Simulation-optimization method for groundwater pollution control in in-situ leaching uranium mining final mining area

Abstract

The application discloses a hydraulic control simulation-optimization method for groundwater pollution in a terminal mining area of in-situ leaching uranium mining, and utilizes MODFLOW and PHT3D simulation software to construct a three-dimensional unsteady groundwater flow and pollution component reaction migration simulation model of an ore-bearing aquifer; determines a management objective function and constraint conditions of hydraulic control of the groundwater pollution range in the terminal mining area of in-situ leaching uranium mining, and establishes an objective function of a hydraulic control optimization management model; takes the groundwater level and the pollutant concentration of each grid as a state variable, and takes the pumping flow of each well as a decision variable; the state variable is constantly updated through the three-dimensional unsteady groundwater flow and pollution component reaction migration simulation model of the ore-bearing aquifer, and the particle swarm optimization algorithm is utilized to select an optimal solution of the objective function of the hydraulic control optimization management model. The application can predict the hydraulic control situation of the pollution plume of the ore-bearing aquifer in the terminal mining area, is helpful to find potential problems in advance, and can take corresponding measures for adjustment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of water pollution hydraulic control simulation, in particular to a simulation-optimization method for groundwater pollution hydraulic control in a terminal mining area of in-situ leaching uranium mining. BACKGROUND

[0002] In-situ leaching uranium mining is the main process of natural uranium production in China. In the production process, a certain amount of chemical reagent is injected into the ore-bearing aquifer to obtain uranium and by-products by using the chemical reaction between minerals and aqueous solution. This mining method has the characteristics of low production cost, good safety, high automation, and is suitable for low-grade and small-scale mining of mineral resources. However, although in-situ leaching uranium mining has relatively small damage to the surface ecological environment, it has a significant impact on the groundwater environment.

[0003] During the in-situ leaching uranium mining process, due to equipment conditions and human operational negligence, there may be running, leaking and dripping phenomena, and a small amount of leaching solution may also be brought out during the exhaust operation of the injection well, which may cause pollution to the surrounding environment. Especially in the process of groundwater remediation, waste water containing heavy metal ions and trace uranium may be extracted, which will cause great harm to the environment if not effectively treated.

[0004] Therefore, it is very important to predict the pollution of groundwater in the terminal mining area of in-situ leaching. SUMMARY

[0005] The purpose of the present application is to provide a simulation-optimization method for groundwater pollution hydraulic control in a terminal mining area of in-situ leaching uranium mining, which selects the optimal hydraulic control scheme by numerical simulation method to predict the hydraulic control of the pollution plume of the ore-bearing aquifer in the terminal mining area.

[0006] In order to solve the above technical problems, the present application provides the following technical scheme: a simulation-optimization method for groundwater pollution hydraulic control in a terminal mining area of in-situ leaching uranium mining, the steps of which include:

[0007] A three-dimensional non-steady-state groundwater flow and pollution component reaction and transport simulation model of the ore-bearing aquifer is constructed by using MODFLOW and PHT3D simulation software;

[0008] The management objective function and constraint conditions of the hydraulic control of the groundwater pollution range in the terminal mining area of in-situ leaching uranium mining are determined, and the objective function of the hydraulic control optimization management model is established;

[0009] The groundwater level and the pollutant concentration of each grid are taken as state variables, and the pumping flow of each well is taken as a decision variable. The state variables are constantly updated by using the three-dimensional non-steady-state groundwater flow and pollution component reaction and transport simulation model of the ore-bearing aquifer, and the particle swarm optimization algorithm is used to select the optimal solution of the objective function of the hydraulic control optimization management model; ​

[0010] The hydraulic control scheme corresponding to the optimal solution of the objective function of the hydraulic control optimization management model is selected and substituted into the numerical simulation model to predict the hydraulic control of the pollution plume of the ore-bearing aquifer in the terminal mining area, and to determine the reliability of the optimal solution.

[0011] The groundwater flow model of the terminal mining area is constructed based on the MODFLOW program, and the reaction and transport model considering the adsorption and desorption of uranium components on the surface complex of the ore-bearing aquifer is constructed based on PHT3D. The terminal mining area groundwater flow model and the reaction and transport model considering the adsorption and desorption of uranium components on the surface complex of the ore-bearing aquifer jointly constitute a three-dimensional unsteady groundwater flow and pollution component reaction and transport simulation model of the ore-bearing aquifer. In the three-dimensional unsteady groundwater flow and pollution component reaction and transport simulation model of the ore-bearing aquifer, the surface complex reaction of UO2 2+ under acidic conditions is:

[0012] S_ssOH+UO2 2+ →S_ssOUO2 + +H +

[0013] S_sOH+UO2 2+ →S_sOUO2 + +H + .

[0014] According to the above technical scheme, the constraint conditions of the hydraulic control optimization management model include total number of pumping wells constraint, water head constraint, hydraulic gradient constraint, characteristic pollutant concentration constraint, and single well pumping flow constraint.

[0015] According to the above technical scheme, the objective function of the hydraulic control optimization management model is:

[0016]

[0017] Where P i is the pumping flow of the i th well, n is the total number of pumping wells, L is the distance of the pollution diffusion front, P represents the total pumping amount, X(m) represents the diffusion distance of the m th tracer particle, and m represents the solute tracer particle.

[0018] The decision target of a multi-objective problem is usually contradictory and conflicting, and meeting the needs of one sub-target may lead to deterioration of the results of other sub-targets. Since there are two conditions in the objective function of the hydraulic control optimization management model, conflicts may exist in the optimization process, therefore, the INSGA-II algorithm is used in the particle swarm optimization algorithm, which is better suitable for handling multi-objective optimization problems.

[0019] According to the above technical scheme, the particle swarm optimization algorithm execution steps include:

[0020] S1, initialize the particle swarm, including random position and speed, and determine the maximum capacity of the Pareto table;

[0021] S2, use the NSGA-II algorithm to perform non-dominated sorting on the initialized particle swarm, generate the first generation of particle swarm, and calculate the fitness of each particle in the first generation of particle swarm;

[0022] S3, determine the current global optimal solution of the particle swarm based on the fitness of each particle;

[0023] S4, update the current global optimal solution to the Pareto table, judge whether the capacity of the updated Pareto table is greater than the maximum capacity of the Pareto table, if greater, calculate the selection probability of each solution in the Pareto table, and remove the solution with cumulative probability greater than threshold A from the Pareto table;

[0024] S5, update the position and speed of each particle, calculate the fitness of each particle and jump to step S3 for continuous execution until the iteration condition is met, and output the final Pareto table;

[0025] S6, calculate the selection probability and crowding degree of each solution in the Pareto table, and select the solution corresponding to the minimum ratio of selection probability and crowding degree as the optimal solution in the objective function value of the hydraulic control optimization management model.

[0026] In the optimization process, multiple results can be generated, so it is necessary to select the optimal solution from multiple results, and the combination of selection probability and crowding degree can better select the optimal solution.

[0027] According to the above technical scheme, the NSGA-II algorithm specifically performs the following steps:

[0028] S201, initialize the particle swarm, set t=0;

[0029] S202, calculate the fitness function value of each particle in the particle swarm Pt, and then perform non-dominated sorting, after non-dominated sorting, cross and mutation operators generate a child population Qt, and calculate the fitness function value of each individual in the child population;

[0030] S203, combine the parent and child populations into Rt, Rt=Pt∪Qt;

[0031] S204, update t, t=t+1, judge whether t is equal to 2, if equal to 2, start merging the parent and child populations, calculate the fitness function value of each individual in Rt, and perform fast non-dominated sorting, calculate the crowding degree value, and select the first Z individuals as the newly generated individuals;

[0032] S205, jump to step S204 to continue execution until the iteration condition is met, and output the first generation of particle swarm.

[0033] According to the above technical solution, the crowding degree calculation step comprises:

[0034] Calculate the Euclidean distance d between the solutions x1 and x2;

[0035] According to the Euclidean distance d and the niche radius value σ s , calculate the fitness sharing function value h;

[0036] Based on the fitness sharing function value h of the solution, the crowding degree value m of the solution, the crowding degree value m = ∑h.

[0037] According to the above technical solution, the Euclidean distance calculation formula:

[0038]

[0039] In the formula, N represents the number of objective functions of the hydraulic control optimization management model, Max(f i (x)) represents the maximum value of the i-th objective function of the hydraulic control optimization management model in all solutions, Min(f i (x)) represents the minimum value of the i-th objective function of the hydraulic control optimization management model in all solutions x, f i (x1) represents the objective function value of the i-th hydraulic control optimization management model when the solution is x1, and f i (x2) represents the objective function value of the i-th hydraulic control optimization management model when the solution is x2.

[0040] According to the above technical solution, the fitness sharing function value:

[0041]

[0042] In the formula, h represents the fitness sharing function value, d represents the Euclidean distance, and σ s represents the niche radius value.

[0043] According to the above technical solution, the selection probability:

[0044]

[0045] In the formula, P(x i ) represents the selection probability of the solution i, f(x i ) represents the fitness value of the i-th solution, represents the sum of the fitness values of all solutions, f(x j ) represents the fitness value of the solution j, and M represents the number of solutions.

[0046] Compared with the prior art, the present application has the beneficial effects that: the present application can more accurately describe the dynamic changes of the groundwater system by taking the groundwater level and the pollutant concentration of each grid as the state variable, can constantly update the state variable by using the three-dimensional non-steady-state groundwater flow and pollutant component reaction migration simulation model of the ore-bearing aquifer, so as to reflect the real-time changes of the groundwater system, and can quickly find the optimal solution of the objective function in the complex decision space by using the particle swarm optimization algorithm, and the hydraulic control scheme corresponding to the optimal solution can be substituted into the numerical simulation model to predict the hydraulic control situation of the pollution plume of the ore-bearing aquifer in the final mining area, which helps to find potential problems in advance and take corresponding measures to adjust, so as to ensure that the pollution plume is effectively controlled. BRIEF DESCRIPTION OF DRAWINGS

[0047] The accompanying drawings are included to provide a further understanding of the present application, and constitute a part of the specification, illustrate the present application, and are used to explain the present application, and do not constitute a limitation on the present application. In the drawings:

[0048] Figure 1 is a step flow chart of the in-situ leaching uranium final mining area groundwater pollution hydraulic control simulation-optimization method of the present application;

[0049] Figure 2 is a mining area groundwater monitoring well position and range map of the embodiment;

[0050] Figure 3 is a layout map of the pumping well under different simulation scenarios of the embodiment;

[0051] Figure 4 is a U(VI) and concentration change map at the monitoring point under different simulation scenarios of the embodiment. DETAILED DESCRIPTION

[0052] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0053] This case is a groundwater pollution control case in a uranium in-situ leaching final mining area in Xinjiang. The study area contains C1, C2 and C3 three mining areas, which were formally put into operation in 2006, with a total of 44 liquid pumping wells and 87 liquid injection wells. During the acidification period, sulfuric acid and hydrogen peroxide were mainly used as leaching agents, the leaching environment pH value was less than 2, and the sulfuric acid concentration injected into the groundwater was 12-15 g / L. Since 2010, the liquid pumping and injection wells in each mining area have been retired in succession, and by 2020, the mining area was completely retired and the liquid injection was completely stopped. Only one pumping well was retained in each mining area to continue pumping. The pumping rate of P1 well in C1 mining area was 3.58 m 3 / h, the pumping rate of P2 well in C2 mining area was 4.3 m 3 / h, and the pumping rate of P3 well in C3 mining area was 2.63 m 3 / h. There are 8 groundwater long-term monitoring wells in the mining area, the monitoring well layout is shown in Figure 2 (a), the monitoring is carried out once a month on average, and the monitoring items include groundwater level and U(Ⅵ), pH, Cl - , Ca 2+ , Mg 2+ , etc. Figure 2 (b) is the profile of the ore-bearing aquifer; Figure 2 (c) is the change of monthly liquid pumping volume after 2020 in each mining area. Overall, from January 2020 to October 2021, the liquid pumping volume in each mining area showed an increasing trend first and then a decreasing trend; after October 2021, the pumping flow rate tended to be stable, and the pumping was carried out at a constant flow rate.

[0054] The groundwater pollution hydraulic control simulation-optimization method for in-situ leaching uranium mining final mining area is used to select the hydraulic control scheme, and the specific implementation steps include:

[0055] Step one, use MODFLOW and PHT3D simulation software to build a three-dimensional non-steady-state groundwater flow and pollution component reaction transport simulation model of the ore-bearing aquifer, which specifically considers the surface complexation reaction of UO2 2+ under acidic conditions. Use MODFLOW program to build a groundwater flow model of the final mining area, and use PHT3D to build a reaction transport model considering the adsorption and desorption surface complexation of uranium components in the ore-bearing aquifer. The three-dimensional non-steady-state groundwater flow and pollution component reaction transport simulation model of the ore-bearing aquifer is built by the groundwater flow model of the final mining area and the reaction transport model considering the adsorption and desorption surface complexation of uranium components in the ore-bearing aquifer;

[0056] Among them, the parameter information of the three-dimensional non-steady-state groundwater flow and pollution component reaction transport simulation model of the ore-bearing aquifer includes: longitudinal dispersion coefficient is 1 m, reaction equilibrium constant logK1=1.277, logK2=0.044, adsorption site density ρ1=2.578 g / cm3 p1 = 1.047 g / cm 3

[0057] Step two, determine the management objective function and constraint conditions of groundwater pollution range of in-situ leaching uranium mining terminal area hydraulic control, and establish the objective function of the hydraulic control optimization management model; the hydraulic control optimization management model:

[0058]

[0059] Where, P i is the pumping flow of the i th well, n is the total number of pumping wells, L is the distance of pollution diffusion front, P represents the total pumping amount, X(m) represents the diffusion distance of the m th tracer particle, and m represents the solute tracer particle.

[0060] Step three, taking the groundwater level and the pollutant concentration of each grid as the state variable, and the pumping flow of each well as the decision variable, the state variable is constantly updated through the three-dimensional unsteady groundwater flow and pollution component reaction and transport simulation model, and the optimal solution of the objective function of the hydraulic control optimization management model is selected by using the particle swarm optimization algorithm;

[0061] The execution steps of the particle swarm optimization algorithm include:

[0062] S1, initialize the particle swarm, including random position and speed, and determine the maximum capacity of the Pareto table; the initial solution (well flow) is set to 60 (m 3 / d), and the maximum iteration number N iter = 100;

[0063] S2, the NSGA-II algorithm is used to non-dominantly sort the initialized particle swarm to generate the first generation of particle swarm, and the fitness of each particle in the first generation of particle swarm is calculated;

[0064] S3, determine the current global optimal solution of the particle swarm based on the fitness of each particle;

[0065] S4, update the current global optimal solution to the Pareto table, judge whether the capacity of the updated Pareto table is greater than the maximum capacity of the Pareto table, if greater, calculate the selection probability of each solution in the Pareto table, and remove the solution with cumulative probability less than or equal to the threshold A from the Pareto table;

[0066] S5, update the position and speed of each particle, calculate the fitness of each particle and jump to step S3 for continuous execution until the iteration condition is met, and output the final Pareto table;

[0067] S6, for each group of solutions in the Pareto table, select the probability and the crowding distance, and select the solution corresponding to the maximum ratio of the selection probability and the crowding distance as the optimal solution in the objective function value of the hydraulic control optimization management model.

[0068] wherein the selection probability is:

[0069]

[0070] wherein P(x i ) represents the selection probability of the solution i, f(x i ) represents the fitness value of the solution i, represents the sum of the fitness values of all solutions, f(x j ) represents the fitness value of the solution j, and M represents the number of solutions;

[0071] The crowding distance calculation step includes:

[0072] The Euclidean distance d between the solution x1 and the solution x2 is calculated:

[0073]

[0074] wherein N represents the number of objective functions of the hydraulic control optimization management model, Max(f i (x)) represents the maximum value of the i-th objective function of the hydraulic control optimization management model in all solutions x, Mim(f i (x)) represents the minimum value of the i-th objective function of the hydraulic control optimization management model in all solutions x, f i (x1) represents the value of the i-th objective function of the hydraulic control optimization management model when the solution is x1, and f i (x2) represents the value of the i-th objective function of the hydraulic control optimization management model when the solution is x2.

[0075] According to the Euclidean distance d and the niche radius value σ s , the fitness value sharing function value h is calculated:

[0076]

[0077] wherein h represents the fitness value sharing function value, d represents the Euclidean distance, and σ s represents the niche radius value.

[0078] Based on the fitness value sharing function value h of the solution and the crowding distance value m of the solution, the crowding distance value m = ∑h.

[0079] Step 4: Select the hydraulic control scheme corresponding to the optimal solution of the objective function of the hydraulic control optimization management model. The hydraulic control scheme A corresponding to the optimal solution is: hydraulic interception at the downstream boundary of the mining area. By setting up a pumping well at the downstream boundary, the diffusion of solute to the downstream direction is controlled, and the extracted solution is disposed of by evaporation in a surface water pond.

[0080] And randomly select any solution other than the optimal solution from the final output Pareto table to connect to the corresponding hydraulic control scheme B: hydraulic interception at the downstream boundary of the mining area, by setting up a pumping well at the downstream boundary, while using the original pumping well inside the mining area to control the diffusion of solute downstream, and the extracted solution is disposed of by evaporation in the surface water evaporation pond.

[0081] Substituting water control schemes A and B into a numerical simulation model, the hydraulic control of the pollution plume from the ore-bearing aquifer in the final mining area is predicted. The numerical simulation model employs both internal and external methods. and Concentration changes were monitored at three monitoring points (O1, O2, and O3) within mining areas C1, C2, and C3, respectively, and at a monitoring point O4 located 50m downstream of the mining area boundary. The locations are as follows: Figure 3 As shown.

[0082] Table 1 shows the pumping flow rates of water control schemes A and B under different scenarios, where S1 in Table 1 represents... Figure 3 Scenario 1 in the table and S2 in Table 1 represent Figure 3 Scenario 2 in the table and S3 in Table 1 represent Figure 3 Scenario 3 in the simulation, U(VI) and at the monitoring point under different simulation scenarios Concentration changes such as Figure 4 As shown, Figure 4 SO in S1-Q60 indicates that the pumping capacity of the S1 pumping mode is 60m³. 3 / d, S1-Q90 indicates that the pumping capacity of S1 pumping mode is 90m³. 3 / d, S1-Q120 indicates that the pumping capacity of S1 pumping mode is 120m³. 3 / d, S2-Q80 indicates that the pumping capacity of S2 pumping mode is 80m³ / d. 3 / d, S2-110 indicates that the pumping capacity of S2 pumping mode is 110m³. 3 / d, S3-Q40 indicates that the pumping capacity of S3 pumping mode is 40m³ / d. 3 / d, S3-Q60 indicates that the pumping capacity of S3 pumping mode is 60m³ / d. 3 / d, S3-Q80 indicates that the pumping capacity of S3 pumping mode is 80m³ / d. 3 / d.

[0083] Table 1 Pumping flow rate under different scenarios

[0084]

[0085]

[0086] By Figure 4 It can be seen that each pumping scheme in the three scenarios controls the migration of U(VI) and and to some extent, the concentrated pumping at the downstream boundary of the mining area can change the groundwater flow field at the downstream boundary, increase the hydraulic gradient between the upstream and downstream of the mining area, and accelerate the migration process of U(VI) and , and the greater the total pumping volume, the better the control effect of U(VI) and at the downstream; pumping both inside and outside the mining area not only plays a role of hydraulic interception at the downstream of the mining area, but also plays a role of source cutting inside the mining area, and makes the best use of the existing pumping wells in the mining area.

[0087] It should be noted that in this paper, relational terms such as first and second are used merely to distinguish one entity or action from another, without necessarily requiring or implying any such actual relationship or order between or among the entities or actions. Also, the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0088] Finally, it should be noted that the above only describes the preferred embodiments of the present application, and is not intended to limit the present application, although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements to some technical features. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A simulation-optimization method for hydraulic control of groundwater pollution in a final in-situ leaching uranium mining area, characterized in that, The steps include: A three-dimensional non-steady-state groundwater flow and pollution component reaction migration simulation model of the ore-bearing aquifer is constructed by using MODFLOW and PHT3D simulation software; A management objective function and constraint condition of the hydraulic control of the groundwater pollution range of the in-situ leaching uranium mining terminal mining area are determined, and an objective function of the hydraulic control optimization management model is established; the objective function of the hydraulic control optimization management model is: ； wherein, is the number of wells, is the pumping rate of the well, is the total number of wells, is the distance of the pollution front, denotes the total amount of pumping, denotes the distance of diffusion of the mth tracer particle, denotes the solute tracer particle; The groundwater level and the pollutant concentration of each grid are taken as state variables, and the pumping flow of each well is taken as a decision variable; the state variables are continuously updated by the three-dimensional non-steady-state groundwater flow and pollution component reaction migration simulation model of the ore-bearing aquifer, and the optimal solution of the objective function of the hydraulic control optimization management model is selected by using a particle swarm optimization algorithm; the particle swarm optimization algorithm includes the following steps: S1, initializing the particle swarm, including random position and speed, and determining the maximum capacity of the Pareto table; S2, performing non-dominated sorting on the initialized particle swarm by using the NSGA-II algorithm to generate the first generation of particles, and calculating the fitness of each particle in the first generation of particles; S3, determining the current global optimal solution of the particle swarm based on the fitness of each particle; S4, updating the current global optimal solution to the Pareto table, and judging whether the capacity of the updated Pareto table is greater than the maximum capacity of the Pareto table; if yes, calculating the selection probability of each solution in the Pareto table, and removing the solution with the cumulative probability greater than the threshold A from the Pareto table; S5, updating the position and speed of each particle, calculating the fitness of each particle, and jumping to step S3 for continuous execution until the iteration condition is met, and outputting the final Pareto table; S6, selecting the solution corresponding to the minimum ratio of the selection probability to the crowding degree as the optimal solution in the objective function value of the hydraulic control optimization management model. The optimal solution of the objective function of the hydraulic control optimization management model is selected, and the hydraulic control scheme corresponding to the optimal solution is substituted into the numerical simulation model to predict the hydraulic control condition of the pollution plume of the ore-bearing aquifer in the terminal mining area, and the reliability of the optimal solution is judged.

2. The in-situ leachable uranium mining end-of-mine area groundwater pollution hydraulic control simulation-optimization method of claim 1, wherein, The constraint condition of the hydraulic control optimization management model includes the total number of pumping wells, the water head, the hydraulic gradient, the characteristic pollutant concentration, and the single-well pumping flow.

3. The in-situ leachable uranium mining end-of-mine area groundwater pollution hydraulic control simulation-optimization method of claim 1 wherein, The NSGA-II algorithm includes the following steps: S201, initializing the particle swarm, and setting t=0; S202, calculating the fitness function value of each particle in the particle swarm Pt, and then performing non-dominated sorting; after the non-dominated sorting, the parent and child populations are merged into Rt=Pt∪Qt by using the crossover and mutation operators to generate a child population Qt, and the fitness function value of each individual in the child population is calculated; S203, merging the parent and child populations into Rt=Pt∪Qt; S204, updating t, t=t+1, and judging whether t is equal to 2; if yes, the parent and child populations are merged, the fitness function value of each individual in Rt is calculated, and fast non-dominated sorting is performed; the first Z individuals are selected as the newly generated individuals; and S205, jump to step S204 to continue execution until the iteration condition is met, and output the first generation of particle swarm.

4. The in-situ leachable uranium mining end-of-mine area groundwater pollution hydraulic control simulation-optimization method of claim 1 wherein, The crowding degree calculation step comprises: The Euclidean distance d between the computed solution and the solution is calculated. and the solution. between the computed solution and the solution is calculated. According to the Euclidean distance d and the niche radius value , a fitness sharing function value h is calculated; a solution's crowding value based on a solution's fitness sharing function value h , the crowding value .

5. The in-situ leachable uranium mining end-of-mine area groundwater pollution hydraulic control simulation-optimization method of claim 4, wherein, The Euclidean distance calculation formula: ； In the formula, N represents the number of objective functions in the hydraulic control optimization management model. This represents the maximum value of the objective function of the i-th hydraulic control optimization management model among all solutions. Let x represent the minimum value of the objective function of the i-th hydraulic control optimization management model among all solutions x. The solution is represented as The objective function value of the i-th hydraulic control optimization management model is given. The solution is represented as The objective function value of the i-th hydraulic control optimization management model.

6. The in-situ leachable uranium mining end-of-mine area groundwater pollution hydraulic control simulation-optimization method of claim 4 wherein, The fitness value sharing function value: ； wherein represents an adapted value sharing function value, represents a Euclidean distance, represents a niche radius value.

7. The in-situ leachable uranium mining end-of-mine area groundwater pollution hydraulic control simulation-optimization method of claim 1 wherein, The selection probability: ； wherein represents the selection probability of solution i, the fitness value of the i-th solution, is represented as the total of the fitness values of all solutions, represents the fitness value of solution j, and M represents the number of solutions.

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