leaching mother liquor is generated in the in-situ leaching process of the target ionic rare earth ore; finally, based on the predicted process operation parameter information, the leaching mother liquor is hydrometallurgically treated to obtain the ionic rare earth elements leached from the target ionic rare earth ore. Therefore, by constructing a preset rare earth mine digital twin coupling model to digitally present the entire mining process of the rare earth mine, the actual leaching and hydrometallurgical production processes can be observed in real time, and the actual leaching data and hydrometallurgical production data at each time and each process can be collected in real time. The actual leaching data and hydrometallurgical production data are analyzed by using the preset ionic rare earth element leaching model to obtain the seepage field distribution data and concentration field distribution data of the leaching process. At the same time, the seepage field distribution data and concentration field distribution data of the leaching mother liquor obtained by the preset ionic rare earth element leaching model are input into the preset hydrometallurgical treatment model for analysis through data coupling to obtain the predicted process operation parameter information of the hydrometallurgical treatment process. Finally, the leaching mother liquor is hydrometallurgically treated according to the predicted process operation parameter information to obtain the ionic rare earth elements leached from the rare earth ore, thereby improving the acquisition rate and leaching efficiency of rare earth elements in the rare earth ore.

[0084] Further, in order to better illustrate the above-mentioned process of leaching ionic rare earth ores, as a refinement and extension of the above-mentioned embodiment, the embodiment of the present invention provides another ionic rare earth ores leaching method based on digital twins, such as Figure 2 As shown, the method includes:

[0085] 201. Based on the actual leaching property data corresponding to the target ionic rare earth ore, a preset rare earth ore digital twin coupling model is constructed, wherein the preset rare earth ore digital twin coupling model includes a preset ionic rare earth element leaching model and a preset hydrometallurgical treatment model.

[0086] Specifically, the entire rare earth mine, leaching process and leaching data of the target ionic rare earth mine are twin mapped to construct a preset rare earth mine digital twin coupling model, which integrates the key core production processes of in-situ leaching and hydrometallurgical process (impurity removal, precipitation and filtration press). The preset ionic rare earth element leaching model in the preset rare earth mine digital twin coupling model includes: leaching agent seepage process, solute migration process and ion exchange process, etc. The preset hydrometallurgical treatment model in the preset rare earth mine digital twin coupling model is based on the preset ionic rare earth element leaching model, and the data model coupling and iterative method realizes the digital modeling of all elements and the whole process of the hydrometallurgical process by continuously accumulating the production data of the hydrometallurgical treatment process. The construction of the preset hydrometallurgical treatment model makes full use of production data, sensor data, equipment data and other automation data, and maps based on actual leaching conditions and parameters to realize the twin interaction of physical process and digital process and the correction of the coupling model.

[0087] 202. In response to a leaching signal corresponding to a target ionic rare earth ore, obtain leaching agent property information of a leaching agent used for leaching the target ionic rare earth ore, rare earth ore property information corresponding to the target ionic rare earth ore, and operating parameters of an in-situ leaching process for the target ionic rare earth ore.

[0088] Specifically, after receiving a leaching signal for leaching a target ionic rare earth ore, sensors and other devices are used to obtain the leaching agent property information of the leaching agent used for leaching the target ionic rare earth ore, the rare earth ore property information corresponding to the target ionic rare earth ore, and the operating parameters of the in-situ leaching process for the target ionic rare earth ore. Thereafter, the above information is analyzed using a preset ionic rare earth element leaching model, and the seepage field distribution data and concentration field distribution data of the in-situ leaching process for the target ionic rare earth ore can be obtained.

[0089] 203. Input the leaching agent property information, rare earth ore property information and operating parameters into a preset ionic rare earth element leaching model to analyze the leaching process, and obtain the seepage field distribution data and concentration field distribution data of the in-situ leaching process for the target ionic rare earth ore.

[0090] Among them, the preset ionic rare earth element leaching model includes a saturated seepage module, a solute transport module, and an ion exchange reaction module.

[0091] For the embodiment of the present invention, after obtaining the leaching agent property information, the rare earth ore property information and the operating parameters, it is necessary to use a preset ionic rare earth element leaching model based on the above information to predict the seepage field distribution data and concentration field distribution data of the in-situ leaching process for the target ionic rare earth ore. Based on this, step 203 specifically includes: inputting the leaching agent property information, the rare earth ore property information and the operating parameters into the saturated seepage module for seepage analysis, and obtaining the seepage field distribution data of the in-situ leaching process for the target ionic rare earth ore. In the method, the seepage field distribution data includes the head distribution data of groundwater in the target ionic rare earth ore and the seepage velocity distribution data of the leaching agent in the target ionic rare earth ore; the seepage velocity distribution data is input into the solute transport module for convection-diffusion analysis to obtain the initial concentration field distribution data of the target ionic rare earth ore before the ion exchange reaction occurs; the initial concentration field distribution data is input into the ion exchange reaction module for ion exchange to obtain the concentration field distribution data of the in-situ leaching process for the target ionic rare earth ore.

[0092] Among them, the saturated seepage module contains the saturated seepage equation of the leaching agent, the solute transport module contains the convection diffusion equation of the solute ions, and the ion exchange reaction module contains the ion exchange reaction equation of the solute ions.

[0093] Specifically, firstly, the leaching agent property information (ammonium ion concentration and rare earth ion concentration in the leaching agent), the rare earth ore property information (the permeability coefficient of the ore body along the x, y, and z directions, the unit water storage coefficient of the ore body, the porosity of the ore body, the hydrodynamic diffusion coefficient of the ore body, the concentration values ​​of different substances in the rare earth ore before leaching, etc.), and the working condition parameters (three-dimensional axis coordinate information, leaching time, etc.) are input into the saturated seepage module for seepage analysis. The saturated seepage equation in the saturated seepage module is used to calculate the seepage field distribution data of the leaching mother liquor in the in-situ leaching process for the target ionic rare earth ore, wherein the saturated seepage equation is as follows:

[0094]

[0095] Among them, x, y, z are the three-dimensional axis coordinates, t is the leaching time, h is the water head distribution in the ionic rare earth ore body, K xx , K yy , K zz are the permeability coefficients of the ore body along the x, y, and z directions, respectively, S sis the unit water storage coefficient of the ore body. Using the above saturated seepage equation, the water head distribution data in the ionic rare earth ore body can be calculated. Then, the following formula is used to calculate the seepage velocity distribution data of the leaching agent in the target ionic rare earth ore:

[0096]

[0097] Among them, represents the seepage velocity distribution data. Thus, using the above formula, the seepage velocity distribution data of the leaching agent in the target ionic rare earth ore can be calculated. The seepage field distribution data is composed of the water head distribution data and the seepage velocity distribution data.

[0098] Furthermore, the seepage field distribution data is input into the solute transport module for convective-dispersion analysis. Through the convective-dispersion equation in the solute transport module, the initial concentration field distribution data of the target ionic rare earth ore before the ion exchange reaction is calculated. Among them, the convective-dispersion equation is as follows:

[0099]

[0100]

[0101] Among them, before indicates the initial concentration field distribution data of ammonium ions in the leaching agent before the ion exchange reaction, before indicates the initial concentration field distribution data of rare earth ions in the leaching agent before the ion exchange reaction. D is the hydrodynamic dispersion coefficient of the ore body, and n is the porosity of the ore body, represents the seepage velocity distribution data. Thus, the initial concentration field distribution data of the target ionic rare earth ore before the ion exchange reaction can be calculated through the above equation.

[0102] Furthermore, the seepage field distribution data is input into the ion exchange reaction module for ion exchange. Through the ion exchange reaction equation in the ion exchange reaction module, the concentration field distribution data for the in-situ leaching process of the target ionic rare earth ore is calculated. Among them, the ion exchange reaction equation is as follows:

[0103]

[0104] Among them, after indicates the concentration field distribution data of the leaching mother liquor, before indicates the initial concentration field distribution data of ammonium ions in the leaching agent before the ion exchange reaction, before indicates the initial concentration field distribution data of rare earth ions in the leaching agent before the ion exchange reaction, K kis the thermodynamic equilibrium constant of the ion exchange reaction, and a and b are calculation constants. |before represents the concentration value of ammonium ions in the rare earth ore before leaching. |before represents the concentration value of rare earth ions in the rare earth ore before leaching. Thus, the concentration field distribution data of the leaching mother liquor in the in-situ leaching process of the target ionic rare earth ore can be calculated through the above equation.

[0105] 204. Based on the data corresponding to the collecting roadway extracted from the seepage field distribution data and the concentration field distribution data, determine the flow rate data, ammonium ion concentration data, and rare earth ion concentration data of the leaching mother liquor in the in-situ leaching process of the target ionic rare earth ore.

[0106] Specifically, after determining the seepage field distribution data and the concentration field distribution data, the data corresponding to the collecting roadway (i.e., the seepage field distribution data and the concentration field distribution data corresponding to the collecting roadway) can be directly extracted. By mapping, processing, and extracting the obtained seepage field distribution data and concentration field distribution data according to the physical model of the collecting roadway, the flow rate data, ammonium ion concentration data, and rare earth ion concentration data of the leaching mother liquor can be obtained. The process of specifically obtaining the flow rate data, ammonium ion concentration data, and rare earth ion concentration data of the leaching mother liquor by using the physical model of the collecting roadway includes: in the target ionic rare earth ore, determining the leaching surface corresponding to the leaching mother liquor based on the structure of the collecting roadway, and determining the area of the leaching surface corresponding to the leaching surface; based on the seepage velocity distribution data in the seepage field distribution data, determining the seepage surface velocity data of the leaching surface; multiplying the area of the leaching surface by the seepage surface velocity data to obtain the flow rate data of the leaching mother liquor in the in-situ leaching process of the target ionic rare earth ore; based on the concentration field distribution data and the structure of the collecting roadway, determining the ammonium ion concentration data and rare earth ion concentration data of the leaching mother liquor in the in-situ leaching process of the target ionic rare earth ore.

[0107] Specifically, by obtaining the coupling interface between the physical model of the liquid collection tunnel and the three-dimensional ore body twin model, that is, the leaching surface representing the leaching mother liquor, based on the corresponding coordinate data, the corresponding leaching surface area can be calculated, and based on the seepage velocity distribution data in the seepage field distribution data, the seepage surface velocity data of the leaching surface can be determined, and then the flow data of the leaching mother liquor (m3 / d) = seepage surface area (m2) × seepage surface velocity data (m / d). Further, the ammonium ion concentration data and the rare earth ion concentration data can be directly obtained from the concentration field distribution data describing the solute migration. It should be noted that when the preset rare earth mine digital twin coupling model is calculated, the three-dimensional twin ore body model and the tunnel model will be discretized first to form a discretized model composed of a large number of tiny tetrahedral units, including the corresponding node coordinate information and numbers, and mapped one by one with the physical model, and then the calculated seepage field and concentration field data are stored and mapped based on the nodes. By extracting the nodes corresponding to the tunnel model, the result data mapped by the tunnel model can be extracted, including: seepage head distribution, velocity field distribution, and concentration field distribution of root ions and rare earth ions.

[0108] 205. The flow rate data, ammonium ion concentration data, and rare earth ion concentration data of the leaching mother liquor are input into a preset hydrometallurgical treatment model for analysis and processing to obtain predicted process operation parameter information for the hydrometallurgical treatment process of the leaching mother liquor.

[0109] Among them, the hydrometallurgical treatment process includes: impurity removal process, precipitation process, liquid preparation process, etc. The preset hydrometallurgical treatment model includes an impurity removal parameter prediction module, a precipitation parameter prediction module, a liquid preparation parameter prediction module, etc. For the embodiment of the present invention, a preset initial hydrometallurgical treatment model is first constructed, and then the preset initial hydrometallurgical treatment model is continuously accumulated for the process parameters of each cycle of the hydrometallurgical treatment process (mother liquor, impurity removal, precipitation, incubation, liquid preparation), the overflow at the beginning and end of each cycle, the bottom flow and the composition of the precipitate, and other production data, and the model is continuously optimized to construct a preset hydrometallurgical treatment model. Therefore, according to the leaching mother liquor flow data, ammonium ion concentration data, and rare earth ion concentration data output by the preset ionic rare earth element leaching model, the optimal process operation parameters of each hydrometallurgical cycle can be dynamically planned, and at the same time, by tracking the flow of elements and components, the element balance is constructed, and the actual production process is dynamically tracked.

[0110] For the embodiment of the present invention, after obtaining the flow data, ammonium ion concentration data, and rare earth ion concentration data of the leaching mother liquor, it is necessary to predict the predicted process operation parameter information of the hydrometallurgical treatment process of the leaching mother liquor based on the above data using a preset hydrometallurgical treatment model. Based on this, step 205 specifically includes: inputting the flow data, ammonium ion concentration data, and rare earth ion concentration data of the leaching mother liquor as initial parameters into the preset hydrometallurgical treatment model for analysis and processing to obtain the predicted impurity removal process operation parameter information for the impurity removal process of the leaching mother liquor; based on the flow data, ammonium ion concentration data, rare earth ion concentration data of the leaching mother liquor and the predicted impurity removal process operation parameters, determining the predicted precipitation process operation parameter information for the precipitation process of the leaching mother liquor after the impurity removal treatment; based on the flow data, ammonium ion concentration data, rare earth ion concentration data of the leaching mother liquor and the predicted precipitation process operation parameter information, determining the predicted liquid preparation process operation parameter information for the liquid preparation process of the leaching mother liquor after the precipitation treatment.

[0111] Among them, the preset impurity removal process operation parameter information includes: the liquid level value, pH value, and the amount of ammonium carbonate added during the impurity removal process; the predicted precipitation process operation parameter information includes: the liquid level value, pH value, and the amount of ammonium carbonate added during the precipitation process; the predicted liquid preparation process operation parameter information includes: the liquid level value, pH value, and the amount of ammonium carbonate added during the liquid preparation process.

[0112] Specifically, the flow data, ammonium ion concentration data, and rare earth ion concentration data of the leaching mother liquor are input into the impurity removal parameter prediction module in the preset hydrometallurgical treatment model for analysis and processing, and the predicted impurity removal process operation parameter information for the impurity removal process of the leaching mother liquor is obtained. Then, the flow data, ammonium ion concentration data, rare earth ion concentration data, and predicted impurity removal process operation parameters of the leaching mother liquor are input into the precipitation parameter prediction module for processing, and the predicted precipitation process operation parameter information for the precipitation process of the leaching mother liquor after the impurity removal treatment is obtained. Then, the flow data, ammonium ion concentration data, rare earth ion concentration data, and predicted precipitation process operation parameter information of the leaching mother liquor are input into the liquid preparation parameter prediction module for processing, and the predicted liquid preparation process operation parameter information for the liquid preparation process of the leaching mother liquor after the precipitation treatment is obtained.

[0113] 206. Based on the predicted process operation parameter information, the leaching mother liquor is subjected to hydrometallurgical treatment to obtain the ionic rare earth elements leached from the target ionic rare earth ore.

[0114] For the embodiment of the present invention, after determining the predicted impurity removal process operation parameter information, predicted precipitation process operation parameter information and other operation parameter information of the hydrometallurgical treatment process of the leaching mother liquor, it is necessary to use the above-mentioned operation parameter information to perform hydrometallurgical treatment on the leaching mother liquor. Based on this, step 206 includes: based on the predicted impurity removal process operation parameter information, performing impurity removal treatment on the leaching mother liquor to obtain the leaching mother liquor after impurity removal treatment; based on the predicted precipitation process operation parameter information, performing precipitation treatment on the leaching mother liquor after impurity removal treatment to obtain a supernatant and a precipitate, and separating ammonium ions from the supernatant, and determining the precipitate as an ionic rare earth element.

[0115] Specifically, the leaching mother liquor is subjected to impurity removal treatment by using the predicted impurity removal process operation parameter information to obtain the leaching mother liquor after impurity removal treatment. The chemical reaction equation involved in the impurity removal process is as follows:

[0116] Al2(SO4)3+6NH4HCO3=2Al(OH)3↓+3(NH4)2SO4+6CO2↑

[0117] Furthermore, according to the predicted precipitation process operation parameter information, the leaching mother liquor after impurity removal treatment is subjected to precipitation treatment to obtain a supernatant and a precipitate, and finally ammonium ions are separated from the supernatant. The obtained precipitate is the ionic rare earth element. The chemical reaction equation involved in the precipitation process is as follows:

[0118] Re2(SO4)3+6NH4HCO3=Re2(CO3)3↓+3(NH4)2SO4+3H2CO3

[0119] Furthermore, after obtaining ammonium ions and ionic rare earth elements in the leaching mother liquor, the ionic rare earth elements are stored and the ammonium ions need to be reused. Based on this, the method includes: based on the predicted liquid preparation process operation parameter information, the ammonium ions are subjected to liquid preparation treatment to obtain a recycled leaching agent, so as to utilize the recycled leaching agent to perform a cyclic leaching treatment on the target ionic rare earth ore to obtain the ionic rare earth elements cyclically leached in the target ionic rare earth ore.

[0120] Specifically, ammonium ions are converted into new leaching agents through liquid preparation during the hydrometallurgical process and returned to the in-situ leaching injection system for recycling, thus realizing model coupling from hydrometallurgical treatment to in-situ leaching.

[0121] The embodiment of the present invention realizes the full process tracking of ammonia nitrogen elements and rare earth elements through the bidirectional coupling of a preset ionic rare earth element leaching model and a preset hydrometallurgical treatment model. Based on the full process tracking of ammonia nitrogen elements, the specific flow direction of ammonia nitrogen elements can be analyzed. The specific flow direction of ammonia nitrogen elements includes: recycling through leaching mother liquor within the digital twin mine system, remaining in the ore body through ion exchange, and being lost through the groundwater system. Based on the full process tracking of rare earth elements, the specific flow direction of rare earth elements can be analyzed. The specific flow direction of rare earth elements includes: converting into rare earth carbonate products through leaching mother liquor in the digital twin mine system, being lost through the groundwater system, and remaining in the ore body. Therefore, through the above-mentioned full process and continuous analysis, the rare earth ion leaching rate of the in-situ leaching process, the rare earth element recovery rate of the hydrometallurgical process and the rare earth output rate of the whole process can be improved.

[0122] Furthermore, by constructing a preset ionic rare earth element leaching model and a preset hydrometallurgical treatment model, the groundwater seepage field (head distribution), ion exchange reaction field (ammonium ion concentration distribution), solute migration field (rare earth ion concentration distribution) in the in-situ leaching of ionic rare earth ores, and the impurity removal and precipitation processes in the subsequent hydrometallurgical process can be digitally expressed and modeled. At the same time, the full-process coupling model can be corrected according to the ammonia nitrogen analysis data of the leaching mother liquor, so as to achieve data analysis and display of the mother liquor collection situation in the liquid collection tunnel used and the ammonia nitrogen loss of the leaching agent in the in-situ leaching process. In the actual generation process, it is possible to effectively prevent accidents such as ore slope collapse and leaching liquid leakage, realize sustainable mineral mining and ecological sustainable development, and improve the production efficiency of rare earth products in the hydrometallurgical process.

[0123] According to another ionic rare earth ore leaching method based on digital twins provided by the present invention, compared with the current method of leaching rare earth ores through the experience accumulated by on-site engineering personnel, the present invention constructs a preset rare earth ore digital twin coupling model based on the actual leaching property data corresponding to the target ionic rare earth ore, wherein the preset rare earth ore digital twin coupling model includes a preset ionic rare earth element leaching model and a preset hydrometallurgical treatment model; and in response to the leaching signal corresponding to the target ionic rare earth ore, obtains the leaching agent property information of the leaching agent used for leaching the target ionic rare earth ore, the rare earth ore property information corresponding to the target ionic rare earth ore, and the operating parameters of the in-situ leaching process for the target ionic rare earth ore; then the leaching agent property information, the rare earth ore property information, the water-soluble rare earth element ... The soil ore property information and the operating parameters are input into the preset ionic rare earth element leaching model for leaching process analysis to obtain the seepage field distribution data and concentration field distribution data for the in-situ leaching process of the target ionic rare earth ore; at the same time, the data corresponding to the liquid collecting channel in the seepage field distribution data and the concentration field distribution data are input into the preset hydrometallurgical treatment model as initial parameters for analysis and processing to obtain the predicted process operation parameter information for the hydrometallurgical treatment process of the leaching mother liquor, wherein the leaching mother liquor is generated in the in-situ leaching process of the target ionic rare earth ore; finally, based on the predicted process operation parameter information, the leaching mother liquor is hydrometallurgically treated to obtain the ionic rare earth elements leached from the target ionic rare earth ore. Therefore, by constructing a preset rare earth mine digital twin coupling model to digitally present the entire mining process of the rare earth mine, the actual leaching and hydrometallurgical production processes can be observed in real time, and the actual leaching data and hydrometallurgical production data at each time and each process can be collected in real time. The actual leaching data and hydrometallurgical production data are analyzed by using the preset ionic rare earth element leaching model to obtain the seepage field distribution data and concentration field distribution data of the leaching process. At the same time, the seepage field distribution data and concentration field distribution data of the leaching mother liquor obtained by the preset ionic rare earth element leaching model are input into the preset hydrometallurgical treatment model for analysis through data coupling to obtain the predicted process operation parameter information of the hydrometallurgical treatment process. Finally, the leaching mother liquor is hydrometallurgically treated according to the predicted process operation parameter information to obtain the ionic rare earth elements leached from the rare earth ore, thereby improving the acquisition rate and leaching efficiency of rare earth elements in the rare earth ore.

[0124] Further, as Figure 1 The specific implementation of the present invention provides an ionic rare earth ore leaching device based on digital twin, such as Figure 3 As shown, the device includes: a construction unit 31, an acquisition unit 32, a leaching analysis unit 33, a parameter prediction unit 34, and a hydrometallurgical treatment unit 35.

[0125] The construction unit 31 can be used to construct a preset rare earth mine digital twin coupling model based on the actual leaching property data corresponding to the target ionic rare earth ore, wherein the preset rare earth mine digital twin coupling model includes a preset ionic rare earth element leaching model and a preset hydrometallurgical treatment model.

[0126] The acquisition unit 32 can be used to respond to the leaching signal corresponding to the target ionic rare earth ore, obtain the leaching agent property information of the leaching agent used for leaching the target ionic rare earth ore, the rare earth ore property information corresponding to the target ionic rare earth ore, and the operating parameters of the in-situ leaching process for the target ionic rare earth ore.

[0127] The leaching analysis unit 33 can be used to input the leaching agent property information, the rare earth ore property information and the operating parameters into the preset ionic rare earth element leaching model to perform leaching process analysis, and obtain seepage field distribution data and concentration field distribution data for the in-situ leaching process of the target ionic rare earth ore.

[0128] The parameter prediction unit 34 can be used to input the data corresponding to the liquid collection channel in the seepage field distribution data and the concentration field distribution data as initial parameters into the preset hydrometallurgical treatment model for analysis and processing, so as to obtain predicted process operation parameter information for the hydrometallurgical treatment process of the leaching mother liquor, wherein the leaching mother liquor is produced by the target ionic rare earth ore in the in-situ leaching process.

[0129] The hydrometallurgical treatment unit 35 can be used to perform hydrometallurgical treatment on the leaching mother liquor based on the predicted process operation parameter information to obtain the ionic rare earth elements leached from the target ionic rare earth ore.

[0130] In a specific application scenario, in order to determine the seepage field distribution data and concentration field distribution data of the leaching mother solution generated in the in-situ leaching process, such as Figure 4 As shown, the leaching analysis unit 33 includes: a seepage analysis module 331 , a migration analysis module 332 , and an ion exchange module 333 .

[0131] The seepage analysis module 331 can be used to input the leaching agent property information, the rare earth ore property information and the operating parameters into the saturated seepage module for seepage analysis, and obtain the seepage field distribution data of the in-situ leaching process for the target ionic rare earth ore, wherein the seepage field distribution data includes the head distribution data of the groundwater in the target ionic rare earth ore, and the seepage velocity distribution data of the leaching agent in the target ionic rare earth ore.

[0132] The migration analysis module 332 can be used to input the seepage velocity distribution data into the migration analysis module for convection-diffusion analysis to obtain the initial concentration field distribution data of the target ionic rare earth ore before the ion exchange reaction occurs.

[0133] The ion exchange module 333 can be used to input the initial concentration field distribution data into the ion exchange reaction module for ion exchange, so as to obtain concentration field distribution data for the in-situ leaching process of the target ionic rare earth ore.

[0134] In a specific application scenario, in order to determine the predicted process operation parameter information of the hydrometallurgical treatment process during the leaching process, the parameter prediction unit 34 includes a determination module 341 and an analysis module 342.

[0135] The determination module 341 can be used to determine the flow data of the leaching mother liquor, the ammonium ion concentration data, and the rare earth ion concentration data during the in-situ leaching of the target ionic rare earth ore based on the data corresponding to the liquid collection channel extracted from the seepage field distribution data and the concentration field distribution data.

[0136] The analysis module 342 can be used to input the flow data, ammonium ion concentration data, and rare earth ion concentration data of the leaching mother liquor as initial parameters into the preset hydrometallurgical treatment model for analysis and processing, so as to obtain predicted process operation parameter information for the hydrometallurgical treatment process of the leaching mother liquor.

[0137] In a specific application scenario, in order to determine the flow data, ammonium ion concentration data, and rare earth ion concentration data of the leaching mother liquor, the determination module 341 can be specifically used to determine the leaching surface corresponding to the leaching mother liquor in the target ionic rare earth ore based on the structural data of the liquid collecting tunnel, and determine the leaching surface area corresponding to the leaching surface; determine the seepage surface velocity data of the leaching surface based on the seepage velocity distribution data in the seepage field distribution data; multiply the leaching surface area by the seepage surface velocity data to obtain the flow data of the in-situ leaching process for the target ionic rare earth ore; determine the ammonium ion concentration data and rare earth ion concentration data for the in-situ leaching process for the target ionic rare earth ore based on the concentration field distribution data and the structural data of the liquid collecting tunnel.

[0138] In a specific application scenario, in order to determine the predicted process operation parameter information, the analysis module 342 can also be used to input the flow data, ammonium ion concentration data, and rare earth ion concentration data of the leaching mother liquor as initial parameters into the preset hydrometallurgical treatment model for analysis and processing, so as to obtain the predicted impurity removal process operation parameter information for the impurity removal process of the leaching mother liquor.

[0139] The analysis module 341 can also be used to determine the predicted precipitation process operating parameter information for the precipitation process of the leaching mother liquor after impurity removal based on the flow data, ammonium ion concentration data, rare earth ion concentration data of the leaching mother liquor and the predicted impurity removal process operating parameters.

[0140] The analysis module 341 can also be used to determine the predicted liquid preparation process operation parameter information for the liquid preparation process of the leachate mother liquor after precipitation treatment based on the flow data of the leachate mother liquor, the ammonium ion concentration data, the rare earth ion concentration data and the predicted precipitation process operation parameter information.

[0141] In a specific application scenario, in order to obtain the ionic rare earth elements leached from the target ionic rare earth ore, the hydrometallurgical treatment unit 35 includes an impurity removal module 351 and a precipitation module 352 .

[0142] The impurity removal module 351 can be used to perform impurity removal treatment on the leaching mother liquor based on the predicted impurity removal process operation parameter information to obtain the impurity-removed leaching mother liquor.

[0143] The precipitation module 352 can be used to perform precipitation treatment on the leaching mother liquor after the impurity removal treatment based on the predicted precipitation process operation parameter information to obtain a supernatant and a precipitate, separate ammonium ions from the supernatant, and determine the precipitate as an ionic rare earth element.

[0144] In a specific application scenario, in order to obtain a recycled leaching agent, the hydrometallurgical treatment unit 35 further includes a liquid preparation module 353 .

[0145] The liquid preparation module 353 can be used to prepare liquid for the ammonium ions based on the predicted liquid preparation process operation parameter information to obtain a recycled leaching agent, so as to use the recycled leaching agent to perform a cyclic leaching treatment on the target ionic rare earth ore to obtain the ionic rare earth elements cyclically leached in the target ionic rare earth ore.

[0146] In a specific application scenario, in order to construct a preset hydrometallurgical treatment model, the acquisition unit 32 can also be used to acquire sample production data, sample sensor data, and sample hydrometallurgical equipment data, wherein the sample production data includes sample seepage field distribution data and the concentration field distribution data, and the sample hydrometallurgical equipment data includes actual process operation parameters; based on the preset mechanism model, the data model is coupled with an iterative algorithm to continuously accumulate sample production data, sample sensor data, and sample hydrometallurgical equipment data of the sample hydrometallurgical treatment process, and digitally model all elements and processes of the sample hydrometallurgical process to obtain the preset hydrometallurgical treatment model.

[0147] In a specific application scenario, in order to track elements, the hydrometallurgical treatment unit 35 can also be used to track the flow of key elements generated by the preset hydrometallurgical treatment model during the hydrometallurgical treatment process based on an element balance algorithm to obtain element flow tracking results; based on the element flow tracking results, the next leaching process corresponding to the preset ionic rare earth element leaching model is determined to achieve a two-way coupling between the preset ionic rare earth element leaching model and the preset hydrometallurgical treatment model, and the digital twin coupling model of the preset rare earth mine in the whole process is corrected according to the ammonia nitrogen analysis data of the leaching mother liquor, and data analysis is performed on the mother liquor collection situation using the liquid collection tunnel and the ammonia nitrogen loss of the leaching agent during the in-situ leaching process.

[0148] In a specific application scenario, in order to display the leaching process, the acquisition unit 32 can also be used to determine the leaching rate of ionic rare earth elements in the in-situ leaching process and the recovery rate of ionic rare earth elements in the hydrometallurgical treatment process; and send the leaching rate of ionic rare earth elements and the recovery rate of ionic rare earth elements to the display terminal of the preset rare earth mine digital twin coupling model for display.

[0149] It should be noted that for other corresponding descriptions of the functional modules involved in the ionic rare earth ore leaching device based on digital twin provided in the embodiment of the present invention, please refer to Figure 1 The corresponding description of the method shown will not be repeated here.

[0150] Based on the above Figure 1The method shown, accordingly, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the following steps are implemented: based on the actual leaching property data corresponding to the target ionic rare earth ore, a preset rare earth ore digital twin coupling model is constructed, wherein the preset rare earth ore digital twin coupling model includes a preset ionic rare earth element leaching model and a preset hydrometallurgical treatment model; in response to the leaching signal corresponding to the target ionic rare earth ore, the leaching agent property information of the leaching agent used for leaching the target ionic rare earth ore, the rare earth ore property information corresponding to the target ionic rare earth ore, and the operating condition parameters of the in-situ leaching process for the target ionic rare earth ore are obtained; the leaching agent property information, the rare earth ore property information, the operating condition parameters of the in-situ leaching process for the target ionic rare earth ore are obtained; and the leaching agent property information, the rare earth ore property information, the operating condition parameters of the in-situ leaching process for the target ionic rare earth ore are obtained. The soil ore property information and the operating condition parameters are input into the preset ionic rare earth element leaching model for leaching process analysis to obtain seepage field distribution data and concentration field distribution data for the in-situ leaching process of the target ionic rare earth ore; the data corresponding to the liquid collecting channel in the seepage field distribution data and the concentration field distribution data are input into the preset hydrometallurgical treatment model as initial parameters for analysis and processing to obtain predicted process operation parameter information for the hydrometallurgical treatment process of the leaching mother liquor, wherein the leaching mother liquor is generated in the in-situ leaching process of the target ionic rare earth ore; based on the predicted process operation parameter information, the leaching mother liquor is hydrometallurgically treated to obtain the ionic rare earth elements leached from the target ionic rare earth ore.

[0151] Based on the above Figure 1 The method shown and Figure 3 The embodiment of the device shown in the figure, the embodiment of the present invention also provides a physical structure diagram of a computer device, such as Figure 5As shown, the computer device includes: a processor 41, a memory 42, and a computer program stored in the memory 42 and executable on the processor, wherein the memory 42 and the processor 41 are both arranged on a bus 43; when the processor 41 executes the program, the following steps are implemented: based on the actual leaching property data corresponding to the target ionic rare earth ore, a preset rare earth ore digital twin coupling model is constructed, wherein the preset rare earth ore digital twin coupling model includes a preset ionic rare earth element leaching model and a preset hydrometallurgical treatment model; in response to a leaching signal corresponding to the target ionic rare earth ore, the leaching agent property information of the leaching agent used for leaching the target ionic rare earth ore, the rare earth ore property information corresponding to the target ionic rare earth ore, and the working conditions of the target ionic rare earth ore under the in-situ leaching process are obtained. parameters; inputting the leaching agent property information, the rare earth ore property information and the operating condition parameters into the preset ionic rare earth element leaching model for leaching process analysis, and obtaining the seepage field distribution data and concentration field distribution data for the in-situ leaching process of the target ionic rare earth ore; inputting the data corresponding to the liquid collecting tunnel in the seepage field distribution data and the concentration field distribution data as initial parameters into the preset hydrometallurgical treatment model for analysis and processing, and obtaining the predicted process operation parameter information for the hydrometallurgical treatment process of the leaching mother liquor, wherein the leaching mother liquor is generated in the in-situ leaching process of the target ionic rare earth ore; based on the predicted process operation parameter information, the leaching mother liquor is hydrometallurgically treated to obtain the ionic rare earth elements leached from the target ionic rare earth ore.

[0152] Through the technical solution of the present invention, the present invention constructs a preset rare earth mine digital twin coupling model based on the actual leaching property data corresponding to the target ionic rare earth ore, wherein the preset rare earth mine digital twin coupling model includes a preset ionic rare earth element leaching model and a preset hydrometallurgical treatment model; and in response to the leaching signal corresponding to the target ionic rare earth ore, obtains the leaching agent property information of the leaching agent used for leaching the target ionic rare earth ore, the rare earth ore property information corresponding to the target ionic rare earth ore, and the operating condition parameters of the in-situ leaching process for the target ionic rare earth ore; then inputs the leaching agent property information, the rare earth ore property information and the operating condition parameters into the preset ionic rare earth ore. The earth element leaching model is used to analyze the leaching process to obtain seepage field distribution data and concentration field distribution data for the in-situ leaching process of the target ionic rare earth ore; at the same time, the data corresponding to the liquid collecting channel in the seepage field distribution data and the concentration field distribution data are input as initial parameters into the preset hydrometallurgical treatment model for analysis and processing to obtain predicted process operation parameter information for the hydrometallurgical treatment process of the leaching mother liquor, wherein the leaching mother liquor is generated in the in-situ leaching process of the target ionic rare earth ore; finally, based on the predicted process operation parameter information, the leaching mother liquor is hydrometallurgically treated to obtain the ionic rare earth elements leached from the target ionic rare earth ore. Therefore, by constructing a preset rare earth mine digital twin coupling model to digitally present the entire mining process of the rare earth mine, the actual leaching and hydrometallurgical production processes can be observed in real time, and the actual leaching data and hydrometallurgical production data at each time and each process can be collected in real time. The actual leaching data and hydrometallurgical production data are analyzed by using the preset ionic rare earth element leaching model to obtain the seepage field distribution data and concentration field distribution data of the leaching process. At the same time, the data corresponding to the liquid collecting channel in the seepage field distribution data and concentration field distribution data obtained by the preset ionic rare earth element leaching model are input as initial parameters into the preset hydrometallurgical treatment model for analysis through data coupling to obtain the predicted process operation parameter information of the hydrometallurgical treatment process. Finally, the leaching mother liquor is hydrometallurgically treated according to the predicted process operation parameter information to obtain the ionic rare earth elements leached from the rare earth ore, thereby improving the acquisition rate and leaching efficiency of rare earth elements in the rare earth ore.

[0153] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0154] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for leaching ionic rare earth ores based on digital twins, characterized in that: include: Based on the actual leaching property data corresponding to the target ionic rare earth ore, a preset rare earth ore digital twin coupling model is constructed, wherein the preset rare earth ore digital twin coupling model includes a preset ionic rare earth element leaching model and a preset hydrometallurgical treatment model, and the preset ionic rare earth element leaching model includes a saturated seepage module, a solute migration module, and an ion exchange reaction module. The method for constructing the preset hydrometallurgical treatment model includes: based on the preset mechanism model, the data model coupling iterative algorithm continuously accumulates the sample production data, sample sensor data, and sample hydrometallurgical equipment data of the sample hydrometallurgical treatment process, and digitally models all elements and the entire process of the sample hydrometallurgical process to obtain the preset hydrometallurgical treatment model; In response to a leaching signal corresponding to a target ionic rare earth ore, obtaining leaching agent property information of a leaching agent used for leaching the target ionic rare earth ore, rare earth ore property information corresponding to the target ionic rare earth ore, and operating parameters of an in-situ leaching process for the target ionic rare earth ore; Input the leaching agent property information, the rare earth ore property information and the operating condition parameters into the saturated seepage module for seepage analysis to obtain the seepage field distribution data for the in-situ leaching process of the target ionic rare earth ore; input the seepage velocity distribution data in the seepage field distribution data into the solute transport module for convection-diffusion analysis to obtain the initial concentration field distribution data of the target ionic rare earth ore before the ion exchange reaction occurs; input the initial concentration field distribution data into the ion exchange reaction module for ion exchange to obtain the concentration field distribution data for the in-situ leaching process of the target ionic rare earth ore; The seepage field distribution data and the data corresponding to the liquid collecting tunnel in the concentration field distribution data are input as initial parameters into the preset hydrometallurgical treatment model for analysis and processing, so as to obtain the predicted process operation parameter information for the hydrometallurgical treatment process of the leaching mother liquor, wherein the leaching mother liquor is generated in the in-situ leaching process of the target ionic rare earth ore; Based on the predicted process operation parameter information, the leaching mother liquor is subjected to hydrometallurgical treatment to obtain the ionic rare earth elements leached from the target ionic rare earth ore.

2. The method according to claim 1, characterized in that The data corresponding to the liquid collecting channel in the seepage field distribution data and the concentration field distribution data are input as initial parameters into the preset hydrometallurgical treatment model for analysis and processing to obtain predicted process operation parameter information for the hydrometallurgical treatment process of the leaching mother liquor, including: Based on the data corresponding to the liquid collecting tunnel extracted from the seepage field distribution data and the concentration field distribution data, determining the flow data, ammonium ion concentration data, and rare earth ion concentration data of the leaching mother solution during the in-situ leaching of the target ionic rare earth ore; The flow data, ammonium ion concentration data, and rare earth ion concentration data of the leaching mother liquor are input into the preset hydrometallurgical treatment model for analysis and processing to obtain predicted process operation parameter information for the hydrometallurgical treatment process of the leaching mother liquor.

3. The method according to claim 2, characterized in that The method of determining the flow rate data, ammonium ion concentration data, and rare earth ion concentration data of the leaching mother solution during in-situ leaching of the target ionic rare earth ore based on the data corresponding to the liquid collecting tunnel extracted from the seepage field distribution data and the concentration field distribution data includes: In the target ionic rare earth ore, based on the structural data of the liquid collecting tunnel, a leaching surface corresponding to the leaching mother solution is determined, and a leaching surface area corresponding to the leaching surface is determined; Determining the seepage surface velocity data of the leaching surface based on the seepage velocity distribution data in the seepage field distribution data; Multiplying the leaching surface area by the seepage surface velocity data to obtain flow data of leaching mother liquor during in-situ leaching of the target ionic rare earth ore; Based on the concentration field distribution data and the structure data of the liquid collecting tunnel, the ammonium ion concentration data and the rare earth ion concentration data of the leaching mother liquor during the in-situ leaching of the target ionic rare earth ore are determined.

4. The method according to claim 2, characterized in that The hydrometallurgical treatment process includes: impurity removal process, precipitation process, and liquid preparation process. The flow data, ammonium ion concentration data, and rare earth ion concentration data of the leaching mother liquor are input into the preset hydrometallurgical treatment model for analysis and processing to obtain the predicted process operation parameter information for the hydrometallurgical treatment process of the leaching mother liquor, including: The flow rate data, ammonium ion concentration data, and rare earth ion concentration data of the leaching mother liquor are input as initial parameters into the preset hydrometallurgical treatment model for analysis and processing to obtain the predicted impurity removal process operation parameter information for the impurity removal process of the leaching mother liquor; Determine the predicted precipitation process operating parameter information for the precipitation process of the leaching mother liquor after the impurity removal treatment based on the flow data, ammonium ion concentration data, rare earth ion concentration data and the predicted impurity removal process operating parameters of the leaching mother liquor; Based on the flow data, ammonium ion concentration data, rare earth ion concentration data of the leaching mother liquor and the predicted precipitation process operation parameter information, the predicted liquid preparation process operation parameter information for the liquid preparation process of the leaching mother liquor after precipitation treatment is determined.

5. The method according to claim 1, characterized in that The predicted process operation parameter information includes: predicted impurity removal process operation parameter information, predicted precipitation process operation parameter information, and predicted liquid preparation process operation parameter information; based on the predicted process operation parameter information, the leaching mother liquor is subjected to hydrometallurgical treatment to obtain the ionic rare earth elements leached from the target ionic rare earth ore, including: Based on the predicted impurity removal process operation parameter information, the leaching mother liquor is subjected to impurity removal treatment to obtain the impurity-removed leaching mother liquor; Based on the predicted precipitation process operation parameter information, the leaching mother liquor after the impurity removal treatment is subjected to precipitation treatment to obtain a supernatant and a precipitate, and ammonium ions are separated from the supernatant, and the precipitate is determined to be an ionic rare earth element; Based on the predicted liquid preparation process operation parameter information, the ammonium ions are prepared to obtain a recycling leaching agent, so that the target ionic rare earth ore can be cyclically leached using the recycling leaching agent to obtain ionic rare earth elements cyclically leached in the target ionic rare earth ore.

6. The method according to claim 1, characterized in that The method further comprises: Based on the element balance algorithm, the flow direction of the key elements generated by the preset hydrometallurgical treatment model during the hydrometallurgical treatment process is tracked to obtain the element flow direction tracking result; Based on the element flow tracking results, the next leaching process corresponding to the preset ionic rare earth element leaching model is determined to achieve two-way coupling between the preset ionic rare earth element leaching model and the preset hydrometallurgical treatment model, and the preset rare earth mine digital twin coupling model of the whole process is corrected according to the ammonia nitrogen analysis data of the leaching mother liquor, and data analysis is performed on the mother liquor collection situation using the liquid collection tunnel and the ammonia nitrogen loss of the leaching agent during the in-situ leaching process.

7. The method according to claim 1, characterized in that After the leaching mother liquor is subjected to hydrometallurgical treatment based on the predicted process operation parameter information to obtain the ionic rare earth elements leached from the target ionic rare earth ore, the method further comprises: Determining the leaching rate of the ionic rare earth elements in the in-situ leaching process and the recovery rate of the ionic rare earth elements in the hydrometallurgical treatment process; The leaching rate of the ionic rare earth elements and the recovery rate of the ionic rare earth elements are sent to the display terminal of the preset rare earth mine digital twin coupling model for display.

8. An ionic rare earth ore leaching device based on digital twin, characterized in that: include: A construction unit is used to construct a preset rare earth mine digital twin coupling model based on actual leaching property data corresponding to the target ionic rare earth mine, wherein the preset rare earth mine digital twin coupling model includes a preset ionic rare earth element leaching model and a preset hydrometallurgical treatment model, and the preset ionic rare earth element leaching model includes a saturated seepage module, a solute migration module, and an ion exchange reaction module. The method for constructing the preset hydrometallurgical treatment model includes: based on the preset mechanism model, a data model coupling iterative algorithm continuously accumulates sample production data, sample sensor data, and sample hydrometallurgical equipment data of the sample hydrometallurgical treatment process, and digitally models all elements and the entire process of the sample hydrometallurgical process to obtain the preset hydrometallurgical treatment model; An acquisition unit, configured to obtain, in response to a leaching signal corresponding to a target ionic rare earth ore, property information of a leaching agent used for leaching the target ionic rare earth ore, property information of a rare earth ore corresponding to the target ionic rare earth ore, and operating parameters of an in-situ leaching process for the target ionic rare earth ore; A leaching analysis unit, for inputting the leaching agent property information, the rare earth ore property information and the operating condition parameters into the saturated seepage module for seepage analysis, and obtaining the seepage field distribution data for the in-situ leaching process of the target ionic rare earth ore; inputting the seepage velocity distribution data in the seepage field distribution data into the solute transport module for convection-diffusion analysis, and obtaining the initial concentration field distribution data of the target ionic rare earth ore before the ion exchange reaction occurs; inputting the initial concentration field distribution data into the ion exchange reaction module for ion exchange, and obtaining the concentration field distribution data for the in-situ leaching process of the target ionic rare earth ore; A parameter prediction unit, used to input the seepage field distribution data and the data corresponding to the liquid collecting tunnel in the concentration field distribution data as initial parameters into the preset hydrometallurgical treatment model for analysis and processing, and obtain predicted process operation parameter information for the hydrometallurgical treatment process of the leaching mother liquor, wherein the leaching mother liquor is generated in the in-situ leaching process of the target ionic rare earth ore; A hydrometallurgical treatment unit is used to perform hydrometallurgical treatment on the leaching mother liquor based on the predicted process operation parameter information to obtain the ionic rare earth elements leached in the target ionic rare earth ore.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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