CN115659719A - Method for optimizing in-situ bioremediation scheme of chlorohydrocarbon pollution in aquifer - Google Patents

Method for optimizing in-situ bioremediation scheme of chlorohydrocarbon pollution in aquifer Download PDF

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CN115659719A
CN115659719A CN202211161500.4A CN202211161500A CN115659719A CN 115659719 A CN115659719 A CN 115659719A CN 202211161500 A CN202211161500 A CN 202211161500A CN 115659719 A CN115659719 A CN 115659719A
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concentration
pumping
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dechlorination
repair
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骆乾坤
赵梦
郭明
孔志伟
孙猛
邓亚平
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Hefei University of Technology
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Abstract

The invention discloses an optimization method of an aquifer chlorinated hydrocarbon pollution in-situ bioremediation scheme, and belongs to the field of groundwater pollution remediation. The optimization method comprises the following steps: defining a chlorohydrocarbon remediation area and a pollution area, constructing a chlorohydrocarbon pollution in-situ bioremediation simulation model in an aquifer, constructing a chlorohydrocarbon pollution in-situ bioremediation scheme multi-target optimization model in the aquifer, constructing a chlorohydrocarbon pollution remediation simulation-optimization model in the aquifer, and solving the simulation-optimization model based on an NSGA-II algorithm to obtain an optimal remediation scheme set. The optimization method of the invention improves the simulation precision of the in-situ bioremediation process of the chlorohydrocarbon, balances the contradiction relationship between the remediation cost and the remediation effect, and provides decision basis for a demander to select a final remediation scheme through the optimal remediation scheme set.

Description

Method for optimizing in-situ bioremediation scheme of chlorohydrocarbon pollution in aquifer
Technical Field
The invention belongs to the field of groundwater pollution remediation, and relates to an optimization method of an aquifer chlorinated hydrocarbon pollution in-situ bioremediation scheme.
Background
In recent years, volatile chlorinated hydrocarbon is widely used in the fields of electronics and industrial cleaning as a common chemical raw material and an organic solvent, and unreasonable discharge in the use process makes the chlorinated hydrocarbon become one of the most common toxic and harmful pollutants in underground water, thereby seriously harming the public environmental safety. Wherein, shallow groundwater in northern China has serious chlorinated hydrocarbon pollution, and tetrachloroethylene (PCE) and Trichloroethylene (TCE) have the highest pollution degree. Since most chlorinated hydrocarbons have a "triogenic" effect: the potential toxicity of carcinogenesis, teratogenesis and mutagenesis seriously threatens the human health and the water environment safety. Therefore, remediation of groundwater chlorinated hydrocarbon pollution is not slow enough.
At present, scholars at home and abroad adopt a series of physical, chemical and biological remediation technologies to treat chlorinated hydrocarbon pollution in underground water, such as surfactant-enhanced aquifer remediation (SEAR) technology, in-situ biological remediation (ISB) technology and the like. Among them, ISB technology can realize complete destruction of organic pollutants, and becomes one of the accepted technologies for repairing chlorinated hydrocarbons in underground water. In the process of restoring chlorinated hydrocarbon pollution in an aquifer by using the ISB technology, most of students do not consider the nature of natural attenuation of the chlorinated hydrocarbon, which causes the problems of low simulation precision and unsatisfactory restoration efficiency and effect, and corresponding papers such as "modeled chlorinated solvent utilization and repair (HRC) (simulation of chlorinated solvent bioremediation by hydrogen releasing compound) (Ryan C w., junqi h., mark N g.modeled chlorinated solvent bioremediation (HRC) [ J ] biological-repair Journal,2007, 10 (3): 129-141.) and the like, in order to improve the restoration efficiency, a simulation model capable of accurately simulating the chlorinated hydrocarbon in-situ bioremediation process must be established. On the other hand, most of the repair schemes take the chlorinated hydrocarbon pollution repair problem as a single target problem, however, the scheme only pursuing a single target function inevitably causes resource waste, and in the design process of the aquifer chlorinated hydrocarbon in-situ bioremediation scheme, not only the repair effect but also the repair cost are considered. Therefore, optimization of the aquifer chlorinated hydrocarbon pollution remediation scheme is a multi-objective optimization problem.
In summary, the existing solutions for restoring chlorinated hydrocarbon pollution in underground aquifers have many disadvantages in terms of simulation precision, balancing restoration total cost and residual pollutant concentration, and methods for optimizing the chlorinated hydrocarbon pollution restoration solutions are urgently needed to be proposed.
Disclosure of Invention
The invention aims to solve the problems in the prior art, namely, a multi-objective optimization model which ensures simulation accuracy and considers two objective functions of the optimal repair effect and the minimum repair cost is established and solved by an NSGA-II algorithm to obtain an optimal repair scheme set which can be selected by a demander.
The technical scheme of the invention is as follows:
an optimization method for an in-situ bioremediation scheme of chlorinated hydrocarbon pollution in an aquifer comprises the following steps:
step 1, definition of a chlorohydrocarbon remediation zone and a pollution zone
The chlorinated hydrocarbon restoration zone and the chlorinated hydrocarbon pollution zone included in the chlorinated hydrocarbon restoration zone are three-dimensional isotropic confined aquifer; simplifying the chlorohydrocarbon restoration area into a rectangular body with the height parallel to the ground plane, taking any cross section perpendicular to the ground plane as a restoration area A, wherein the restoration area A is a rectangle, the long side of the restoration area A is alpha, and the short side of the restoration area A is beta;
establishing a plane coordinate system by taking one end point of the repair area A as an original point, wherein the long side of the repair area A is parallel to the X axis, and the short side of the repair area A is parallel to the Y axis; making two long edges as water-resisting boundaries and two short edges as constant water head boundaries, wherein the short edge superposed with the Y axis is marked as a boundary 1, the other short edge is marked as a boundary 2, the water flow direction is the same as the positive direction of the X axis, and the water head of the boundary 1 is larger than that of the boundary 2;
marking a chlorohydrocarbon polluted area in the restoration area A as a polluted area B, and setting the polluted area B to be positioned in an interval of 0.04 alpha to X to 0.64a and 0.35 beta to Y to 0.7 beta;
the restoration area A also comprises a concentration restriction area which is a rectangular area, the right boundary of the concentration restriction area is overlapped with the boundary 2, and the straight line distance from the left boundary to the right boundary of the pollution area is about 0.04 alpha-0.08 alpha;
the center line which is arranged in the restoration area A and is parallel to the X axis is uniformly provided with gamma pumping and injection wells, and any one of the pumping and injection wells is marked as a pumping and injection well I i I =1,2, r pumping well I i Sequentially arranged from left to right;
arranged in the repairing process through gamma pumping injection wells I i Injecting lactic acid into the chlorohydrocarbon repair area, and fermenting to generate hydrogen at the moment of injecting the lactic acid, wherein the total concentration of the hydrogen is known;
in the repairing process, the hydraulic gradient of the hydrochloric ether repairing area is 0.0048, the porosity is 0.3, and the longitudinal dispersion coefficient is 10m 2 S, permeability coefficient of 1.8m/d, repair cycle N s The longitudinal dispersion coefficient is the area of pollutants dispersed to the X axis in the positive direction every second, and is 1000 days;
the total time period of N is set to be one time period every day in the repairing process s A repair time period, wherein any one repair time period is recorded as a repair time period s, s =1,2 s ；
Equally dividing the repair area A into finite difference grids, wherein each intersection point of the finite difference grids is a node, and any one node is marked as a node j 0 0=1, 2., N is the number of nodes in the repair area a;
step 2, constructing a simulation model for in-situ bioremediation of chlorohydrocarbon pollution in an aquifer
Marking the model 1 as an in-situ biological remediation simulation model of the chlorinated hydrocarbon pollution in the aquifer, wherein the model comprises a model for degrading chlorinated hydrocarbon by microbial population, a model for growing and attenuating microbes and a model for naturally attenuating chlorinated hydrocarbon;
the contaminants in the chlorinated hydrocarbon remediation zone include the following four: tetrachloroethylene, trichloroethylene, dichloroethylene, and vinyl chloride; two kinds of dechlorination microorganisms exist in the chlorohydrocarbon restoration area, wherein one kind of dechlorination microorganism participates in degrading tetrachloroethylene and trichloroethylene and is called microorganism 1, and the other kind of microorganism participates in degrading dichloroethylene and chloroethylene and is called microorganism 2;
the expression of the model for degrading chlorinated hydrocarbon by microbial population is as follows:
Figure BDA0003858126080000041
in the formula, V 1 Concentration of microorganism 1 participating in the reaction, V 2 In order to obtain the concentration of the microorganisms 2 involved in the reaction,
Figure BDA0003858126080000042
the degradation rate of tetrachloroethylene under the action of dechlorination participated by the microorganism 1,
Figure BDA0003858126080000043
maximum dechlorination constant, C, of tetrachloroethylene by microbial 1 dechlorination PCE Is the initial concentration of the tetrachloroethylene and,
Figure BDA0003858126080000044
is the half-saturation constant of tetrachloroethylene by the dechlorination of microorganisms 1, C TCE Is the initial concentration of the trichloroethylene to be,
Figure BDA0003858126080000045
is the half-saturation constant of trichloroethylene by microbial 1-dechlorination, C i，s For repairing pumping well I in time period s i The concentration of the lactic acid injected in the process,
Figure BDA0003858126080000051
dechlorination reaction H in the presence of hydrogen 2 A threshold value for the concentration of the aqueous phase, said threshold value being the lowest value required to maintain normal survival of the microorganisms,
Figure BDA0003858126080000052
for dechlorination of H by the microorganism 1 2 The constant of the half-saturation is constant,
Figure BDA0003858126080000053
is the degradation rate of the trichloroethylene under the dechlorination action of the microorganism 1,
Figure BDA0003858126080000054
is the maximum dechlorination constant of the trichloroethylene through microbial 1 dechlorination,
Figure BDA0003858126080000055
is the degradation rate of dichloroethylene under the action of dechlorination participated by microorganism 2,
Figure BDA0003858126080000056
maximum dechlorination constant, C, for the dechlorination of ethylene dichloride by microorganism 2 DCE Is the initial concentration of the ethylene dichloride in the reactor,
Figure BDA0003858126080000057
is the half-saturation constant of dichloroethylene by microbial 2-dechlorination, C VC Is the initial concentration of vinyl chloride and is,
Figure BDA0003858126080000058
is the half-saturation constant of the dechlorination of vinyl chloride by the microorganism 2,
Figure BDA0003858126080000059
for dechlorination of H by microorganisms 2 2 The half-saturation constant of the liquid crystal,
Figure BDA00038581260800000510
the degradation rate of the chloroethylene under the action of dechlorination participated by the microorganism 2,
Figure BDA00038581260800000511
is the maximum dechlorination constant of the dechlorination of vinyl chloride by the microorganism 2,
Figure BDA00038581260800000512
to pass through H 2 The utilization rate of the catalyst is increased under the action of dechlorination,
Figure BDA00038581260800000513
consumption of H for tetrachloroethylene 2 The stoichiometric coefficient of (a) is,
Figure BDA00038581260800000514
consumption of H for trichloroethylene 2 The stoichiometric coefficient of (a) is,
Figure BDA00038581260800000515
consumption of H for dichloroethylene 2 The stoichiometric coefficient of (a) is,
Figure BDA00038581260800000516
consumption of H for vinyl chloride 2 The stoichiometric coefficient of (a);
the expression of the microorganism growth and decay model is as follows:
Figure BDA00038581260800000517
in the formula (I), the compound is shown in the specification,
Figure BDA00038581260800000518
for the utilization of the microorganism 1 in the dechlorination, D 1 Is a yield coefficient of the microorganism 1,
Figure BDA00038581260800000519
is the decay rate constant, V, of microorganism 1 1，min For a given minimum concentration of microorganism 1 participating in the reaction,
Figure BDA00038581260800000520
for the utilization of microorganism 2 in the dechlorination, D 2 Is a factor in the yield of the microorganism 2,
Figure BDA00038581260800000521
is the decay rate constant, V, of microorganism 2 2，min The lowest microorganism 2 concentration for a given reaction participation;
the expression of the natural attenuation model of the chlorohydrocarbon is as follows:
Figure BDA0003858126080000061
in the formula, V PCE Is the concentration of tetrachloroethylene after decay, K pce Is the first order degradation rate of tetrachloroethylene, V TCE Concentration of trichloroethylene after decay, Y tce/pce Is the natural attenuation coefficient of tetrachloroethylene, K tce First order degradation rate of trichloroethylene, V DCE Is the concentration of ethylene dichloride after decay, Y dce/tce Is the natural attenuation coefficient of trichloroethylene, K dce Is the first order degradation rate of dichloroethylene, V VC Is the concentration of vinyl chloride after decay, Y vc/dce Is the natural attenuation coefficient of dichloroethylene;
step 3, constructing a multi-objective optimization model of the chlorinated hydrocarbon pollution in-situ bioremediation scheme in the aquifer
Recording a multi-objective optimization model of the chlorinated hydrocarbon pollution in-situ bioremediation scheme in the aquifer as a model 2, and constructing the model 2 under a set constraint condition;
the set constraint conditions are as follows:
Figure BDA0003858126080000062
under the above constraints, the expression of model 2 is:
Figure BDA0003858126080000071
in the formula, f 1 Min f for total cost of repair 1 To restore the lowest value of the total cost, a 1 For drilling cost factor, a 2 For installation of well cost factor, a 3 For pumping water charge factor, a 4 Cost factor for injection of lactic acid, the drilling cost factor a 1 For drilling one suction injection well I i Cost of required underground space, installation well cost factora 2 For installing a pumping well I i The required cost, the water pumping and injecting cost coefficient a 3 For in a pumping and injecting well I i Extract 1m 3 Water or in a pumping well I i Is injected into the space of 1m 3 Cost of water, cost coefficient of injected lactic acid a 4 For in a pumping and injecting well I i The cost required for injecting 1mol of lactic acid;
b is the presence coefficient of the pumping well, b is equal to 0 or 1, and when b =1, the pumping well I i Use, when coefficient b =0 is present, pump well I i Not in use, d i For pumping and injecting well I i Well depth of (Q) i，s For pumping and injecting well I i The water pumping and injecting rate in the repairing time period s has a negative value of pumping water, a positive value of water injection, delta s of the duration of the repairing time period s and min f 2 Is the lowest value of the concentration ratio of the remaining contaminants, f 2 Mass as the remaining contaminant concentration ratio ini To restore the initial concentration of contaminants in the aquifer, mass end The concentration of the pollutants in the aquifer at the end of remediation;
Figure BDA0003858126080000072
for repairing a node j within a time period s 0 The concentration of the contaminants in the (c) is,
Figure BDA0003858126080000073
for repairing a node j within a time period s 0 The maximum allowable concentration of the contaminant(s) in (c),
Figure BDA0003858126080000074
for a full term, meaning that for any, e denotes belonging,
Figure BDA0003858126080000075
for repairing the pumping well I in the time period s i The lower limit of the concentration of the injected lactic acid,
Figure BDA0003858126080000076
for repairing the pumping well I in the time period s i The upper limit of the concentration of injected lactic acid of (c),
Figure BDA0003858126080000077
for repairing a node j within a time period s 0 The lower limit of the water head of (c),
Figure BDA0003858126080000078
for repairing node j in time interval s 0 The height of the water head of (b),
Figure BDA0003858126080000079
for repairing node j in time interval s 0 The upper limit of the water head of (b),
Figure BDA00038581260800000710
for repairing a node j within a time period s 0 The hydraulic gradient of the upper gradient water head is obtained,
Figure BDA00038581260800000711
for repairing j within time period s 0 The hydraulic gradient of the lower gradient water head at the node,
Figure BDA00038581260800000712
Figure BDA00038581260800000713
for repairing the well I in the time period s i The lower limit of the flow rate is,
Figure BDA00038581260800000714
for repairing pumping well I in time period s i Upper limit of flow, Q s，total For repairing all pumping and injecting wells I in time period s i The total water injection amount of (c);
step 4, constructing a simulation-optimization model for restoring chlorinated hydrocarbon pollution in an aquifer
The model 1 and the model 2 are coupled to form a simulation-optimization model for restoring the chlorinated hydrocarbon pollution in the aquifer, and the model is recorded as a model 3, and the expression is as follows:
Figure BDA0003858126080000081
when the restoration is finished, the concentrations of four pollutants in the concentration restriction area reach the standard of IV-class water, and specifically, the concentration V of tetrachloroethylene after attenuation PCE Concentration of trichloroethylene after attenuation V TCE Concentration of ethylene dichloride after decay V DCE Vinyl chloride concentration after damping V VC Is recorded as the concentration W of the pollutant L L is the number of the four chlorinated hydrocarbons, L =1,2,3,4, where 1 is tetrachloroethylene, 2= trichloroethylene, 3 is dichloroethylene, and 4 is vinyl chloride, according to which the constraints of model 3 are as follows:
Figure BDA0003858126080000091
in the formula (I), the compound is shown in the specification,
Figure BDA0003858126080000092
for repairing node j in time interval s 0 The concentration of the contaminants in the (c) is,
Figure BDA0003858126080000093
for repairing j within time period s 0 Maximum allowable contaminant concentration at the node;
step 5, solving the model 3 obtained in the step 4 based on the NSGA-II algorithm
Firstly, an objective function is set, and input variables are as follows: to total cost of repair f 1 Is a first objective function, the remaining pollutant concentration ratio f 2 Is a second objective function, and the first and second objective functions are denoted as an objective function F (F) 1 ，f 2 ) (ii) a Taking the number gamma of pumping and injection wells as a first input variable E 1 Pumping and injecting well I i Well depth d i Is a second input variable E 2 Pumping and injection well I i Water pumping and injection rate Q in repair period s i，s Is a third input variable E 3 Pumping and injection well I i Injected lactic acid concentration C over repair time period s i，s Is a fourth input variable E 4 And the four input variables are denoted as input variables E (Γ, d) i ，Q i，s ，C i，s )；
Secondly, solving the model 3 obtained in the step 4 based on an NSGA-II algorithm to obtain G optimal solutions F yk (f 1 ，f 2 ) And corresponding G optimal repair solutions E yk (Γ，d i ，Q i，s ，C i，s ) G optimal repair solutions E yk (Γ，d i ，Q i，s ，C i，s ) Composing an optimal set of repair solutions
Figure BDA0003858126080000094
k =1, 2.., G is the maximum number of iterations;
step 6, drawing f 1 -f 2 Curve
With f 1 Is the abscissa, f 2 Marking each optimal solution F in a plane coordinate system for the ordinate yk (f 1 ，f 2 ) Corresponding data points are selected to carry out fitting on G data points by an exponential function to obtain a strip f 1 -f 2 A curve;
step 7, selection of final repair scheme
On demand by f 1 -f 2 Curve selection data points, i.e. total cost f for repair 1 And the remaining contaminant concentration ratio f 2 Then from the optimal repair solution set
Figure BDA0003858126080000095
Selecting a final remediation scheme of in-situ organisms polluted by the chlorohydrocarbon.
Preferably, the specific steps of solving the model 3 obtained in step 4 based on the NSGA-II algorithm in step 5 are as follows:
step 5.1, initializing the population and randomly selecting N z Set input variables E (Γ, d) i ，Q i，s ，C i，s ) Value of (A) forms N z Group non-dominated solution, resulting in an initial parent population P 0 In which N is z The population scale is adopted;
step 5.2, adding N z Set of input variables E (Γ, d) i ，Q i，s ，C i，s ) Calculating the kth iteration parent population P in the substitution model 3 k To obtain N z Group objective function F k (f 1 ，f 2 ) N of the group z Group objective function F k (f 1 ，f 2 ) Composition target solution set T k ；
Step 5.3, apply pareto to sort the target solution T k N in (1) z Group objective function F k (f 1 ，f 2 ) Sorting from good to bad in descending order, and selecting an objective function F with the first rank k (f 1 ，f 2 ) Is recorded as an optimal solution F yk (f 1 ，f 2 ) And the optimal solution F is yk (f 1 ，f 2 ) Corresponding input variables E (Γ, d) i ，Q i，s ，C i，s ) Is recorded as an optimal repair scheme E yk (Γ，d i ，Q i，s ，C i，s )；
Target solution set T k In the unselected objective function F k (f 1 ，f 2 ) Corresponding input variable E (Γ, d) i ，Q i，s ，C i，s ) Obtaining the population size N through selection, variation and crossing z Progeny population Q of-1 k ；
Step 5.4, the parent population P k And progeny population Q k Are combined into a population R k To the population R k Performing fast non-dominant sorting, sorting from good to bad in descending order, and taking the top N z As a new parent population P k+1 ；
Step 5.5, repeating the steps 5,2 and 5.4 until the maximum iteration number G is reached, and outputting G optimal solutions F obtained in G iterations yk (f 1 ，f 2 ) And G optimal repair solutions E yk (Γ，d i ，Q i，s ，C i，s ) G optimal repair solutions E yk (Γ，d i ，Q i，s ，C i，s ) And forming an optimal repair scheme set B.
Compared with the prior art, the invention has the beneficial effects that:
1. the in-situ bioremediation simulation model of the chlorohydrocarbon pollution in the aquifer is established for the first time, wherein the natural attenuation of the chlorohydrocarbon is considered, so that the simulation precision of the in-situ bioremediation process of the chlorohydrocarbon pollution in the aquifer is improved;
2. establishing a multi-objective optimization model of the chlorinated hydrocarbon pollution in-situ bioremediation scheme in the aquifer, and then coupling the multi-objective optimization model with the chlorinated hydrocarbon pollution in-situ bioremediation simulation model in the aquifer to form a chlorinated hydrocarbon pollution remediation simulation-optimization model in the aquifer;
3. solving the obtained chlorohydrocarbon pollution restoration simulation-optimization model in the aquifer through an NSGA-II algorithm, balancing the contradiction relation between the restoration cost and the restoration effect, and providing decision basis for a demander to select a final restoration scheme through an optimal restoration scheme set.
Drawings
FIG. 1 is a schematic view of the initial contamination of a hydrochloric ether remediation zone;
FIG. 2 shows f obtained in an example of the present invention 1 -f 2 A curve;
Detailed Description
The invention is further illustrated by the figures and the examples.
The invention provides an optimization method of an in-situ bioremediation scheme for chlorohydrocarbon pollution in an aquifer, which comprises the following steps of:
step 1, definition of a chlorohydrocarbon remediation zone and a pollution zone
The chlorinated hydrocarbon restoration zone and the chlorinated hydrocarbon pollution zone included in the chlorinated hydrocarbon restoration zone are three-dimensional isotropic confined aquifer; simplifying the chlorohydrocarbon restoration area into a rectangular body with the height parallel to the ground plane, taking any cross section perpendicular to the ground plane as a restoration area A, wherein the restoration area A is a rectangle, the long side of the restoration area A is alpha, and the short side of the restoration area A is beta;
establishing a plane coordinate system by taking one end point of the repair area A as an original point, wherein the long side of the repair area A is parallel to the X axis, and the short side of the repair area A is parallel to the Y axis; setting two long edges as water-resisting boundaries and two short edges as constant water head boundaries, wherein the short edge superposed with the Y axis is marked as a boundary 1, the other short edge is marked as a boundary 2, the water flow direction is the same as the positive direction of the X axis, and the water head of the boundary 1 is greater than that of the boundary 2;
recording a chlorinated hydrocarbon polluted area in the restoration area A as a polluted area B, and setting the polluted area B to be positioned in an interval of more than 0.04 alpha and less than X and less than 0.64 alpha and more than 0.35 beta and less than Y and less than 0.7 beta;
the restoration area A also comprises a concentration restriction area which is a rectangular area, the right boundary of the concentration restriction area is overlapped with the boundary 2, and the straight line distance from the left boundary to the right boundary of the pollution area is about 0.04 alpha-0.08 alpha;
the center line which is arranged in the repair area A and is parallel to the X axis is uniformly provided with gamma pumping and injecting wells, and any one of the pumping and injecting wells is marked as a pumping and injecting well I i I =1, 2., Γ, Γ number of pumping wells I i Sequentially arranged from left to right;
arranged in the repairing process through gamma-shaped pumping and injecting wells I i Injecting lactic acid into the chlorohydrocarbon repair area, and fermenting to generate hydrogen at the moment of injecting the lactic acid, wherein the total concentration of the hydrogen is known;
in the repairing process, the hydraulic gradient of the chlorohydrocarbon repairing area is 0.0048, the porosity is 0.3, and the longitudinal dispersion coefficient is 10m 2 S, permeability coefficient of 1.8m/d, repair cycle N s The longitudinal dispersion coefficient is the area of pollutants dispersed to the X axis in the positive direction every second, and is 1000 days;
the total time period of N is set to be one time period every day in the repairing process s A repair time period, wherein any one repair time period is recorded as a repair time period s, s =1,2 s ；
Equally dividing the repair area A into finite difference grids, wherein each intersection point of the finite difference grids is a node, and any one node is marked as a node j 0 0=1, 2., N is the number of nodes in the repair area a;
FIG. 1 is a schematic view of the initial contamination of a chlorinated hydrocarbon remediation zone and shows the specific locations of a remediation zone A, a contamination zone B, and a concentration-limiting zone.
Step 2, constructing a simulation model for in-situ bioremediation of chlorohydrocarbon pollution in an aquifer
And (3) marking the model 1 as an in-situ biological remediation simulation model of the chlorinated hydrocarbon pollution in the aquifer, wherein the model comprises a model for degrading chlorinated hydrocarbon by microbial population, a model for growing and attenuating microbes and a model for naturally attenuating chlorinated hydrocarbon.
The contaminants in the chlorinated hydrocarbon remediation zone include the following four: tetrachloroethylene, trichloroethylene, dichloroethylene, and vinyl chloride; two kinds of dechlorination microorganisms exist in the chlorohydrocarbon restoration area, wherein one kind of dechlorination microorganism participates in degrading tetrachloroethylene and trichloroethylene and is called microorganism 1, and the other kind of microorganism participates in degrading dichloroethylene and chloroethylene and is called microorganism 2;
the expression of the model for degrading chlorinated hydrocarbon by microbial population is as follows:
Figure BDA0003858126080000131
in the formula, V 1 Concentration of microorganism 1 participating in the reaction, V 2 In order to obtain the concentration of the microorganisms 2 involved in the reaction,
Figure BDA0003858126080000132
the degradation rate of tetrachloroethylene under the action of dechlorination participated by microorganism 1,
Figure BDA0003858126080000133
maximum dechlorination constant, C, of tetrachloroethylene by microorganism 1 dechlorination PCE Is the initial concentration of the tetrachloroethylene and,
Figure BDA0003858126080000134
is the half-saturation constant of tetrachloroethylene by the dechlorination of microorganisms 1, C TCE Is the initial concentration of the trichloroethylene and is,
Figure BDA0003858126080000135
is the half-saturation constant of trichloroethylene by microbial 1-dechlorination, C i，s For repairing pumping well I in time period s i The concentration of the lactic acid injected in the process,
Figure BDA0003858126080000141
dechlorination reaction H in the presence of hydrogen 2 A threshold value for the concentration of the aqueous phase, said threshold value being the lowest value required to maintain normal survival of the microorganisms,
Figure BDA0003858126080000142
for dechlorination of H by microorganisms 1 2 The half-saturation constant of the liquid crystal,
Figure BDA0003858126080000143
the degradation rate of the trichloroethylene under the action of dechlorination participated by the microorganism 1,
Figure BDA0003858126080000144
is the maximum dechlorination constant of the trichloroethylene through microbial 1 dechlorination,
Figure BDA0003858126080000145
is the degradation rate of dichloroethylene under the action of dechlorination participated by microorganism 2,
Figure BDA0003858126080000146
the maximum dechlorination constant, CD, of ethylene dichloride by microorganism 2 CE Is the initial concentration of the ethylene dichloride in the reactor,
Figure BDA0003858126080000147
is the half-saturation constant of dichloroethylene by microbial 2-dechlorination, C VC Is the initial concentration of vinyl chloride and is,
Figure BDA0003858126080000148
is the half-saturation constant of the dechlorination of vinyl chloride by the microorganism 2,
Figure BDA0003858126080000149
for dechlorination of H by microorganisms 2 2 The half-saturation constant of the liquid crystal,
Figure BDA00038581260800001410
the degradation rate of the chloroethylene under the action of dechlorination participated by the microorganism 2,
Figure BDA00038581260800001411
is the maximum dechlorination constant of the dechlorination of vinyl chloride by the microorganism 2,
Figure BDA00038581260800001412
to pass through H 2 The utilization rate of the catalyst under the action of dechlorination is improved,
Figure BDA00038581260800001413
consumption of H for tetrachloroethylene 2 The stoichiometric coefficient of (a) is,
Figure BDA00038581260800001414
consumption of H for trichloroethylene 2 The stoichiometric coefficient of (a) is,
Figure BDA00038581260800001415
consumption of H for dichloroethylene 2 The stoichiometric coefficient of (a) is,
Figure BDA00038581260800001416
consumption of H for vinyl chloride 2 The stoichiometric coefficient of (a);
the expression of the microorganism growth and decay model is as follows:
Figure BDA00038581260800001417
in the formula (I), the compound is shown in the specification,
Figure BDA00038581260800001418
for the utilization of the microorganism 1 in the dechlorination, D 1 The yield coefficient of the microorganism 1 is taken as,
Figure BDA00038581260800001419
is the decay rate constant, V, of microorganism 1 1，min For a given minimum concentration of microorganism 1 involved in the reaction,
Figure BDA00038581260800001420
for the utilization of microorganism 2 in the dechlorination, D 2 Is a factor in the yield of the microorganism 2,
Figure BDA00038581260800001421
is the decay rate constant, V, of microorganism 2 2，min The lowest microorganism 2 concentration for a given participating reaction.
The expression of the chlorinated hydrocarbon natural attenuation model is as follows:
Figure BDA0003858126080000151
in the formula, V PCE Is the concentration of tetrachloroethylene after decay, K pce Is the first order degradation rate of tetrachloroethylene, V TCE Concentration of trichloroethylene after decay, Y tce/pce Is the natural attenuation coefficient of tetrachloroethylene, K tce First order degradation rate of trichloroethylene, V DCE Is the concentration of ethylene dichloride after decay, Y dce/tce Is the natural attenuation coefficient of trichloroethylene, K dce First order degradation Rate of ethylene dichloride, V VC Concentration of vinyl chloride after decay, Y vc/dce Is the natural attenuation coefficient of dichloroethylene.
Step 3, constructing a multi-objective optimization model of the chlorinated hydrocarbon pollution in-situ bioremediation scheme in the aquifer
Marking a multi-objective optimization model of the chlorinated hydrocarbon pollution in-situ bioremediation scheme in the aquifer as a model 2, and constructing the model 2 under a set constraint condition;
the set constraint conditions are as follows:
Figure BDA0003858126080000152
under the above constraints, the expression of model 2 is:
Figure BDA0003858126080000161
in the formula (f) 1 Min f for total cost of repair 1 To restore the lowest value of the total cost, a 1 For the drilling cost factor, a 2 For installation of well cost factor, a 3 For pumping water charge factor, a 4 B is the coefficient of existence of the pumping well, and the drilling cost coefficient a 1 For drilling one suction injection well I i Cost of required underground space, installation well cost factor a 2 For installing a pumping well I i The required cost, the water pumping and injecting cost coefficient a 3 For in a pumping and injecting well I i Extract 1m 3 Water or in a pumping well I i Is injected into the position of 1m 3 Cost of water, cost coefficient of injected lactic acid a 4 For in a pumping well I i The cost required for injecting 1mol of lactic acid;
b is equal to 0 or 1, when b =1, pumping well I i Use, when the coefficient b =0 is present, the well I is pumped i Not in use, d i For pumping and injecting well I i Well depth of (Q) i，s For pumping and injecting well I i The water pumping and injecting rate in the repairing time period s has a negative value of pumping water, a positive value of water injection, delta s of the duration of the repairing time period s and min f 2 Is the lowest value of the concentration ratio of the remaining contaminants, f 2 Mass as the ratio of the concentration of the remaining contaminants ini To restore the concentration of contaminants in the aquifer at the beginning of the repair, mass end Is the concentration of the contaminant in the aquifer at the end of remediation.
Figure BDA0003858126080000162
For repairing a node j within a time period s 0 The concentration of the contaminant(s) in the fluid,
Figure BDA0003858126080000163
for repairing node j in time interval s 0 The maximum allowable concentration of the contaminant(s) in (c),
Figure BDA0003858126080000164
for a full term, meaning that for any, e denotes belonging,
Figure BDA0003858126080000165
for repairing the pumping well I in the time period s i The lower limit of the concentration of the injected lactic acid,
Figure BDA0003858126080000166
for repairing pumping well I in time period s i The upper limit of the concentration of the injected lactic acid of (1),
Figure BDA0003858126080000167
for repairing node j in time interval s 0 The lower limit of the water head of (c),
Figure BDA0003858126080000168
for repairing a node j within a time period s 0 The height of the water head of the water pump,
Figure BDA0003858126080000169
for repairing node j in time interval s 0 The upper limit of the water head of (b),
Figure BDA00038581260800001610
for repairing a node j within a time period s 0 The hydraulic gradient of the upper gradient water head is obtained,
Figure BDA00038581260800001611
for repairing j within time period s 0 The hydraulic gradient of the lower gradient water head at the node,
Figure BDA00038581260800001612
Figure BDA00038581260800001613
for repairing the well I in the time period s i The lower limit of the flow rate is,
Figure BDA00038581260800001614
for repairing pumping well I in time period s i Upper limit of flow, Q s，total For repairing all pumping and injecting wells I in time period s i The total water injection amount.
Step 4, constructing a simulation-optimization model for restoring chlorinated hydrocarbon pollution in an aquifer
The model 1 and the model 2 are coupled to form a simulation-optimization model for restoring the chlorinated hydrocarbon pollution in the aquifer, and the model is recorded as a model 3, and the expression is as follows:
Figure BDA0003858126080000171
when the restoration is finished, the concentrations of four pollutants in the concentration restriction area reach the standard of IV-class water, and specifically, the concentration V of tetrachloroethylene after attenuation PCE Concentration of trichloroethylene after attenuation V TCE Concentration of ethylene dichloride after decay V DCE Vinyl chloride concentration after decay V VC Is recorded as the concentration W of the pollutant L L is the number of the four chlorinated hydrocarbons, L =1,2,3,4, where 1 is tetrachloroethylene, 2= trichloroethylene, 3 is dichloroethylene, and 4 is vinyl chloride, according to which the constraints of model 3 are as follows:
Figure BDA0003858126080000181
in the formula (I), the compound is shown in the specification,
Figure BDA0003858126080000182
for repairing node j in time interval s 0 The concentration of the contaminant(s) in the fluid,
Figure BDA0003858126080000183
for repairing j within time period s 0 The maximum allowable contaminant concentration at the node.
Step 5, solving the model 3 obtained in the step 4 based on the NSGA-II algorithm
Firstly, an objective function is set, and input variables are as follows: to repair the total cost f 1 Is a first objective function, the remaining pollutant concentration ratio f 2 Is a second objective function, and the first and second objective functions are denoted as an objective function F (F) 1 ，f 2 ) (ii) a Taking the number gamma of pumping and injection wells as the firstAn input variable E 1 Pumping and injecting well I i Well depth d i Is a second input variable E 2 Pumping and injecting well I i Water pumping and injection rate Q in repair period s i，s Is a third input variable E 3 Pumping and injection well I i Injected lactic acid concentration C over a repair time period s i，s Is a fourth input variable E 4 And the four input variables are recorded as input variables E (Γ, d) i ，Q i，s ，C i，s )。
Secondly, solving the model 3 obtained in the step 4 based on an NSGA-II algorithm to obtain G optimal solutions F yk (f 1 ，f 2 ) And corresponding G optimal repair solutions E yk (Γ，d i ，Q i，s ，C i，s ) G optimal repair schemes E yk (Γ，d i ，Q i，s ，C i，s ) Composing an optimal set of repair solutions
Figure BDA0003858126080000184
k =1,2.., G is the maximum number of iterations.
Step 6, drawing f 1 -f 2 Curve
With f 1 Is the abscissa, f 2 Marking each optimal solution F in a plane coordinate system for the ordinate yk (f 1 ，f 2 ) Corresponding data points are selected, an exponential function is selected to fit the G data points to obtain a strip f 1 -f 2 Curve line.
Step 7, selection of final repair scheme
On demand by f 1 -f 2 Curve selection data points, i.e. total cost f for repair 1 And the remaining contaminant concentration ratio f 2 Then from the optimal repair solution set
Figure BDA0003858126080000185
Selecting a final remediation scheme of in-situ organisms polluted by the chlorohydrocarbon.
In this embodiment, the specific steps of solving the model 3 obtained in step 4 based on the NSGA-II algorithm in step 5 are as follows:
step 5.1, initializing the population and randomly selecting N z Set input variables E (Γ, d) i ，Q i，s ，C i，s ) Value of (A) forms N z Group non-dominated solution, resulting in an initial parent population P 0 In which N is z The population scale is adopted;
step 5.2, adding N z Set input variables E (Γ, d) i ，Q i，s ，C i，s ) Calculating the kth iteration parent population P in the substitution model 3 k To obtain N z Group objective function F k (f 1 ，f 2 ) N of the group z Group objective function F k (f 1 ，f 2 ) Composition target solution set T k ；
Step 5.3, apply pareto ordering to solve T target solution set k N in (1) z Group objective function F k (f 1 ，f 2 ) Sorting from good to bad in descending order, and selecting an objective function F with the first rank k (f 1 ，f 2 ) Is recorded as an optimal solution F yk (f 1 ，f 2 ) And the optimal solution F is yk (f 1 ，f 2 ) Corresponding input variables E (Γ, d) i ，Q i，s ，C i，s ) Is recorded as an optimal repair scheme E yk (Γ，d i ，Q i，s ，C i，s )；
Target solution set T k Is not selected as an objective function F k (f 1 ，f 2 ) Corresponding input variable E (Γ, d) i ，Q i，s ，C i，s ) Obtaining the population size N through selection, variation and crossing z Progeny population Q of-1 k ；
Step 5.4, the parent population P k And progeny population Q k Are combined into a population R k For population R k Sorting in descending order from good to bad, and taking N before ranking z As a new parent population P k+1 ；
Step 5.5, repeating the step 5, 2-step 5.4 until reaching the maximum iteration number G, outputting G optimal solutions F obtained in G iterations yk (f 1 ，f 2 ) And G optimal repair solutions E yk (Γ，d i ，Q i，s ，C i，s ) G optimal repair solutions E yk (Γ，d i ，Q i，s ，C i，s ) Composing an optimal set of repair solutions
Figure BDA0003858126080000191
Data for implementation according to the above protocol are as follows.
In the present embodiment, the long side α =1000m, the short side β =800m, and the depth of the repair area a is 20m.
In this embodiment, d i ＝20m，a 1 +a 2 d i =10000 yuan/well, a 3 =3.7 yuan/m 3 ，a 4 =0.66 yuan/mol.
In this embodiment, the maximum number of iterations G =100 is taken, and 100 optimal solutions F are obtained yk (f 1 ，f 2 ) The data of (a) are as follows:
Figure BDA0003858126080000201
Figure BDA0003858126080000211
FIG. 2 shows f obtained by fitting the above-mentioned 100 optimal solutions 1 -f 2 Curve line.
When selecting the final repair solution, the demander can select different optimal solutions according to the actual situation, or f should be avoided in order to make different targets have the same importance 1 -f 2 At both ends of the curve, choose f as much as possible 1 -f 2 The turning part of the curve. For example, in this embodiment, the optimal solution F corresponding to the Option 46 is selected y46 (f 1 ，f 2 ) Its corresponding optimal repair scenario E y46 (Γ，d i ，Q i，s ，C i，s ) I.e. a final repair scenario, which isIn the middle, the number of pumping and injection wells is gamma =3, and the pumping and injection wells are respectively pumping and injection wells I 2 Pumping and injecting well I 3 Pumping and injecting well I 4 .3 pumping and injecting wells I i Well depth d i The same is 5 m. Pumping and injecting well I 2 Pumping and injecting well I 3 Pumping and injecting well I 4 Injection rate Q of i，s Are respectively 262m 3 /d、2500m 3 /d、655m 3 D, concentration of injected lactic acid C i，s Respectively 175mg/L, 156mg/L and 48mg/L.
In the final repair scenario, the total repair cost f 1 =20.62 million yuan, remaining contaminant concentration ratio f 2 ＝2.38％。

Claims (2)

1. An optimization method for an in-situ bioremediation scheme of chlorinated hydrocarbon pollution in an aquifer is characterized by comprising the following steps of:
step 1, definition of a chlorohydrocarbon remediation zone and a pollution zone
The chlorinated hydrocarbon restoration zone and the chlorinated hydrocarbon pollution zone included in the chlorinated hydrocarbon restoration zone are three-dimensional isotropic confined aquifer; simplifying the chlorohydrocarbon restoration area into a rectangular body with the height parallel to the ground plane, taking any cross section perpendicular to the ground plane as a restoration area A, wherein the restoration area A is a rectangle, the long side of the restoration area A is alpha, and the short side of the restoration area A is beta;
establishing a plane coordinate system by taking one end point of the repair area A as an original point, wherein the long side of the repair area A is parallel to the X axis, and the short side of the repair area A is parallel to the Y axis; making two long edges as water-resisting boundaries and two short edges as constant water head boundaries, wherein the short edge superposed with the Y axis is marked as a boundary 1, the other short edge is marked as a boundary 2, the water flow direction is the same as the positive direction of the X axis, and the water head of the boundary 1 is larger than that of the boundary 2;
marking a chlorohydrocarbon polluted area in the restoration area A as a polluted area B, and setting the polluted area B to be positioned in an interval of 0.04 alpha to X to 0.64a and 0.35 beta to Y to 0.7 beta;
the restoration area A also comprises a concentration restriction area which is a rectangular area, the right boundary of the concentration restriction area is overlapped with the boundary 2, and the straight line distance from the left boundary to the right boundary of the pollution area is about 0.04 alpha-0.08 alpha;
the center line which is arranged in the repair area A and is parallel to the X axis is uniformly provided with gamma pumping and injecting wells, and any one of the pumping and injecting wells is marked as a pumping and injecting well I i I =1,2, r pumping well I i Sequentially arranging from left to right;
arranged in the repairing process through gamma-shaped pumping and injecting wells I i Injecting lactic acid into the chlorohydrocarbon repair area, and fermenting to generate hydrogen at the moment of injecting the lactic acid, wherein the total concentration of the hydrogen is known;
in the repairing process, the hydraulic gradient of the chlorohydrocarbon repairing area is 0.0048, the porosity is 0.3, and the longitudinal dispersion coefficient is 10m 2 S, permeability coefficient of 1.8m/d, repair cycle N s The time length is 1000 days, and the longitudinal dispersion coefficient is the area of pollutants dispersed to the X axis in the positive direction per second;
the total time period of N is set to be one time period every day in the repairing process s A repair time period, wherein any one repair time period is recorded as a repair time period s, s =1,2 s ；
Equally dividing the repair area A into finite difference grids, wherein each intersection point of the finite difference grids is a node, and any one node is marked as a node j 0 0=1, 2., N is the number of nodes in the repair area a;
step 2, constructing a simulation model for in-situ bioremediation of chlorohydrocarbon pollution in an aquifer
Recording the simulation model of chlorinated hydrocarbon pollution in-situ bioremediation in the aquifer as a model 1, wherein the model comprises a microbial population degradation chlorinated hydrocarbon model, a microbial growth and attenuation model and a chlorinated hydrocarbon natural attenuation model;
the contaminants in the chlorinated hydrocarbon remediation zone include the following four: tetrachloroethylene, trichloroethylene, dichloroethylene, and vinyl chloride; two kinds of dechlorination microorganisms exist in the chlorohydrocarbon remediation area, one kind of dechlorination microorganism participates in degrading tetrachloroethylene and trichloroethylene and is called microorganism 1, and the other kind of microorganism participates in degrading dichloroethylene and chloroethylene and is called microorganism 2;
the expression of the microorganism population degradation chlorinated hydrocarbon model is as follows:
Figure FDA0003858126070000021
Figure FDA0003858126070000031
in the formula, V 1 Concentration of microorganism 1 participating in the reaction, V 2 In order to obtain the concentration of the microorganisms 2 involved in the reaction,
Figure FDA0003858126070000032
the degradation rate of tetrachloroethylene under the action of dechlorination participated by the microorganism 1,
Figure FDA0003858126070000033
maximum dechlorination constant, C, of tetrachloroethylene by microorganism 1 dechlorination PCE Is the initial concentration of the tetrachloroethylene and,
Figure FDA0003858126070000034
is the half-saturation constant, C, of tetrachloroethylene by microbial 1 dechlorination TCE Is the initial concentration of the trichloroethylene and is,
Figure FDA0003858126070000035
is the half-saturation constant, C, of trichloroethylene by microbial 1 dechlorination i，s For repairing the pumping well I in the time period s i The concentration of the lactic acid injected in the process,
Figure FDA0003858126070000036
dechlorination reaction H in the presence of hydrogen 2 A threshold value for the concentration of the aqueous phase, said threshold value being the lowest value required to maintain normal survival of the microorganisms,
Figure FDA0003858126070000037
for dechlorination of H by microorganisms 1 2 The half-saturation constant of the liquid crystal,
Figure FDA0003858126070000038
is the degradation rate of the trichloroethylene under the dechlorination action of the microorganism 1,
Figure FDA0003858126070000039
is the maximum dechlorination constant of the trichloroethylene through microbial 1 dechlorination,
Figure FDA00038581260700000310
is the degradation rate of dichloroethylene under the action of dechlorination participated by microorganism 2,
Figure FDA00038581260700000311
maximum dechlorination constant, C, of ethylene dichloride by microorganism 2 DCE Is the initial concentration of the ethylene dichloride in the reactor,
Figure FDA00038581260700000312
is the half-saturation constant of dichloroethylene by microbial 2-dechlorination, C VC Is the initial concentration of vinyl chloride and is,
Figure FDA00038581260700000313
is the half-saturation constant of the dechlorination of vinyl chloride by the microorganism 2,
Figure FDA00038581260700000314
for dechlorination of H by microorganisms 2 2 The half-saturation constant of the liquid crystal,
Figure FDA00038581260700000315
the degradation rate of the chloroethylene under the action of dechlorination participated by the microorganism 2,
Figure FDA00038581260700000316
is the maximum dechlorination constant of the dechlorination of vinyl chloride by the microorganism 2,
Figure FDA00038581260700000317
to pass through H 2 The utilization rate of the catalyst is increased under the action of dechlorination,
Figure FDA00038581260700000318
consumption of H for tetrachloroethylene 2 The stoichiometric coefficient of (a) is,
Figure FDA00038581260700000319
consumption of H for trichloroethylene 2 The stoichiometric coefficient of (a) is,
Figure FDA00038581260700000320
consumption of H for dichloroethylene 2 The stoichiometric coefficient of (a) is,
Figure FDA00038581260700000321
consumption of H for vinyl chloride 2 The stoichiometric coefficient of (a);
the expression of the microorganism growth and decay model is as follows:
Figure FDA0003858126070000041
in the formula (I), the compound is shown in the specification,
Figure FDA0003858126070000042
for the utilization of microorganism 1 in the dechlorination, D 1 The yield coefficient of the microorganism 1 is taken as,
Figure FDA0003858126070000043
is the decay rate constant, V, of microorganism 1 1，min For a given concentration of the lowest microorganism 1 participating in the reaction,
Figure FDA0003858126070000044
for the utilization of microorganism 2 in the dechlorination, D 2 Is a factor in the yield of the microorganism 2,
Figure FDA0003858126070000045
is the decay rate constant, V, of microorganism 2 2，min The given concentration of the lowest microorganism 2 participating in the reaction;
the expression of the natural attenuation model of the chlorohydrocarbon is as follows:
Figure FDA0003858126070000046
in the formula, V PCE Is the concentration of tetrachloroethylene after decay, K pce Is the first order degradation rate of tetrachloroethylene, V TCE Is the concentration of trichloroethylene after decay, Y tce/pce Is the natural attenuation coefficient of tetrachloroethylene, K tce Is the first order degradation rate of trichloroethylene, V DCE Is the concentration of ethylene dichloride after decay, Y dce/tce Is the natural attenuation coefficient of trichloroethylene, K dce Is the first order degradation rate of dichloroethylene, V VC Concentration of vinyl chloride after decay, Y vc/dce Is the natural attenuation coefficient of dichloroethylene;
step 3, constructing a multi-objective optimization model of the chlorinated hydrocarbon pollution in-situ bioremediation scheme in the aquifer
Marking a multi-objective optimization model of the chlorinated hydrocarbon pollution in-situ bioremediation scheme in the aquifer as a model 2, and constructing the model 2 under a set constraint condition;
the set constraint conditions are as follows:
Figure FDA0003858126070000051
under the above constraints, the expression of model 2 is:
Figure FDA0003858126070000052
in the formula, f 1 For total cost of repair, minf 1 To restore the lowest value of the total cost, a 1 For drilling cost factor, a 2 For installation of well cost factor, a 3 To extract the water charge factor, a 4 Cost factor for injection of lactic acid, the drilling cost factor a 1 For drilling one suction injection well I i Cost of required underground space, installation well cost factor a 2 For installing a pumping well I i The required cost, the pumping and injection water cost coefficient a 3 For in a pumping and injecting well I i Extract 1m 3 Water or in a pumping well I i Is injected into the space of 1m 3 Cost of water, cost coefficient of injected lactic acid a 4 For in a pumping and injecting well I i The cost required for injecting 1mol of lactic acid;
b is a pumping well I i B equals 0 or 1, when b =1, pumping well I i Use, when the coefficient b =0 is present, the well I is pumped i Not in use, d i For pumping and injecting well I i Well depth of (Q) i，s For pumping and injecting well I i The water pumping and injecting rate in the repairing time period s is that the negative value is water pumping, the positive value is water injection, delta s is the duration of the repairing time period s, and minf 2 Is the lowest value of the concentration ratio of the remaining contaminants, f 2 Mass as the ratio of the concentration of the remaining contaminants ini To restore the initial concentration of contaminants in the aquifer, mass end The concentration of the pollutants in the aquifer at the end of remediation;
Figure FDA0003858126070000061
for repairing a node j within a time period s 0 The concentration of the contaminants in the (c) is,
Figure FDA0003858126070000062
for repairing node j in time interval s 0 The maximum allowable concentration of the contaminant(s) in (b),
Figure FDA0003858126070000063
for a full term, meaning that for any, e denotes belonging,
Figure FDA0003858126070000064
for repairing pumping well I in time period s i The lower limit of the concentration of the injected lactic acid,
Figure FDA0003858126070000065
for repairing pumping well I in time period s i The upper limit of the concentration of the injected lactic acid of (1),
Figure FDA0003858126070000066
for repairing node j in time interval s 0 The lower limit of the water head of (c),
Figure FDA00038581260700000615
for repairing node j in time interval s 0 The height of the water head of the water pump,
Figure FDA0003858126070000067
for repairing node j in time interval s 0 The upper limit of the water head of (c),
Figure FDA0003858126070000068
for repairing node j in time interval s 0 The hydraulic gradient of the upper gradient water head is obtained,
Figure FDA0003858126070000069
for repairing j within time period s 0 The hydraulic gradient of the lower gradient water head at the node,
Figure FDA00038581260700000610
Figure FDA00038581260700000611
Figure FDA00038581260700000612
for repairing the pumping well I in the time period s i The lower limit of the flow rate is,
Figure FDA00038581260700000613
for repairing pumping well I in time period s i Upper limit of flow, Q s，total For repairing all pumping and injecting wells I in time period s i The total water injection amount of (2);
step 4, constructing a simulation-optimization model for restoring chlorinated hydrocarbon pollution in an aquifer
The model 1 and the model 2 are coupled to form a simulation-optimization model for restoring the chlorinated hydrocarbon pollution in the aquifer, and the model is marked as a model 3, and the expression of the model is as follows:
Figure FDA00038581260700000614
Figure FDA0003858126070000071
when the restoration is finished, the concentrations of four pollutants in the concentration restriction area reach the standard of IV-class water, and specifically, the concentration V of tetrachloroethylene after attenuation PCE Concentration of trichloroethylene after attenuation V TCE Concentration of ethylene dichloride after decay V DCE Vinyl chloride concentration after decay V VC Is recorded as the concentration W of the pollutant L L is the number of the four chlorinated hydrocarbons, L =1,2,3,4, where 1 is tetrachloroethylene, 2= trichloroethylene, 3 is dichloroethylene, and 4 is vinyl chloride, according to which the constraints of model 3 are as follows:
Figure FDA0003858126070000072
in the formula (I), the compound is shown in the specification,
Figure FDA0003858126070000073
for repairing node j in time interval s 0 The concentration of the contaminants in the (c) is,
Figure FDA0003858126070000074
for repairing j within time period s 0 Maximum allowable contaminant concentration at the node;
step 5, solving the model 3 obtained in the step 4 based on the NSGA-II algorithm
Firstly, an objective function is set, and input variables are as follows: to repair the total cost f 1 Is a first objective function, the remaining pollutant concentration ratio f 2 Is a second objective function, and the first and second objective functions are denoted as an objective function F (F) 1 ，f 2 ) (ii) a Taking the number gamma of pumping and injection wells as a first input variable E 1 Pumping and injecting well I i Well depth d i Is a second input variable E 2 Pumping and injecting well I i Water pumping and injection rate Q in repair time period s i，s Is a third input variable E 3 And pumping and injecting well I in repairing time period s i Concentration of lactic acid C injected i，s Is a fourth input variable E 4 And the four input variables are recorded as input variables E (Γ, d) i ，Q i，s ，C i，s )；
Secondly, solving the model 3 obtained in the step 4 based on an NSGA-II algorithm to obtain G optimal solutions F yk (f 1 ，f 2 ) And corresponding G optimal repair solutions E yk (Γ，d i ，Q i，s ，C i，s ) G optimal repair solutions E yk (Γ，d i ，Q i，s ，C i，s ) Composing an optimal set of repair solutions
Figure FDA0003858126070000081
k =1, 2.., G is the maximum number of iterations;
step 6, drawing f 1 -f 2 Curve
With f 1 Is the abscissa, f 2 Marking each optimal solution F in a plane coordinate system for the ordinate yk (f 1 ，f 2 ) Corresponding data points are selected, an exponential function is selected to fit the G data points to obtain a strip f 1 -f 2 A curve;
step 7, selection of final repair scheme
On demand through f 1 -f 2 Curve selection data points, i.e. total cost f for repair 1 And the remaining contaminant concentration ratio f 2 Then from the optimal repair solution set
Figure FDA0003858126070000082
Selecting a final remediation scheme of in-situ organisms polluted by the chlorohydrocarbon.
2. The optimization method for in-situ bioremediation protocol of chlorinated hydrocarbon pollution in aquifers according to claim 1, wherein the specific steps of solving the model 3 obtained in the step 4 based on the NSGA-II algorithm in the step 5 are as follows:
step 5.1, initializing the population and randomly selecting N z Set input variables E (Γ, d) i ，Q i，s ，C i，s ) Value of (A) forms N z Group non-dominated solution, resulting in an initial parent population P 0 In which N is z The population scale is adopted;
step 5.2, adding N z Set of input variables E (Γ, d) i ，Q i，s ，C i，s ) Calculating the kth iteration parent population P in the substitution model 3 k To obtain N z Group objective function F k (f 1 ，f 2 ) N of the group z Group objective function F k (f 1 ，f 2 ) Composition target solution set T k ；
Step 5.3, apply pareto ordering to solve T target solution set k N in (1) z Group objective function F k (f 1 ，f 2 ) Sorting from good to bad in descending order, and selecting an objective function F with the first rank k (f 1 ，f 2 ) Is recorded as an optimal solution F yk (f 1 ，f 2 ) And the optimal solution F is yk (f 1 ，f 2 ) Corresponding input variables E (Γ, d) i ，Q i，s ，C i，s ) Is recorded as an optimal repair scheme E yk (Γ，d i ，Q i，s ，C i，s )；
Target solution set T k Is not selected as an objective function F k (f 1 ，f 2 ) Corresponding input variable E (Γ, d) i ，Q i，s ，C i，s ) Obtaining the population size N through selection, variation and crossing z Progeny population Q of-1 k ；
Step 5.4, the parent population P k And progeny population Q k Are combined into a population R k For population R k Sorting in descending order from good to bad, and taking N before ranking z As a new parent population P k+1 ；
Step 5.5, repeating the steps 5,2 and 5.4 until the maximum iteration number G is reached, and outputting G optimal solutions F obtained in G iterations yk (f 1 ，f 2 ) And G optimal repair solutions E yk (Γ，d i ，Q i，s ，C i，s ) G optimal repair schemes E yk (Γ，d i ，Q i，s ，C i，s ) Composing an optimal set of repair solutions
Figure FDA0003858126070000091
CN202211161500.4A 2022-09-22 2022-09-22 Method for optimizing in-situ bioremediation scheme of chlorohydrocarbon pollution in aquifer Pending CN115659719A (en)

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CN116060426A (en) * 2023-02-24 2023-05-05 山东大成环境修复有限公司 Soil and groundwater collaborative remediation system

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116060426A (en) * 2023-02-24 2023-05-05 山东大成环境修复有限公司 Soil and groundwater collaborative remediation system
CN116060426B (en) * 2023-02-24 2024-02-06 山东大成环境修复有限公司 Soil and groundwater collaborative remediation system

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