CN106600025A - Multi-level urban sewage water reuse-and-recycle configuration data's dynamic processing method based on multi-objective hybrid genetic algorithm - Google Patents
Multi-level urban sewage water reuse-and-recycle configuration data's dynamic processing method based on multi-objective hybrid genetic algorithm Download PDFInfo
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Abstract
The invention discloses a multi-level urban sewage water reuse-and-recycle configuration data's dynamic processing method based on multi-objective hybrid genetic algorithm, comprising: 1) obtaining the basic information data of multi-level water resources relationship; establishing a multi-level data configuration relationship between urban centralized sewage treatment plants and centralized water regeneration plants; 2) obtaining basic information data and other data information about the urban centralized sewage treatment plants and the centralized water regeneration plants; 3) according to the above basic information data, establishing a multi-objective data configuration model which considers the conditions of water quality balancing, water supply capability, water transporting capability, water requiring capability and risk constraints wherein the objective function is a multi-objective function with the largest total water resources utilization, the maximum benefit of water resources reuse and the minimum per capita water deficit in sub-areas; and 4) using the multi-level urban sewage water reuse-and-recycle configuration data's dynamic processing method based on multi-objective hybrid genetic algorithm to seek data configuration schemes for different scenes. The method of the invention can meet the reuse-and-recycle data configuration requirement for urban multi-objective and multi-level sewage water.
Description
Technical field
The present invention relates to the centralized recycling sewage generated by making field in city, is related to multi-level municipal sewage dynamic data processing side
Method, more particularly to a kind of multi-level regeneration of urban sewage reuse configuration data dynamic process based on multiobjective genetic algorithms
Method.
Technical background
The centralized recycling sewage generated by making in city is one and is related to multiple spatial dispersion water sources, water resources type and various
The complex data process problem of reuse approach, first, the water outlet tail water after the centralized Sewage Plant in city is processed can be directly as collection
Chinese style regenerates the water source of water factory, also there is the approach of allotment reprocessing reuse over long distances, again, city in the city of shortage of water resources
The water outlet of centralized recycled water factory of city can be back to city use mixedly, industry, landscape, the classification such as agricultural, the centralized sewage in Gucheng city
Regeneration is related to relation between multi-level allocation data and its data, and its reuse approach and configuration amount decision problem finally can be general
Turn to the multistage dynamic of multiple constraint, the long-term, control problem of complex linear system.Especially in view of sewage source property
Particularity, the interests decision-making between the ecological environment that may cause during its reuse, society and economic investment impact, further increases
The complexity of its Optimization Solution.The research for being now directed to centralized recycling sewage generated by making data configuration method is less, focuses mostly on
In the process of reservoir Water resources data and allocation problem, problem solves target and also transits to multifactor collaboration most from single factors
Excellent, the solution of multi-objective optimization scheduling successively has traditional algorithm and all kinds of bionic intelligence algorithms, and traditional algorithm is more in solution
Need just to be solved to form simple target by giving each target weight during target problem, and most only produce a solution at last,
Being applied to final decision problem the method more；Researcher is now more to be passed through bionic principle to solve multi-objective problem using intelligent algorithm
Collaboration it is optimum, representative including genetic algorithm, ant group algorithm, particle cluster algorithm etc., intelligent algorithm is solving many mesh
The characteristics of having global optimizing effect preferable during mark optimization problem.
Genetic algorithm (Genetic Algorithm) was proposed by John H.Holland in 1975, was with natural selection
With the algorithm based on theory of heredity, nature biotechnology evolutionary process is simulated, object space is entered by the way of artificial evolution
Row random optimization search, the feasible solution in Problem Areas regarded as an individual or chromosome for colony for it, and by it is each each and every one
Body is encoded into symbol string form, simulates the biological evolution process of Darwinian heredity selection and natural selection, and colony is entered repeatedly
Row is based on genetic operation (heredity, intersection and variation), so as to reach the purpose of efficient global optimizing.What is progressively developed afterwards changes
Enter the method that genetic algorithm is achieved by application dynamic, improve original algorithm global excellent present on solution multi-objective problem
The problems such as changing undesirable, is to be applied to the focus algorithm that Programming for Multiobjective Water Resources distributes field rationally.But the genetic algorithm is being solved
The problem that global convergence is slow, gained solution may not be global optimum is still suffered from during multiple target multiple constraint problem.And due to heredity calculation
Method will produce more than one solution, be unfavorable for that policymaker provides planning year city centralized recycling sewage generated by making concrete scheme.And
Genetic algorithm solves the linear programming problem of the centralized recycling sewage generated by making configuration in multiple target multiple constraint city and reduces last solution
Integrate scope as decision-making provide with reference to aspect pertinent literature at present still without.
The content of the invention
The technical problem to be solved is, for the multiple target of the multi-level regeneration of the centralized sewage in city
Multiple constraint Linear Control problem, by setting up multiple objective function and related constraint model, introduces genetic algorithm and model is entered
Row is solved and carries out pre-screening to disaggregation, there is provided a kind of multi-level regeneration of urban sewage based on multiobjective genetic algorithms is returned
Configuration data method for dynamically processing is used, the multi-level long-term optimization control of the centralized recycling sewage generated by making in city is realized, is city
Recycling sewage generated by making configuration provides objective basis.
Specifically, the technical problem to be solved, further for：By the strong centralized sewage disposal of spatial dispersion
The incidence relation data of inter-plant, carry out the macroscopical reuse path analysis on city level, there is provided one kind is lost based on multiple target mixing
The multi-level regeneration of urban sewage reuse configuration data method for dynamically processing of propagation algorithm, the method goes out centralized sewage treatment plant
Water tail water (whereabouts includes the centralized regeneration water factory of supply or further remote allotment), centralized recycled water plant effluent (whereabouts
To supply each regeneration purposes in urban area) multi-level multiple objective programming year urban sewage recycling as a dynamic total system
Carry out the analysis of dynamic relationship between the analysis of water yield data and data, for reusing sewage further decision-making provide objective data according to
According to.Set up multiple objective function and related constraint linear model based on water balance and water resource cost first, and then introduce
Constructed model is solved based on the mixing improved adaptive GA-IAGA of interior point and to gained Pareto disaggregation further cluster and
Desired value pre-screening, finally gives the centralized reusing sewage protocol in city, is needed with solving existing centralized reusing sewage
Fixed confession, the systematic problem of city level reuse design shortcoming.
In order to solve above-mentioned technical problem, the invention provides a kind of regeneration effluent based on multiobjective genetic algorithms
Reuse data processing method, including step：
Multilayer water resources relationship essential information data are obtained, is set up based on the centralized sewage treatment plant in city, centralized
Recycled water inter-plant multi-levels data configuration relation；
The centralized sewage treatment plant in city, centralized regeneration water factory essential information data are obtained, tail water and recycled water is obtained
The supply demand data of each reuse approach of two kinds of water resources, obtain transmission ＆ distribution water number according to, urban development data.Preferably, can adopt
In the following manner：
In units of each sub-district, each centralized sewage treatment plant's number data and locus distributed data information, design are obtained
Disposal ability data xi, water outlet tail water water quality, water outlet vacuum in draft tube purposes information data xw, obtain centralized regeneration water factory number
And locus distributed data information, Design Treatment capacity data xi’, water inlet source-information, water outlet recycled water water quality, reuse use
Way information data xj, municipal transmission ＆ distribution water energy force data, cost coefficient c are obtained, obtain each sub-district socio-economic development data and hold
Carry the information such as demographic data b；
Set up according to information above data and consider water balance, water supply capacity, conveyance power of water, need outlet capacity, Risk Constraint
Under the conditions of Multi-target Data allocation models, object function therein is using total amount is maximum, resource regeneration is returned with water resource
With benefit maximum, the sub-district multiple objective function that per capita water deficit minimum is set up；
Using the multi-level regeneration of urban sewage reuse configuration data dynamic process side based on multiobjective genetic algorithms
Method is solved and obtains data configuration scheme under different scenes.
Implement the present invention, have the advantages that：
(1) demand of the multi-level regeneration data configuration of the centralized sewage in city is met；
(2) genetic algorithm initial population is produced using traditional interior-point algohnhm, ergodic, the randomness of initial population can be improved
And multiformity, genetic algorithm random initial population under the conditions of multi-restriction Multi-Objective can be avoided of poor quality and some local are concentrated on
Region is so that algorithm is absorbed in the problem of local optimum.
(3) genetic algorithm files collection method using the outside of Dynamic Updating Mechanism, to further ensure that noninferior solution multiformity, plus
Fast global convergence.
(4) based on dynamic seriality model for water quantity allocation principle, using clustering procedure and desired value sieve method to genetic algorithm disaggregation
Carry out decision-making pre-screening process, it is ensured that water yield stability and the effectiveness of water factory's longtime running that single water factory allocates for a long time.
(5) the scene decision-making pre-screening method based on desired value, can further from based on dynamic seriality model for water quantity allocation
Select different optimal allocation schemes in Pareto optimal solution sets for many years, effectively increase arithmetic speed and the side of data configuration scheme
Case execution efficiency.
Description of the drawings
Fig. 1 is that the multi-level regeneration of urban sewage reuse based on multiobjective genetic algorithms configures method for dynamically processing stream
Cheng Tu；
Fig. 2 is the flow chart of multiobjective genetic algorithms；
Fig. 3 is the flow chart of multiobjective genetic algorithms Pareto optimal solution set decision-making pre-screenings.
Specific embodiment
The present invention is illustrated further below in conjunction with the accompanying drawings.Described embodiment is only that a part of the invention is implemented
Example, rather than the embodiment of whole.
Multiple target multiple constraint linear long term data configuration of the present invention for the multi-level regeneration of the centralized sewage in city
Problem, by setting up multiple objective function and related constraint model, there is provided a kind of based on the multi-level of multiobjective genetic algorithms
Urban sewage recycling data dynamically-processing method, the method is generated initial population, is adopted dynamic renewal using traditional interior-point algohnhm
The genetic algorithm of the outside filing collection method principle of mechanism, realizes algorithm global optimizing ability, and on Pareto optimal solution sets basis
On carry out cluster analyses and objective decision pre-screening technology, it is ensured that the water yield data stability and water of single water factory allotment for a long time
The effectiveness of factory's longtime running, realizes the multi-level long-term optimization control of the centralized recycling sewage generated by making data in city.
Fig. 1 is multi-level regeneration of urban sewage reuse configuration data dynamic of the present invention based on multiobjective genetic algorithms
Process flow figure, as shown in figure 1, including step：
Step 1, obtain Multilayer water resources relationship essential information data, set up based on the centralized sewage treatment plant in city,
Centralized recycled water inter-plant multi-levels data configuration relation；
Step 101, according to constraintss such as administrative division, water project situation, water resource utilizing works, city is divided into many
Individual sub-district；
Step 102, in units of each sub-district, obtains centralized sewage treatment plant in each sub-district, centralized regeneration water factory sky
Between positional information, obtain centralized sewage disposal plant effluent tail water whereabouts information, centralized regeneration water factory water inlet source-information, collection
Chinese style recycled water plant effluent whereabouts information；
Step 103, based on step 102, sets up and is based on centralized sewage treatment plant, the multilamellar number of times of centralized regeneration water factory
According to configuration relation, the incidence relation set up between each centralized sewage disposal plant effluent tail water, the water inlet of centralized regeneration water factory, build
Each centralized sewage disposal plant effluent vacuum in draft tube whereabouts incidence relation is found, centralized recycled water plant effluent reuse whereabouts is set up and is closed
Connection relation；
Step 2, obtains the centralized sewage treatment plant in city, centralized regeneration water factory essential information data, obtain tail water and
The supply demand data of each reuse approach of two kinds of water resources of recycled water, obtain transmission ＆ distribution water number evidence, urban development data.
Preferably, in units of each sub-district, obtain each centralized sewage treatment plant's number data and locus distribution number it is believed that
Breath, Design Treatment capacity data xi, water outlet tail water water quality, water outlet vacuum in draft tube purposes information data xw, obtain centralized recycled water
Factory's number and locus distributed data information, Design Treatment capacity data xi”, water inlet source-information, water outlet recycled water water quality,
Reuse purposes information data xj, municipal transmission ＆ distribution water energy force data, cost coefficient c are obtained, obtain each sub-district socio-economic development number
According to carrying population data b；
Wherein, it is contemplated that reuse water source is sewage, cost coefficient c answers comprehensive suite containing the city produced during water reuse
Political affairs engineering construction maintenance costs, road transport cost, user according to self-demand again advanced treating expense, reach row's lake standard
Environmental pollution Reduction of Students' Study Load processing cost and the public receive wish risk equal weight factor.
Step 3, according to information above data, sets up and considers water balance, water supply capacity, conveyance power of water, needs outlet capacity, wind
Multi-target Data allocation models under the constraintss such as danger, object function is with the optimum multiple target letter set up of diversiform data
Number；The diversiform data includes environmental data, benefit data, approach data.The information data, at least including essential information number
According to, supply demand data, transmission ＆ distribution water number evidence, urban development data etc.；In a specific embodiment, the environmental data, effect
Beneficial data, approach data are respectively and utilize maximum total amount, resource regeneration reuse benefit maximum, sub-district hydropenia per capita with water resource
Amount is minimum, and sets up multiple objective function with it.
Step 301, the variable for determining data configuration model is (xi’j, xiw)；
Step 302, sets up water resource using total amount object function F1With the relationship between multi-level water regulation, its
Middle water resource is sign to the maximum using total amount object function with centralized sewage disposal plant effluent utilization data；
MinF1(x)=Min ∑ (xi-xi’j-xw)
Wherein, i is centralized sewage treatment plant's sequence number, and i ' is centralized regeneration water factory sequence number, and j is the reuse of centralized regeneration water factory
Purposes sequence number, the reuse purposes sequence number that w is centralized sewage treatment plant in addition to supply regeneration water factory, xi’j、xiwRespectively each concentration
Formula recycled water plant effluent difference reuse purposes allotment amount and each centralized sewage disposal plant effluent in addition to supply regeneration water factory not
With reuse purposes allotment amount.
Step 303, sets up resource regeneration reuse benefit goal function F2With the mathematical relationship between multi-level water regulation
Formula, and with the minimum quantization of Multilayer water resource allocation benefit data；
MinF2(x)=Min ∑ (xi’j×ci’j+xw×cw)
Wherein, ci’j、cwRespectively centralized recycled water plant effluent and centralized sewage disposal plant effluent are in addition to supply regeneration water factory
Cost coefficient of the different purposes in the range of different spaces.
Step 304, sets up sub-district water deficit object function F per capita3With the relationship between each level water regulation, it is somebody's turn to do
Object function includes number depending on sub-district number, and per capita hydropenia data volume is most with the supply and demand of each sub-district difference reuse approach
It is little for quantify；
MinF3(x)=Min ∑ (xi’-xi’j)/b
Step 305, the multiple objective function for determining the data configuration model is Min F (x)={ F1(x), F2(x), F3
(x)…Fn(x)}；
Step 306, sets up water regulation constraints, including water supply capacity constraint, conveyance power of water are constrained, need outlet capacity about
The water quantity restraint condition such as beam；
Step 307, every information data is set up centralized based on the centralized sewage treatment plant in city, city according to more than
Regeneration water factory Pedicled multi-layer facelifting data configuration model, wherein, the data configuration model is：
Min F (x)={ F1(x), F2(x), F3(x)…Fn(x)}
In formula, xi’jmin、xi’jmaxThe lower limit of respectively centralized regeneration water factory i ' allocation datas in different reuse purposes j is with
Limit, xiwmin、xiwmaxRespectively centralized sewage treatment plant i allocation datas in other reuse purposes w in addition to supply regeneration water factory
Lower limit and the upper limit.
Step 4, using the multi-level regeneration of urban sewage reuse dynamic data processing based on multiobjective genetic algorithms
Method is solved and obtains data configuration scheme under different scenes.In a specific embodiment, following steps can be specifically adopted：
Step 401, according to the information data that step 1- step 3 is obtained, it is determined that enter mould parameter for many years, including many year T,
xi、xi‘、xi’jmin、xi’jmax、xiwmin、xiwmax、ci’j、cw、b；
Step 402, sets genetic algorithm parameter：Determine initial population number G, sub- population number SN, per individual sub- population
Individual amount M, genetic algebra GEN；
Step 403, row constraint process is entered to object function：When object function decision variable is unsatisfactory for constraints, will
Its fitness function value is taken as the numerical value less than predetermined threshold value close to zero, it is determined that the decision variable of output meets constraint
Condition；
Step 404, based on interior point method to xi’j, xiwCarry out initialization population；
Step 405, with binary codes express variable, according to the size of individual adaptation degree using constraint non-dominated ranking side
Method is ranked up；
Step 406, calculates each target function value of each population at individual, is selected, is intersected, mutation operation, sequence
Individuality is intersected at random, is made a variation and paired comparison, according to fitness size fiducial value, successively by the noninferior solution in current population
First and n-th leading surface are classified as, and representative individuality, root are chosen from each leading surface successively according to dynamic archiving method
According to the individual amount after screening, dynamic adjustment population is individual to reaching set definite value until selecting；
Step 407, judges whether to reach predetermined genetic algebra GEN, if being not reaching to, goes to step 405, continues next
The global search of wheel, otherwise, exports the Pareto optimal solution sets that dynamic outer filing is concentrated；
Step 408, substitutes into respectively data in step 401 for many years, and repeat step 402- step 409 obtains Pareto for many years
Optimal solution set P1, P2, P3…PN；
Step 409, respectively to Pareto optimal solution sets P for many years1, P2, P3…PNCluster screening is carried out, is respectively obtained discrete
The minimum P of degree1K、P2K、…PNKOptimal solution set for many years based on the continuous planning of dynamic；In a specific embodiment, should
Step 409 can also adopt following concrete mode to realize：
Step 4091, sets up different K values, is separately operable step 4, with data set standard deviation as index, is produced by comparing K values
Discreteness between raw clustering schemes, determines the best K values of Clustering Effect and used by next step；
Step 4092, respectively to Pareto optimal solution sets P for many years1, P2, P3…PNCluster screening is carried out, P is respectively obtained1、
P2、…PNK phylogenetic group；
Step 4093, with data set standard deviation as index, respectively to P1、P2、…PNK phylogenetic group carry out data discrete
Property analysis, obtain the minimum P of dispersion1K、P2K、…PNKOptimal solution set for many years based on the continuous planning of dynamic；
Step 410, according to optimal solution set P for many years based on the continuous planning of dynamic1K、P2K、…PNK, further set up difference
Targeting constraints scene simultaneously carries out desired value screening, obtains based on the optimum dynamic disaggregation of simulated target.It is specific at one
In embodiment, the step 410 can also adopt following concrete mode to realize：
Step 4101, environmental data, benefit data and the approach datum target set up according to model, sets up respectively based on water
Utilization of resources total amount is maximum, resource regeneration reuse benefit is maximum, per capita water deficit is minimum and above-mentioned each constraints for sub-district
Average scene；
Step 4102, according to step 409 gained P1K、P2K、…PNKBased on dynamic process optimal solution set for years,
With simulated target value to sieve condition, the data configuration scheme of scene condition making policy decision scene set by step 4101 is respectively obtained.
Now by taking the distributing rationally of the centralized recycling sewage generated by making data in Kunming as an example, illustrate the effectiveness of inventive method with
Reasonability.Kunming possesses 27 centralized sewage treatment plants, wherein there is the water outlet tail water portion of 25 centralized sewage treatment plants
Divide reuse as the water sources of corresponding 25 centralized regeneration water factories, have at 15 sewage in this 25 centralized sewage treatment plants
Reason plant effluent tail water part using by city water diversion project as the industrial supply water in Dianchi Lake Basin downstream city raw water.25
The water outlet of the centralized regeneration water factory of seat will realize industry, landscape (park landscape water, river course according to administrative division in one's respective area
Moisturizing), city use mixedly (municipal administration greening, road pour spill, flush the toilet, carwash) etc. multiple use regeneration.Obtain the year two thousand twenty with
The year two thousand thirty per centralized sewage treatment plant and centralized reused water processing factory Design Treatment ability, each factory place administrative region
The maximum water transfer ability of the demand upper lower limit value of various regeneration approach, city water diversion project, water delivery parameter and other economy
Social parameter.Determine three object functions, respectively centralized reusing sewage amount is maximum, Financial cost is minimum, water deficit per capita
At least, determine that water balance is constrained, founding mathematical models.Genetic algorithm is adopted to be optimized configuration to realize three targets
Optimization.Jing measuring and calculations repeatedly, it is determined that the optimal parameter for solving the multiple-objection optimization allocation problem is:Initial population number G
=180, sub- population number SN=3, per individual sub- population at individual quantity M=60, genetic algebra GEN=100；
Based on the multiple disaggregation of the genetic algorithm for obtaining, disaggregation is clustered using K-means clustering algorithms, wherein K values
It is defined as 7, obtains the year two thousand twenty and distinguish 7 clustering schemes with the year two thousand thirty, according to discrete data seriality, obtains the year two thousand twenty and 2030
Year continuous clustering schemes collection, and further according to desired value sieve method obtain based on water resource using total amount is maximum, water resource again
Give birth to reuse benefit maximum, sub-district three minimum scene decision-making suggested designs of water deficit per capita.
The above is only the preferred embodiments of the present invention, it is noted that based on the embodiment in the present invention, this area
The every other embodiment that those of ordinary skill is obtained under the premise of creative work is not made, belongs to protection of the present invention
Scope.
Claims (7)
1. the multi-level regeneration of urban sewage reuse configuration data method for dynamically processing of multiobjective genetic algorithms is based on, and it is special
Levy and be, including step：
Step 1, obtains Multilayer water resources relationship essential information data, sets up and is based on the centralized sewage treatment plant in city, concentration
Formula recycled water inter-plant multi-levels data configuration relation；
Step 2, obtains the centralized sewage treatment plant in city, centralized regeneration water factory essential information data, obtains tail water and regeneration
The supply demand data of each reuse approach of two kinds of water resources of water, obtain transmission ＆ distribution water number evidence, urban development data；
Step 3, sets up according to above essential information data and considers water balance, water supply capacity, conveyance power of water, needs outlet capacity, wind
Multi-target Data allocation models under dangerous constraints, object function therein is with the optimum multiple target set up of diversiform data
Function；The diversiform data includes environmental data, benefit data, approach data；
Step 4, is processed using the multi-level regeneration of urban sewage reuse configuration data dynamic based on multiobjective genetic algorithms
Method is solved and obtains data configuration scheme under different scenes.
2. the method for claim 1, it is characterised in that the step 1 is specifically included：
Step 101, according to administrative division, water project situation, the constraints of water resource utilizing works is included, city is divided into many
Individual sub-district；
Step 102, in units of each sub-district, obtains centralized sewage treatment plant, centralized regeneration water factory space bit in each sub-district
Confidence ceases, and obtains centralized sewage disposal plant effluent tail water whereabouts information, centralized regeneration water factory water inlet source-information, centralized
Recycled water plant effluent whereabouts information；
Step 103, based on step 102, is set up and is matched somebody with somebody based on centralized sewage treatment plant, the multi-levels data of centralized regeneration water factory
Relation is put, the incidence relation set up between each centralized sewage disposal plant effluent tail water, the water inlet of centralized regeneration water factory sets up each
Centralized sewage disposal plant effluent vacuum in draft tube whereabouts incidence relation, sets up centralized recycled water plant effluent reuse whereabouts association and closes
System.
3. the method for claim 1, it is characterised in that the step 2 is specifically included：
In units of each sub-district, each centralized sewage treatment plant's number data and locus distributed data information, design are obtained
Disposal ability data xi, water outlet tail water water quality, water outlet vacuum in draft tube purposes information data xw, obtain centralized regeneration water factory number
And locus distributed data information, Design Treatment capacity data xi’, water inlet source-information, water outlet recycled water water quality, reuse use
Way information data xj, municipal transmission ＆ distribution water energy force data, cost coefficient c are obtained, obtain each sub-district socio-economic development data and hold
Carry demographic data b.
4. the method for claim 1, it is characterised in that the step 3 is specifically included：
Step 301, the variable for determining data configuration model is (xi’j, xiw)；
Step 302, sets up water resource using total amount object function F1With the relationship between multi-level water regulation, wherein water
Utilization of resources total amount object function is sign to the maximum with centralized sewage disposal plant effluent utilization data：
MinF1(x)=Min ∑ (xi-xi’j-xw)
Wherein, i is centralized sewage treatment plant's sequence number, and i ' is centralized regeneration water factory sequence number, and j is the reuse of centralized regeneration water factory
Purposes sequence number, the reuse purposes sequence number that w is centralized sewage treatment plant in addition to supply regeneration water factory, xi’j、xiwRespectively each concentration
Formula recycled water plant effluent difference reuse purposes allotment amount and each centralized sewage disposal plant effluent in addition to supply regeneration water factory not
With reuse purposes allotment amount；
Step 303, sets up resource regeneration reuse benefit goal function F2With the relationship between multi-level water regulation, and
With the minimum quantization of Multilayer water resource allocation benefit data：
MinF2(x)=Min ∑ (xi’j×ci’j+xw×cw)
Wherein, ci’j、cwRespectively centralized recycled water plant effluent and centralized sewage disposal plant effluent are in addition to supply regeneration water factory
Cost coefficient of the different purposes in the range of different spaces；
Step 304, sets up sub-district water deficit object function F per capita3With the relationship between each level water regulation, the target
Function includes number depending on sub-district number, and per capita hydropenia data volume is minimum with the supply and demand of each sub-district difference reuse approach
Quantify：
MinF3(x)=Min ∑ (xi’-xi’j)/b；
Step 305, the multiple objective function for determining the data configuration model is Min F (x)={ F1(x), F2(x), F3(x)…Fn
(x)}；
Step 306, sets up water regulation constraints, including water supply capacity constraint, conveyance power of water are constrained, need water energy force constraint etc.
Water quantity restraint condition；
Step 307, sets up according to above essential information data and is based on the centralized sewage treatment plant in city, the centralized recycled water in city
Factory's Pedicled multi-layer facelifting data configuration model, wherein, the data configuration model is：
Min F (x)={ F1(x), F2(x), F3(x)…Fn(x)}
In formula, xi’jmin、xi’jmaxThe lower limit of respectively centralized regeneration water factory i ' allocation datas in different reuse purposes j is with
Limit, xiwmin、xiwmaxRespectively centralized sewage treatment plant i allocation datas in other reuse purposes w in addition to supply regeneration water factory
Lower limit and the upper limit.
5. the method for claim 1, it is characterised in that the step 4 is specifically included：
Step 401, according to the information data that step 1- step 3 is obtained, it is determined that enter mould parameter for many years, including many year T, xi、
xi‘、xi’jmin、xi’jmax、xiwmin、xiwmax、ci’j、cw、b；
Step 402, sets genetic algorithm parameter：Determine initial population number G, sub- population number SN, per individual sub- population at individual
Quantity M, genetic algebra GEN；
Step 403, row constraint process is entered to object function：When object function decision variable is unsatisfactory for constraints, fitted
Response functional value is taken as the numerical value less than predetermined threshold value close to zero, it is determined that the decision variable of output meets constraint bar
Part；
Step 404, based on interior point method to xi’j, xiwCarry out initialization population；
Step 405, with binary codes express variable, is entered according to the size of individual adaptation degree using constraint non-dominated ranking method
Row sequence；
Step 406, calculates each target function value of each population at individual, is selected, is intersected, mutation operation, and sequence is individual
Intersected at random, made a variation and paired comparison, according to fitness size fiducial value, be successively classified as the noninferior solution in current population
First with n-th leading surface, and representative individuality is chosen from each leading surface successively according to dynamic archiving method, according to sieve
Individual amount after choosing, dynamic adjustment population is individual to reaching set definite value until selecting；
Step 407, judges whether to reach predetermined genetic algebra GEN, if being not reaching to, goes to step 405, continues next round
Global search, otherwise, exports the Pareto optimal solution sets that dynamic outer filing is concentrated；
Step 408, substitutes into respectively many annual datas in step 401, repeat step 402- step 409, obtains for many years that Pareto is most
Excellent disaggregation P1, P2, P3…PN；
Step 409, respectively to Pareto optimal solution sets P for many years1, P2, P3…PNCluster screening is carried out, dispersion is respectively obtained minimum
P1K、P2K、…PNKOptimal solution set for many years based on the continuous planning of dynamic；
Step 410, according to optimal solution set P for many years based on the continuous planning of dynamic1K、P2K、…PNK, further set up different target
Constraints scene simultaneously carries out desired value screening, obtains based on the optimum dynamic disaggregation of simulated target.
6. the method that such as claim 5 is stated, it is characterised in that the step 409 is specifically included：
Step 4091, sets up different K values, is separately operable step 401- step 407, with data set standard deviation as index, by than
The discreteness between clustering schemes is produced compared with K values, the best K values of Clustering Effect is determined and used by next step；
Step 4092, respectively to Pareto optimal solution sets P for many years1, P2, P3…PNCluster screening is carried out, P is respectively obtained1、P2、…PN
K phylogenetic group；
Step 4093, with data set standard deviation as index, respectively to P1、P2、…PNK phylogenetic group carry out data discrete point
Analysis, obtains the minimum P of dispersion1K、P2K、…PNKOptimal solution set for many years based on the continuous planning of dynamic.
7. the multi-level regeneration of urban sewage reuse configuration based on multiobjective genetic algorithms as claimed in claim 5 is dynamic
Processing method, it is characterised in that the step 410 is specifically included：
Step 4101, the target set up according to model is set up respectively based on water resource using total amount maximum, resource regeneration reuse
Benefit is maximum, per capita water deficit is minimum and average scene of above-mentioned each constraints for sub-district；
Step 4102, according to step 409 gained P1K、P2K、…PNKBased on dynamic process optimal solution set for years, with mould
Type desired value is screening condition, respectively obtains the data configuration scheme of scene condition making policy decision scene set by step 4101.
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107798471A (en) * | 2017-10-19 | 2018-03-13 | 扬州大学 | More storehouse multiple station systems water resource optimal allocation methods of canal are directly mended under a kind of fully irrigation conditions |
| CN108337486A (en) * | 2018-04-19 | 2018-07-27 | 北京软通智城科技有限公司 | A kind of device and method of the video analysis of the algorithm configuration based on scene |
| CN108549234A (en) * | 2018-05-11 | 2018-09-18 | 江南大学 | A kind of multiobjective optimization control method based on dynamic variate |
| CN110032755A (en) * | 2019-02-27 | 2019-07-19 | 石家庄铁道大学 | Municipal sewage treatment process Multipurpose Optimal Method under multi-state |
| CN110084410A (en) * | 2019-05-31 | 2019-08-02 | 华北电力大学 | A kind of reutilization of the sewage pattern synthesis energy system operation optimization method |
| CN110910006A (en) * | 2019-11-19 | 2020-03-24 | 昆明市生态环境科学研究院 | Multisource data processing method for comprehensively utilizing regional reclaimed water resources |
| CN113591257A (en) * | 2021-07-27 | 2021-11-02 | 中国水利水电科学研究院 | Urban raw water scheduling scheme compiling method for multi-water-source multi-target comprehensive application |
| CN115204062A (en) * | 2022-09-15 | 2022-10-18 | 中国地质大学（武汉） | Reinforced hybrid differential evolution method and system for interplanetary exploration orbit design |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1900956A (en) * | 2006-07-11 | 2007-01-24 | 南京大学 | Design method for improved mixed genetic algorithm optimizing water quality model parameter |
| CN102708406A (en) * | 2012-05-10 | 2012-10-03 | 湖北省电力公司 | Scheduling graph optimizing method based on multi-target genetic algorithm |
| US20140039849A1 (en) * | 2011-04-20 | 2014-02-06 | Massachusetts Institute Of Technology | Method for the construction of a water distribution model |
| CN104268712A (en) * | 2014-10-11 | 2015-01-07 | 中冶南方工程技术有限公司 | Energy balancing and scheduling method based on improved mixed multi-population evolutionary algorithm |
| CN105297827A (en) * | 2015-10-16 | 2016-02-03 | 贵州省水利水电勘测设计研究院 | Water resource allocation method taking multi-user water demand and multi-source water supply into consideration |
| CN105809279A (en) * | 2016-03-03 | 2016-07-27 | 河海大学 | Multi-objective quantum Shuffled Frog Leaping Algorithm (SFLA) based water resource optimization and diversion method |
-
2016
- 2016-10-10 CN CN201610883619.0A patent/CN106600025B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1900956A (en) * | 2006-07-11 | 2007-01-24 | 南京大学 | Design method for improved mixed genetic algorithm optimizing water quality model parameter |
| US20140039849A1 (en) * | 2011-04-20 | 2014-02-06 | Massachusetts Institute Of Technology | Method for the construction of a water distribution model |
| CN102708406A (en) * | 2012-05-10 | 2012-10-03 | 湖北省电力公司 | Scheduling graph optimizing method based on multi-target genetic algorithm |
| CN104268712A (en) * | 2014-10-11 | 2015-01-07 | 中冶南方工程技术有限公司 | Energy balancing and scheduling method based on improved mixed multi-population evolutionary algorithm |
| CN105297827A (en) * | 2015-10-16 | 2016-02-03 | 贵州省水利水电勘测设计研究院 | Water resource allocation method taking multi-user water demand and multi-source water supply into consideration |
| CN105809279A (en) * | 2016-03-03 | 2016-07-27 | 河海大学 | Multi-objective quantum Shuffled Frog Leaping Algorithm (SFLA) based water resource optimization and diversion method |
Non-Patent Citations (1)
| Title |
|---|
| 唐瑶等: "基于混合遗传算法的城市再生水系统优化探索", 《净水技术》 * |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107798471A (en) * | 2017-10-19 | 2018-03-13 | 扬州大学 | More storehouse multiple station systems water resource optimal allocation methods of canal are directly mended under a kind of fully irrigation conditions |
| CN107798471B (en) * | 2017-10-19 | 2019-08-02 | 扬州大学 | More libraries-multiple station systems water resource optimal allocation method of canal is directly mended under a kind of abundant irrigation conditions |
| CN108337486A (en) * | 2018-04-19 | 2018-07-27 | 北京软通智城科技有限公司 | A kind of device and method of the video analysis of the algorithm configuration based on scene |
| CN108549234A (en) * | 2018-05-11 | 2018-09-18 | 江南大学 | A kind of multiobjective optimization control method based on dynamic variate |
| CN110032755A (en) * | 2019-02-27 | 2019-07-19 | 石家庄铁道大学 | Municipal sewage treatment process Multipurpose Optimal Method under multi-state |
| CN110032755B (en) * | 2019-02-27 | 2022-11-01 | 石家庄铁道大学 | Multi-objective optimization method for urban sewage treatment process under multiple working conditions |
| CN110084410A (en) * | 2019-05-31 | 2019-08-02 | 华北电力大学 | A kind of reutilization of the sewage pattern synthesis energy system operation optimization method |
| CN110910006A (en) * | 2019-11-19 | 2020-03-24 | 昆明市生态环境科学研究院 | Multisource data processing method for comprehensively utilizing regional reclaimed water resources |
| CN110910006B (en) * | 2019-11-19 | 2023-04-18 | 昆明市生态环境科学研究院 | Multisource data processing method for comprehensively utilizing regional reclaimed water resources |
| CN113591257A (en) * | 2021-07-27 | 2021-11-02 | 中国水利水电科学研究院 | Urban raw water scheduling scheme compiling method for multi-water-source multi-target comprehensive application |
| CN115204062A (en) * | 2022-09-15 | 2022-10-18 | 中国地质大学（武汉） | Reinforced hybrid differential evolution method and system for interplanetary exploration orbit design |
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