Underground water DNAPL simulation method coupling mathematical model and optimization algorithm

Abstract

The invention provides a method for simulating heavy non-aqueous phase liquid in underground water by coupling a mathematical model and an optimization algorithm. The method comprises the following steps: respectively acquiring underground water dynamic change data, environmental index change data and DNAPL concentration data; hydrogeological conditions of the site and the DNAPL pollution migration and transformation process are analyzed; performing simulation based on a multiphase flow and solute transport coupling model; and constructing a simulation system in which a mathematical model and an optimization algorithm are coupled, simulating and evaluating the transfer process of the DNAPL in the underground water according to the acquired data, and acquiring a simulation result matched with an actual monitoring condition. According to the method, programming is carried out based on the Python language, through mutual coupling of open source programs, simulation of the whole process of different phase states of DNAPL pollution is achieved, meanwhile, model parameters are corrected based on an optimization algorithm, an optimal simulation result conforming to actual monitoring data is finally obtained, and a foundation is laid for subsequent deeper DNAPL simulation work.

Description

Technical Field

[0001] The present invention relates to the technical field of groundwater pollution control, and in particular to a groundwater DNAPL simulation method coupling a mathematical model and an optimization algorithm. Background Art

[0002] Dense non-aqueous phase liquids (DNAPLs) are a type of pollutant that has attracted much attention in the field of groundwater pollution. DNAPLs have the following special properties: (1) The density is greater than that of water, so that pollutants not only migrate horizontally with groundwater, but also migrate independently to the depths of the formation in the vertical direction; (2) Low water solubility, a small part of the pollutants dissolve in water, and then present a state of coexistence of free phase and dissolved phase of DNAPLs, which greatly increases the complexity of pollution; (3) Low viscosity and low interfacial tension promote the migration of DNAPLs in the groundwater environment; (4) Low biodegradability, so that pollutants can exist in the groundwater environment for a long time, and the risk hazards are difficult to eliminate.

[0003] For groundwater DNAPLs pollution, accurately understanding the distribution characteristics of DNAPLs in contaminated plots can help reduce remediation costs and improve the pertinence of remediation measures, which is crucial to the formulation of remediation plans. At present, there are many software in the field of groundwater pollution simulation. For groundwater flow field and solute transport simulation, there are GMS, MODFLOW, VisualModflow, FEFLOW, etc., and for DNAPLs multiphase flow simulation, the commonly used modules are TOUGH2's T2VOC and TMVOC. However, there are still two major drawbacks: on the one hand, the above commonly used software are all commercial software, which cannot be used as open source, and the application promotion is limited; on the other hand, there is independence between multiphase flow simulation software and solute transport software. For the multiphase simulation problem of DNAPLs pollution, it is impossible to unify the simulation on a unified platform, which increases the difficulty of coupling optimization algorithms and methods. Summary of the invention

[0004] In view of this, the present invention proposes a groundwater DNAPL simulation method that couples a mathematical model and an optimization algorithm to solve the problems existing in the above-mentioned prior art.

[0005] To achieve the above object, the present invention proposes a groundwater DNAPL simulation method that couples a mathematical model and an optimization algorithm, comprising:

[0006] The groundwater dynamic change data is obtained based on the groundwater online monitoring system, the groundwater-related environmental index change data is obtained based on the water quality multi-parameter instrument, and the DNAPL concentration data of the corresponding points is obtained based on regular groundwater sample collection and analysis;

[0007] Analyze the hydrogeological conditions of the site and the migration and transformation process of DNAPL pollution;

[0008] Preliminarily estimate the model parameter range, set the scenario to be corrected, and perform simulation based on the multiphase flow and solute transport coupling model;

[0009] An optimization algorithm is used to construct a simulation system that couples a mathematical model with the optimization algorithm. The migration, transformation, and transport processes of DNAPL in groundwater are simulated and evaluated based on the groundwater dynamic change data, environmental indicator change data, and DNAPL concentration data to obtain simulation results that are consistent with the actual monitoring conditions.

[0010] Optionally, the process of simulating and evaluating the migration, transformation and transport of DNAPL in groundwater according to the groundwater dynamic change data, environmental indicator change data and DNAPL concentration data includes:

[0011] Simulate the DNAPL phase migration process based on the multiphase flow module of the established simulation system;

[0012] A DNAPL dissolution mass transfer model is constructed, and a dissolution process from a DNAPL phase to a water phase is simulated based on the DNAPL dissolution mass transfer model;

[0013] The migration and reverse diffusion processes of DNAPL were simulated based on the groundwater flow field module and the halogenated hydrocarbon dissolved phase migration module of the established simulation system.

[0014] Optionally, in the process of simulating the DNAPL phase migration process based on the multiphase flow model, the release of pollution sources, the heterogeneity of the aquifer, the constitutive relationship characterizing the permeability-saturation-capillary pressure correlation, and the influence of the DNAPL's own properties and the permeability of the aquifer on the migration rate are considered.

[0015] Optionally, the DNAPL dissolution mass transfer model includes a REV scale model and a site scale model, and the influencing factors and change laws of DNAPL dissolution are obtained through the REV scale model; and the model is matched with field measurement data through the site scale model.

[0016] Optionally, the simulation method of the DNAPL dissolution mass transfer model includes:

[0017] Based on the local equilibrium assumption, the mass transfer rate is estimated according to the equilibrium distribution relationship to simulate the interphase mass transfer process, or based on the dissolution kinetics, an empirical rate limiting expression is established to simulate the interphase mass transfer process.

[0018] Optionally, the simulation method of the site scale model includes:

[0019] The field-scale effective mass transfer coefficient is modified based on site size, soil heterogeneity, and NAPL distribution, or a power-law relationship is established between the effluent concentration and the remaining DNAPL mass.

[0020] Optionally, the simulation process of the groundwater flow field model includes:

[0021] Constructing the differential equation for groundwater flow based on Darcy’s law;

[0022] The groundwater flow differential equation is differentially solved by using a finite difference method, a set of discrete points in a simulation area is used to replace the seepage zone, and a difference quotient is used to approximately replace the differential quotient, so as to convert the differential equation and its boundary conditions into a differential equation with an approximate value of an unknown function at discrete points as an unknown quantity, and the differential equation is solved to obtain the approximate value of the solution of the groundwater flow differential equation at the discrete points;

[0023] The differential equation is embedded in the groundwater flow field module, and the relevant parameters are set according to the hydrogeological information during simulation.

[0024] Optionally, the simulation process of the halogenated hydrocarbon dissolved phase migration model includes:

[0025] Based on the groundwater flow field model and Fick's law, a convection-dispersion equation is constructed to describe the dual process of flow and dispersion of dissolved halogenated hydrocarbons in the groundwater system.

[0026] The injection or extraction process of pollutants is described based on the convection-diffusion equation, while the adsorption process of halogenated hydrocarbons on soil and the degradation process of halogenated hydrocarbons are considered.

[0027] The advection-dispersion equation is embedded in the halogenated hydrocarbon dissolved phase migration module, and the relevant parameters are set according to the information such as the pollutant leakage situation and the pollutant properties during the simulation.

[0028] Optionally, the process of constructing a simulation system that couples the optimization algorithm with the mathematical model includes:

[0029] The simulation model of DNAPL migration and transformation was adapted into a function form with the parameter to be determined as the dependent variable, and the function was imported into the optimization algorithm model;

[0030] The model parameters are optimized based on the optimization algorithm, and training is repeated. When the convergence condition is reached, the optimal solution of the coupling model output is obtained.

[0031] Optionally, the process of optimizing the model parameters based on the optimization algorithm includes:

[0032] Set constraints and convergence conditions;

[0033] Initialize the decision variable parameters based on the population size to form the first generation population;

[0034] Based on the first generation population, a DNAPL migration and transformation simulation program is called to obtain simulation results;

[0035] Determine whether the simulation results meet the constraint conditions, calculate the objective function value, evaluate each feasible solution, and obtain a new generation of population;

[0036] When the convergence condition is reached, the optimal solution of the coupled model output is obtained.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] The present invention helps to solve the two major drawbacks of current DNAPL simulations.

[0039] On the one hand, the present invention establishes a groundwater DNAPL pollution migration and transformation simulation system based on Python language, including a multiphase flow module for simulating NAPL phase migration, a groundwater flow field and a halogenated hydrocarbon dissolved phase migration module, realizing open source simulation of the entire DNAPL process and having scalability.

[0040] On the other hand, the simulation system of the mathematical model coupled with the optimization algorithm established by the present invention unifies the simulation of the DNAPL pollution process and the optimization of the model parameters on the Python platform, which enhances the convenience of model setting. Most importantly, the introduction of the optimization algorithm can determine the optimal model parameters, thereby making the simulation prediction results consistent with the actual monitoring data, and improving the accuracy of the model. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0042] Figure 1 Schematic diagram of a groundwater DNAPL simulation method based on an optimization algorithm in an embodiment of the present invention;

[0043] Figure 2 Schematic diagram of the structure of the groundwater online monitoring system in an embodiment of the present invention;

[0044] Figure 3 It is a schematic diagram of the appearance of the Leveline-mini-CTD monitoring device in an embodiment of the present invention;

[0045] Figure 4 Schematic diagram of the AP2000 water quality multi-parameter analyzer in an embodiment of the present invention;

[0046] Figure 5 The plane location diagram of the pollution source and the observation well in the simulated area of ​​Case 1 in the embodiment of the present invention;

[0047] Figure 6 This is the multiphase flow simulation result of Case 1 in the embodiment of the present invention;

[0048] Figure 7 This is a schematic diagram of the simulation area of ​​Case 2 in an embodiment of the present invention;

[0049] Figure 8 1 is a comparison diagram of the steady-state simulation results of the groundwater flow field model in the embodiment of the present invention; wherein (a) is the simulation result of the present invention using the Flopy module, and (b) is the result of the GMS software operation;

[0050] Fig. 9 Schematic diagram for comparing the unsteady-state simulation results of the groundwater flow field model in the embodiment of the present invention; wherein (a) is the simulation result of the present invention using the Flopy module, and (b), (c), and (d) are the results of the GMS software operation;

[0051] Fig.10 This is a schematic diagram of the simulation area of ​​Case 4 in an embodiment of the present invention;

[0052] Fig.11 This is a comparison chart of simulation results of Case 4 in an embodiment of the present invention;

[0053] Fig.12 It is the structural framework of the DNAPL simulation system in the embodiment of the present invention;

[0054] Fig.13 It is the pollution plume distribution result based on the whole process simulation of DNAPL in the embodiment of the present invention; Among them, (a) is the NAPL phase distribution result based on the UTCHEM module simulation in the present invention, and (b) is the dissolved phase distribution result based on the Flopy module simulation;

[0055] Fig.14 It is the model structure framework of the coupling optimization algorithm in the embodiment of the present invention;

[0056] Fig.15 It is the convergence curve of the coupling model objective function (RMSE) in the embodiment of the present invention. DETAILED DESCRIPTION

[0057] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to be able to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0058] This embodiment proposes a groundwater DNAPL simulation method that couples a mathematical model and an optimization algorithm, such as Figure 1 As shown, including:

[0059] Groundwater flow field monitoring program

[0060] (1) Layout of pollution monitoring sites

[0061] Survey the hydrogeological environment of the site contaminated by halogenated hydrocarbons, select several locations to set up groundwater pollution monitoring stations, and form a detection point network for the contaminated site. The basic principles for the layout of the groundwater pollution monitoring point network are as follows: 1) Points are arranged on the basis of identifying the hydrogeochemical environment, pollutant components and main pollution sources in the monitoring area; 2) The point network should be able to control urban residential areas, industrial and mining enterprise areas, and existing and planned water supply sources; 3) In key investigation areas, such as severely polluted areas, water sources, and major pollution sources, the points should be densely distributed, and vice versa; 4) Monitoring points should also be arranged in non-polluted areas as control points.

[0062] (2) Composition of the online groundwater monitoring system

[0063] like Figure 2 As shown in the figure, the groundwater online monitoring system is mainly composed of multiple water quality parameter probes, data communication host, wireless network and monitoring center station. The center station exchanges information with each monitoring instrument through the wireless network to complete the recovery of monitoring data and the management of monitoring equipment. The on-site detection instrument includes an automatic monitoring and data transmission system for groundwater environmental parameters. The long-term automatic monitoring of groundwater environmental parameters is achieved through the composite probe. The monitoring data can be exchanged and communicated regularly through the wireless network according to the agreed protocol to realize the monitoring network of intelligent collection, transmission, monitoring and management.

[0064] (3) On-site installation

[0065] Put the water level meter into the groundwater monitoring well, and place the water level sensor terminal in the protection device at the wellhead. Use a cylindrical load-bearing rod to fix the water level meter cable at the wellhead baffle, so that the load-bearing rod bears the weight of the water level meter and cable, which not only prevents the water level meter from dragging the sensor terminal, but also eliminates the risk of the water level meter falling off and falling into the well.

[0066] (4) Description of technical performance of main equipment

[0067] The model of the groundwater flow field online automatic monitoring equipment selected in this embodiment is Leveline-mini-CTD. This equipment is used for automatic long-term monitoring of groundwater level, water temperature and conductivity. The groundwater level, water temperature and conductivity sensors have high consistency in measured data, accurate measured data, and rapid response to instantaneous water head changes; the sensor is easy to install. By forming a monitoring network with data communication equipment, remote data transmission can be achieved.

[0068] like Figure 3 As shown in the figure, the instrument uses a high-precision piezoresistive ceramic pressure sensor and a four-electrode conductivity sensor. Standard data protocol, LeveLine can be integrated into a third-party monitoring system to measure absolute pressure or gauge pressure, water temperature and conductivity up to 100m deep. Its related parameters are shown in Table 1.

[0069] Table 1

[0070]

[0071] (5) Monitoring indicators

[0072] The dynamic change data of groundwater level, temperature, conductivity, etc. are measured through the online monitoring system to provide data support for the establishment of groundwater flow model. The groundwater flow field monitoring indicators are shown in Table 2.

[0073] Table 2

[0074]

[0075] Groundwater Quality Monitoring Program

[0076] Water quality monitoring is carried out on the basis of the groundwater flow field monitoring program. With the help of the pollution monitoring stations used when establishing the groundwater online monitoring system, the portable water quality multi-parameter analyzer is used to collect groundwater quality related data on site.

[0077] (1) Description of main equipment technical performance

[0078] The model of the groundwater quality monitoring equipment selected in this embodiment is the AP-2000 portable multi-parameter water quality analyzer. The AP-2000 system is standardly configured with basic parameters such as optical dissolved oxygen, pH, ORP, EC, and temperature. In addition to these five probes, the AP-2000 also has an additional ISE electrode interface and an optical probe interface, and the required probe can be installed. The system can read data on-site through the Aquaread GPS Aquameter and save data through the Aquaread Logger.

[0079] like Figure 4 As shown in the figure, the AP2000 host has a built-in GPS system, which can store 3,000 monitoring data and output them through the USB port, and can be superimposed with maps or satellite photos in conjunction with the operating software. It has a high level of protection and is suitable for use in field environments. The probe of this device can respond quickly, does not require preheating, does not need to be immersed in electrolyte, and is not interfered by sulfide, sulfate, chloride, carbon dioxide or ammonia nitrogen. AP-2000 has unique advantages for groundwater, especially water quality measurement in boreholes. Its ultra-small size can adapt to most boreholes, and each water quality detection probe has its own flow cell, which allows sample water to enter the probe directly, which is ideal for groundwater monitoring and some process applications. In addition, its marine-grade aluminum alloy shell can fully protect the instrument's measuring probe. The relevant parameters are shown in Table 3.

[0080] Table 3

[0081]

[0082]

[0083] (2) Monitoring indicators

[0084] In this embodiment, the AP2000 water quality multi-parameter analyzer is mainly used for some conventional water chemical parameters in groundwater, such as pH, negative oxidation-reduction potential (ORP), total dissolved solids (TDS), optical dissolved oxygen (DO), etc. Table 4 lists the range and accuracy of some detectable items.

[0085] Table 4

[0086]

[0087] DNAPL pollution migration and transformation process

[0088] After DNAPL is released into the soil-groundwater system, the pollution formation process is as follows:

[0089] In the first stage, NAPL phase migration. Due to its high density and low viscosity, once a leak occurs, DNAPL will penetrate deep into the formation, pass through the vadose zone, pollute the groundwater aquifer, and eventually accumulate on the top of the aquiclude to form a pool. Under the influence of soil heterogeneity, hydraulic gradient, capillary force and other factors, DNAPL forms an extremely irregular and complex distribution pattern on its seepage path, such as ganglia (accounting for 1-20% of the pore space, also known as residual saturation) and continuous pools (accounting for more than 50% of the pore openings). The infiltrated DNAPL can exist stably in this form for a long time, and continue to slowly dissolve into the groundwater, becoming a source area for groundwater pollution.

[0090] In the second stage, DNAPL partially dissolves in groundwater through the interphase mass transfer process. When groundwater flows through the DNAPL pollution source area, there is an obvious concentration gradient in the boundary layer area, which promotes the dissolution of DNAPL. It is worth noting that due to the influence of the pollution concentration in the upstream water, the dissolution rate at different locations in the source area is different. Most DNAPLs have low water solubility, and the groundwater flow rate is low. Therefore, the dissolution process of the NAPL phase is slow and long-lasting.

[0091] The third stage is the migration process of the dissolved phase. The dissolved DNAPL flows with the groundwater and forms a pollution plume downstream of the source area. The pollution plume may extend hundreds or thousands of meters away from the pollution source area, expanding the scope of pollution. The distribution and life of the pollution plume are affected by a combination of factors such as groundwater flow, soil heterogeneity, source area dissolution intensity, and biodegradation.

[0092] In the fourth stage, DNAPL diffuses in the reverse direction from the low permeability zone to the high permeability zone. Over a long period of time, the DNAPL source zone is continuously dissolved and consumed. At the same time, part of the dissolved DNAPL diffuses from the high permeability zone to the low permeability zone and is intercepted and accumulated. This is the process of forward diffusion (the first three stages). When the source zone is exhausted and the concentration gradient between the two zones is reversed, reverse diffusion (the fourth stage) occurs, and pollutants migrate in the reverse direction from the low permeability zone to the aquifer. Reverse diffusion greatly increases the persistence of the pollution plume, allowing the pollution concentration in the groundwater to remain above the MCL for decades or even hundreds of years.

[0093] Simulation of DNAPL migration, transformation and transport in groundwater

[0094] (1) Simulating DNAPL phase migration process based on multiphase flow model

[0095] From a spatial perspective, the key factors affecting DNAPL migration include the release of pollution sources, the heterogeneity of aquifers, and the permeability-saturation-capillary pressure (K r,N -S w -P c ) correlation. From the perspective of time scale, the migration rate of DNAPL is mainly affected by its own properties (density and viscosity) and the permeability of the aquifer. Therefore, the above factors need to be considered when setting up the multiphase model.

[0096] (2) Dissolution process from DNAPL phase to water phase based on mass transfer model

[0097] The mass transfer models describing the DNAPL dissolution process are mainly divided into three categories: pore scale model, REV scale model and site scale model. Through the pore scale model, the details of mass transfer in the DNAPL source area dissolution process are revealed; through the REV scale model, the influencing factors and changing laws of DNAPL dissolution are studied; through the site scale model, the model can match the field measurement data and can truly predict the distribution of pollutant concentrations.

[0098] There are two major methods for simulating interphase mass transfer processes. One is based on the local equilibrium assumption and estimates the mass transfer rate based on the equilibrium distribution relationship. The other is based on dissolution kinetics and establishes an empirical rate limiting expression, such as:

[0099]

[0100] Where J is the mass flux from the NAPL phase to the water phase during the dissolution process, [ML -3 T -1 ]; is the average mass transfer coefficient of the NAPL-water interface, [LT -1 ]; a nw is the effective specific interfacial area between the NAPL phase and the water phase, [L -1 ]; C s is the aqueous phase concentration under equilibrium conditions, i.e., effective solubility, [ML -3 ]; C is the aqueous phase concentration, [ML -3 ].

[0101] The scale-up models used for site scale can also be divided into two categories. One category considers the comprehensive effects of site scale, soil heterogeneity, NAPL distribution, etc., and the field scale effective mass transfer coefficient K eff The method for correction is as follows. For example, the widely used expression of field scale effective mass transfer coefficient is:

[0102]

[0103] In the formula, C out is the average flow concentration at the descending boundary, L is the length of the source region parallel to the flow direction, is the average Darcy velocity of groundwater, is the effective saturated hydraulic conductivity, M0 and M are the residual concentrations of DNAPL at the initial moment and at present, respectively, and k o , α, and β are all model parameters.

[0104] The other is to establish a power-law relationship between the effluent concentration and the remaining DNAPL mass, that is, the source intensity function.

[0105] Widely adopted source intensity functions, such as

[0106]

[0107] In the formula, C s (t) and M(t) correspond to the concentration of DNAPL dissolved in the source region and the mass of residual DNAPL at time t, respectively. C0 and M0 correspond to the concentration of DNAPL dissolved in the initial source region and the mass of residual DNAPL, respectively. Γ is a model parameter.

[0108] (3) Dissolved phase migration process

[0109] The factors that need to be considered in the migration process of the dissolved phase of DNAPL mainly include the velocity and water level of the groundwater flow field, as well as the diffusion mixing effect, biodegradation, etc. This process is usually simulated based on the coupling of the groundwater flow field model and the solute transport model. Most of them characterize the release process of the dissolved phase source area by assuming the source strength function. A few studies couple it with the multiphase flow model to simulate the whole process of DNAPL migration.

[0110] (4) Reverse diffusion

[0111] Back diffusion refers to the process in which pollutants accumulated in low permeability areas migrate back to high permeability areas due to the reversal of concentration gradients between high permeability areas and low permeability areas. It is essentially the process of reverse migration of DNAPL dissolution on a long time scale. When the pollutants in the source area are dissolved and exhausted or isolated and repaired, reverse diffusion will occur after the pollutant concentration in the aquifer drops to a certain level, becoming a secondary pollution source, so that the plume concentration remains above the MCL for a long time. Factors affecting reverse diffusion include solubility, heterogeneity, adsorption-desorption, degradation, etc. The simulation of the reverse diffusion process is generally divided into two stages marked by the removal or isolation of the source area. The first stage: accumulation of pollutants in the low permeability area through forward diffusion in the presence of the source area. The second stage: the source area is removed, and the simulation continues for a longer time range, and the reverse diffusion process is studied by observing the tailing phenomenon of the plume.

[0112] Example 1: Modeling and verification of the multiphase flow module

[0113] According to the different occurrence states of DNAPL, the equilibrium equations of different phases are established respectively. At the same time, based on the transformation law between the phases of halogenated hydrocarbons, the mutual coupling between the multiphase equations is realized, and the multiphase flow migration equation is established as follows:

[0114] For the water phase:

[0115]

[0116] For halocarbon free phase:

[0117]

[0118] in, It represents the process of free phase halogenated hydrocarbons dissolving into the water phase. It represents the process of halogenated hydrocarbons in the water phase being adsorbed by soil particles and desorbed after adsorption.

[0119] This embodiment performs simulation based on the open source program UTCHEM to obtain the distribution of NAPL phase in groundwater in DNAPL pollution.

[0120] Case 1: The simulated research site is a three-dimensional area of ​​140m×60m×20m, a phreatic aquifer, and the aquifer medium is a homogeneous isotropic medium. The model is discretized in space. In the plane, the study area is divided into 70 rows and 30 columns, a total of 2100 units; vertically, the study area is divided into 10 layers. The volume of each unit is 2×2×2m 3 The groundwater flows from left to right. The left and right boundaries are constant pressure boundaries (the left boundary pressure is 102.972 kPa, and the right boundary pressure is 101.325 kPa), and the other boundaries are water-proof boundaries. In the study area, there is a nitrobenzene leakage point with a leakage volume of 8m 3 / d, the leakage time is 10d. The nitrobenzene concentration is 70% (volume fraction). The initial concentration of contaminants in the phreatic aquifer is 0. The simulation is carried out under this scenario, and the simulation time lasts for 300d. The values ​​of the parameters in the model are shown in Table 5. The plane positions of the pollution sources and observation wells in the study area are shown in Figure 5 .

[0121] Table 5

[0122]

[0123] The simulation results are as follows Figure 6 As shown. For the DNAPL free phase, the concentration is measured in saturation. In the first 10 days of the pollutant leakage stage, a small amount of pollutants appeared in the 5th layer, indicating that it takes a few days for DNAPL to infiltrate to the soil depth here under the action of gravity. From the 10th to the 30th day, as the DNAPL that is difficult to dissolve in the water phase gradually infiltrates, a DNAPL free phase distribution area is formed around the leakage point, and the saturation gradually increases. At this time, the DNAPL free phase shows an accumulation trend, and the effect of gravity migration is greater than the groundwater convection diffusion effect. From the 90th to the 300th day, the distribution range of the DNAPL free phase did not change significantly, but the saturation showed a gradual downward trend, indicating that the DNAPL free phase has been stably present in the soil at this stage. Under the influence of groundwater convection diffusion, a small amount of DNAPL gradually dissolves into the water phase, resulting in a decrease in saturation. At the same time, the dissolved DNAPL diffuses with the water flow, expanding the scope of pollution, so that DNAPL pollutants of different concentrations (in mg / L) can be detected at each monitoring well.

[0124] Example 2: Modeling and verification of groundwater flow field module and halogenated hydrocarbon dissolved phase transport module

[0125] Groundwater flow model

[0126] The groundwater flow field model is the basis for simulating the migration of groundwater pollution. The groundwater flow differential equation is established based on Darcy's law:

[0127]

[0128] This embodiment is programmed based on Python language, and MODFLOW is run through the open source program Flopy to achieve the spatiotemporal discretization of the contaminated site and the solution of the model differential equation, and obtain the distribution of groundwater level and flow direction of the contaminated site.

[0129] Halocarbon Dissolved Phase Migration Model

[0130] For halogenated hydrocarbon pollutants dissolved in groundwater, they flow in a single phase with the groundwater flow, and the process is controlled by convection and diffusion. Therefore, by coupling the groundwater flow field model and Fick's law, a convection-dispersion equation is established to describe the dual process of flow and dispersion of soluble halogenated hydrocarbons in the groundwater system:

[0131]

[0132] This embodiment runs MT3DMS based on the open source program Flopy to achieve the simulation of the concentration distribution of soluble halogenated hydrocarbon pollutants.

[0133] Model Testing

[0134] Different case scenarios were set up to verify the groundwater flow field model program and halogenated hydrocarbon dissolved phase migration model program that had been written, and the simulation results were compared with the GMS software operation results to verify the feasibility and accuracy of the established models.

[0135] (1) Groundwater flow field model under steady-state conditions

[0136] Case 2: If Figure 7 As shown in the figure, the simulation area is a single-layer rectangular area with an area of ​​190m in the x direction and 250m in the y direction. The top elevation of the area is 5m and the bottom elevation is -15m. The area is discretized into a uniform grid with 26 rows and 20 columns. Under steady-state conditions, the upper and lower boundaries of the simulation area are constant head boundaries, the left and right boundaries are no-flux boundaries, and the initial water level of the entire area is 3m. The permeability coefficient hk = 1m / d, and the rainfall recharge = 0.001m / d.

[0137] The schematic diagram for comparison with the GMS software running results is as follows Figure 8As shown in the figure, in this scenario, rainfall falls in the simulation area in the form of surface source and flows out from the upper and lower boundaries. After calculation, the steady-state rainfall ≈ the outflow from the upper boundary + the outflow from the lower boundary, which is theoretically reasonable. In the steady state, the water level in the middle of the area is the highest, about 3.4m. From the middle to the upper and lower boundaries, the water level gradually decreases to the fixed head boundary value of 3m. This is consistent with the simulation results of the GMS software.

[0138] (2) Groundwater flow field model under non-steady-state conditions

[0139] Case 3: An unsteady-state simulation test was conducted based on the simulation area of ​​Case 2. Under unsteady-state conditions, the upper and lower boundaries of the simulation area were fixed head boundaries, the left and right boundaries were no-flux boundaries, the initial water level of the entire area was 3m, and the permeability coefficient hk = 1m / d. Different from the steady state, the rainfall (recharge) was divided into three stress periods, namely, the 1st to 10th day: 0.001m / d; the 10th to 20th day: 0.0015m / d; the 20th to 30th day: 0.0005m / d.

[0140] The simulation results are compared as shown in the figure Fig. 9 As shown in the figure, the overall trend of water level distribution is similar to that in steady state, that is, the water level is highest in the middle of the area, and gradually decreases from the middle to the upper and lower boundaries to the fixed head boundary value. By reading the water level data, it can be found that the highest water level in the middle is about 3.4m on the 10th day, about 3.6m on the 20th day, and about 3.3m on the 30th day, which is basically consistent with the GMS data distribution.

[0141] (3) Halocarbon Dissolved Phase Migration Model

[0142] Case 4: If Fig.10 As shown, the simulation area is a single-layer rectangular area with an area of ​​190m in the x direction and 250m in the y direction. The top elevation of the area is 5m and the bottom elevation is -15m. The area is discretized into a uniform grid of 26 rows and 20 columns. The upper boundary water level of the simulation area is 3m, the lower boundary water level is 4m, the left and right boundaries are flux-free boundaries, and the initial water level of the entire area is 3m. The permeability coefficient hk = 1m / d. There is a pumping well at (5, 5) with a pumping capacity of 5m3 / d; there is an injection well at (15, 15) with an injection capacity of 5m 3 / d. The pollutant concentration in the injected water is 50 mg / L. The porosity is 0.3, the longitudinal diffusion is 20 m, trpt = 0.2, trpv = 0.01.

[0143] The simulation results are compared as shown in the figure Fig.11 As shown, it can be seen that the water flow direction is from bottom to top, and the pollutants migrate along the water flow direction after injection. At the same time, under the influence of diffusion, the pollution range along the left and right directions has also expanded to a certain extent. From the comparison of the simulation results on the 61st day and the 361st day, it can be seen that it is basically consistent with the GMS simulation.

[0144] Example 3: The multiphase flow module is coupled with the groundwater flow field module and the halogenated hydrocarbon dissolved phase migration module to construct a migration and transformation simulation system.

[0145] The key to coupling the two is to transform the permeability field and saturation field of the NAPL distribution source area. The relevant formulas that can be used are as follows:

[0146] K eff =K i ·K r

[0147]

[0148] Where: K i ——Intrinsic hydraulic conductivity (permeability coefficient); S rw ——Liquid phase residual saturation, 0.2; λ b ——Brooks-Corey model parameters, the above parameters can take a fixed value, 2.0.

[0149] Another aspect is to reasonably set the above mass transfer model.

[0150] like Fig.12 As shown in the figure, the constructed simulation system consists of three parts. The first part is the simulation of the free phase of DNAPLs. By importing the UTCHEM program input file and entering the relevant parameters of the model, the free phase distribution of DNAPLs is calculated and solved, and the results are characterized by saturation. Under the state of DNAPLs free phase, the permeability of soil to groundwater changes, and the free phase distribution also affects the dissolution of DNAPLs. Therefore, the key parameters of solute migration under the condition of free phase existence are corrected through formulas and models. The second part, the simulation of DNAPLs dissolved phase, is coupled with the open source program Flopy to call MODFLOW and the solute transport MT3D model for solution and calculation to obtain the head distribution H and DNAPLs dissolved phase distribution Cn.

[0151] Case 5: Based on the NAPL phase simulation results of Case 1, the groundwater flow field module and the halogenated hydrocarbon dissolved phase migration module are further coupled to predict the dissolved phase distribution. The results are as follows: Fig.13 shown.

[0152] Example 4: Establish a groundwater DNAPL simulation method based on an optimization algorithm, determine the DNAPL source information according to monitoring data, and achieve more accurate simulation prediction.

[0153] The key to establishing a groundwater DNAPL simulation method based on an optimization algorithm is to convert the forward simulation model (DNAPL simulation program) into a subroutine form that can be called by the inverse optimization model (optimization algorithm), and then embed it into the optimization process to achieve seamless data transmission between the two. Taking the genetic algorithm used in this embodiment as an example, its optimization is a cyclic process. When the cycle starts, it is necessary to initialize the parameters in the decision variables according to the set population size to form the first generation population. Take this population as a feasible solution set, and each individual in it needs to call a Python simulation program once to perform groundwater flow field and dissolved phase migration simulation calculations under the corresponding scheme to obtain corresponding simulation results. Then the optimization algorithm will judge whether the scheme meets the set constraints based on the simulation results, calculate the objective function value, evaluate each feasible solution, and obtain a new generation of population. Repeat the above steps until the convergence condition is reached. The specific process architecture of the coupled optimization algorithm model is as follows Fig.14 shown.

[0154] Case 6: Groundwater DNAPL inversion case based on optimization algorithm

[0155] Scenario setting: Assume that a halogenated hydrocarbon pollutant leaked somewhere on a site. Horizontally, the site is 50m×30m in size and homogeneous. During the simulation, the plane area is discretized into a uniform grid of 25 rows and 15 columns. Vertically, the site has a top elevation of 2m and a bottom elevation of -10m, which can be divided into three strata. The left and right boundaries of the simulation area are fixed head boundaries (1.5m on the left and 0m on the right), and the upper and lower boundaries are no-flux boundaries. The initial head of the entire area is 1.5m. The pollutant leak point is located at (row 7, column 5, layer 1), and the leakage volume is 5m3 / d. Among them, the concentration of the leaked chlorobenzene pollutant is 50mg / L. The leakage lasted for 30 days, after which measures were taken to terminate the pollution source. The porosity during pollutant migration is 0.3, the longitudinal dispersion is 0.5m, the ratio of the lateral dispersion to the longitudinal dispersion (TRPT) is 0.1, and the ratio of the vertical dispersion to the longitudinal dispersion (TRVT) is 0.01. A non-steady-state simulation was performed in this scenario, with a simulation duration of 100 days, of which pollutants flowed in during the first 30 days.

[0156] Reference field: The above simulation scenario is used as the reference field to invert and determine the amount of pollutant leakage and leakage concentration at the pollution source. In order to obtain the monitoring data required for the inversion model, it is assumed that three monitoring wells are set up in the contaminated site, and their coordinates are J1 (7, 9, 1), J2 (6, 15, 2) and J3 (9, 20, 1) (the third digit represents the monitored stratum).

[0157] Through the simulation calculation of the reference field, it can be known that when the pollution leakage is 5m3 / d and the pollutant concentration is 50mg / L, the pollutant concentrations at the three monitoring wells after 100 days of simulation are C1 (34.27mg / L), C2 (0.65mg / L), and C3 (1.32mg / L). The reference value is then used as the actual monitoring value (true value) of the inversion model.

[0158] Inversion scenario setting: Assume that a pollutant leak occurred at a certain point P (7, 5, 1) in the study area of ​​the above case. After 30 days, the source of pollution was discovered and measures were taken to stop the pollution. The amount and concentration of the pollutant were unknown. After 100 days, site remediation began. The concentration values ​​of pollutants in groundwater measured at the three monitoring wells were C1 (34.27), C2 (0.65), and C3 (1.32). The monitoring data was used to invert the pollution source strength (leakage amount and leakage concentration) in order to accurately determine the pollution source information and achieve accurate pollution plume prediction.

[0159] In this embodiment, a classic genetic algorithm is used for inversion solution, wherein the relevant elements are set as follows:

[0160] (1) Forward modeling model call. Since the forward model itself is a code program that can be successfully run, in order to make it a subroutine that can be called by the optimization algorithm, the model needs to be adapted into a function form with the parameters to be determined as the dependent variables. In this embodiment, the forward modeling model is defined as the fordwardingmodel function, and the variables of the function are the model parameters to be determined, which are the leakage amount and leakage concentration of the pollution source. At the same time, the return value of the function is set to the required model simulation value in order to read the simulation results, which is the pollutant concentration at the three monitoring wells in this case. Finally, the forward model function to be called is imported into the optimization model program to realize the coupling of the forward simulation model and the inverse optimization model.

[0161] (2) Inverse optimization model setting. The optimization model has three elements: objective function, decision variables and constraints.

[0162] Objective function: The root mean square error of the simulated value and the observed value is used as the objective function to determine the degree of agreement between the measured pollutant concentration in the monitoring well and the pollutant concentration simulated by the model. The formula is as follows:

[0163]

[0164] Decision variables: the parameters to be determined, in this case the amount of pollutant leakage P[0] and concentration P[1].

[0165] Constraints: impose upper and lower limits on the decision variables. In this case, 0≤P[0]≤10m 3 / d; 40≤P[1]≤60mg / L.

[0166] Inversion result analysis

[0167] This example uses a genetic algorithm to invert the leakage and concentration of the source and sink items of the forward model. When the population size is 30 and 100 generations are simulated, the convergence curve of the corresponding objective function (RMSE) is as follows: Fig.15 As shown in the figure. During the optimization process, when the iterations reached about 10 times, the objective function value dropped significantly; when the iterations reached about 38 times, the objective function value basically reached convergence. It can be seen that solving the optimization model based on the genetic algorithm can search for the global optimum at a faster speed.

[0168] The optimal solution obtained by solving the above nonlinear optimization model is the identification result of the pollutant leakage and concentration in this embodiment. The pollutant leakage at the pollution source obtained by inversion is 5.05m 3 / d, compared to the true value of 5m 3 / d, the simulated value is only 1% higher than the true value; the pollutant leakage concentration obtained by inversion is 49.63mg / L, compared with the true value of 50mg / L, the simulated value is only 0.7% lower than the true value. The root mean square error (RSME) of the final simulation result is 0.05, and the inversion result is relatively accurate.

[0169] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A groundwater DNAPL simulation method that couples a mathematical model and an optimization algorithm, characterized in that: The following steps are involved: The groundwater dynamic change data is obtained based on the groundwater online monitoring system, the groundwater-related environmental index change data is obtained based on the water quality multi-parameter instrument, and the DNAPL concentration data of the corresponding points is obtained based on regular groundwater sample collection and analysis; Analyze the hydrogeological conditions of the site and the migration and transformation process of DNAPL pollution; Preliminarily estimate the model parameter range, set the scenario to be corrected, and perform simulation based on the multiphase flow and solute transport coupling model; An optimization algorithm is used to construct a simulation system that couples a mathematical model with the optimization algorithm. The migration, transformation, and transport processes of DNAPL in groundwater are simulated and evaluated based on the groundwater dynamic change data, environmental indicator change data, and DNAPL concentration data to obtain simulation results that are consistent with the actual monitoring conditions.

2. The groundwater DNAPL simulation method of coupling mathematical model and optimization algorithm according to claim 1 is characterized in that: The process of simulating and evaluating the migration, transformation and transport of DNAPL in groundwater based on the groundwater dynamic change data, environmental indicator change data and DNAPL concentration data includes: Simulate the DNAPL phase migration process based on the multiphase flow module of the established simulation system; A DNAPL dissolution mass transfer model is constructed, and a dissolution process from a DNAPL phase to a water phase is simulated based on the DNAPL dissolution mass transfer model; The migration and reverse diffusion processes of DNAPL were simulated based on the groundwater flow field module and the halogenated hydrocarbon dissolved phase migration module of the established simulation system.

3. The groundwater DNAPL simulation method of coupling mathematical model and optimization algorithm according to claim 2 is characterized in that: In the process of simulating the DNAPL phase migration process based on the multiphase flow model, the release of pollution sources, the heterogeneity of the aquifer, the constitutive relationship characterizing the permeability-saturation-capillary pressure correlation, and the influence of the DNAPL's own properties and the permeability of the aquifer on the migration rate are considered.

4. The groundwater DNAPL simulation method of coupling mathematical model and optimization algorithm according to claim 2 is characterized in that: The DNAPL dissolution mass transfer model includes a REV scale model and a site scale model. The REV scale model is used to obtain the influencing factors and change laws of DNAPL dissolution; the site scale model is used to match the model with field measurement data.

5. The groundwater DNAPL simulation method of coupling mathematical model and optimization algorithm according to claim 4 is characterized in that: The simulation method of the DNAPL dissolution mass transfer model includes: Based on the local equilibrium assumption, the mass transfer rate is estimated according to the equilibrium distribution relationship to simulate the interphase mass transfer process, or based on the dissolution kinetics, an empirical rate limiting expression is established to simulate the interphase mass transfer process.

6. The groundwater DNAPL simulation method of coupling mathematical model and optimization algorithm according to claim 4 is characterized in that: The simulation method of the site scale model includes: The field-scale effective mass transfer coefficient is modified based on site size, soil heterogeneity, and NAPL distribution, or a power-law relationship is established between the effluent concentration and the remaining DNAPL mass.

7. The groundwater DNAPL simulation method of coupling mathematical model and optimization algorithm according to claim 2 is characterized in that: The simulation process of the groundwater flow field model includes: Constructing the differential equation for groundwater flow based on Darcy’s law; The groundwater flow differential equation is differentially solved by using a finite difference method, a set of discrete points in a simulation area is used to replace the seepage zone, and a difference quotient is used to approximately replace the differential quotient, so as to convert the differential equation and its boundary conditions into a differential equation with an approximate value of an unknown function at discrete points as an unknown quantity, and the differential equation is solved to obtain the approximate value of the solution of the groundwater flow differential equation at the discrete points; The differential equation is embedded in the groundwater flow field module, and the relevant parameters are set according to the hydrogeological information during simulation.

8. The groundwater DNAPL simulation method of coupling mathematical model and optimization algorithm according to claim 2 is characterized in that: The simulation process of the halocarbon dissolved phase migration model includes: Based on the groundwater flow field model and Fick's law, a convection-dispersion equation is constructed to describe the dual process of flow and dispersion of dissolved halogenated hydrocarbons in the groundwater system. The injection or extraction process of pollutants is described based on the convection-diffusion equation, while the adsorption process of halogenated hydrocarbons on soil and the degradation process of halogenated hydrocarbons are considered. The advection-dispersion equation is embedded in the halogenated hydrocarbon dissolved phase migration module, and the relevant parameters are set according to the information such as the pollutant leakage situation and the pollutant properties during the simulation.

9. The groundwater DNAPL simulation method of coupling mathematical model and optimization algorithm according to claim 1 is characterized in that: The process of building a simulation system that couples the optimization algorithm with the mathematical model includes: The simulation model of DNAPL migration and transformation was adapted into a function form with the parameter to be determined as the dependent variable, and the function was imported into the optimization algorithm model; The model parameters are optimized based on the optimization algorithm, and training is repeated. When the convergence condition is reached, the optimal solution of the coupling model output is obtained.

10. The groundwater DNAPL simulation method of coupling mathematical model and optimization algorithm according to claim 9 is characterized in that: The process of optimizing the model parameters based on the optimization algorithm includes: Set constraints and convergence conditions; Initialize the decision variable parameters based on the population size to form the first generation population; Based on the first generation population, a DNAPL migration and transformation simulation program is called to obtain simulation results; Determine whether the simulation results meet the constraint conditions, calculate the objective function value, evaluate each feasible solution, and obtain a new generation of population; When the convergence condition is reached, the optimal solution of the coupled model output is obtained.

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