A numerical simulation method of an electrically assisted microbial remediation process

Abstract

The application discloses a numerical simulation method of an electrically assisted microbial remediation process, and belongs to a numerical simulation method system. First, a conceptual model, chemical substance setting, initial condition and boundary condition of a research area are determined through initialization research area setting. Then, the number of simulation nodes is set based on grid self-adaptive division. A migration model established by Nernst-Planck-Poisson equation is considered in COMSOL software based on a migration-reaction operator separation method, so as to describe processes such as convection, dispersion, electromigration and electroosmotic flow related to material migration in groundwater. In PHREEQC, the influence of microbial growth on the permeability coefficient and the multivariate inhibition Monod degradation reaction are considered. Finally, the CPM-BIO numerical simulation method is formed by combination, so that the electrically assisted microbial remediation process can be simulated in detail. Compared with existing calculation models, the CPM-BIO can realize the detailed description of the electrically assisted microbial remediation numerical simulation process with multiple dimensions and multiple reactions.

Description

Technical Field

[0001] This invention relates to a numerical simulation methodology, specifically a numerical simulation method that can be used to precisely characterize electrically assisted microbial remediation processes. Background Technology

[0002] In recent years, due to economic and social development, some sites have suffered from soil and groundwater pollution to a certain extent. The presence of low-permeability media has brought significant challenges to the application of water-soil remediation technologies. Electrically assisted microbial remediation offers a new approach to this field, while numerical simulation of the remediation process can predict and assess material migration and remediation efficiency, providing guidance for the remediation process.

[0003] Current numerical simulation methods for groundwater are mostly limited to hydraulic gradient-controlled flow or one-dimensional conditions, and the simulated reactions are relatively simple. They do not fully characterize the multi-field coupling processes and microbial growth and reaction processes involved in electrically assisted microbial remediation (EMR). Numerical simulation of EMR processes requires multi-field and multi-reaction coupling, which has a high technical threshold and is complex to operate. Establishing a general numerical simulation method system for EMR can improve the efficiency and accuracy of numerical simulation. PHREEQC, as a general simulation software in the field of multi-field coupling and reaction, can serve as the basis for method construction, and can be further extended and applied to the numerical simulation process of electrically assisted microbial remediation. Summary of the Invention

[0004] This invention addresses the shortcomings of existing groundwater numerical simulation programs, such as their inability to accurately simulate electrically assisted microbial remediation processes and the difficulty in accurately assessing microbial growth during simulation. It constructs a COMSOL-based system... The numerical simulation method system of PHREEQC, utilizing COMSOL This study calculates convection, dispersion, and electromigration terms related to mass migration. PHREEQC is used to handle multivariate monod reactions involving aqueous phase material distribution, microbial growth, and pollutant consumption. It considers processes such as microbial growth, degradation, and media blockage involved in microbial remediation, and employs a microbial growth blockage model and a GBM model to assess the impact of microbial growth on permeability. Finally, an extended EK-BIO coupled model is established to accurately characterize electrically assisted microbial remediation.

[0005] This invention is implemented as follows:

[0006] A numerical simulation method for electrically assisted microbial remediation processes, characterized by the following steps:

[0007] Step 1: For COMSOL Initialize the settings within the PHREEQC software to determine the conceptual model of the study area, define the structure of the study area, set chemical substances, set initial and boundary conditions, and set the number of simulation nodes based on adaptive mesh partitioning;

[0008] Step 2: Using the transport-reaction operator separation method, calculate the migration and reaction processes separately; whereby the migration term is calculated by COMSOL. The multi-field coupled environment was characterized, including convection under hydraulic gradient, dispersion under concentration gradient, electromigration, electroosmosis and possible electrode reactions under potential gradient; the reaction terms were characterized by PHREEQC for aqueous phase material distribution and the Monod equation for multi-dimensional inhibition of microbial remediation.

[0009] Step 3, in The present invention defines the parameter settings of a model for assessing the impact of microbial growth on permeability, namely the microbial growth blocking model (GBM). The microbial growth blocking model (GBM) proposed in this invention is used to characterize the impact of microbial growth on permeability. This model establishes the relationship between microbial growth and permeability, making the electrically assisted microbial remediation model more accurate.

[0010] Step four, the coupling process of migration and reaction simulation is carried out in a step-by-step manner: within this step, COMSOL The calculated spatial distribution results of the substances were imported into PHREEQC to calculate various reactions; then, the new concentration data calculated in PHREEQC were used as initial conditions for transport simulation in the next step; this was then performed using MATLAB. Internally compiled subroutines control the simulation step size and simulation duration; based on The self-compiled coupled computation module is combined to form the CPM-BIO numerical simulation process, which realizes the fine simulation of the electrically assisted microbial remediation process; the stepwise long coupling process is separated by the self-compiled operator.

[0011] Step 5: After completing the numerical simulation with the specified number of steps, import all results into COMSOL. Alternatively, post-processing can be performed in an external program.

[0012] Furthermore, in step two, multi-field environment characterization is based on Nernst-Plänk-Poisson:

[0013] On a macroscopic scale, the Nernst-Planck equation is used to describe the movement behavior of a certain substance i. Its flux expression mainly consists of the dispersion term. Activity item and electromigration terms composition:

[0014]

[0015] in It is the diffusion coefficient of aqueous substances, c i It is the concentration of aqueous phase substances, γ i It is the activity coefficient, z i Ion valence state, F is Faraday constant, R is ideal gas constant, T is temperature, Φ is electric potential;

[0016] In groundwater aquifers, the presence of a solid phase restricts the diffusion of substance i within the aqueous phase; therefore, the aqueous diffusion coefficient in Equation 1... Replaced with D i =D i aq τ, where τ is the tortuosity; similarly, the porosity n of the medium has a similar effect on the diffusion of matter; in addition, the total flux... Considering the impact of convection

[0017]

[0018] Depending on the driving force, the convection term, q * There are two main categories: First, under an applied hydraulic gradient, pressurized water flow, which is the traditional concept of groundwater flow; second, under an applied electric field, electroosmotic flow will occur between the media. Therefore, q * Represented as q * =q+u e Where q is the flux formed under the hydraulic gradient, expressed under macroscopic conditions according to Darcy's law:

[0019]

[0020] Where k h Where ρ is the permeability, g is the fluid density, μ is the acceleration due to gravity, and h is the head difference.

[0021] According to the Holmhertz-Smolukowski theory, electroosmotic flow is expressed as u e =-k e ▽Φ, where k e This is the electroosmotic coefficient, corresponding to the osmotic coefficient in Darcy's law; when the pH of the solution inside the medium is close to neutral, this value is 10. -8 -10 -9 m 2 / (V·s);

[0022] Therefore, substituting the mass conservation equation, the migration constitutive equation of substance i is mainly expressed by the following equation:

[0023]

[0024] Among them, R i For source and sink items;

[0025] When charged ions move in a directed manner under the influence of an applied electric field or external force, they affect the surrounding potential distribution. The Poisson equation is used to quantify this process.

[0026]

[0027] Where ρ e The charge density is denoted as ρ. e =F∑z i c i ε₀ is the permittivity of vacuum, ε r It is the relative permittivity;

[0028] The combined equations (2), (4), and (5) are the Nernst-Plunk-Poisson equations, which are constitutive equations describing the transport of matter in a medium.

[0029] Furthermore, step two characterizes the microbial remediation process based on multi-monod:

[0030] In assessing microbial degradation rates, the Monod equation, based on multiple inhibition, is used to characterize microbial growth and organic matter degradation. The Monod kinetic equation can characterize the competitive inhibition phenomena that occur during the chain degradation process.

[0031]

[0032] Among them, C EA,i With C ED , , represent the concentrations of the i-th electron acceptor EA and electron donor ED, respectively; t is time (s); Maximum apparent degradation rate (1 / s); K s,ED and K s,EA,i The half-saturation coefficients of ED and EA, respectively; K inh,j X represents the inhibition coefficient of the target EA by the j-th EA (excluding the i-th EA); k The number of microorganisms;

[0033] The change in the number of microorganisms over time caused by the degradation process is characterized by the following kinetic reaction:

[0034]

[0035] Where Y is the microbial growth coefficient, b k This represents the microbial extinction coefficient.

[0036] Furthermore, step three considers the microbial growth blocking model GBM:

[0037] In actual remediation, it is necessary to assess the impact of biological growth on hydrodynamic parameters. When performing migration-reaction simulation, the GBM model considers two phases of microorganisms: adsorbed on solid surfaces and free in liquids. It is assumed that adsorbed microorganisms are the cause of media blockage. The two phases of microorganisms are further considered to have two forms: active and dead. Active organisms that can be degraded by microorganisms and dead organisms that cannot be degraded by microorganisms are distributed in both phases.

[0038] The relevant settings of the model are as follows: (1) The maximum adsorbed biomass (X) is set. surface_max ) and the reduction factor of maximum permeability (P) max ), and the total amount of microorganisms adsorbed on the surface and the decrease in permeability satisfy a linear change; (2) dead microorganisms have the same effect as active microorganisms in blocking the medium; (3) microorganisms in the solution and microorganisms adsorbed on the medium obey equilibrium adsorption and once adsorbed, they will not fall off until the maximum bioadsorption amount is reached.

[0039] The GBM model mainly consists of four parts, and a simulation cycle includes four periods: migration, growth, update, and allocation.

[0040] 1) Migration phase: Contains microorganisms with active effects (X) solution_Live ) and dead microorganisms (X solution_Dead Liquid-phase microorganisms (X) including solution All of them migrated; while those containing microorganisms with active effects (X) surface_Live ) and dead microorganisms (X surface_Dead Microorganisms (X) on solid surfaces, including surface Then remain stationary; simultaneously set three ratio variables: the ratio of active microorganisms in the liquid phase R solution The ratio of active microorganisms on the solid phase, R surface and the ratio R of active microorganisms on the solid-liquid two-phase system. solu_surf ;

[0041] Solution phase:

[0042] X solution_Live =X solution *R solution (8)

[0043] X solution_Dead =X solution *(1-R solution (9)

[0044]

[0045] Solid surfaces:

[0046] X surface_Live =X surface *R surface (11)

[0047] X surface_Dead =X surface *(1-R surface (12)

[0048]

[0049] Solution-solid phase:

[0050]

[0051] Variables (X) during this time period solution_Live X solution_Dead X surface_Dead X surface_Live ) represents the first-level variable within this step size; variable (R) solution R surface R solu_surf ) represents the secondary variable within this step size; in the description, the superscript i used to indicate this step size is omitted, and the result is saved for subsequent calculations;

[0052] 2) Growth period: Active microorganisms (X) in the two phases Live Microbial degradation and substrate consumption occur, and the total active biomass is calculated at the end of this step based on the growth pattern defined by the multivariate inhibition Monod equation; simultaneously, the number of dead microorganisms (X) in both phases during the degradation period... Dead It was also calculated to assess the impact of the presence of dead microorganisms on the permeability coefficient;

[0053] X Live =X solution_Live +X surface_Live (15)

[0054] X Dead =X solution_Dead +X surface_Dead (16)

[0055] Variables (X) during this time period Live X Dead After a reaction cycle, the value changes; in the description, the superscript value used to indicate the change of step size is omitted. The superscript of the variable within the current step size is i, and the superscript of the variable changes to the next step size after the reaction is completed is i+1.

[0056] 3) Update period: Based on 2), the variable (X) Live X DeadUpdate the biomass values ​​of each part of the microorganisms in the two phases, that is, update the initial values ​​of the primary variables before the allocation period under the new step size; the primary variables are not allocated after the update, so they are distinguished by superscript tilde (~);

[0057]

[0058]

[0059]

[0060]

[0061] Where i+1 represents the next step size;

[0062] 4) Distribution period: Based on the biomass in the two phases, the biomass is redistributed, and the biomass between the solution phase and the solid phase is set to follow equilibrium adsorption, with the adsorption parameter being K. d Until the surface reaches the maximum adsorption capacity X max Furthermore, all organisms in the solution phase, including both living and dead organisms, have an equal probability of adsorbing onto the surface of the solid phase.

[0063]

[0064] After determining the biological allocation, the primary variables are recalculated, and the remaining secondary variables are updated based on the primary variables to complete a numerical simulation process;

[0065] Finally, based on the biomass X adsorbed on the solid surface... surface A permeability coefficient variation model was established and the variation coefficient P was obtained. coef ;

[0066]

[0067] Where K a X represents the permeability coefficient under biological influence. max For maximum biological growth, P max The reduction factor for the maximum permeability coefficient, where X is the amount of biological growth and K0 is the background permeability coefficient value;

[0068] By combining the above constitutive equations with numerical simulation methods, a detailed characterization of the electrically assisted microbial remediation process can be achieved.

[0069] Furthermore, in step four, COMSOL is used as a basis. The calculated spatial distribution results of the substances are imported into the system to calculate various reactions; then, the new concentration data calculated in PHREEQC are used as initial conditions for the next step of transport simulation; this is then performed using MATLAB. Internally compiled subroutines control the simulation step size and simulation duration; based on The self-compiled coupled computation module is combined to form the CPM-BIO numerical simulation method, which realizes the precise simulation of the electrically assisted microbial remediation process.

[0070] The advantages of this invention compared with the prior art are as follows: This invention can provide a general numerical simulation method system for electrically assisted microbial remediation, which can consider the influence of microbial growth on the permeability coefficient under multidimensional conditions, and provide support for a precise and comprehensive characterization of the electrically assisted microbial remediation process. Attached Figure Description

[0071] Figure 1 This is a flowchart of the numerical simulation of electrically assisted microbial remediation according to the present invention. Detailed Implementation

[0072] To make the objectives, technical solutions, and effects of this invention clearer and more explicit, the following examples provide a more detailed description of the invention. It should be noted that the specific embodiments described herein are merely illustrative and not intended to limit the scope of the invention.

[0073] like Figure 1 As shown, the specific steps of this invention are as follows:

[0074] Step 1: For COMSOL Initialize the settings within the PHREEQC software to determine the conceptual model of the study area, define the structure of the study area, set chemical substances, set initial and boundary conditions, and set the number of simulation nodes based on adaptive mesh partitioning;

[0075] Step 2: Using the transport-reaction operator separation method, calculate the migration and reaction processes separately; whereby the migration term is calculated by COMSOL. The multi-field coupled environment was characterized, including convection under hydraulic gradient, dispersion under concentration gradient, electromigration, electroosmosis and possible electrode reactions under potential gradient; the reaction terms were characterized by PHREEQC for aqueous phase material distribution and the Monod equation for multi-dimensional inhibition of microbial remediation.

[0076] Step 3, in The model for assessing the impact of microbial growth on permeability coefficient is defined in the paper, namely the parameter settings of the microbial growth blocking model (GBM).

[0077] Step four, the coupling process of migration and reaction simulation is carried out in a step-by-step manner: within this step, COMSOL The calculated spatial distribution results of the substances were imported into PHREEQC to calculate various reactions; then, the new concentration data calculated in PHREEQC were used as initial conditions for transport simulation in the next step; this was then performed using MATLAB. Internally compiled subroutines control the simulation step size and simulation duration; based on The self-compiled coupled computing module is combined to form the CPM-BIO numerical simulation process, which realizes the precise simulation of the electrically assisted microbial remediation process.

[0078] Step 5: After completing the numerical simulation with the specified number of steps, import all results into COMSOL. Alternatively, post-processing can be performed in an external program.

[0079] The specific implementation involves three parts: (i) characterization of the multi-field environment, including flow field, concentration field, and electric field; (ii) characterization of the microbial remediation reaction process; and (iii) microbial growth blockage model (GBM).

[0080] (i) Characterization of multi-field environment including flow field, concentration field, and electric field

[0081] On a macroscopic scale, the Nernst-Planck equation (NPE) is typically used to describe the movement behavior of a substance i, and its flux expression mainly consists of the dispersion term. Activity item and electromigration terms composition:

[0082]

[0083] in It is the diffusion coefficient of aqueous substances, c i It is the concentration of aqueous phase substances, γ i It is the activity coefficient, z i Ion valence state, F is Faraday constant, R is ideal gas constant, T is temperature, Φ is electric potential.

[0084] In groundwater aquifers, the presence of solid media such as soil restricts the diffusion of substance i within the aqueous phase. Therefore, the aqueous diffusion coefficient in Equation 1... It is usually replaced with D. i =D i aq τ, where τ is the tortuosity. Similarly, the porosity n of the medium has a similar effect on the diffusion of matter. In addition, the total flux... The impact of convection also needs to be considered.

[0085]

[0086] Depending on the driving force, the convection term, q * It is mainly divided into two categories: i.) Under the condition of an applied hydraulic gradient, pressurized water flow will be generated, which is the traditional groundwater flow; ii.) Under the condition of an applied electric field, electroosmotic flow will be generated between the media. Therefore, q * It can be represented as q * =q+u e Where q is the flux formed under the hydraulic gradient, which can be expressed under macroscopic conditions according to Darcy's law:

[0087]

[0088] Where k h ρ is the permeability, g is the fluid density, μ is the gravitational acceleration, and h is the head difference.

[0089] According to the Helmholtz–Smoluchowski theory, electroosmotic flow can usually be expressed as u e =-k e ▽Φ, where k e This is the electroosmotic coefficient, which corresponds to the osmotic coefficient in Darcy's law. When the pH of the solution inside the medium is close to neutral, this value is approximately 10. -8 -10 -9 m 2 / (V·s).

[0090] Therefore, substituting the mass conservation equation, the migration constitutive equation of substance i is mainly expressed by the following equation:

[0091]

[0092] Among them, R i For source and sink items.

[0093] The directional movement of charged ions under the influence of an applied electric field or external force will affect the surrounding potential distribution. Poisson's equation (PE) is used to quantify this process.

[0094]

[0095] Where ρ e Charge density, generally expressed as ρ e =F∑z i c i ε₀ is the permittivity of vacuum, ε r is the relative permittivity.

[0096] The combined equations (2), (4), and (5) are the Nernst-Planck-Poisson Equation (NPPE), which is the constitutive equation describing the transport of matter in a medium.

[0097] (ii) Characterization of the microbial remediation response process

[0098] In assessing microbial degradation rates, the Monod equation, based on multiple inhibition, is generally used to characterize microbial growth and organic matter degradation.

[0099] The Monod kinetic equation can take into account the competitive inhibition phenomenon that occurs during the degradation process:

[0100]

[0101] Among them, C EA,i With C ED , , represent the concentrations of the i-th electron acceptor (EA) and electron donor (ED), respectively; t represents time (s); Maximum apparent degradation rate (1 / s); K s,ED and K s,EA,i The half-saturation coefficients of ED and EA, respectively; K inh,j X represents the inhibition coefficient of the target EA by the j-th EA (excluding the i-th EA); k This represents the number of microorganisms.

[0102] The change in the number of microorganisms over time caused by the degradation process is characterized by the following kinetic reaction:

[0103]

[0104] Where Y is the microbial yield coefficient, b k This is the Decay Coefficient, representing the microbial extinction rate.

[0105] (iii) Microbial growth blocking model (GBM)

[0106] During site microbial remediation, microorganisms adsorb onto the surface of the medium or aggregate and remain free in the pores. This causes changes in hydrodynamic parameters such as permeability and dispersion. Therefore, in actual remediation, the impact of biological growth on hydrodynamic parameters must be assessed. The GBM model proposed in this patent considers two phases of microorganisms in migration-reaction simulation: adsorbed on solid surfaces and free in liquids. It is assumed that the adsorbed phase microorganisms are the cause of medium blockage. The two phases of microorganisms are further considered in terms of both active and dead forms, with active organisms capable of microbial degradation and dead organisms not capable of microbial degradation distributed in both phases.

[0107] The model makes the following assumptions: (a) It is assumed that there exists a maximum adsorbed biomass (X). surface_max ) and the reduction factor of maximum permeability (P) max (a) The total amount of microorganisms adsorbed on the surface and the decrease in permeability satisfy a linear relationship; (b) Dead microorganisms have the same effect on blocking the medium as active microorganisms; (c) It is assumed that microorganisms in the solution and microorganisms adsorbed on the medium follow an equilibrium adsorption, and once adsorbed, they will not fall off until the maximum biosorption capacity is reached. The GBM model mainly consists of four parts, and a simulation cycle includes four periods: migration, growth, renewal, and distribution.

[0108] 1) Migration phase: Contains microorganisms with active effects (X) solution_Live ) and dead microorganisms (X solution_Dead Liquid-phase microorganisms (X) including solution All of them migrated; while those containing microorganisms with active effects (X) surface_Live ) and dead microorganisms (X surface_Dead Microorganisms (X) on solid surfaces, including surface Then remain stationary; simultaneously set three ratio variables: the ratio of active microorganisms in the liquid phase R solution The ratio of active microorganisms on the solid phase, R surface and the ratio R of active microorganisms on the solid-liquid two-phase system. solu_surf .

[0109] Solution phase:

[0110] X solution_Live =X solution *R solution (8)

[0111] X solution_Dead =X solution *(1-R solution (9)

[0112]

[0113] Solid surfaces:

[0114] X surface_Live =X surface *R surface (11)

[0115] X surface_Dead =X surface *(1-R surface (12)

[0116]

[0117] Solution-solid phase:

[0118]

[0119] Variables (X) during this time period solution_Live X solution_Dead X surface_Dead X surface_Live ) represents the first-level variable within this step size; variable (R) solution R surface R solu_surf ) represents a secondary variable within this step size. In the description, the superscript i used to indicate this step size is omitted, and the result is saved for subsequent calculations.

[0120] 2) Growth period: Active microorganisms (X) in the two phases Live Microbial degradation and substrate consumption occur, and the total active biomass at the end of this step is calculated based on the growth pattern defined by the multivariate inhibition Monod equation. Simultaneously, the number of dead microorganisms (Xg) in both phases during the degradation period... Dead The value was also calculated to assess the impact of the presence of dead microorganisms on the permeability coefficient.

[0121] X Live =X solution_Live +X surface_Live (15)

[0122] X Dead =X solution_Dead +X surface_Dead (16)

[0123] Variables (X) during this time period Live X Dead The value changes after one reaction cycle. In the description, the superscript value used to indicate the step size change is omitted. Within the current step size, the variable's superscript is i; after the reaction ends, the value changes to the next step size, and the superscript is i+1.

[0124] 3) Update period: Based on 2), the variable (X) Live X DeadUpdate the biomass values ​​of each microorganism within the two phases, i.e., update the initial values ​​of the primary variables before the allocation period under the new step size. The primary variables are not allocated after the update, so they are distinguished by the superscript tilde (~).

[0125]

[0126]

[0127]

[0128]

[0129] Here, i+1 represents the next step size.

[0130] 4) Distribution period: Based on the biomass in the two phases, the biomass is redistributed, assuming that the biomass between the solution phase and the solid phase follows equilibrium adsorption, with the adsorption parameter being K. d Until the surface reaches the maximum adsorption capacity X max Furthermore, all organisms (active and dead organisms) in the solution phase have an equal probability of adsorbing onto the surface of the solid phase.

[0131]

[0132] After determining the biological allocation, the primary variables are recalculated, and the remaining secondary variables are updated based on the primary variables to complete a numerical simulation process.

[0133] Finally, based on the biomass X adsorbed on the solid surface... surface A permeability coefficient variation model was established and the variation coefficient P was obtained. c o ef

[0134]

[0135] Where K a X represents the permeability coefficient under biological influence. max For maximum biological growth, P max The maximum permeability coefficient is reduced by a factor of X, where X is the amount of biological growth and K0 is the background permeability coefficient value.

[0136] By combining the above constitutive equations Figure 1 The computational logic flowchart can be used to achieve a detailed depiction of the electrically assisted microbial repair process.

[0137] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements can be made without departing from the principle of the present invention, and these improvements should also be considered within the scope of protection of the present invention.

Claims

1. A numerical simulation method of an electrically assisted microbial remediation process, characterized in that, The method steps are as follows: Step one, initialization of settings in COMSOL and PHREEQC software, determination of conceptual model of the study area, definition of the study area structure, setting of chemical substances, initial and boundary conditions, and setting of the number of simulation nodes based on adaptive meshing; Step two, the transport-reaction operator splitting method is used to calculate the transport and reaction process respectively; the transport term is calculated by COMSOL The multi-field coupling environment is depicted, specifically including the convection under the hydraulic gradient, the dispersion under the concentration gradient, the electromigration and electroosmotic flow under the electric potential gradient, and the possible electrode reaction; the reaction term is calculated by PHREEQC to depict the water phase material distribution and the multi-element inhibition Monod equation for the microbial remediation; step two is the depiction of the microbial remediation process based on the multi-element Monod equation; In the evaluation of microbial degradation rate, the Monod equation based on multiple inhibition is used to describe the microbial growth and organic matter degradation; the Monod kinetic equation can describe the competitive inhibition phenomenon occurring in the material degradation process: where C EA,i and C ED are the concentrations of the ith electron acceptor EAand electron donor ED, respectively; t is time (s); is the maximum apparent degradation rate (1 / s); K s,ED and K s,EA,i are the half-saturation coefficients for EDand EA, respectively; K inh,j is the inhibition coefficient of j other EAs on the target EA; X k is the microbial quantity; The change of the number of microorganisms with time caused by the degradation process is described by the following kinetic reaction: where Y is the coefficient of microbial growth, b k is the coefficient of microbial death. Step three, in the model for the evaluation of the effect of microbial growth on the permeability coefficient, i.e. the parameter setting of the microbial growth blockage model (GBM); in step three in the microbial growth blockage model GBM: In the actual repair process, the influence of biological growth process on the hydrodynamic parameters must be evaluated, the GBM model considers two phases of microorganisms in the migration-reaction simulation when microorganisms exist, namely adsorbed on the solid surface and free in the liquid, and assumes that the adsorbed phase microorganisms are the cause of the medium blockage; the two phases of microorganisms further consider the active and dead forms, and the active microorganisms that can degrade microorganisms and the dead microorganisms that cannot degrade microorganisms are distributed in the two phases; The relevant settings of the model are as follows: (1) the maximum adsorbed biomass (X surface max ) and the maximum reduction rate of the permeability coefficient (P max ) are set, and the total amount of microorganisms adsorbed on the surface and the reduction of the permeability coefficient satisfy linear variation; (2) the dead microorganisms have the same effect on the blocking medium capacity as the active microorganisms; (3) the microorganisms in the solution and the microorganisms adsorbed on the medium obey the equilibrium adsorption and do not fall off once adsorbed until the maximum biological adsorption capacity is reached;​ The GBM model mainly consists of four parts, and one simulation cycle includes four cycles of migration, growth, update and distribution: 1) Migration phase: Contains microorganisms with active effects (X) solution_Live ) and dead microorganisms (X solution_Dead Liquid-phase microorganisms (X) including solution All of them migrated; while those containing microorganisms with active effects (X) surface_Live ) and dead microorganisms (X surface_Dead Microorganisms (X) on solid surfaces, including surface Then remain stationary; simultaneously set three ratio variables: the ratio of active microorganisms in the liquid phase R solution The ratio of active microorganisms on the solid phase, R surface and the ratio R of active microorganisms on the solid-liquid two-phase system. solu_surf ; Solution phase: X solution_Live = X solution * R solution (8) X solution_Dead = X solution (1 - R solution ) (9) Solid surface: X surface_Live = X surface R surface (11) X surface_Dead = X surface (1 - R surface ) (12) Solution-solid phase: The variable (X solution_Live , X solution_Dead , X surface_Dead , X surface_Live ) is a primary variable in this step; the variable (R solution , R surface , R solu_surf ) is a secondary variable in this step. In terms of expression, the superscript i indicating the current step is omitted, and the result is saved for subsequent calculation; 2) Growth phase: active microorganisms (X Live ) in both phases undergo microbial degradation, consume substrate, and the total active biomass at the end of this step is calculated according to the growth pattern set by the multi-inhibition Monod equation; at the same time, dead microorganisms (X Dead ) in both phases are also calculated to assess the impact of the presence of dead microorganisms on the permeability coefficient; X Live = X solution_Live + X surface_Live (15) X Dead = X solution_Dead + X surface_Dead (16) The variable (X Live , X Dead ) undergoes a reaction cycle and the value changes in this time period; in terms of expression, the superscript value indicating the step change is omitted, the variable superscript is i in this step, and the variable superscript is i+1 after the calculation reaction ends and changes to the next step. 3) Update phase: update the biomass values of the microorganisms in each part of the two phases according to the variables (X Live , X Dead ) obtained in 2), i.e. update the initial values of the primary variables before the distribution phase in the new step length; the primary variables are not distributed after being updated, and are therefore distinguished by a tilde (~) superscript; Where, i+1 represents the next step; 4) Distribution phase: Re-distribution of biomass in two phases, set the biomass between the solution phase and the solid phase to obey the equilibrium adsorption, the adsorption parameters are K d , until the surface reaches the maximum adsorption capacity X max , and all the biomass in the solution phase, i.e. the active and dead biomass, has an equal probability of being adsorbed on the surface of the solid phase: After determining the biological distribution, the primary variable is recalculated, and the remaining secondary variables are updated according to the primary variable to complete a numerical simulation process; Finally, the biomass X adsorbed on the solid phase surface is determined surface , a permeability coefficient change model is established, and the change coefficient P coef is obtained where K a is the permeability coefficient under biological influence, X max is the maximum biological growth, P max is the maximum permeability coefficient reduction rate, X is the biological growth, and K0 is the background permeability coefficient value; By combining the above constitutive equation with the numerical simulation method, the fine description of the electromotive auxiliary microbial repair process can be realized; Step four, the coupling process of migration and reaction simulation is carried out in a step-by-step manner: within this step, COMSOL The calculated spatial distribution results of the substances were imported into PHREEQC to calculate various reactions; then, the new concentration data calculated in PHREEQC were used as initial conditions for transport simulation in the next step; this was then performed using MATLAB. Internally compiled subroutines control the simulation step size and simulation duration; based on The self-compiled coupled computing module is combined to form the CPM-BIO numerical simulation process, which realizes the precise simulation of the electrically assisted microbial remediation process. Step five, after the numerical simulation of the specified number of steps is completed, all results are imported into COMSOL or post-processed in an external program.

2. The numerical simulation method of an electrically-assisted microbial remediation process according to claim 1, characterized in that, The multi-field environment description based on Nernst-Planck-Poisson in step two: At the macroscopic scale, the Nernst-Planck equation is used to describe the movement behavior of a certain substance i, whose flux expression is mainly composed of a diffusion term an activity term and an electromigration term consists of: wherein is the water phase substance diffusion coefficient, c i is the water phase substance concentration, γ i is the activity coefficient, z i the ionic valence, F is the Faraday constant, R is the ideal gas constant, T is the temperature, and Φ is the electric potential; In an aquifer, the diffusion of a substance i in the water phase is limited by the presence of the solid phase; therefore, the water phase diffusion coefficient in equation 1 is replaced by D = D i = D i aq τ, where τ is the tortuosity; similarly, the size of the medium porosity n also has a similar effect on the diffusion of the substance; in addition, the total flux Taking into account the effect of convection According to the different driving forces, the flow term, q * There are two main types:

1. Under the condition of external hydraulic gradient, pressure water flow will be generated, which is the traditional sense of groundwater flow; 2. Under the condition of external electric field, electroosmotic flow will be generated between the medium bodies; therefore, q * is expressed as q * = q + u e , wherein q is the flux formed under the hydraulic gradient, which is expressed according to Darcy's law under macroscopic conditions: where k h is the permeability, p is the fluid density, g is the acceleration of gravity, μ is the dynamic viscosity coefficient, is the head difference; According to the Holm-Hsorohovsky theory, the electroosmotic flow is expressed as where k e is the electroosmotic coefficient, corresponding to the permeability coefficient in Darcy's law; this value is 10 -8 -10 -9 m 2 / (V·s) when the pH of the solution inside the medium body is close to neutral; Therefore, by substituting the mass conservation equation, the migration constitutive equation of material i is mainly represented by the following formula: wherein R i is a source sink term; Under the action of an external electric field or the directional movement of charged ions under the action of an external force, the potential distribution around will be affected, and the Poisson equation is used to quantify this process: where p e is the charge density, expressed as p e = F∑z i c i , ε0is the permittivity of free space, and ε r is the relative permittivity; The combination of equations (2), (4) and (5) is the Nernst-Planck-Poisson equation, and this set of equations is the constitutive equation describing the migration of materials in the medium.

3. The numerical simulation method of an electrically assisted bioremediation process according to claim 1, characterized in that, COMSOL-based in step four The calculated spatial distribution of mass transport results were imported into PHREEQC to calculate various reactions; the newly calculated concentration data in PHREEQC were then taken as initial conditions to be brought into the next step for transport simulation; the simulation step and simulation duration were controlled by a subroutine compiled in MATLAB from MATLAB Based on The coupling calculation module is obtained from the self-compilation, and a CPM-BIO numerical simulation method is formed by combination to realize a numerical simulation process of fine electrically assisted microbial remediation.

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