Resource utilization method for heavy metal polluted soil

Abstract

The invention discloses a resource utilization method for heavy metal polluted soil, relates to the technical field of treatment of heavy metal polluted soil, and aims to optimize the proportion and predict the performance of a mixture using the heavy metal polluted soil as roadbed filler. The method comprises the following steps: collecting and determining basic physicochemical properties of the polluted soil; the method comprises the following steps: evaluating the initial heavy metal pollution degree of polluted soil, preparing a mixture in a laboratory by using the polluted soil, and simulating a roadbed structure: constructing a small roadbed model by using the mixture, simulating an actual pavement structure, carrying out durability and dynamic loading tests, and monitoring the deformation and long-term leaching toxicity of a roadbed under the action of a load; predicting roadbed performance: constructing a roadbed structure numerical model based on ABAQUS and the obtained deformation amount, and analyzing mechanical and deformation characteristics of the mixed roadbed material under different load and water immersion conditions; and the ratio of the roadbed mixture is optimized. The resource utilization of the solidified heavy metal polluted soil as the roadbed filling material is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of treatment of heavy metal contaminated soil, and in particular to a method for resource utilization of heavy metal contaminated soil. Background Art

[0002] The treatment and resource utilization of heavy metal contaminated soil is an important topic in the current field of environmental protection and sustainable development. The existing methods for treating heavy metal contaminated soil mainly include physical, chemical and biological remediation technologies. However, these methods generally have problems such as long treatment cycle, high cost, and great damage to soil structure. For example, although chemical leaching can effectively remove heavy metals from the soil, it will also reduce soil fertility and affect the balance of the ecosystem. Although bioremediation technology is environmentally friendly, it is inefficient and difficult to meet the needs of large-scale pollution control. In recent years, research on the resource utilization of heavy metal contaminated soil as roadbed filling materials has gradually attracted attention. This method can not only achieve on-site treatment of contaminated soil, but also reduce the mining of natural materials, with significant economic and environmental benefits. However, there are still some problems with existing resource utilization technologies: (1) Poor heavy metal stabilization effect: Although the risk of heavy metal leaching can be reduced by adding curing agents, the long-term stability has not been fully verified and there is a potential risk of secondary pollution.

[0003] (2) Insufficient performance of roadbed engineering: After treatment, some heavy metal contaminated soils have indicators such as strength, compaction and water stability that are difficult to meet the technical requirements of roadbed filling, limiting their widespread application.

[0004] (3) Imperfect environmental safety assessment system: Existing assessment methods mainly focus on the total amount of heavy metals or leaching concentration, ignoring the migration and transformation of heavy metals in the environment and their bioavailability, making it difficult to fully reflect the environmental risks of treated soil.

[0005] Therefore, there is an urgent need to develop a method that can simultaneously achieve the stabilization and resource utilization of heavy metal contaminated soil, ensuring environmental safety while meeting the technical requirements of roadbed engineering. The present invention aims to solve the above problems and proposes a method for resource utilization of heavy metal contaminated soil. Summary of the Invention

[0006] To address the shortcomings of existing technologies, the present invention aims to provide a method for resource-based utilization of heavy metal-contaminated soil. This method distinguishes between the bioavailable portion of heavy metals and their steady-state toxicity. While accurately assessing the degree of heavy metal contamination, it uses mineral powder (a byproduct of industrial and agricultural waste) and corn straw biochar as curing agents, taking into account both road performance and pollution remediation effectiveness. This method allows for the resource-based utilization of the cured heavy metal-contaminated soil as roadbed fill material.

[0007] According to one aspect of the present disclosure, a method for resource utilization of heavy metal contaminated soil is provided, characterized in that the method comprises: Step S1: collecting and measuring the basic physical and chemical properties of contaminated soil; Step S2: Assessing the initial heavy metal contamination level of the contaminated soil in step S1, including: determining the content of target heavy metals and their different chemical forms in the soil, distinguishing the contribution of different heavy metal types and forms to environmental risks through the analytic hierarchy process, and calculating and determining the initial heavy metal contamination level using the single factor index method and the improved Nemerow index method; Step S3: Preparing a mixture using the contaminated soil from step S1: According to a uniform test design, the contaminated soil is mixed with a preselected curing agent to prepare mixtures of different proportions. Standard compaction tests are performed on the mixtures of different proportions. Standard specimens are prepared based on the measured compaction characteristics. The standard specimens are cured under standard conditions to form a solidified body. The physical and mechanical properties and heavy metal content of the solidified body are measured, and the degree of heavy metal contamination is determined using the evaluation method in step S2. Step S4: Simulating roadbed structure: using the mixture in step S3 to cast a small roadbed model, simulating the actual road structure, conducting durability and loading tests, monitoring and recording the response data of the roadbed model under load and long-term leaching toxicity; Step S5: Predicting roadbed performance: A numerical model of the roadbed structure is constructed based on the finite element software ABAQUS and the test response data obtained in step S4. The mechanical and deformation properties of the roadbed material under different load and immersion conditions in step S4 are analyzed. Based on the analysis results, a prediction model of the roadbed material performance is established in combination with a neural network to predict the mechanical and deformation properties of the mixed roadbed material. Step S6: Optimizing the proportion of mixed roadbed materials: performing regression analysis based on the properties of the mixture and solidified soil obtained in step S3, the degree of soil contamination after treatment, and the mechanical and deformation characteristics of the mixed roadbed materials obtained in steps S4 and S5; Based on the road performance indicators of the mixture, heavy metal pollution limit values ​​and roadbed deformation requirements, a multi-objective optimization model is constructed to determine the optimal ratio of mixed roadbed materials.

[0008] In a possible implementation, the basic physical and chemical properties of the contaminated soil in step S1 include moisture content, pH value, density, particle size distribution, permeability coefficient, and plasticity index.

[0009] In one possible implementation, the target heavy metal species in step S2 is determined by an X-ray fluorescence spectrometer; The different chemical forms of heavy metals in step S2 include exchangeable state, reducible state, oxidizable state and residual state; The initial contamination degree of the contaminated soil in step S2 includes the contamination degree of a single heavy metal and the contamination degree of multiple heavy metals.

[0010] In a possible implementation, the mixture prepared in step S3 includes contaminated soil, a solvent, and a curing agent; the solvent is deionized water; the curing agent includes cement, mineral powder, and corn straw biochar; The proportions in step S3 include water-ash ratio, ore-ash ratio, and carbon-ash ratio; The compaction characteristics of the mixture in step S3 are measured by compaction tests, including the optimal moisture content, maximum dry density and compaction degree of mixtures with different proportions under normal temperature and pressure, cyclic loading, dry-wet cycle, and freeze-thaw cycle conditions, and standard specimens are prepared accordingly; The physical and mechanical properties of the solidified body to be measured in step S3 include: 28-day California bearing ratio, 28-day unconfined compressive strength, and permeability coefficient under normal temperature and pressure, cyclic loading, dry-wet cycle, and freeze-thaw cycle conditions; The operation of determining the degree of heavy metal contamination of the solidified body in step S3 includes: performing leaching toxicity tests on the solidified body under different test conditions, measuring the heavy metal concentration in the leachate, and calculating the corresponding index value according to the evaluation method in step S2.

[0011] In one possible implementation, the durability test in step S4 includes a long-term immersion test on the roadbed model and a heavy metal concentration test on the leachate; the loading test in step S4 includes applying a static load and a cyclic load simulating vehicle driving to the roadbed model, and arranging strain gauges and displacement sensors to monitor the mechanical response data of the roadbed under different loads, wherein the mechanical response data includes stress, strain distribution and deformation.

[0012] In a possible implementation, the specific steps of simulating the mixed material roadbed in ABAQUS in step S5 include: Step S51: Geometric model construction: The research goal is to analyze the mechanical and deformation characteristics of the roadbed structure under different load and water immersion conditions, using a three-dimensional geometric model to more realistically reflect the spatial distribution and stress state of the roadbed structure; Step S52: Physical field settings: The mechanical behavior of the subgrade structure is mainly based on elastic-plastic deformation. The Mohr-Coulomb model provided by ABAQUS is used to simulate the mechanical properties of the soil. At the same time, the pore water pressure field is combined to simulate the changes in the mechanical properties of the soil under immersion conditions. Step S53: Parameter definition: Based on the material property data obtained in steps S3 and S4, the key parameters of the elastoplastic constitutive model are defined, including the elastic modulus, Poisson's ratio, internal friction angle, and cohesion of the soil. The immersion effect is simulated by setting the pore water pressure field and soil softening parameters. At the same time, the load size, action area, and boundary conditions are defined. Step S54: Mesh division and optimization: The ABAQUS meshing tool was used to mesh the roadbed structure. Finer meshes were used in the load-bearing and flooded areas to improve calculation accuracy, while coarser meshes were used in other areas to reduce the amount of calculation. Step S55: Solve: Static and dynamic analyses were performed using the Standard module and the Explicit solver in ABAQUS. Multi-step analysis was used to simulate the gradual application of loads and the dynamic changes in immersion conditions. The stability and accuracy of the solution process were ensured by adjusting the time step and convergence criteria. Step S56: Result extraction and analysis: The post-processing function of ABAQUS is used to extract the stress, strain and displacement distribution results of the roadbed structure; by drawing stress cloud maps, deformation maps and displacement-time curves of key nodes, the mechanical and deformation characteristics of the roadbed under different load and immersion conditions are analyzed.

[0013] In one possible implementation, the validity of the roadbed model established by ABAQUS in step S5 is verified by comparing it with the test results of step S4; the ABAQUS numerical analysis results are organized into a data set, and a neural network is used to establish a mechanical property and deformation prediction model of the hybrid roadbed material.

[0014] In one possible implementation, in step S6, SPSS software is used to perform regression analysis to fit the contamination degree, deformation, 28-day California bearing ratio, and 28-day unconfined compressive strength change formula of mixed roadbed materials with different water-cement ratios, mineral ash ratios, and carbon ash ratios under normal temperature and pressure, cyclic loads, dry-wet cycles, and freeze-thaw cycles.

[0015] In one possible implementation, the multi-objective optimization model in step S6 adopts a non-dominated sorting genetic algorithm II, with the road performance of the mixed material, the degree of pollution and the roadbed deformation as the optimization objectives, and determines the optimal ratio of the mixed roadbed material according to the project cost and resource utilization efficiency.

[0016] In a possible implementation, the optimal ratio includes the optimal performance ratio, the lowest pollution ratio, and the lowest cost ratio; Optimal performance ratio: The mixture achieves the maximum 28-day California bearing ratio and 28-day unconfined compressive strength while meeting pollution and cost control requirements; Minimum pollution ratio: The mixture achieves the lowest heavy metal leaching concentration while meeting the requirements of mechanical properties and cost control; Optimal cost ratio: The mixture achieves the lowest cost while meeting the pollution and mechanical property control requirements; Mechanical properties and pollution control requirements include: 28-day California bearing ratio not less than 8%, 28-day compressive strength not less than 1.0MPa, and pollution index improved Nemerow index PN meets the national heavy metal contaminated soil treatment standards.

[0017] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention proposes a method for resource utilization of heavy metal-contaminated soil. By accurately assessing the degree of contamination, optimizing the mixture ratio, and simulating the roadbed structure, it achieves efficient resource utilization of contaminated soil and significantly reduces environmental risks. 2. The present invention uses an improved Nemerow index method to evaluate the degree of heavy metal pollution, taking into account multiple forms of soil heavy metals, such as the total amount, exchangeable content, and reducible content, thereby improving the accuracy and comprehensiveness of pollution assessment; 3. This invention introduces ABAQUS finite element analysis software to construct a numerical model of the roadbed structure. Durability and dynamic loading tests are conducted on the actual roadbed structure, which more realistically simulates the mechanical and deformation characteristics of the roadbed under different load and immersion conditions. 4. Using SPSS software for regression analysis, a quantitative relationship between the contamination level and mechanical properties of mixed roadbed materials under cyclic loading, dry-wet cycles, and freeze-thaw cycles was established, providing a theoretical basis for optimizing mix ratios. 5. A non-dominated sorting genetic algorithm II (NSGA-II) was used for multi-objective optimization, simultaneously considering the road performance of the mixed material, pollution level, roadbed deformation, project cost, and resource utilization efficiency, to scientifically determine the optimal mix ratio. 6. The method of the present invention establishes a complete performance evaluation system, including mechanical indicators such as the 28-day California bearing ratio and the 28-day unconfined compressive strength, as well as environmental indicators such as heavy metal leaching concentration, to ensure the engineering applicability and environmental safety of the treated soil. By combining ABAQUS numerical analysis results with neural networks, a prediction model for the mechanical properties and deformation of hybrid roadbed materials was established, improving the accuracy and efficiency of the prediction. 7. The method of the present invention fully considers the requirements of relevant national standards, such as the "Land Environmental Quality Construction Land Soil Pollution Risk Control Standards", ensuring that the treated soil meets the dual requirements of environmental protection and engineering application. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1A schematic diagram of a hierarchical analysis structure for distinguishing the contributions of different heavy metal pollution using the hierarchical analysis method according to an embodiment of the present disclosure is shown.

[0019] Figure 2 A flowchart of a method for resource utilization of heavy metal contaminated soil according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0020] Various exemplary embodiments, features, and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise indicated.

[0021] The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0022] In addition, numerous specific details are provided in the following detailed description to better illustrate the present disclosure. Those skilled in the art will appreciate that the present disclosure can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art are not described in detail in order to highlight the main points of the present disclosure.

[0023] The present invention provides a method for resource utilization of heavy metal contaminated soil, comprising the following steps: Step S1: collecting and measuring the basic physical and chemical properties of contaminated soil; Contaminated soil samples (0-20 cm) were collected from a heavy metal-contaminated site. After sampling, the samples were placed in a well-ventilated area to air dry, debris and other debris removed, ground, and passed through a 200-mesh sieve. The samples were then stored away from light until further use. Indoor geotechnical tests were conducted to determine the basic physical and chemical properties of the soil.

[0024] Step S2: Evaluate the initial heavy metal contamination level of the contaminated soil in step S1, including: determining the content of target heavy metals and their different chemical forms in the soil, distinguishing the contribution of different heavy metal types and forms to environmental risks through the analytic hierarchy process, and calculating and determining the initial heavy metal contamination level using the single factor index method and the improved Nemerow index method, specifically: X-ray fluorescence spectrometry was used to determine the types of heavy metals such as lead, cadmium, mercury, arsenic, and chromium in the soil. Based on the BCR continuous extraction method, the content of heavy metals in different forms, including exchangeable, reducible, oxidizable, and residual forms, was determined by inductively coupled plasma emission spectrometry. The contribution of different heavy metal pollution was distinguished by the hierarchical analysis method, and the modified Nemerow index method P was used to determine the content of heavy metals in the soil. N To evaluate the degree of heavy metal pollution, the calculation formula is as follows: ; Where, 、 、 、 、 They are the total amount of soil heavy metals, exchangeable content, reducible content, oxidizable content, and residual content, with the unit being mg / kg; 、 are the bioavailable content of heavy metals and the background value of heavy metal i, respectively, in mg / kg; is the single factor pollution index of heavy metal i; The weight coefficients determined for the AHP method; 、 、 They are the comprehensive average index of heavy metal pollution, the largest single factor index of heavy metal pollution, and the improved Nemerow index; the hierarchical analysis structure is shown in Figure 1 The pollution degree classification is shown in Table 1-Table 2, and the heavy metal toxicity coefficient, limit value in food, and limit value in surface water are shown in Table 3.

[0025] Table 1 Pollution degree classification using single factor index method ; Table 2 Nemerow index method pollution degree classification ; Table 3 Heavy metal toxicity coefficients, food limit values, and surface water limit values ; Step S3: Preparation of mixture in laboratory According to the uniform test design, different proportions of mixtures were prepared and solidified soil was formed. The properties and pollution degree of the solidified body under different proportions were measured, including: Using cement, mineral powder and biochar as curing agents and deionized water as solvent, mixtures were prepared in different proportions according to a uniform test design. Standard compaction tests were carried out on the mixtures with different proportions. Standard specimens were prepared according to the measured compaction characteristics. The standard specimens were cured under standard conditions to form a solidified body, and the physical and mechanical properties and heavy metal content information of the solidified body were measured.

[0026] Step S4: Roadbed structure simulation Construct a small-scale roadbed model to simulate actual road structures. Conduct durability and loading tests, as well as static and dynamic loading tests, to simulate the effects of vehicle cyclic loads on the roadbed. Monitor and record the deformation response data and long-term leaching toxicity of the roadbed model under load.

[0027] Step S5: Predict roadbed performance A numerical model of the subgrade structure is constructed using the finite element software ABAQUS and the test response data obtained in step S4. The mechanical and deformation properties of the subgrade material under different load and water immersion conditions in step S4 are analyzed. Based on the analysis results, a performance prediction model for the hybrid subgrade material is established using a neural network. This model is used to predict the mechanical and deformation properties of the hybrid subgrade material. The specific steps include: Step S51: Geometric model construction Since the research goal is to analyze the mechanical and deformation characteristics of the roadbed structure under different load and immersion conditions, a three-dimensional geometric model is used to more realistically reflect the spatial distribution and stress state of the roadbed structure.

[0028] Step S52: Physical field settings The mechanical behavior of the subgrade structure is primarily based on elastic-plastic deformation. The Mohr-Coulomb model provided by ABAQUS is used to simulate the mechanical properties of the soil. The pore water pressure field is also used to simulate the changes in the mechanical properties of the soil under flooding conditions.

[0029] Step S53: Parameter definition Based on the material property data obtained in steps S3 and S4, define the key parameters of the elastoplastic constitutive model, including the soil's elastic modulus, Poisson's ratio, internal friction angle, and cohesion. Simulate the effects of water immersion by setting the pore water pressure field and soil softening parameters. Also, define the load magnitude, applied area, and boundary conditions.

[0030] Step S54: Mesh division and optimization The ABAQUS meshing tool was used to mesh the roadbed structure. Finer meshes were used in the load-bearing and flooded areas to improve calculation accuracy, while coarser meshes were used in other areas to reduce the amount of calculation.

[0031] Step S55: Solving Static and dynamic analyses were performed using the ABAQUS / Standard and ABAQUS / Explicit solvers. Multi-step analysis was implemented to simulate the gradual application of loads and the dynamic changes in immersion conditions. The stability and accuracy of the solution were ensured by adjusting the time step and convergence criteria.

[0032] Step S56: Result extraction and analysis The post-processing capabilities of ABAQUS were used to extract the stress, strain, and displacement distributions of the roadbed structure. By plotting stress contours, deformation diagrams, and displacement-time curves of key nodes, the mechanical and deformation characteristics of the roadbed under different load and water immersion conditions were analyzed.

[0033] A neural network was used to establish a model for predicting the mechanical properties and deformation of hybrid roadbed materials. Specifically, the ABAQUS analysis results were compared with the test results from step S4 to verify the validity of the model. The ABAQUS numerical analysis results were compiled as a data set, and the neural network was used to predict the mechanical properties and deformation of the hybrid roadbed materials under different conditions.

[0034] Step S6: Optimization of roadbed mixture ratio Based on the mixture performance and pollution degree obtained in step S3 and the mechanical and deformation characteristics of the mixed roadbed material obtained in step S5, regression analysis was performed using SPSS software to fit the pollution degree, deformation, 28-day California bearing ratio, and 28-day unconfined compressive strength change formulas of mixed roadbed materials with different water-cement ratios, mineral ash ratios, and carbon ash ratios under normal temperature and pressure, cyclic load, dry-wet cycle, and freeze-thaw cycle conditions.

[0035] Furthermore, the basic physical and chemical properties of the contaminated soil in step S1 include moisture content, pH, density, permeability coefficient and plasticity index.

[0036] Furthermore, the target heavy metal species in step S2 are determined by X-ray fluorescence spectrometry (XRF); The different chemical forms of heavy metals in step S2 include exchangeable state, reducible state, oxidizable state and residual state; The initial contamination degree of the contaminated soil in step S2 includes the contamination degree of a single heavy metal and the contamination degree of multiple heavy metals.

[0037] Furthermore, the mix ratio in step S3 includes water-cement ratio, mineral ash ratio, and carbon ash ratio. The measured mixture properties include the optimal moisture content, maximum dry density, compaction degree, 28-day California bearing ratio, 28-day unconfined compressive strength, and permeability coefficient of the contaminated soil under normal temperature and pressure, cyclic load, dry-wet cycle, and freeze-thaw cycle conditions. The improved Nemerow index is calculated by detecting the heavy metal concentration of the leachate of the solidified body after 28 days of solidification to obtain the contamination degree of the remediated contaminated soil. The leaching toxicity test refers to the "Solid Waste Leaching Toxicity Leaching Method Sulfuric Acid and Nitric Acid Method" (HJ / T 299-2007).

[0038] Determine the optimal moisture content, maximum dry density, compaction degree, 28-day California bearing ratio, 28-day unconfined compressive strength, and permeability coefficient of different mixture ratios under normal temperature and pressure, cyclic loading, dry-wet cycling, and freeze-thaw cycling conditions. Determine the degree of heavy metal contamination in the solidified material in step S3 by conducting a leaching toxicity test on the solidified material under different test conditions, measuring the heavy metal concentration in the leachate, and calculating the corresponding index value according to the evaluation method in step S2.

[0039] Furthermore, the durability test in step S4 includes a long-term immersion test on the roadbed model and a heavy metal concentration test of the leachate, simulating the impact of vehicle cyclic loads on the roadbed through static and dynamic loading tests, and arranging strain gauges and displacement sensors to monitor the stress, strain distribution and deformation of the roadbed under different loads.

[0040] Furthermore, in step S5, the effectiveness of the roadbed model established by ABAQUS is verified by comparing it with the test results in step S4; the ABAQUS numerical analysis results are used as a data set, and a neural network is used to establish a mechanical property and deformation prediction model of the hybrid roadbed material.

[0041] Furthermore, in step S6, SPSS software is used to perform regression analysis to fit the quantitative relationship formula between the pollution degree of mixed roadbed materials with different water-cement ratios, mineral ash ratios, and carbon ash ratios under normal temperature and pressure, cyclic loads, dry-wet cycles, and freeze-thaw cycles, and the California bearing ratio and 28-day unconfined compressive strength after curing for 28 days.

[0042] Furthermore, the multi-objective optimization model in step S6 adopts a non-dominated sorting genetic algorithm II (NSGA-II), takes the road performance of the mixed material, the pollution degree and the roadbed deformation as the optimization objectives, and takes the project cost and resource utilization efficiency into consideration to determine the optimal ratio of the mixed roadbed material.

[0043] Furthermore, the optimal ratio in step S6 includes the optimal performance ratio, the lowest pollution ratio, and the lowest cost ratio. The optimal performance ratio means that the mixed material reaches the maximum 28-day California bearing ratio and 28-day unconfined compressive strength while meeting the pollution and cost control requirements. The lowest pollution ratio means that the mixed material reaches the lowest heavy metal leaching concentration while meeting the mechanical properties and cost control requirements. The optimal cost ratio means that the mixed material reaches the lowest cost while meeting the pollution and mechanical properties control requirements. The mechanical properties and pollution control requirements include: the 28-day California bearing ratio is not less than 8%, the 28-day compressive strength is not less than 1.0 MPa, and the pollution index improved Nemerow index P N Comply with national standards for contaminated soil treatment.

[0044] Furthermore, the national standards for contaminated soil treatment include the "Land Environmental Quality - Construction Land Soil Pollution Risk Control Standard" (GB36600-2018), the "Soil Environmental Quality - Agricultural Land Soil Pollution Risk Control Standard" (GB15618-2018), and the "Hazardous Waste Identification Standard - Leaching Toxicity Identification" (GB5085.3-2007).

[0045] While various embodiments of the present disclosure have been described above, the foregoing description is intended to be illustrative, non-exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is selected to best explain the principles of the embodiments, their practical applications, or technological improvements in the marketplace, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for resource utilization of heavy metal contaminated soil, characterized in that: The method comprises: Step S1: collecting and measuring the basic physical and chemical properties of contaminated soil; Step S2: Assessing the initial heavy metal contamination level of the contaminated soil in step S1, including: determining the content of target heavy metals and their different chemical forms in the soil, distinguishing the contribution of different heavy metal types and forms to environmental risks through the analytic hierarchy process, and calculating and determining the initial heavy metal contamination level using the single factor index method and the improved Nemerow index method; Step S3: Preparing a mixture using the contaminated soil from step S1: According to a uniform test design, the contaminated soil is mixed with a preselected curing agent to prepare mixtures of different proportions. Standard compaction tests are performed on the mixtures of different proportions. Standard specimens are prepared based on the measured compaction characteristics. The standard specimens are cured under standard conditions to form a solidified body. The physical and mechanical properties and heavy metal content of the solidified body are measured, and the degree of heavy metal contamination is determined using the evaluation method in step S2. Step S4: Simulating roadbed structure: using the mixture in step S3 to cast a small roadbed model, simulating the actual road structure, conducting durability and loading tests, monitoring and recording the response data of the roadbed model under load and long-term leaching toxicity; Step S5: Predicting roadbed performance: A numerical model of the roadbed structure is constructed based on the finite element software ABAQUS and the test response data obtained in step S4. The mechanical and deformation properties of the roadbed material under different load and immersion conditions in step S4 are analyzed. Based on the analysis results, a prediction model of the roadbed material performance is established in combination with a neural network to predict the mechanical and deformation properties of the mixed roadbed material. Step S6: Optimizing the proportion of mixed roadbed materials: performing regression analysis based on the properties of the mixture and solidified soil obtained in step S3, the degree of soil contamination after treatment, and the mechanical and deformation characteristics of the mixed roadbed materials obtained in steps S4 and S5; Based on the road performance indicators of the mixture, heavy metal pollution limit values ​​and roadbed deformation requirements, a multi-objective optimization model is constructed to determine the optimal ratio of mixed roadbed materials.

2. The method for resource utilization of heavy metal contaminated soil according to claim 1, characterized in that: The basic physical and chemical properties of the contaminated soil in step S1 include moisture content, pH value, density, particle size distribution, permeability coefficient and plasticity index.

3. The method for resource utilization of heavy metal contaminated soil according to claim 1, characterized in that: The target heavy metal species in step S2 are determined by X-ray fluorescence spectrometry; The different chemical forms of heavy metals in step S2 include exchangeable state, reducible state, oxidizable state and residual state; The initial contamination degree of the contaminated soil in step S2 includes the contamination degree of a single heavy metal and the contamination degree of multiple heavy metals.

4. The method for resource utilization of heavy metal contaminated soil according to claim 1, characterized in that: The mixed material prepared in step S3 includes contaminated soil, solvent, and curing agent; the solvent is deionized water; the curing agent includes cement, mineral powder, and corn straw biochar; The proportions in step S3 include water-ash ratio, ore-ash ratio, and carbon-ash ratio; The compaction characteristics of the mixture in step S3 are measured by compaction tests, including the optimal moisture content, maximum dry density and compaction degree of mixtures with different proportions under normal temperature and pressure, cyclic loading, dry-wet cycle, and freeze-thaw cycle conditions, and standard specimens are prepared accordingly; The physical and mechanical properties of the solidified body to be measured in step S3 include: 28-day California bearing ratio, 28-day unconfined compressive strength, and permeability coefficient under normal temperature and pressure, cyclic loading, dry-wet cycle, and freeze-thaw cycle conditions; The operation of determining the degree of heavy metal contamination of the solidified body in step S3 includes: performing leaching toxicity tests on the solidified body under different test conditions, measuring the heavy metal concentration in the leachate, and calculating the corresponding index value according to the evaluation method in step S2.

5. The method for resource utilization of heavy metal contaminated soil according to claim 1, characterized in that: The durability test in step S4 includes a long-term immersion test on the roadbed model and a heavy metal concentration test in the leachate; the loading test in step S4 includes applying a static load and a cyclic load simulating vehicle driving to the roadbed model, arranging strain gauges and displacement sensors to monitor the mechanical response data of the roadbed under different loads, and the mechanical response data includes stress, strain distribution and deformation.

6. The method for resource utilization of heavy metal contaminated soil according to claim 1, characterized in that: The specific steps of simulating the mixed material roadbed in ABAQUS in step S5 include: Step S51: Geometric model construction: The research goal is to analyze the mechanical and deformation characteristics of the roadbed structure under different load and water immersion conditions, using a three-dimensional geometric model to more realistically reflect the spatial distribution and stress state of the roadbed structure; Step S52: Physical field settings: The mechanical behavior of the subgrade structure is mainly based on elastic-plastic deformation. The Mohr-Coulomb model provided by ABAQUS is used to simulate the mechanical properties of the soil. At the same time, the pore water pressure field is combined to simulate the changes in the mechanical properties of the soil under immersion conditions. Step S53: Parameter definition: Based on the material property data obtained in steps S3 and S4, the key parameters of the elastoplastic constitutive model are defined, including the elastic modulus, Poisson's ratio, internal friction angle, and cohesion of the soil. The immersion effect is simulated by setting the pore water pressure field and soil softening parameters. At the same time, the load size, action area, and boundary conditions are defined. Step S54: Mesh division and optimization: The ABAQUS meshing tool was used to mesh the roadbed structure. Finer meshes were used in the load-bearing and flooded areas to improve calculation accuracy, while coarser meshes were used in other areas to reduce the amount of calculation. Step S55: Solve: Static and dynamic analyses were performed using the Standard module and the Explicit solver in ABAQUS. Multi-step analysis was used to simulate the gradual application of loads and the dynamic changes in immersion conditions. The stability and accuracy of the solution process were ensured by adjusting the time step and convergence criteria. Step S56: Result extraction and analysis: The post-processing function of ABAQUS is used to extract the stress, strain and displacement distribution results of the roadbed structure; by drawing stress cloud maps, deformation maps and displacement-time curves of key nodes, the mechanical and deformation characteristics of the roadbed under different load and immersion conditions are analyzed.

7. The method for resource utilization of heavy metal contaminated soil according to claim 1, characterized in that: The effectiveness of the subgrade model established by ABAQUS in step S5 is verified by comparing it with the test results in step S4; The ABAQUS numerical analysis results were organized into a data set, and a neural network was used to establish a mechanical property and deformation prediction model for hybrid roadbed materials.

8. The method for resource utilization of heavy metal contaminated soil according to claim 1, characterized in that: In step S6, SPSS software is used to perform regression analysis to fit the contamination degree, deformation, 28-day California bearing ratio, and 28-day unconfined compressive strength change formulas of mixed roadbed materials with different water-cement ratios, mineral-cement ratios, and carbon-cement ratios under normal temperature and pressure, cyclic loads, dry-wet cycles, and freeze-thaw cycles.

9. The method for resource utilization of heavy metal contaminated soil according to claim 1, characterized in that: The multi-objective optimization model in step S6 adopts a non-dominated sorting genetic algorithm II, takes the road performance of the mixed material, the pollution degree and the roadbed deformation as the optimization objectives, and determines the optimal ratio of the mixed roadbed material according to the project cost and resource utilization efficiency.

10. The method for resource utilization of heavy metal contaminated soil according to claim 8, characterized in that: The optimal ratio includes the optimal performance ratio, the lowest pollution ratio, and the lowest cost ratio; Optimal performance ratio: The mixture achieves the maximum 28-day California bearing ratio and 28-day unconfined compressive strength while meeting pollution and cost control requirements; Minimum pollution ratio: The mixture achieves the lowest heavy metal leaching concentration while meeting the requirements of mechanical properties and cost control; Optimal cost ratio: The mixture achieves the lowest cost while meeting the pollution and mechanical property control requirements; Mechanical properties and pollution control requirements include: 28-day California bearing ratio not less than 8%, 28-day compressive strength not less than 1.0MPa, pollution index improved Nemerow index P N It complies with national standards for the treatment of heavy metal contaminated soil.

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