A method for revegetation of a mine spoil

Abstract

The application provides a vegetation restoration method for mining wasteland, and relates to the technical field of mining wasteland restoration.The vegetation restoration method for mining wasteland specifically comprises the following steps: S1, data collection;S2, establishment of an ecological model;S3, preparation of a nanometer particle material;S4, synthesis of a nanometer particle;S5, application of a nanometer particle;S6, design of a biomimetic ecological substrate;S7, application of microorganisms;S8, VR restoration simulation.The nanometer particle has efficient adsorption and slow-release functions, can penetrate into the interior of soil, completely remove pollutants, and will not cause secondary pollution.The method provides long-term stable soil improvement effects and persistent support for plant growth through the slow-release function of the nanometer particle and the application of the biomimetic substrate.Real-time data analysis and feedback of an AI system can dynamically adjust the restoration strategy, realize a precise and efficient restoration process, and greatly improve the restoration success rate.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of mining waste land restoration, in particular to a vegetation restoration method for mining waste land. BACKGROUND

[0002] Mining waste land refers to land that has been damaged due to mining and processing activities of mineral resources. Such land often suffers from severe environmental damage, including but not limited to soil pollution, landform destruction, water resource pollution, loss of biodiversity, etc. Mining activities often result in waste land with sparse or missing vegetation, with a large amount of toxic and harmful substances (such as heavy metals, acid minerals, etc.) accumulated in the soil, and a severely damaged ecosystem that is difficult to restore on its own. Mining waste land often contains high concentrations of pollutants, especially heavy metals and toxic chemicals, which pose a threat to the surrounding environment and human health. Through vegetation restoration, harmful substances in the soil and water can be reduced, and the risk of environmental pollution can be reduced. Vegetation restoration helps to restore the ecosystem of mining waste land, and through the re-establishment of vegetation, it can promote the improvement of soil structure, water retention, and the increase of biodiversity, thereby re-establishing a healthy ecosystem.

[0003] Existing vegetation restoration techniques for mining waste land mainly improve soil structure and environmental conditions through physical means, such as covering uncontaminated soil on the surface of contaminated soil to reduce exposure to pollutants. Or through engineering means to reshape the terrain, reduce soil erosion and improve vegetation growth conditions. There are also chemical means to fix or decompose pollutants, such as: applying lime, phosphate, etc. to adjust the pH value of the soil and fix heavy metals in the soil. Or use oxidizing or reducing agents to change the chemical properties of pollutants, making them less toxic. Furthermore, plants, microorganisms or animals are used to restore contaminated soil, such as: planting plants that can absorb, decompose or fix pollutants, commonly used plants include sunflowers, reeds, etc. Or use specific microorganisms to decompose or transform pollutants, such as using bacteria to decompose organic pollutants into harmless substances. The above-mentioned physical and chemical restoration methods usually only solve the problem of surface soil, and it is difficult to completely remove pollutants in deep soil, and the restoration speed is slow. The use of chemical modifiers may introduce secondary pollution. Traditional soil improvement methods often have limited effect, especially in highly polluted areas, and it is difficult to provide a stable environment for long-term plant growth. Biological remediation technology is limited by environmental conditions (such as climate, soil type, etc.), and the restoration effect is unstable, and it is difficult to accurately control the restoration process and progress.

[0004] Therefore, the present application proposes a vegetation restoration method for mining waste land to solve the above-mentioned problems. SUMMARY

[0005] In view of the deficiencies of the prior art, the vegetation restoration method for the mining wasteland is provided, and the problems of low pollutant removal efficiency and insufficient soil improvement effect of the prior method are solved.

[0006] To achieve the above object, the present application is realized by the following technical scheme: a vegetation restoration method for a mining wasteland, specifically comprising the following steps:

[0007] S1. Data collection

[0008] The mine area is preliminarily surveyed by using a UAV and a ground detection device, and topographic data, soil samples and water samples are collected, the samples are analyzed in a laboratory, the types and concentrations of pollutants are determined, and the soil structure, pH value and organic matter content are evaluated;

[0009] S2. Establishment of an ecological model

[0010] The collected field data are input into an artificial intelligence system, a digital ecological model of the mine area is established, and the effects of different restoration strategies are simulated by using a machine learning algorithm;

[0011] S3. Preparation of nanoparticles

[0012] A metal oxide precursor zinc sulfate, silica sol, activated carbon, calcium phosphate, chitosan or polyethyleneimine is prepared;

[0013] S4. Synthesis of nanoparticles

[0014] The metal oxide precursor zinc sulfate and activated carbon are dissolved in deionized water, silica sol and calcium phosphate are added, and the solution is stirred uniformly, heated at 90 DEG C, ammonia solution is added dropwise to adjust the pH, and the reaction is carried out for 1-2 hours, so that the metal oxide is precipitated on the silica sol matrix, the precipitated nanoparticles are mixed with a chitosan solution, stirred for 2 hours, and then dried to obtain modified nanoparticles;

[0015] S5. Application of nanoparticles

[0016] The nanoparticles are dispersed in deionized water at a ratio of 1 g / L, treated with ultrasonic waves for 30 minutes to ensure uniform dispersion, and applied to the contaminated soil through a spraying device or an irrigation system, and irrigation is carried out immediately after the nanoparticles are applied to ensure that the particles penetrate into the deep soil and adsorb the pollutants;

[0017] S6. Design of biomimetic ecological matrix

[0018] A sodium polyacrylate biomimetic material is selected, a 3D printing technology is used to manufacture a biomimetic root network of the biomimetic ecological matrix, the matrix can be used as a support structure for plant growth, and the soil stability is enhanced, the biomimetic ecological matrix is laid on the surface of the mine area, and selected plants are planted on the biomimetic matrix to ensure rooting and growth.

[0019] S7. Microbial application

[0020] Screening microorganisms capable of forming a symbiotic relationship with plants, and co-culturing the microorganisms with plants to form a symbiotic body, and then applying the symbiotic body to the soil of the mining area to observe its remediation effect in the actual environment;

[0021] S8. VR remediation simulation

[0022] Establish a VR platform for ecological remediation of mining areas, users can simulate different remediation strategies in a virtual environment and predict their effects, input field data and remediation schemes into the VR platform to establish a real mining area model for simulation experiments, test and optimize remediation schemes in the VR platform, and determine the best practices.

[0023] Preferably, the determination of the type of pollutants in the S1 data collection includes detection of heavy metal pollution and detection of acidic substance pollution.

[0024] Preferably, in the step S3 of preparing the material of the nanoparticles, the metal oxide precursor zinc sulfate is used for adsorbing heavy metals, silica sol is used for forming a stable nanoparticle structure, activated carbon is used for enhancing adsorption capacity, calcium phosphate is used as a slow-release fertilizer to gradually release phosphorus elements, and chitosan or polyethyleneimine is used for nanoparticle surface modification to improve stability and functionality.

[0025] Preferably, in the step S3 of preparing the material of the nanoparticles, the proportions of the raw materials are: metal oxide precursor zinc sulfate 5-15 parts, silica sol 10-25 parts, activated carbon 5-15 parts, calcium phosphate 5-15 parts, chitosan or polyethyleneimine 1-10 parts, and deionized water 30-60 parts.

[0026] Preferably, in the step S4 of synthesizing the nanoparticles, the ammonia solution is adjusted to a pH value of 7.0-7.5.

[0027] Preferably, in the step S5 of applying the nanoparticles, 0.5-1 L of the suspension is used per square meter of soil.

[0028] 1. The present application provides a vegetation remediation method for mining waste land. It has the following beneficial effects:

[0029] The application provides a vegetation restoration method for mining wasteland, and the method can effectively adsorb and fix heavy metal pollutants in the soil, and the nanoparticles can efficiently capture and fix harmful substances due to their large specific surface area and surface energy, in addition, the slow-release fertilizer component in the nanoparticles can provide long-term and stable nutrient supply for plants, avoiding the problem of rapid loss of nutrient elements in the use of traditional fertilizers, and in combination with the application of the biomimetic ecological matrix, the soil structure can be further improved, the water retention capacity and air permeability of the soil are enhanced, and a better environment is provided for plant growth.

[0030] 2. The application provides a vegetation restoration method for mining wasteland.

[0031] The application introduces AI technology, and the decision and operation in the restoration process can be dynamically adjusted based on real-time data, so that the intelligent restoration method can better adapt to environmental changes, provide an optimal restoration scheme, and improve the restoration efficiency and success rate. DETAILED DESCRIPTION

[0032] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the application.

[0033] Embodiment 1

[0034] The application provides a vegetation restoration method for mining wasteland, and the method can effectively adsorb and fix heavy metal pollutants in the soil, and the nanoparticles can efficiently capture and fix harmful substances due to their large specific surface area and surface energy, in addition, the slow-release fertilizer component in the nanoparticles can provide long-term and stable nutrient supply for plants, avoiding the problem of rapid loss of nutrient elements in the use of traditional fertilizers, and in combination with the application of the biomimetic ecological matrix, the soil structure can be further improved, the water retention capacity and air permeability of the soil are enhanced, and a better environment is provided for plant growth.

[0035] S1. Data collection

[0036] The unmanned aerial vehicle and the ground detection equipment are used to preliminarily survey the mining area, collect topographic data, soil samples and water samples, perform laboratory analysis on the samples, determine the types (such as heavy metals and acidic substances) and concentrations of the pollutants, and evaluate the soil structure, pH value and organic matter content;

[0037] S2. Establishment of an ecological model

[0038] The collected field data are input into an artificial intelligence system to establish a digital ecological model of the mining area, and the effects of different restoration strategies are simulated through a machine learning algorithm (such as deep learning and support vector machine);

[0039] S3. Material preparation of nanoparticles

[0040] Preparation of metal oxide precursor zinc sulfate (for heavy metal adsorption), silica sol (for stable nanoparticle structure formation), activated carbon (to enhance adsorption capacity), calcium phosphate (as slow-release fertilizer, gradually releasing phosphorus elements), chitosan or polyethyleneimine (for nanoparticle surface modification to improve stability and functionality);

[0041] The ratio of raw materials is:

[0042] Metal oxide precursor zinc sulfate: 5 parts, silica sol: 10 parts, activated carbon: 5 parts, calcium phosphate: 5 parts, chitosan or polyethyleneimine: 1 part, deionized water: 30 parts.

[0043] S4. Synthesis of nanoparticles

[0044] Dissolve the metal oxide precursor zinc sulfate and activated carbon in deionized water, add silica sol and calcium phosphate, stir until uniform, heat the solution to 90°C, adjust the pH to 7.0 by adding ammonia solution dropwise, react for 1 hour to precipitate the metal oxide on the silica sol matrix, mix the precipitated nanoparticles with a chitosan solution, stir for 2 hours, then dry to obtain modified nanoparticles.

[0045] S5. Application of nanoparticles

[0046] Disperse the nanoparticles in deionized water at a ratio of 1 g / L, use ultrasonic treatment for 30 minutes to ensure uniform dispersion, apply the nanoparticle suspension to the contaminated soil through spraying equipment or irrigation systems. Use 0.5L of suspension per square meter of soil. Irrigate immediately after nanoparticle application to ensure that the particles penetrate deep into the soil and adsorb pollutants.

[0047] S6. Design of biomimetic ecological matrix

[0048] Select sodium polyacrylate biomimetic material, use 3D printing technology to manufacture biomimetic ecological matrix biomimetic root network, these matrices can serve as support structures for plant growth, enhance soil stability, lay biomimetic ecological matrix on the surface of the mining area, plant selected plants on the biomimetic matrix to ensure rooting and growth.

[0049] S7. Microbial application

[0050] Screen microorganisms that can form symbiotic relationships with plants, cultivate microorganisms with plants to form symbiotic organisms, then apply symbiotic organisms to mining area soil to observe their repair effect in actual environment.

[0051] S8. VR repair simulation

[0052] A VR platform for mine ecological restoration is established, users can simulate different restoration strategies in a virtual environment and predict their effects, input field data and restoration schemes into the VR platform, establish a real mine model for simulation experiments, test and optimize restoration schemes in the VR platform, and determine the best practices.

[0053] Embodiment 2

[0054] The embodiment of the present application provides a vegetation restoration method for mining wasteland, which specifically comprises the following steps:

[0055] S1. Data collection

[0056] Preliminary survey of the mine area is conducted using unmanned aerial vehicles and ground detection equipment, topographic data, soil samples and water samples are collected, laboratory analysis is performed on the samples to determine the types and concentrations of pollutants (such as heavy metals and acidic substances), and the soil structure, pH value and organic matter content are evaluated;

[0057] S2. Establishment of ecological model

[0058] The collected field data is input into an artificial intelligence system to establish a digital ecological model of the mine area, and the effects of different restoration strategies are simulated through machine learning algorithms (such as deep learning and support vector machines);

[0059] S3. Preparation of nanoparticles

[0060] Prepare metal oxide precursor zinc sulfate (for adsorbing heavy metals), silica sol (for forming stable nanoparticle structure), activated carbon (for enhancing adsorption capacity), calcium phosphate (as slow-release fertilizer, gradually releasing phosphorus elements), chitosan or polyethyleneimine (for nanoparticle surface modification, improving stability and functionality);

[0061] The proportions of raw materials are as follows:

[0062] Metal oxide precursor zinc sulfate: 10 parts, silica sol: 17 parts, activated carbon: 10 parts, calcium phosphate: 10 parts, chitosan or polyethyleneimine: 5 parts, deionized water: 45 parts.

[0063] S4. Synthesis of nanoparticles

[0064] Dissolve the metal oxide precursor zinc sulfate and activated carbon in deionized water, add silica sol and calcium phosphate, stir uniformly, heat the solution at 90°C, add ammonia solution dropwise to adjust the pH to 7.3, react for 1.5 hours, make the metal oxide precipitate on the silica sol matrix, mix the precipitated nanoparticles with the chitosan solution, stir for 2 hours, then dry to obtain the modified nanoparticles.

[0065] S5. Application of nanoparticles

[0066] The nanoparticles are dispersed in deionized water at a ratio of 1 g / L, treated with ultrasonic waves for 30 minutes to ensure uniform dispersion, and applied to the contaminated soil through a spraying device or irrigation system. 0.7 L of the suspension is used per square meter of soil. Irrigation is performed immediately after the nanoparticles are applied to ensure that the particles penetrate deep into the soil and adsorb the pollutants.

[0067] S6. Design of biomimetic ecological matrix

[0068] A sodium polyacrylate biomimetic material is selected, and a 3D printing technology is used to manufacture a biomimetic root network of the biomimetic ecological matrix. These matrices can serve as support structures for plant growth and enhance soil stability. The biomimetic ecological matrix is laid on the surface of the mining area, and selected plants are planted on the biomimetic matrix to ensure their rooting and growth.

[0069] S7. Microbial application

[0070] Microorganisms capable of forming a symbiotic relationship with plants are screened, and the microorganisms are cultured with plants to form a symbiotic body. The symbiotic body is then applied to the soil in the mining area to observe its remediation effect in the actual environment.

[0071] S8. VR remediation simulation

[0072] A VR platform for ecological remediation of mining areas is established. Users can simulate different remediation strategies in a virtual environment and predict their effects. Field data and remediation schemes are input into the VR platform to establish a real mining area model for simulation experiments. Remediation schemes are tested and optimized in the VR platform to determine the best practices.

[0073] Example 3

[0074] The embodiment of the present application provides a vegetation remediation method for mining wasteland, which specifically comprises the following steps:

[0075] S1. Data collection

[0076] A preliminary survey of the mining area is conducted using a drone and ground detection equipment to collect topographic data, soil samples, and water samples. Laboratory analysis is performed on the samples to determine the types and concentrations of pollutants (such as heavy metals and acidic substances), and to evaluate soil structure, pH value, and organic matter content.

[0077] S2. Establishment of ecological model

[0078] The collected field data is input into an artificial intelligence system to establish a digital ecological model of the mining area. Machine learning algorithms (such as deep learning and support vector machines) are used to simulate the effects of different remediation strategies.

[0079] S3. Material preparation of nanoparticles

[0080] Preparation of metal oxide precursor zinc sulfate (for heavy metal adsorption), silica sol (for forming stable nanoparticle structures), activated carbon (to enhance adsorption capacity), calcium phosphate (as a slow-release fertilizer, gradually releasing phosphorus elements), chitosan or polyethyleneimine (for nanoparticle surface modification to improve stability and functionality);

[0081] The ratio of raw materials is:

[0082] Metal oxide precursor zinc sulfate: 15 parts, silica sol: 25 parts, activated carbon: 15 parts, calcium phosphate: 15 parts, chitosan or polyethyleneimine: 10 parts, deionized water: 60 parts.

[0083] S4. Synthesis of nanoparticles

[0084] Dissolve the metal oxide precursor zinc sulfate and activated carbon in deionized water, add silica sol and calcium phosphate, stir until uniform, heat the solution to 90°C, adjust the pH to 7.5 by adding ammonia solution dropwise, react for 2 hours, precipitate the metal oxide on the silica sol matrix, mix the precipitated nanoparticles with a chitosan solution, stir for 2 hours, then dry to obtain modified nanoparticles.

[0085] S5. Application of nanoparticles

[0086] Disperse the nanoparticles in deionized water at a ratio of 1g / L, use ultrasonic treatment for 30 minutes to ensure uniform dispersion, apply the nanoparticle suspension to the contaminated soil through spraying equipment or irrigation systems. Use 1L of suspension per square meter of soil. Irrigate immediately after nanoparticle application to ensure that the particles penetrate deep into the soil and adsorb pollutants.

[0087] S6. Design of biomimetic ecological matrix

[0088] Select sodium polyacrylate biomimetic material, use 3D printing technology to manufacture biomimetic ecological matrix biomimetic root network, these matrices can serve as support structures for plant growth, enhance soil stability, lay the biomimetic ecological matrix on the surface of the mining area, plant selected plants on the biomimetic matrix to ensure rooting and growth.

[0089] S7. Microbial application

[0090] Screen microorganisms that can form symbiotic relationships with plants, cultivate microorganisms with plants to form symbiotic bodies, then apply the symbiotic bodies to the soil in the mining area to observe their repair effect in the actual environment.

[0091] S8. VR repair simulation

[0092] A VR platform for mine ecological restoration is established, users can simulate different restoration strategies in a virtual environment and predict their effects, input field data and restoration schemes into the VR platform, establish a real mine model for simulation experiments, test and optimize restoration schemes in the VR platform, and determine the best practices.

[0093] The specific framework of the VR platform for mine ecological restoration is as follows:

[0094] In the front-end framework, VR head-mounted devices can be used to create a Unity game engine, and input methods such as VR controllers, gesture recognition, and voice recognition can be set to enhance the flexibility of interaction. Real-time rendering technology is used to display the 3D scene of mine ecological restoration, supporting dynamic lighting, shadows, and physical effects.

[0095] The middleware layer manages the loading, switching, and saving of different ecological restoration scenes. Based on physical and biological models, the dynamic changes of the mine ecological system are simulated, such as water flow, soil changes, and plant growth. GIS data, remote sensing data, sensor data, and other data in various formats are converted into visual models and simulation parameters. In a multi-user collaboration environment, data consistency and real-time synchronization between users are ensured.

[0096] In the back-end system architecture, relational database PostgreSQL and non-relational database MongoDB are used to store mine-related geographic information, ecological restoration data, and user operation logs. Data warehouse is used to centrally manage historical restoration schemes, simulation results, and user feedback, supporting data mining and analysis.

[0097] The server layer runs the core application logic, handles user requests, data queries, and simulation calculations, and is developed using Java, Python, or C++. Through simulation calculation servers, complex ecological system simulation and effect prediction are handled, which requires distributed computing or cloud computing resources.

[0098] The ecological model library contains the characteristics of bionic materials, plant growth models, and pollution diffusion models for different mine ecological environments. Artificial intelligence algorithms and machine learning are used to analyze and predict the effects of ecological restoration.

[0099] Although embodiments of the present application have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and alterations can be made without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A method for revegetation of a mine spoil, characterized by, Specifically comprising the following steps: S1. Data collection Conducting a preliminary survey of the mining area using drones and ground detection equipment, collecting topographic data, soil samples, and water samples, conducting laboratory analysis on the samples to determine the types and concentrations of pollutants, and evaluating soil structure, pH, and organic matter content; S2. Establishing an ecological model Inputting the collected field data into an artificial intelligence system to establish a digital ecological model of the mining area, and simulating the effects of different remediation strategies through machine learning algorithms; S3. Nanoparticle material preparation Preparing metal oxide precursors such as zinc sulfate, silica sol, activated carbon, calcium phosphate, chitosan, or polyethyleneimine; S4. Nanoparticle synthesis Dissolving the metal oxide precursor zinc sulfate and activated carbon in deionized water, adding silica sol and calcium phosphate, stirring until uniform, heating the solution to 90°C, adjusting the pH by adding ammonia solution dropwise, and allowing the metal oxide to precipitate on the silica sol matrix for 1-2 hours, then mixing the precipitated nanoparticles with a chitosan solution, stirring for 2 hours, and then drying to obtain modified nanoparticles; S5. Nanoparticle application Dispersing the nanoparticles in deionized water at a ratio of 1 g / L, using ultrasonic treatment for 30 minutes to ensure uniform dispersion, and applying the nanoparticle suspension to the contaminated soil through an irrigation system, and immediately irrigating after nanoparticle application to ensure that the particles penetrate deep into the soil and adsorb pollutants; S6. Design of biomimetic ecological matrix Selecting a sodium polyacrylate biomimetic material and using 3D printing technology to manufacture a biomimetic root network for the biomimetic ecological matrix, which can serve as a support structure for plant growth and enhance soil stability, laying the biomimetic ecological matrix on the surface of the mining area, and planting selected plants on the biomimetic matrix to ensure root growth and development; S7. Microbial application Screening for microorganisms that can form a symbiotic relationship with plants, and co-culturing the microorganisms with the plants to form a symbiotic body, then applying the symbiotic body to the soil in the mining area to observe its remediation effect in the actual environment; S8. VR remediation simulation Establishing a VR platform for ecological remediation of the mining area, allowing users to simulate different remediation strategies in a virtual environment and predict their effects, inputting field data and remediation schemes into the VR platform to establish a realistic model of the mining area for simulation experiments, testing and optimizing remediation schemes in the VR platform, and determining the best practices.

2. A method of revegetation of a mine spoil according to claim 1, characterised in that: The determination of the type of pollutant in the S1 data collection includes detection of heavy metal pollution and detection of acidic substance pollution.

3. The method for revegetation of a mining wasteland according to claim 1, characterized in that: The machine learning algorithm in the S2 ecological model establishment is deep learning or support vector machine.

4. The method for re-vegetation of a mine wasteland according to claim 1, characterized in that: In the step S3, the metal oxide precursor zinc sulfate is used to adsorb heavy metals, the silica sol is used to form a stable nanoparticle structure, the activated carbon is used to enhance adsorption capacity, the calcium phosphate is used as a slow-release fertilizer to gradually release phosphorus elements, and the chitosan or polyethyleneimine is used for surface modification of the nanoparticles to improve stability and functionality.

5. The method for re-vegetation of a mine wasteland according to claim 1, characterized in that: The raw material ratio in the material preparation of the step S3 nanoparticles is: metal oxide precursor zinc sulfate 5-15 parts, silica sol 10-25 parts, activated carbon 5-15 parts, calcium phosphate 5-15 parts, chitosan or polyethyleneimine 1-10 parts, and deionized water 30-60 parts.

6. The method for re-vegetation of a mine wasteland according to claim 1, characterized in that: In the synthesis of the step S4 nanoparticles, the ammonia solution is adjusted to a pH value of 7.0-7.

5.

7. The method for re-vegetation of a mine wasteland according to claim 1, characterized in that: In the application of the step S5 nanoparticles, 0.5-1 L of the suspension is used per square meter of soil.