Intelligent control system for in-situ negative pressure leaching of ionic rare earth ore and control method of intelligent control system

Abstract

The invention relates to the technical field of leaching mining, in particular to an intelligent control system for in-situ negative pressure leaching of ionic rare earth ore and a control method thereof.A data processing module processes data collected by a data acquisition module in real time through edge computing, and the data are optimized and fused through cloud computing; a PID (Proportion Integration Differentiation) control algorithm is adopted to calculate the parameter range of the required pressure and the liquid level of the ore leaching liquid in the ion type rare earth ore body, a fuzzy control algorithm is adopted to calculate the parameter range of the required permeation coefficient in the ion type rare earth ore body, and an intelligent optimization algorithm is adopted to calculate the parameter range of the required pH and flow of the ore leaching liquid. According to data monitored in real time, various parameter ranges required in the ore body are accurately calculated, so that a control system can accurately control the ore leaching process according to the actual situation of the ore body and the ore leaching requirement, the leaching efficiency of rare earth elements is improved, energy consumption and environmental pollution are reduced, and the method is suitable for popularization and application. And efficient utilization of resources is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of in-situ leaching mining, and particularly to an intelligent control system and a control method for in-situ negative pressure leaching of ionic rare earth ores. Background Art

[0002] In the process of mining ionic rare earth ores, the in-situ negative pressure leaching technology has received extensive attention due to its high efficiency and environmental protection characteristics. This technology applies negative pressure inside the ore body and uses a specific leaching solution for leaching, effectively improving the leaching efficiency of rare earth elements while reducing environmental pollution. However, the implementation of the in-situ negative pressure leaching technology requires precise control of multiple parameters, including the pressure inside the ore body, the liquid level of the leaching solution, the permeability coefficient, the pH value of the leaching solution, and the flow rate, etc. The precise control of these parameters is crucial for improving the leaching efficiency and ensuring safe production.

[0003] Traditional control systems mostly adopt a centralized control architecture, that is, all data is first transmitted to the central control room for processing, and then control instructions are issued according to the processing results. This architecture has problems such as data transmission delay and low processing efficiency, and it is difficult to meet the high requirements of the in-situ negative pressure leaching technology for real-time performance and accuracy. In addition, traditional control systems also have deficiencies in data processing and algorithm optimization, and it is difficult to accurately calculate the optimal values of each parameter, thus affecting the leaching efficiency and production safety. Summary of the Invention

[0004] Aiming at the problems of low data processing efficiency, low real-time performance and accuracy of the control system existing in the above-mentioned prior art, the present invention provides an intelligent control system and a control method for in-situ negative pressure leaching of ionic rare earth ores.

[0005] To achieve the above object, the technical solution of the present invention is as follows:

[0006] An intelligent control system for in-situ negative pressure leaching of ionic rare earth ores, comprising a data acquisition module, a data processing module and a control module;

[0007] The data acquisition module includes a pressure sensor, a liquid level sensor, a permeability coefficient sensor, a pH sensor and a flow sensor; the pressure sensor and the permeability coefficient sensor are arranged inside the ionic rare earth ore body, the liquid level sensor is arranged inside the leaching solution storage device and the ionic rare earth ore body, the pH sensor is arranged in the leaching solution storage device, and the flow sensor is arranged on the liquid injection pipeline and the liquid collection pipeline;

[0008] The control module is connected to a leaching solution injection pump, a negative pressure pump and a pipeline valve.

[0009] Furthermore, it also includes a security module, which includes a system failure alarm unit and an operation anomaly warning unit.

[0010] Furthermore, it also includes a system optimization module, which includes an adaptive unit and a deep learning unit.

[0011] Furthermore, the resource consumption includes the consumption of leaching solution, precipitant, and impurity removal agent.

[0012] Furthermore, it also includes a user interface module, which includes a visualization interface and a historical database.

[0013] The present invention also includes the following technical solutions:

[0014] A control method for an intelligent control system of in-situ negative pressure leaching of ionic rare earth ores, comprising the following steps:

[0015] S1. Various sensors transmit the collected data to the data acquisition module;

[0016] S2. The data processing module uses edge computing to clean and filter the data collected by the data acquisition module, judge the rationality of the collected data, and store the reasonable data; uses cloud computing to fuse the data collected by the same type of sensors at different locations and the data collected by different types of sensors, analyze the rationality and correlation of the fused data, and adjust the relevant process parameters according to the collected data;

[0017] The cloud computing includes the following steps:

[0018] Calculate the theoretical parameter ranges of the pressure P and the leaching solution level L required in the ionic rare earth ore body according to formula (1) and formula (2): range and L range :

[0019] P range = f(porosity, hydraulic conductivity, ore body shape, empirical parameter) (1)

[0020] L range = f(porosity, hydraulic conductivity, ore body shape, empirical parameter) (2)

[0021] And calculate the actual parameter ranges of the pressure P and the leaching solution level L required in the ionic rare earth ore body by using the PID control algorithm according to formula (3):

[0022]

[0023] where u(t) is the parameter range of the pressure or the leaching solution level, and e(t) is the deviation between the theoretical parameter value and the actually measured value; K p 、Ki , K d are the proportional, integral, and differential coefficients, respectively;

[0024] Calculate the required infiltration coefficient K in the ionic rare earth ore body using the fuzzy control algorithm according to formula (4): 浸透 Parameter range of:

[0025] K 浸透 = FuzzyControl(leaching cycle, ore body reserve, ore body grade distribution) (4)

[0026] Among them, the fuzzy control calculates the parameter range of the infiltration coefficient through the fuzzy rule base and membership function;

[0027] Calculate the required pH range of the leaching solution and flow rate parameter Q according to formulas (5) and (6): pH range and Q range :

[0028] pH range = f(ore body mineral composition, ore body range, ore body grade) (5)

[0029] Q range = f(ore body mineral composition, ore body range, ore body grade) (6)

[0030] After that, calculate the consumption M of the leaching solution using the intelligent optimization algorithm according to formula (7):

[0031] Minimize f(pH, Q) = M (7)

[0032] Obtain the sum E of the optimized negative pressure pump energy consumption and leaching solution consumption according to formula (8):

[0033] Minimize E = α · negative pressure pump energy consumption + β · leaching solution consumption M (8)

[0034] Among them, α and β are weight coefficients used to balance the negative pressure pump energy consumption and leaching solution consumption M;

[0035] S3. The control module adjusts the power of the leaching solution injection pump and the negative pressure pump and the pipeline valve according to the relevant parameters calculated by the data processing module;

[0036] S4. The adaptive unit of the system optimization module evaluates the system operation performance by analyzing the leaching efficiency and resource consumption and automatically adjusts the system control strategy; the deep learning unit analyzes the optimal control parameters under different ore types and different leaching conditions through the deep learning algorithm and automatically generates an optimized operation plan.

[0037] Furthermore, the porosity, hydraulic conductivity, and ore body shape are obtained by fitting empirical formulas or experimental data; the leaching period, ore body reserves, ore body grade distribution, ore body mineral composition, ore body scope, and ore body grade are obtained by statistical analysis of mine data and model prediction.

[0038] Furthermore, the intelligent optimization algorithms include genetic algorithms and particle swarm optimization algorithms.

[0039] The beneficial effects of the present invention compared with the prior art are as follows:

[0040] The present invention provides an intelligent control system and a control method for in-situ negative pressure leaching of ionic rare earth ores. Through a variety of high-precision sensors arranged inside the ore body and on the leaching solution pipeline in the data acquisition module, key process parameters during the leaching process are monitored in real time. Combining with the real-time processing ability of edge computing, data is quickly responded to and processed to ensure the timeliness and accuracy of the data. At the same time, cloud computing is used to further optimize the data processing process, improve the efficiency of data fusion, and provide a solid foundation for subsequent control decisions. The data processing module adopts advanced PID control algorithms, fuzzy control algorithms, and intelligent optimization algorithms. According to the real-time monitored data, the required parameter ranges inside the ore body are accurately calculated, enabling the control system to automatically adjust the power of the leaching solution injection pump and the negative pressure pump, as well as the opening degree of the pipeline valve according to the actual situation of the ore body and the leaching requirements, thereby realizing precise control of the leaching process. This not only improves the leaching efficiency of rare earth elements but also reduces energy consumption and environmental pollution, achieving efficient utilization of resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The following further describes the embodiments of the present invention with reference to the accompanying drawings, where:

[0042] Figure 1 Shows a schematic diagram of the modules of an intelligent control system for in-situ negative pressure leaching of ionic rare earth ores. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0043] In order to make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below through specific embodiments with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0044] The present invention is used in the in-situ negative pressure leaching process of ionic rare earth ores. The in-situ negative pressure leaching system for ionic rare earth ores includes a leaching solution injection system, a negative pressure system, and a liquid collection system. The leaching solution injection system includes a leaching solution storage device, a leaching solution injection pump, and an injection pipeline for injecting the leaching solution into the ionic rare earth ore. The leaching solution injection pump is connected to the leaching solution storage device and the injection pipeline. The negative pressure system includes a negative pressure pipeline and a negative pressure pump, and the negative pressure pipeline is arranged inside the ionic rare earth ore body. The liquid collection system is used to recover the leaching solution after leaching and includes a liquid collection pipeline. Pipeline valves are provided on the injection pipeline, the negative pressure pipeline, and the liquid collection pipeline.

[0045] An intelligent control system for in-situ negative pressure leaching of ionic rare earth ores includes a data acquisition module, a data processing module, and a control module.

[0046] The data acquisition module includes a pressure sensor, a liquid level sensor, a permeability coefficient sensor, a pH sensor, and a flow sensor. The pressure sensor and the permeability coefficient sensor are arranged inside the ionic rare earth ore body. The liquid level sensor is arranged inside the leaching solution storage device and the ionic rare earth ore body. The pH sensor is arranged in the leaching solution storage device, and the flow sensor is arranged on the injection pipeline and the liquid collection pipeline.

[0047] The control module is connected to the leaching solution injection pump, the negative pressure pump, and the pipeline valves.

[0048] In an embodiment of the present invention, a safety module is further included. The safety module includes a system fault alarm unit and an operation abnormality warning unit. When a system fault occurs, the system fault alarm unit will issue an alarm and display the fault location. The operation abnormality warning unit can predict the abnormal operations of the operator and remind the operator to make adjustments through warnings to avoid equipment damage or resource waste.

[0049] In an embodiment of the present invention, a system optimization module is further included. The system optimization module includes an adaptive unit and a deep learning unit.

[0050] In an embodiment of the present invention, the resource consumption includes the consumption of leaching solution, precipitant, and impurity removal agent.

[0051] In an embodiment of the present invention, a user interface module is further included. The user interface module includes a visualization interface and a historical database.

[0052] A control method for an intelligent control system for in-situ negative pressure leaching of ionic rare earth ores includes the following steps:

[0053] S1. Various sensors transmit the collected data to the data acquisition module.

[0054] S2. The data processing module uses edge computing to clean and filter the data collected by the data acquisition module, judge the rationality of the collected data, and store the reasonable data; it uses cloud computing to fuse the data collected by the same type of sensors at different locations and the data collected by different types of sensors, analyze the rationality and relevance of the fused data, and adjust the relevant process parameters according to the collected data;

[0055] The cloud computing includes the following steps:

[0056] Calculate the theoretical parameter ranges of the pressure P and the leaching solution level L required in the ionic rare earth ore body according to Formula (1) and Formula (2): range and L range :

[0057] P range = f(porosity, water conductivity coefficient, ore body shape, empirical parameter) (1)

[0058] L range = f(porosity, water conductivity coefficient, ore body shape, empirical parameter) (2)

[0059] And calculate the actual parameter ranges of the pressure P and the leaching solution level L required in the ionic rare earth ore body by using the PID control algorithm according to Formula (3):

[0060]

[0061] where u(t) is the parameter range of the pressure or the leaching solution level, and e(t) is the deviation between the theoretical parameter value and the actually measured value; K p 、K i 、K d are the proportional, integral, and differential coefficients respectively;

[0062] Calculate the parameter range of the infiltration coefficient K 浸透 required in the ionic rare earth ore body by using the fuzzy control algorithm according to Formula (4):

[0063] K 浸透 = FuzzyControl(leaching cycle, ore body reserve, ore body grade distribution) (4)

[0064] where the fuzzy control calculates the parameter range of the infiltration coefficient through the fuzzy rule base and the membership function;

[0065] Calculate the required ranges of the pH value and the flow rate parameter Q of the leaching solution according to Formula (5) and Formula (6): range and Q range :

[0066] pH range= f(mineral composition of ore body, ore body range, ore body grade) (5)

[0067] Q range = f(mineral composition of ore body, ore body range, ore body grade) (6)

[0068] After that, according to formula (7), the intelligent optimization algorithm is used to calculate the consumption M of the leaching solution:

[0069] Minimize f(pH, Q) = M (7)

[0070] According to formula (8), the sum E of the optimized negative pressure pump energy consumption and the leaching solution consumption is obtained:

[0071] Minimize E = α · negative pressure pump energy consumption + β · leaching solution consumption M (8)

[0072] Among them, α and β are weight coefficients used to balance the negative pressure pump energy consumption and the leaching solution consumption M;

[0073] S3. The control module adjusts the power of the leaching solution injection pump and the negative pressure pump and the pipeline valve according to the relevant parameters calculated by the data processing module;

[0074] S4. The adaptive unit of the system optimization module evaluates the system operation performance by analyzing the leaching efficiency and resource consumption, and automatically adjusts the system control strategy; the deep learning unit analyzes the optimal control parameters under different ore types and different leaching conditions through the deep learning algorithm, and automatically generates an optimized operation plan.

[0075] In an embodiment of the present invention, the porosity, hydraulic conductivity, and ore body shape are obtained by fitting empirical formulas or experimental data; the leaching cycle, ore body reserves, ore body grade distribution, mineral composition of ore body, ore body range, and ore body grade are obtained by mine data statistics and model prediction.

[0076] In an embodiment of the present invention, the intelligent optimization algorithm includes a genetic algorithm and a particle swarm optimization algorithm.

[0077] When using the present invention to control leaching, the data acquisition module collects key parameters in the leaching process in real time through a sensor network and sends them to the data processing module through a bus transmission method; the data processing module receives and stores the real-time data from the data acquisition module, performs data cleaning, outlier detection and processing, and data normalization processing on the data, and then calculates the actual required range of the key parameters in the leaching process based on various algorithms; according to the analysis results of the data processing module, the control module automatically adjusts the key parameters of the leaching process to ensure the maximization of the leaching efficiency.

[0078] The present invention provides an intelligent control system and its control method for in-situ negative pressure leaching of ionic rare earth ores. Through a variety of high-precision sensors arranged inside the ore body and on the leaching solution pipelines in the data acquisition module, key process parameters during the leaching process are monitored in real time. Combining with the real-time processing ability of edge computing, data is quickly responded to and processed to ensure the timeliness and accuracy of the data. At the same time, cloud computing is used to further optimize the data processing process and improve the efficiency of data fusion, providing a solid foundation for subsequent control decisions. The data processing module adopts advanced PID control algorithms, fuzzy control algorithms, and intelligent optimization algorithms. According to the real-time monitored data, the required parameter ranges inside the ore body are accurately calculated, enabling the control system to automatically adjust the power of the leaching solution injection pump, the negative pressure pump, and the opening degree of the pipeline valves according to the actual situation of the ore body and the leaching requirements, thereby realizing precise control of the leaching process. This not only improves the leaching efficiency of rare earth elements but also reduces energy consumption and environmental pollution, achieving efficient utilization of resources.

[0079] Some exemplary embodiments of the present invention are described above. It can be understood that the above embodiments are only used to explain the present invention and do not constitute a limitation to the protection scope of the present invention. The features in these embodiments can be recombined in a suitable manner, and the obtained solutions are still within the protection scope required by the present invention. Based on the above embodiments, all other embodiments obtained by those skilled in the art without creative efforts, that is, all modifications, equivalent replacements, and improvements made within the spirit and principle of this application, fall within the protection scope required by the present invention.

Claims

1. An intelligent control system for in-situ negative pressure leaching of ionic rare earth ores, characterized in that: It includes a data acquisition module, a data processing module and a control module; The data acquisition module includes a pressure sensor, a liquid level sensor, a permeability sensor, a pH sensor and a flow sensor; the pressure sensor and the permeability sensor are arranged inside the ionic rare earth ore body, the liquid level sensor is arranged inside the leaching liquid storage device and the ionic rare earth ore body, the pH sensor is arranged in the leaching liquid storage device, and the flow sensor is arranged on the injection pipe and the liquid collection pipe; The control module is connected to the leaching solution injection pump, the negative pressure pump and the pipeline valve.

2. The intelligent control system for in-situ negative pressure leaching of ionic rare earth ores according to claim 1 is characterized in that: It also includes a safety module, which includes a system failure alarm unit and an operation abnormality early warning unit.

3. The intelligent control system for in-situ negative pressure leaching of ionic rare earth ores according to claim 1 is characterized in that: It also includes a system optimization module, which includes an adaptive unit and a deep learning unit.

4. The intelligent control system for in-situ negative pressure leaching of ionic rare earth ores according to claim 3 is characterized in that: The resource consumption includes the consumption of leaching solution, precipitant and impurity remover.

5. The intelligent control system for in-situ negative pressure leaching of ionic rare earth ores according to claim 1, characterized in that: It also includes a user interface module, which includes a visualization interface and a historical database.

6. A control method for an intelligent control system for in-situ negative pressure leaching of ionic rare earth ores based on any one of claims 1 to 5, characterized in that: The following steps are involved: S1. Various sensors transmit the collected data to the data acquisition module; S2. The data processing module uses edge computing to clean and filter the data collected by the data acquisition module, judge the rationality of the collected data, and store the reasonable data; cloud computing is used to fuse the data collected by the same type of sensors at different locations and the data collected by different types of sensors, analyze the rationality and relevance of the fused data, and adjust the relevant process parameters according to the collected data; The cloud computing includes the following steps: According to formula (1) and formula (2), the theoretical parameter range P of the pressure P and the leaching liquid level L required in the ionic rare earth ore body is calculated: range and L range : P range =f(porosity, hydraulic conductivity, ore body shape, empirical parameters) (1) L range =f(porosity, hydraulic conductivity, ore body shape, empirical parameters) (2) According to formula (3), the PID control algorithm is used to calculate the actual parameter range of the pressure P required in the ionic rare earth ore body and the leaching liquid level L: Where u(t) is the parameter range of pressure or leaching solution level, e(t) is the deviation between the theoretical parameter value and the actual measured value; K p , K i , K d They are proportional, integral and differential coefficients respectively; According to formula (4), the fuzzy control algorithm is used to calculate the required permeability coefficient K in the ionic rare earth ore body. 浸透 Parameter range: K 浸透 =FuzzyControl(leaching cycle, ore reserves, ore grade distribution) (4) Among them, fuzzy control calculates the parameter range of penetration coefficient through fuzzy rule base and membership function; According to formula (5) and formula (6), the required leaching solution pH and flow parameter Q range are calculated. range and Q range : pH range =f(ore body mineral composition, ore body range, ore body grade) (5) Q range =f(ore body mineral composition, ore body range, ore body grade) (6) Afterwards, the leaching solution consumption M is calculated using the intelligent optimization algorithm according to formula (7): Minimize f(pH,Q)=M (7) According to formula (8), the sum of the optimized negative pressure pump energy consumption and leaching solution consumption E is obtained: Minimize E=α·Energy consumption of negative pressure pump+β·Consumption of leaching solution M (8) Among them, α and β are weight coefficients, which are used to balance the energy consumption of the negative pressure pump and the consumption of the leaching solution M; S3, the control module adjusts the power of the leaching solution injection pump and the negative pressure pump and the pipeline valve according to the relevant parameters calculated by the data processing module; S4. The adaptive unit of the system optimization module evaluates the system operating performance by analyzing the leaching efficiency and resource consumption, and automatically adjusts the system control strategy; the deep learning unit analyzes the optimal control parameters under different ore types and different leaching conditions through the deep learning algorithm, and automatically generates the optimized operation plan.

7. The control method of the intelligent control system for in-situ negative pressure leaching of ionic rare earth ores according to claim 6 is characterized in that: The porosity, water conductivity and ore body shape are obtained by fitting empirical formulas or experimental data; the leaching cycle, ore body reserves, ore body grade distribution, ore body mineral composition, ore body range and ore body grade are obtained by mining data statistics and model prediction.

8. The control method of the intelligent control system for in-situ negative pressure leaching of ionic rare earth ores according to claim 6 is characterized in that: The intelligent optimization algorithm includes a genetic algorithm and a particle swarm optimization algorithm.

Images