CN105045945A - Method for quickly constructing economical and efficient artificial wetland - Google Patents
Method for quickly constructing economical and efficient artificial wetland Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 30
- 239000011159 matrix material Substances 0.000 claims abstract description 42
- 238000007418 data mining Methods 0.000 claims abstract description 12
- 239000010865 sewage Substances 0.000 claims abstract description 12
- 239000003344 environmental pollutant Substances 0.000 claims abstract description 9
- 231100000719 pollutant Toxicity 0.000 claims abstract description 9
- 238000005516 engineering process Methods 0.000 claims abstract description 7
- 238000005457 optimization Methods 0.000 claims abstract description 7
- 238000005070 sampling Methods 0.000 claims abstract description 5
- 239000002689 soil Substances 0.000 claims abstract description 4
- 238000010276 construction Methods 0.000 claims abstract 3
- 230000000694 effects Effects 0.000 claims description 12
- 238000010606 normalization Methods 0.000 claims description 10
- 239000002351 wastewater Substances 0.000 claims description 7
- 238000000746 purification Methods 0.000 claims description 6
- 238000011109 contamination Methods 0.000 claims description 5
- 238000012216 screening Methods 0.000 claims description 5
- 238000012423 maintenance Methods 0.000 claims description 4
- 238000007781 pre-processing Methods 0.000 abstract 1
- 241000196324 Embryophyta Species 0.000 description 58
- 230000003203 everyday effect Effects 0.000 description 15
- 238000001556 precipitation Methods 0.000 description 8
- 238000010521 absorption reaction Methods 0.000 description 5
- 239000000463 material Substances 0.000 description 5
- 244000005700 microbiome Species 0.000 description 5
- 239000003403 water pollutant Substances 0.000 description 4
- 235000014676 Phragmites communis Nutrition 0.000 description 3
- 235000000864 Typha angustata Nutrition 0.000 description 3
- 240000001398 Typha domingensis Species 0.000 description 3
- 238000013461 design Methods 0.000 description 3
- 239000002893 slag Substances 0.000 description 3
- 229910052902 vermiculite Inorganic materials 0.000 description 3
- 235000019354 vermiculite Nutrition 0.000 description 3
- 239000010455 vermiculite Substances 0.000 description 3
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 3
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 description 2
- 239000000356 contaminant Substances 0.000 description 2
- 238000005315 distribution function Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 239000000758 substrate Substances 0.000 description 2
- 244000205574 Acorus calamus Species 0.000 description 1
- 241000894006 Bacteria Species 0.000 description 1
- 235000011996 Calamus deerratus Nutrition 0.000 description 1
- 235000005273 Canna coccinea Nutrition 0.000 description 1
- 240000008555 Canna flaccida Species 0.000 description 1
- 244000131316 Panax pseudoginseng Species 0.000 description 1
- 235000005035 Panax pseudoginseng ssp. pseudoginseng Nutrition 0.000 description 1
- 235000003140 Panax quinquefolius Nutrition 0.000 description 1
- OAICVXFJPJFONN-UHFFFAOYSA-N Phosphorus Chemical compound [P] OAICVXFJPJFONN-UHFFFAOYSA-N 0.000 description 1
- 241000746966 Zizania Species 0.000 description 1
- 235000002636 Zizania aquatica Nutrition 0.000 description 1
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 1
- 238000005842 biochemical reaction Methods 0.000 description 1
- 230000008033 biological extinction Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 230000000903 blocking effect Effects 0.000 description 1
- 230000015556 catabolic process Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000000052 comparative effect Effects 0.000 description 1
- 230000000295 complement effect Effects 0.000 description 1
- 238000010219 correlation analysis Methods 0.000 description 1
- 238000006731 degradation reaction Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000004069 differentiation Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 239000000945 filler Substances 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 235000008434 ginseng Nutrition 0.000 description 1
- 229910001385 heavy metal Inorganic materials 0.000 description 1
- 238000005342 ion exchange Methods 0.000 description 1
- 210000003734 kidney Anatomy 0.000 description 1
- 230000000813 microbial effect Effects 0.000 description 1
- 230000007269 microbial metabolism Effects 0.000 description 1
- 244000000010 microbial pathogen Species 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 239000008239 natural water Substances 0.000 description 1
- 229910052757 nitrogen Inorganic materials 0.000 description 1
- 235000015097 nutrients Nutrition 0.000 description 1
- -1 organism Substances 0.000 description 1
- 238000013450 outlier detection Methods 0.000 description 1
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Classifications
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
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- Purification Treatments By Anaerobic Or Anaerobic And Aerobic Bacteria Or Animals (AREA)
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Abstract
The invention relates to a method for quickly constructing an economic and efficient artificial wetland, which comprises the following steps: firstly, analyzing the concentration of each pollutant in sewage, the regional climate soil condition and each index data of different plant types, and sampling, exploring and preprocessing original data based on a data mining technology to obtain useful data information meeting optimization conditions; then constructing a multivariate function model for each piece of mined index data according to specified conditions, and solving a corresponding black plug matrix; and analyzing and judging the blackplug matrix based on the properties of the Gaussian formula and the multivariate function extreme value, and optimizing an economic and efficient constructed wetland construction scheme. The invention provides a method for constructing an economic and efficient artificial wetland, and provides technical support for selecting the most economic and efficient artificial wetland construction scheme under different sewage conditions in different regions.
Description
Technical field
The present invention relates to artificial swamp constructing technology field, be specifically related to a kind of method building economical and efficient type artificial swamp.
Technical background
Along with developing rapidly of environmental protection, people there has also been Wetland Function and are familiar with widely.Wetland, as " kidney of the earth ", is responsible for the purification to earth natural water and processing capacity.Due to minimizing gradually and the extinction of natural bioremediation in city, therefore artificial swamp receives increasing concern and development with its original superiority.
The process of artificial swamp to sewage is internal system plant, the interactional synthesis result of physics, chemistry and bioprocess between matrix and microorganism, and it removes suspension, organism, nitrogen, phosphorus, heavy metal and the pathogenic microorganism in waste water by number of ways such as filtration, absorption, precipitation, ion-exchange, plant absorption and microbial metabolisms.
The key of artificial wet land system water technology is the design of system process route, the selection of plant and matrix and application configuration thereof.In artificial wet land system, plant self can absorb and utilize a part of venomous injurant in waste water to participate in the geochemical cycle of material as nutrients on the one hand, its root district is that microorganism provides necessary attachment and forms the place of bacterium colony on the other hand, promote the growth of microorganism, and transmit oxygen downwards by rhizome, good environment is provided to rhizosphere microbial, promote that microorganism is to biochemical reaction sedimental around rhizosphere, thus indirectly improve wetland to the treatment effect of waste water.To consider when host material is selected that matrix is to the absorption property polluted and substrate clogging problem.Usually, wetland bed matrix should select the filler that specific surface area is comparatively large, voidage is higher, rough surface, absorption property are strong, is convenient to absorption and microorganism colonization, but also will consider the problem that the gentle smoothness of current is logical simultaneously, packing material size is too little, easily causes blocking.
At present, also there is following problem when artificial marsh sewage treatment system being built:
(1) select the randomness of plant larger when artificial swamp builds.In the building process of artificial wet land system, the selection of plant and configuration are vital factors, especially to consider the effect of influencing each other between plant, plant affects by regional climate, the factor such as the kind of dirty water pollutant and concentration, the major function of system and the botanical characteristics of plant will be considered simultaneously.In current artificial swamp building process, mostly the selection of plant is experimental or based on experimental result once, several times, does not combine a large amount of actual result and take into full account.
(2) influence factor of further investigated artificial wetland treatment effect is needed.Affect artificial swamp to pollutant removal factor, except the reaction kinetics of wetland contaminant degradation and the flow-shape of wetland inside, temperature, influent load, hydraulic detention time, wetland is also had to hold water volume and the factor such as aquifer yield, area differentiation and climate characteristic and system type.Because the current research to artificial swamp does not have unified contrast standard, scale and the type of artificial swamp vary, and many achievements in research lack comparability.Therefore, be necessary to set up and be suitable for different regions, environment, weather conditions and the artificial wetland treatment effect of water quality characteristic and the database of influence factor, provide Data support for artificial swamp builds.
(3) artificial swamp front-end investment is larger.Although artificial swamp later stage operating cost is less, early stage infrastructure project, host material fill and plant culture time all need certain fund input, and artificial swamp floor area adds somewhat to more greatly the expenditure of expense.Thus, when artificial wet land system builds, system, consider the influence factor of various investment.
The present invention is just based on considering influencing each other between plant, and the plant collocation making every effort to select makes the life cycle of plant long as far as possible, under the prerequisite that dirt-removing power is high, reduces economic input.Technical support is provided for building Design of Constructed Wetland scheme.
Summary of the invention
The present invention is by analyzing the sewage data of different regions, different pollution level and the information data of different plant, excavate, thus the most economical efficient artificial swamp constructing plan of optimization.
Technical scheme is as follows: a kind of method of rapid build economical and efficient type artificial swamp, comprise the following steps: first each achievement data of pollutant levels each in sewage, regional climate soil regime and different vegetation type is analyzed, based on data mining technology, raw data is sampled, explores, pre-service, obtain and meet the useful data information of optimal conditions; Then each achievement data excavated condition is according to the rules built multivariate function model, and obtain corresponding Hessian matrix; Character based on Gauss formula and multivariate function extreme value carries out analysis judgement to Hessian matrix, the artificial swamp constructing plan of optimization economical and efficient.
Build a method for economical and efficient type artificial swamp, concrete steps are as follows:
Step 1: data mining analysis is carried out to each achievement data of raw data, excavates useful relevant information; First to data sampling by classification, then Data Mining and pre-service are carried out to sample data, then carry out data screening, and for missing values, then use method of weighted mean interpolation;
Step 2: set up multivariate function model
Each achievement data vectorization step 1 excavated, convenient observation and structure multivariate function model, again because of the needs of model, by plant to four kinds of pollutants (COD, TN, TP and turbidity) point other total removal rate Gaussian processes normalization.Consider the impact of several key factor on plant, the life cycle realizing plant reaches the longest, the optimization aim such as maintenance frequency is minimum, and economic cost is minimum, and wastewater purifying efficiency is the most desirable.Its significance level is compared between two to 4 kinds of contamination index's Fuzzy AHPs, determines weight sets S=(s
1, s
2, s
3, s
4), and by flexible strategy s
1, s
2, s
3, s
4give the final purification concentration of COD, TN, TP, turbidity respectively, set up aggregative index F to evaluate clean-up effect, the less clean-up effect of F is better, then set up about k=(k
1, k
2..., k
n) n+m meta-function model be:
f(k)＝s
1c
1′+s
2c
2′+s
3c
3′+s
4c
4′
Step 3: build Hessian matrix and analyze judgement
N+m meta-function f (k) that obvious step 2 builds is at considered field of definition R
n+mbe inside there is continuous single order and second-order partial differential coefficient, f (k) has Hessian matrix
If this n+m meta-function f (k) is at field of definition R
n+m, some k in existing
0make
have at this place
and
then k
0this n+m meta-function f (k) that makes required by us obtains minimizing vector, and namely purchasing choosing vector is k
0time, the artificial swamp scheme set up is optimum, and namely most economical, contaminant removal efficiency is the highest;
Step 4: the minimal value vector k obtained by step 3
0be the optimum arranging scheme of plant and the matrix purchasing choosing, and required economic cost
Patent of the present invention is the method for a kind of rapid build economical and efficient type artificial swamp set up based on data mining technology and multivariate function extremum method.Its advantage comprises:
The method, based on data mining technology, excavates the data that correlativity is stronger, and based on Multivariate Extreme Value method, builds optimization model.Thus algorithm of the present invention can provide the scheme of economical and efficient type effectively for building artificial swamp.
Embodiment
Embodiment 1
The method concrete steps of described structure economical and efficient type artificial swamp are as follows:
Step 1: data mining analysis is carried out to each achievement data of raw data, excavates useful relevant information.First to data sampling by classification, Data Mining and pre-service are being carried out to sample data, is then carrying out data screening, and for missing values, then use method of weighted mean interpolation.
Step 2: set up multivariate function model
Each achievement data vectorization step 1 excavated, convenient observation and structure multivariate function model, again because of the needs of model, use Gaussian processes normalization respectively by the clearance of plant to four kinds of pollutants (COD, TN, TP and turbidity).Consider the impact of several key factor on plant, the life cycle of plant be made to reach the longest, and maintenance frequency is minimum, and economic cost is minimum, and wastewater purifying efficiency is the most desirable.Its significance level is compared between two to 4 kinds of contamination index's Fuzzy AHPs, determines weight sets S=(s
1, s
2, s
3, s
4), and by flexible strategy s
1, s
2, s
3, s
4give the final purification concentration of COD, TN, TP, turbidity respectively, set up aggregative index F to evaluate clean-up effect, the less clean-up effect of F is better, then set up about k=(k
1, k
2..., k
n) n+m meta-function model be:
f(k)＝s
1c
1′+s
2c
2′+s
3c
3′+s
4c
4′
Step 3: build Hessian matrix and analyze judgement
N+m meta-function f (k) that obvious step 2 builds is at considered field of definition R
n+mbe inside there is continuous single order and second-order partial differential coefficient, f (k) has Hessian matrix
If this n+m meta-function f (k) is at field of definition R
n+m, some k in existing
0make
have at this place
and
then k
0this n+m meta-function f (k) that makes required by us obtains minimizing vector, and namely purchasing choosing vector is k
0time, the artificial swamp scheme set up is efficiently most economical.
Step 4: the minimal value vector k obtained by step 3
0be the optimum arranging scheme of plant and the matrix purchasing choosing, and required economic cost
The sub-step that step 1 comprises:
S1.1 sampling by classification: extract many group observationses by source of sewage territorial classification;
S1.2 Data Mining: outlier detection, missing values analysis, correlation analysis, periodicity analysis, sample cross checking are carried out to the data extracted;
S1.3 pre-service:
S1.3.1 data screening: inessential observed reading in observed reading sample is fallen in screening, finally extract dirty water pollutant COD, TN, TP concentration, the mean annual precipitation of turbidity and this area, the life cycle of average temperature of the whole year and different plant, price, clearance to above 3 kinds of different pollutants and turbidity, the price of different substrates and to the capability of influence of plant, go turbid ability.
S1.3.2 missing values process: use method of weighted mean complement;
The sub-step that step 2 comprises:
The vectorization of S2.1 achievement data:
The life cycle of n Plants: t=(t
1, t
2..., t
n);
Plant divides other price: p=(p
1, p
2..., p
n);
Plant is to the clearance of COD average every day: h=(h
1, h
2..., h
n);
Plant is to the clearance of TN average every day: a=(a
1, a
2..., a
n);
Plant is to the clearance of TP average every day: b=(b
1, b
2..., b
n);
Plant is to the clearance of turbidity average every day: d=(d
1, d
2..., d
n);
N Plants divides other influence coefficient: α=(α
1, α
2..., α
n);
The price of m kind matrix: q=(q
n+1, q
n+2... .., q
n+m);
Matrix is to the clearance of TN average every day: u=(u
n+1, u
n+2..., u
n+m);
Matrix is to the clearance of TP average every day: v=(v
n+1, v
n+2..., v
n+m);
Matrix is to the influence coefficient of n plant: λ=(λ
1, λ
2..., λ
n);
Matrix go turbid rate: g=(g
n+1, g
n+2..., g
n+m);
Dirty water pollutant COD concentration c
1, TN concentration c
2, TP concentration c
3, turbidity c
4with quantity of precipitation L, the temperature on average T of this area;
Quantity of precipitation and temperature on average to the influence coefficient l=F (L, T) of plant,
l＝(l
1，l
2，......，l
3)
If purchase the strain number selecting n Plants unit area: k '=(k
1, k
2..., k
n),
Purchase the quality selecting m kind base units area: k "=(k
n+1, k
n+2..., k
n+m),
Then purchase the quantity vector selecting plant and base units area:
k＝(k
1，k
2，...，k
n，k
n+1，...，k
n+m)
S2.2 sets up multivariate function model
S2.2.1 Gaussian normalization
K ' h
tfor all plants are to the total removal rate of COD average every day, be designated as H;
K ' a
tfor all plants are to the total removal rate of TN average every day, be designated as A;
K ' b
tfor all plants are to the total removal rate of TP average every day, be designated as B;
K ' d
t+ k " g
tfor all plants and matrix are to the clearance of turbidity, be designated as C.
By gauss of distribution function
carry out Gaussian normalization to H, A, B, C, wherein σ is variance, and μ is expectation value, then obtaining normalized four kinds of total removal rate is comparatively meet true rule, then become respectively after normalization:
p
H、p
A、p
B、p
C
S2.2.2 Fuzzy AHP determination flexible strategy collection also sets up the multivariate function
Consider the impact of several key factor on plant, the life cycle of plant be made to reach the longest, and maintenance frequency is minimum, and economic cost is minimum, and wastewater purifying efficiency is the most desirable.Now its significance level is compared between two to four kinds of contamination index's Fuzzy AHPs, determine weight sets S=(s
1, s
2, s
3, s
4), and by flexible strategy s
1, s
2, s
3, s
4give the final purification concentration of COD, TN, TP, turbidity respectively, set up aggregative index F to evaluate clean-up effect, the less clean-up effect of F is better, then set up about k=(k
1, k
2..., k
n) n+m meta-function model be:
f(k)＝s
1c
1′+s
2c
2′+s
3c
3′+s
4c
4′
Wherein:
C
1'=c
1(1-p
h)
r, be final COD purified concentrations;
C
2'=c
2(1-p
a)
r, be final TN purified concentrations;
C
3'=c
3(1-p
b)
r, be final TP purified concentrations;
C
4'=c
4(1-p
c)
r, be final turbidity purification result.
R (my god) be overall time, because the life cycle of every Plants reality affects by matrix and quantity of precipitation and temperature on average, therefore obtained by method of weighted mean:
Illustrating of step 3:
Obvious n+m meta-function f (k) is at considered field of definition R
n+mbe inside there is continuous single order and second-order partial differential coefficient, then the Hessian matrix of f (k) is:
Wherein
If this n+m meta-function f (k) is at field of definition R
n+m, some k in existing
0make
have at this place
and
then k
0this n+m meta-function f (k) that makes required by us obtains minimizing vector, and namely purchasing choosing vector is k
0time, the artificial swamp scheme set up is efficiently most economical.
Required economic cost
Embodiment 2
The method in embodiment 1 is utilized to build artificial swamp to certain sewage treatment plant,
In order to verify the validity of this structure economical and efficient type artificial swamp algorithm, the good characteristics of algorithm being described, testing below design and it is verified and comparative studies.
Ginseng this area's temperature on average and quantity of precipitation, estimate that this area builds artificial swamp northeastward.Select 7 Plants of suitable this area growth: typha angustata, canna, calamus, reed, wild rice stem, wilson iris and rush and vermiculite and Hongcomb coal-slag two kinds of matrix are as the structure material of artificial swamp, and the data of 7 Plants and two kinds of matrix are as shown in table 1.
The data of table 17 Plants and two kinds of matrix:
Note: plant life cycle is the growth cycle of plant under good environment
By each achievement data vectorization:
The then life cycle of n Plants: t=(365,365,365,365,365,365,365)
Plant divides other price: p=(0.25,0.7,0.6,0.8,0.665,0.45,0.8)
Plant is to the clearance of COD average every day:
h＝(62.29，53.88，49.98，56.30，53.40，61.41，51.30)
Plant is to the clearance of TN average every day:
a＝(77.2073.65，69.70，47.47，75.04，76.25，65.28)
Plant is to the clearance of TP average every day:
b＝(68.03，74.12，64.33，58.63，62.23，76.60，73.45)
Plant is to the clearance of turbidity average every day:
d＝(22.19，25.23，9.1，33.42，34.32，23.62，51.41)
N Plants divides other influence coefficient: α=(0.95,0.6,0.6,0.95,0.6,0.7,0.5)
The price of m kind matrix: q=(0.54,0.6)
Matrix is to the clearance of TN average every day: u=(38.12,7.16)
Matrix is to the clearance of TP average every day: v=(2.31,19.24)
Matrix go turbid rate: g=(64.87,81.87)
Matrix is to the influence coefficient of n plant: λ=(1,1,1,1,1,1,1)
Quantity of precipitation and temperature on average are to the influence coefficient of plant
l＝(0.9，0.65，0.5，0.9，0.3，0.85，0.2)
If purchase the strain number selecting 7 Plants one square metre: k '=(k
1, k
2..., k
7),
Purchase the quality selecting 2 kinds of matrix one square metre: k "=(k
8, k
9),
Then purchase the quantity vector selecting plant and matrix: k=(k
1, k
2..., k
7, k
8, k
9)
Its significance level is compared between two to four kinds of contamination index's Fuzzy AHPs, determines weight sets S=(3,3,3,1),
Calculate H, A, B, C, then by gauss of distribution function
after Gaussian normalization is carried out to H, A, B, C.
Dirty water pollutant COD concentration c
1'=90.56mg/L, TN concentration c
2=27.32mg/L, TP concentration c
3=2.33mg/L, turbidity c
4the mean annual precipitation L=48.92mm of=122.35NTU and this area, average temperature of the whole year T=10.3 DEG C;
Then 9 meta-function models are:
f(k)＝3c
1′+3c
2′+3c
3′+c
4′
Order
and
then there is k
0=(5,0,0,5,0,0,0,6,3)
For this sewage treatment plant's sewage, optimizing artificial swamp constructing plan is: typha angustata planting density 5 strain/square metre, reed 5 strain/square metre, namely with 6kg vermiculite, 3kg Hongcomb coal-slag as collocation.Namely choose typha angustata and reed with 1: 1 ratio and with 5 strains/square metre, blend proportion be 2: 1 6kg vermiculite and 3kg Hongcomb coal-slag be effectively most economical.
Comprehensive above analysis, one of the present invention builds economical and efficient type artificial swamp method based on Gaussian normalization and Hessian matrix, builds multivariate function model.Thus algorithm of the present invention can build economical and efficient type artificial swamp effectively, automatically realizes the collocation of plant and matrix.
Claims (4)
1. the method for a rapid build economical and efficient type artificial swamp, it is characterized in that: comprise the following steps: first each achievement data of pollutant levels each in sewage and regional climate soil regime and different vegetation type is analyzed, excavate useful data information; Then each achievement data excavated condition is according to the rules built multivariate function model, and obtain corresponding Hessian matrix; Finally carry out analysis to Hessian matrix to judge, the artificial swamp constructing plan of optimization economical and efficient.
2. the method for structure economical and efficient type artificial swamp according to claim 1, it is characterized in that: described to each pollutant levels of sewage and regional climate soil and each achievement data of different plant analyze, based on bulk information data mining technology, raw data is sampled, explores, pre-service, obtain useful related data information.
3. build the method for economical and efficient type artificial swamp according to claim 1, it is characterized in that: described builds multivariate function model by each achievement data excavated condition according to the rules, and obtain corresponding Hessian matrix, and analysis judgement is carried out to Hessian matrix, the character based on Gauss formula and multivariate function extreme value, desired sum data is obtained to the data elementary function excavated, then use Gauss's normalization method by their normalization, finally carry out model construction by normalized data, and analysis and solution Hessian matrix, optimization meets the artificial swamp constructing plan imposed a condition.
4. the method for structure economical and efficient type artificial swamp according to claim 1, is characterized in that: concrete steps are as follows:
Step 1: data mining analysis is carried out to each achievement data of raw data, excavates useful relevant information; First to data sampling by classification, then Data Mining and pre-service are carried out to sample data, then carry out data screening, and for missing values, then use method of weighted mean interpolation;
Step 2: set up multivariate function model
Each achievement data vectorization step 1 excavated, convenient observation and structure multivariate function model, again because of the needs of model, divide other total removal rate Gaussian processes normalization by plant to four kinds of pollutants.Consider the impact of several key factor on plant, the life cycle of plant be made to reach the longest, and maintenance frequency is minimum, and economic cost is minimum, and wastewater purifying efficiency is the most desirable.Its significance level is compared between two to four kinds of contamination index's Fuzzy AHPs, determines weight sets S=(s
1, s
2, s
3, s
4), and by flexible strategy s
1, s
2, s
3, s
4give the final purification concentration of COD, TN, TP, turbidity respectively, set up aggregative index F to evaluate clean-up effect, the less clean-up effect of F is better, then set up about k=(k
1, k
2..., k
n) n+m meta-function model be:
f(k)＝s
1c
1′+s
2c
2′+s
3c
3′+s
4c
4′
Step 3: build Hessian matrix and analyze judgement
N+m meta-function f (k) that obvious step 2 builds is at considered field of definition R
n+mbe inside there is continuous single order and second-order partial differential coefficient, f (k) has Hessian matrix
If this n+m meta-function f (k) is at field of definition R
n+m, some k in existing
0make
have at this place
and
then k
0this n+m meta-function f (k) that makes required by us obtains minimizing vector, and namely purchasing choosing vector is k
0time, the artificial swamp scheme set up is efficiently most economical;
Step 4: the minimal value vector k obtained by step 3
0be the optimum arranging scheme of plant and the matrix purchasing choosing, and required economic cost
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| CN113636657A (en) * | 2021-08-31 | 2021-11-12 | 北京东方园林环境股份有限公司 | Continuous purification method for bottom mud and water pollutants |
| CN116675347A (en) * | 2023-06-09 | 2023-09-01 | 长江生态环保集团有限公司 | Method for preventing constructed wetland from being blocked and synchronously removing nitrogen and phosphorus |
| CN116675347B (en) * | 2023-06-09 | 2024-05-28 | 长江生态环保集团有限公司 | Method for preventing constructed wetland from being blocked and synchronously removing nitrogen and phosphorus |
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