CN108280791B - Sewage deep sea optimized discharge response relation determination method based on tidal current change - Google Patents
Sewage deep sea optimized discharge response relation determination method based on tidal current change Download PDFInfo
- Publication number
- CN108280791B CN108280791B CN201810101668.3A CN201810101668A CN108280791B CN 108280791 B CN108280791 B CN 108280791B CN 201810101668 A CN201810101668 A CN 201810101668A CN 108280791 B CN108280791 B CN 108280791B
- Authority
- CN
- China
- Prior art keywords
- sewage discharge
- sewage
- tidal current
- optimal
- max
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
- 239000010865 sewage Substances 0.000 title claims abstract description 86
- 230000004044 response Effects 0.000 title claims abstract description 22
- 230000008859 change Effects 0.000 title claims abstract description 20
- 238000000034 method Methods 0.000 title claims description 16
- 239000012895 dilution Substances 0.000 claims abstract description 15
- 238000010790 dilution Methods 0.000 claims abstract description 15
- 230000000694 effects Effects 0.000 claims abstract description 12
- 238000005265 energy consumption Methods 0.000 claims abstract description 12
- 238000004422 calculation algorithm Methods 0.000 claims description 39
- 238000004364 calculation method Methods 0.000 claims description 34
- 238000013528 artificial neural network Methods 0.000 claims description 25
- 239000003344 environmental pollutant Substances 0.000 claims description 22
- 230000002068 genetic effect Effects 0.000 claims description 22
- 231100000719 pollutant Toxicity 0.000 claims description 22
- 210000000349 chromosome Anatomy 0.000 claims description 21
- 230000006870 function Effects 0.000 claims description 13
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 13
- 238000004088 simulation Methods 0.000 claims description 12
- 238000009792 diffusion process Methods 0.000 claims description 9
- 238000013178 mathematical model Methods 0.000 claims description 9
- 238000010606 normalization Methods 0.000 claims description 6
- 230000007613 environmental effect Effects 0.000 claims description 4
- 230000009191 jumping Effects 0.000 claims description 4
- 238000005457 optimization Methods 0.000 claims description 4
- NAWXUBYGYWOOIX-SFHVURJKSA-N (2s)-2-[[4-[2-(2,4-diaminoquinazolin-6-yl)ethyl]benzoyl]amino]-4-methylidenepentanedioic acid Chemical compound C1=CC2=NC(N)=NC(N)=C2C=C1CCC1=CC=C(C(=O)N[C@@H](CC(=C)C(O)=O)C(O)=O)C=C1 NAWXUBYGYWOOIX-SFHVURJKSA-N 0.000 claims description 3
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 238000011156 evaluation Methods 0.000 claims description 3
- 238000005259 measurement Methods 0.000 claims description 3
- 230000035772 mutation Effects 0.000 claims description 3
- 230000008569 process Effects 0.000 claims description 3
- 238000012545 processing Methods 0.000 claims description 3
- 239000000243 solution Substances 0.000 claims description 3
- 238000012549 training Methods 0.000 claims description 3
- 108090000623 proteins and genes Proteins 0.000 description 4
- 238000010168 coupling process Methods 0.000 description 2
- 230000008878 coupling Effects 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000008092 positive effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000012827 research and development Methods 0.000 description 1
- 239000007921 spray Substances 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/12—Computing arrangements based on biological models using genetic models
- G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
Abstract
The invention discloses a sewage deep sea optimized discharge response relation based on tidal current change, which is characterized in that the response relation between the flow speed in a tidal current change interval of an engineering sea area and the optimal sewage discharge amount corresponding to the flow speed is established by taking the optimal sewage dilution effect, the lowest energy consumption and the optimal sewage discharge efficiency as targets, the response relation is applied to optimize and adjust the sewage deep sea discharge amount in real time, the sewage can be highly diluted in a space-time range as small as possible so as to improve the initial dilution effect and finally reach the discharge standard, the management level of the sewage deep sea discharge can be improved, and the ecological quality level of the sea area environment can be maintained.
Description
Technical Field
The invention relates to a sewage discharge method, in particular to a sewage deep sea optimized discharge response relation determination method based on tidal current change.
Background
In recent years, with the rapid development of coastal economy in China, a series of major maritime engineering is frequently built, and newly added pollution discharge inevitably causes greater ecological environment pressure on engineering sea areas. By means of the extremely strong mixing and transporting capacity of waves, tides and currents, the offshore deep sea discharge of sewage becomes an engineering measure of priority for the sewage discharge in coastal areas due to the low engineering cost and the low operation cost. However, the current deep sea discharge engineering cannot realize a mechanism for optimizing dynamic regulation in real time along with the motion of complex tidal waves, so that the sewage entering the environmental water body cannot obtain the optimal real-time dilution effect. Therefore, research and development of a sewage deep sea optimized discharge response relation based on tidal current change are urgently needed to optimize and adjust the sewage discharge amount in real time, so that the sewage is highly diluted in a space-time range as small as possible, the initial dilution effect is improved, and the sewage finally reaches the standard and is discharged.
Disclosure of Invention
The invention provides a sewage deep sea optimized discharge response relation based on tidal current change for solving the technical problems in the known technology, the sewage discharge amount controlled by adopting the response relation can be optimized and adjusted in real time along with the motion of complex tidal waves, so that the sewage is highly diluted in a space-time range as small as possible, the initial dilution effect is improved, and the sewage finally reaches the standard and is discharged.
The technical scheme adopted by the invention for solving the technical problems in the prior art is as follows: a sewage deep sea optimized discharge response relation determination method based on tidal current change comprises the following steps:
1) calculating the engineering sea area load flow change interval (u) by adopting a simulation inverse algorithmmin,u1,…,ui,…,un,umax) Middle n +2 tidal flow velocities and optimal sewage discharge (Q) corresponding to the samemin,Q1,…,Qi,…,Qn,Qmax) The method comprises the following specific steps:
1.1) establishing a simulation back calculation method model
1.1.1) adopting a sewage outlet pollutant diffusion mathematical model;
1.1.2) establishing a Multi-objective function
MAX.DD＝C0/C1 (1)
MIN.EC＝0.0414×Q+13.828 (2)
MAX.OE＝OE (3)
In the formula (1), DD is the dilution of pollutants; c0The concentration value of the pollutants in the jet flow water body is obtained; c1The concentration value of the pollutant at a certain point in the environmental water body is obtained; EC is energy consumption value; q is the sewage discharge amount of the pump, m is m multiplied by v multiplied by S, m is the number of the nozzles, v is the discharge rate, and S is the sectional area of the nozzles; OE is the sewage discharge efficiency;
1.1.3) Using a genetic Algorithm model
1.1.3.1) using evolutionary computation operators
1.1.3.2) using cross-computing operators
1.1.3.3) calculation operator by mutation
1.2) initial setting
1.2.1) taking the sewage discharge as a control variable of an optimization process, and adopting a real number coding form;
1.2.2) setting a value range of the control variable, and taking the value range as a constraint condition of the genetic algorithm population value;
1.3) for the engineering sea area tidal current change interval (u)min,u1,…,ui,…,un,umax) The simulation reverse calculation of the optimal sewage discharge amount is carried out according to the following steps for each tidal current flow velocity, and then n +2 optimal sewage discharge amounts (Q) aiming at n +2 tidal current flow velocities can be obtainedmin,Q1,…,Qi,…,Qn,Qmax)
1.3.1) randomly selecting r numerical values in a control variable value range according to each tidal current flow rate to obtain r chromosomes, and forming a population (r) of possible solutions of the optimal sewage discharge amount under the tidal current flow rate condition;
1.3.2) taking each chromosome as a numerical example, adopting a pollutant diffusion mathematical model of a sewage outlet to calculate r times, and simultaneously outputting and storing r pollutant concentration results of observation points in a water area;
1.3.3) carrying out chromosome evaluation by adopting the multi-target function established in the step 1.1.2) with the target of optimal sewage dilution effect, lowest energy consumption and optimal sewage discharge efficiency;
1.3.4) when the maximum generation number is met, jumping out of the program and outputting the best chromosome, namely the optimal sewage discharge amount; if the maximum generation number target is not met, adopting the genetic algorithm model in the step 1.1.3) to perform genetic calculation;
1.3.5) updating the chromosomes selected in the step 1.3.1) through genetic calculation, obtaining a brand new population, and repeating the calculation according to the steps (1.3.2) to (1.3.4) until the best chromosomes, namely the best sewage discharge amount, is output;
2) establishing a data-driven BP algorithm model of the artificial neural network, wherein a hyperbolic Sigmoid function is adopted as a nonlinear conversion function in the BP algorithm model of the artificial neural network, and the formula (9) is as follows:
f(x)＝1/1+e-x+θ (9)
in the formula, theta represents a threshold value;
in the artificial neural network BP algorithm model, Root Mean Square Error (RMSE) is adopted to evaluate the prediction capability of the network, and the formula (10) shows:
in the formula, a is the number of data; y isiIs an actual measurement value; y isi' is a predicted value;
the normalization processing of all data in the artificial neural network BP algorithm model adopts an equation (11):
wherein:
represents a normalized value; y isiRepresents the value before normalization; y ismaxRepresents the maximum of all data; y isminRepresents the minimum of all data;
3) initializing network weight and combining with engineering sea area tidal current change interval (u)min,u1,…,ui,…,un,umax) Middle n +2 tidal flow velocities and optimal sewage discharge (Q) corresponding to the samemin,Q1,…,Qi,…,Qn,Qmax) Setting a network architecture and setting basic parameters of an artificial neural network BP algorithm model;
4) taking n +2 tidal current flow rate data as the input of an artificial neural network BP algorithm model, correspondingly taking n +2 optimal sewage discharge amount as the output of the artificial neural network BP algorithm model, substituting the optimal sewage discharge amount into the artificial neural network model for training and learning, and calculating the root mean square error;
5) when the error requirement of Root Mean Square Error (RMSE) is met, a jump-out program establishes a response relation; if the Root Mean Square Error (RMSE) error requirement is not met, performing subsequent calculation;
6) and (4) based on the root mean square error, after the network weight is reversely adjusted, repeating the calculation according to the steps 3) -5) until a response relation is established.
The invention has the advantages and positive effects that: by taking the optimal sewage dilution effect, the lowest energy consumption and the optimal sewage discharge efficiency as targets, the response relation between the flow speed in the tidal current change interval of the engineering sea area and the optimal sewage discharge amount corresponding to the flow speed is established, the response relation is applied, the deep sea sewage discharge amount is optimized and adjusted in real time, the sewage can be highly diluted in a space-time range as small as possible, the initial dilution effect is improved, the sewage can be finally discharged up to the standard, the management level of the deep sea sewage discharge can be improved, and the ecological quality level of the sea area environment is maintained.
Detailed Description
To further understand the contents, features and effects of the present invention, the following examples are illustrated as follows:
a sewage deep sea optimized discharge response relation determination method based on tidal current change comprises the following steps:
1) calculating the engineering sea area load flow change interval (u) by adopting a simulation inverse algorithmmin,u1,…,ui,…,un,umax) Middle n +2 tidal flow velocities and optimal sewage discharge (Q) corresponding to the samemin,Q1,…,Qi,…,Qn,Qmax) The method comprises the following specific steps:
1.1) establishing a simulation back calculation method model
1.1.1) the mathematical model of pollutant diffusion at sewage outlet is adopted, please refer to the following two documents, namely, Zhoufeng, Liangshuxiu, Sunschen, numerical simulation of the influence of Jet spray angle on Jet characteristics in a Flowing water environment [ J ]. university of continental Engineering, 2007,47(4): 583-.
1.1.2) establishing a Multi-objective function
MAX.DD＝C0/C1 (1)
MIN.EC＝0.0414×Q+13.828 (2)
MAX.OE＝OE (3)
In the formula (1), DD is the dilution of pollutants; c0The concentration value of the pollutants in the jet flow water body is obtained; c1The concentration value of the pollutant at a certain point in the environmental water body is obtained; EC is energy consumption value; q is the sewage discharge amount of the pump, m is m multiplied by v multiplied by S, m is the number of the nozzles, v is the discharge rate, and S is the sectional area of the nozzles; OE is sewage discharge efficiency, and for practical engineers, the higher the efficiency, the better; the formula (2) is obtained by adopting least square fitting according to the flow and the energy consumption of the Germany KSB company K400-500 type horizontal sewage pump under different operation modes.
1.1.3) Using a genetic Algorithm model
The genetic algorithm applies the prior art, comprises three calculation parts, namely evolution, intersection and variation, and specifically comprises a genetic algorithm model:
1.1.3.1) using evolutionary computation operators
1.1.3.2) using cross-computing operators
1.1.3.3) calculation operator by mutation
The genetic algorithm comprises the following steps: evolutionary calculations (roulette selection), cross calculations (as shown in equations (4) and (5)), and variant calculations (as shown in equations (6) to (8)).
Os1＝ω×Pa1+(1-ω)×Pa2 (4)
Os2＝ω×Pa2+(1-ω)×Pa1 (5)
In the formulas (4) and (5), omega is a random parameter between-0.25 and 1.25; os and Pa represent offspring and father, respectively; 1 and 2 are labels of children and parents.
And (3) calculating variation:
X＝X'+Δ(t,y) (6)
in the formulas (6) and (7), X is a mutated gene, X' is an initial gene, T is the current generation number, T is the maximum generation number, r is a random number between 0 and 1, and b is a system parameter. The parameter y takes the value according to the formula (8), LD is the minimum value of the gene, UD is the maximum value of the gene, and i is a random number which is 0 or 1 respectively.
1.2) initial setting
1.2.1) taking the sewage discharge amount as a control variable of an optimization process and adopting a real number coding form.
1.2.2) setting the value range of the control variable, and taking the value range as the constraint condition of the genetic algorithm population value.
1.3) for the engineering sea area tidal current change interval (u)min,u1,…,ui,…,un,umax) The simulation reverse calculation of the optimal sewage discharge amount is carried out according to the following steps for each tidal current flow velocity, and then n +2 optimal sewage discharge amounts (Q) aiming at n +2 tidal current flow velocities can be obtainedmin,Q1,…,Qi,…,Qn,Qmax) The method comprises the following specific steps:
1.3.1) randomly selecting r numerical values in the control variable value range according to each tidal current flow velocity to obtain r chromosomes, and forming a population (r) of possible solutions of the optimal sewage discharge amount under the tidal current flow velocity condition.
1.3.2) taking each chromosome as a numerical example, adopting a pollutant diffusion mathematical model to carry out r times of calculation, and simultaneously outputting and storing r pollutant concentration results of observation points in the water area.
1.3.3) carrying out chromosome evaluation by adopting the multi-objective function established in the step 1.1.2) with the goals of optimal sewage dilution effect, lowest energy consumption and optimal sewage discharge efficiency.
1.3.4) when the maximum generation number is met, jumping out of the program and outputting the best chromosome, namely the optimal sewage discharge amount; if the maximum generation number target is not met, the genetic algorithm model in the step 1.1.3) is adopted for genetic calculation.
1.3.5) updating the chromosomes selected in the step 1.3.1) through genetic calculation, obtaining a brand new population, and repeating the calculation according to the steps (1.3.2) to (1.3.4) until the best chromosomes, namely the best sewage discharge amount, is output.
The simulation inverse algorithm embeds a sewage discharge port pollutant diffusion mathematical model (also called a jet flow model) and an operation energy consumption formula into a genetic algorithm model, takes the optimal sewage dilution effect, the lowest energy consumption and the optimal discharge efficiency as targets, carries out a coupling method model of optimal sewage discharge amount simulation inverse calculation, adopts the sewage discharge port pollutant diffusion mathematical model to calculate each chromosome and outputs pollutant concentration data of an observation point in a calculated water area; calculating and analyzing the dilution, sewage conveying energy consumption and discharge efficiency of each chromosome by adopting a multi-objective function; taking the maximum generation number set by the model as a judgment condition, jumping out of a calculation program when the condition is met, and entering genetic calculation when the condition is not met; and (4) adjusting chromosomes by genetic calculation operators of evolution, intersection and variation to form a brand new population, and substituting the brand new population into a pollutant diffusion mathematical model of the sewage outlet to perform calculation of the next generation.
2) Establishing a data-driven BP algorithm model of the artificial neural network, wherein a hyperbolic Sigmoid function is adopted as a nonlinear conversion function in the BP algorithm model of the artificial neural network, and the formula (9) is as follows:
f(x)＝1/1+e-x+θ (9)
in the formula, theta represents a threshold value;
in the artificial neural network BP algorithm model, Root Mean Square Error (RMSE) is adopted to evaluate the prediction capability of the network, and the formula (10) shows:
in the formula, a is the number of data; y isiIs an actual measurement value; y isi' is a predicted value.
The normalization processing of all data in the artificial neural network BP algorithm model adopts an equation (11):
wherein:
represents a normalized value; y isiRepresents the value before normalization; y ismaxRepresents the maximum of all data; y isminRepresents the minimum of all data.
3) Initializing network weight and combining with engineering sea area tidal current change interval (u)min,u1,…,ui,…,un,umax) Middle n +2 tidal flow velocities and optimal sewage discharge (Q) corresponding to the samemin,Q1,…,Qi,…,Qn,Qmax) Setting a network architecture and setting basic parameters of the BP algorithm model of the artificial neural network. The determination of the basic parameters can be found in the literature: research on sea area combined unit water quality model multi-parameter step-by-step coupling optimization inversion method based on data drive and genetic calculation [ J]Practice and understanding of mathematics 2015,45(12): 167-.
4) And taking n +2 tidal current flow rate data as the input of the BP algorithm model of the artificial neural network, correspondingly taking n +2 optimal sewage discharge amount as the output of the BP algorithm model of the artificial neural network, substituting the optimal sewage discharge amount into the artificial neural network model for training and learning, and calculating the root mean square error.
5) When the error requirement of Root Mean Square Error (RMSE) is met, a jump-out program establishes a response relation; if the Root Mean Square Error (RMSE) error requirement is not met, then the subsequent calculation is entered.
6) And (4) based on the root mean square error, after the network weight is reversely adjusted, repeating the calculation according to the steps 3) -5) until a response relation is established.
The response relation establishing method has the advantages that the data-driven model-artificial neural network BP algorithm is adopted, and the response relation between the system variables can be established only by taking the system state variables and the control variables as model input and output and analyzing the characteristics of the system data on the basis of little knowledge of the system physical knowledge.
While the present invention has been described in connection with the preferred embodiments thereof, it is not intended to limit the present invention to the particular embodiments described above, which are intended to be illustrative rather than limiting, and it will be apparent to those of ordinary skill in the art in light of the teachings of the present invention that many more modifications can be made without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (1)
1. A sewage deep sea optimized discharge response relation determination method based on tidal current change is characterized by comprising the following steps:
1) calculating the engineering sea area load flow change interval (u) by adopting a simulation inverse algorithmmin,u1,…,ui,…,un,umax) Middle n +2 tidal flow velocities and optimal sewage discharge (Q) corresponding to the samemin,Q1,…,Qi,…,Qn,Qmax) The method comprises the following specific steps:
1.1) establishing a simulation back calculation method model
1.1.1) adopting a sewage outlet pollutant diffusion mathematical model;
1.1.2) establishing a Multi-objective function
MAX.DD＝C0/C1 (1)
MIN.EC＝0.0414×Q+13.828 (2)
MAX.OE＝OE (3)
In the formula (1), DD is the dilution of pollutants; c0The concentration value of the pollutants in the jet flow water body is obtained; c1The concentration value of the pollutant at a certain point in the environmental water body is obtained; EC is energy consumption value; q is the sewage discharge amount of the pump, m is m multiplied by v multiplied by S, m is the number of the nozzles, v is the discharge rate, and S is the sectional area of the nozzles; OE is the sewage discharge efficiency;
1.1.3) Using a genetic Algorithm model
1.1.3.1) using evolutionary computation operators
1.1.3.2) using cross-computing operators
1.1.3.3) calculation operator by mutation
1.2) initial setting
1.2.1) taking the sewage discharge as a control variable of an optimization process, and adopting a real number coding form;
1.2.2) setting a value range of the control variable, and taking the value range as a constraint condition of the genetic algorithm population value;
1.3) for the engineering sea area tidal current change interval (u)min,u1,…,ui,…,un,umax) The simulation reverse calculation of the optimal sewage discharge amount is carried out according to the following steps for each tidal current flow velocity, and then n +2 optimal sewage discharge amounts (Q) aiming at n +2 tidal current flow velocities can be obtainedmin,Q1,…,Qi,…,Qn,Qmax)
1.3.1) randomly selecting r numerical values in a control variable value range according to each tidal current flow rate to obtain r chromosomes, and forming a population (r) of possible solutions of the optimal sewage discharge amount under the tidal current flow rate condition;
1.3.2) taking each chromosome as a numerical example, adopting a pollutant diffusion mathematical model of a sewage outlet to calculate r times, and simultaneously outputting and storing r pollutant concentration results of observation points in a water area;
1.3.3) carrying out chromosome evaluation by adopting the multi-target function established in the step 1.1.2) with the target of optimal sewage dilution effect, lowest energy consumption and optimal sewage discharge efficiency;
1.3.4) when the maximum generation number is met, jumping out of the program and outputting the best chromosome, namely the optimal sewage discharge amount; if the maximum generation number target is not met, adopting the genetic algorithm model in the step 1.1.3) to perform genetic calculation;
1.3.5) updating the chromosomes selected in the step 1.3.1) through genetic calculation, obtaining a brand new population, and repeating the calculation according to the steps (1.3.2) to (1.3.4) until the best chromosomes, namely the best sewage discharge amount, is output;
2) establishing a data-driven BP algorithm model of the artificial neural network, wherein a hyperbolic Sigmoid function is adopted as a nonlinear conversion function in the BP algorithm model of the artificial neural network, and the formula (9) is as follows:
f(x)＝1/1+e-x+θ (9)
in the formula, theta represents a threshold value;
in the artificial neural network BP algorithm model, Root Mean Square Error (RMSE) is adopted to evaluate the prediction capability of the network, and the formula (10) shows:
in the formula, a is the number of data; y isiIs an actual measurement value; y'iIs a predicted value;
the normalization processing of all data in the artificial neural network BP algorithm model adopts an equation (11):
wherein:
represents a normalized value; y isiRepresents the value before normalization; y ismaxRepresents the maximum of all data; y isminRepresents the minimum of all data;
3) initializing network weight and combining with engineering sea area tidal current change interval (u)min,u1,…,ui,…,un,umax) Middle n +2 tidal flow velocities and optimal sewage discharge (Q) corresponding to the samemin,Q1,…,Qi,…,Qn,Qmax) Setting a network architecture and setting basic parameters of an artificial neural network BP algorithm model;
4) taking n +2 tidal current flow rate data as the input of an artificial neural network BP algorithm model, correspondingly taking n +2 optimal sewage discharge amount as the output of the artificial neural network BP algorithm model, substituting the optimal sewage discharge amount into the artificial neural network model for training and learning, and calculating the root mean square error;
5) when the error requirement of Root Mean Square Error (RMSE) is met, a jump-out program establishes a response relation; if the Root Mean Square Error (RMSE) error requirement is not met, performing subsequent calculation;
6) and (4) based on the root mean square error, after the network weight is reversely adjusted, repeating the calculation according to the steps 3) -5) until a response relation is established.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810101668.3A CN108280791B (en) | 2018-02-01 | 2018-02-01 | Sewage deep sea optimized discharge response relation determination method based on tidal current change |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810101668.3A CN108280791B (en) | 2018-02-01 | 2018-02-01 | Sewage deep sea optimized discharge response relation determination method based on tidal current change |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108280791A CN108280791A (en) | 2018-07-13 |
| CN108280791B true CN108280791B (en) | 2021-07-27 |
Family
ID=62807359
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810101668.3A Expired - Fee Related CN108280791B (en) | 2018-02-01 | 2018-02-01 | Sewage deep sea optimized discharge response relation determination method based on tidal current change |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108280791B (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103809557A (en) * | 2013-12-30 | 2014-05-21 | 北京工业大学 | Neural network based sewage disposal process optimal control method |
| CN105224772A (en) * | 2015-11-04 | 2016-01-06 | 松辽流域水资源保护局松辽流域水环境监测中心 | A kind of Nenjiang representative region Ecology methods of risk assessment based on system dynamics decision model |
| CN105867124A (en) * | 2016-04-06 | 2016-08-17 | 中国水利水电科学研究院 | Wastewater treatment system based on discharge capacity and pollutant holding capability equalization regulation and control network |
| CN106951983A (en) * | 2017-02-27 | 2017-07-14 | 浙江工业大学 | Injector performance Forecasting Methodology based on the artificial neural network using many parent genetic algorithms |
-
2018
- 2018-02-01 CN CN201810101668.3A patent/CN108280791B/en not_active Expired - Fee Related
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103809557A (en) * | 2013-12-30 | 2014-05-21 | 北京工业大学 | Neural network based sewage disposal process optimal control method |
| CN105224772A (en) * | 2015-11-04 | 2016-01-06 | 松辽流域水资源保护局松辽流域水环境监测中心 | A kind of Nenjiang representative region Ecology methods of risk assessment based on system dynamics decision model |
| CN105867124A (en) * | 2016-04-06 | 2016-08-17 | 中国水利水电科学研究院 | Wastewater treatment system based on discharge capacity and pollutant holding capability equalization regulation and control network |
| CN106951983A (en) * | 2017-02-27 | 2017-07-14 | 浙江工业大学 | Injector performance Forecasting Methodology based on the artificial neural network using many parent genetic algorithms |
Non-Patent Citations (3)
| Title |
|---|
| 动水射流多参数多目标非线性遗传优化耦合反演方法研究;李明昌;《水力发电学报》;20140831;第33卷(第4期);正文全文 * |
| 动水环境中射流喷角对射流特性影响数值模拟;周丰;《大连理工大学学报》;20070731;第47卷(第4期);正文全文 * |
| 基于数据驱动与遗传计算的海域组合单元水质模型多参数分步耦合优化反演方法研究;李明昌;《数学的实践与认识》;20150630;第45卷(第12期);正文全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108280791A (en) | 2018-07-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110160526B (en) | Air route planning method based on genetic algorithm | |
| CN104616060B (en) | Insulator dirty degree Forecasting Methodology based on BP neural network and fuzzy logic | |
| CN103809557B (en) | A kind of sewage disposal process optimal control method based on neutral net | |
| CN101441728B (en) | Neural network method of crane optimum design | |
| CN107694337A (en) | Coal unit SCR denitrating flue gas control methods based on network response surface | |
| CN107728477A (en) | A kind of industrialized aquiculture water quality dissolved oxygen prediction control method and system | |
| Hu et al. | Multi-objective ecological reservoir operation based on water quality response models and improved genetic algorithm: A case study in Three Gorges Reservoir, China | |
| CN107967803A (en) | Traffic congestion Forecasting Methodology based on multi-source data and variable-weight combined forecasting model | |
| CN106873379A (en) | A kind of sewage disposal method for optimally controlling based on iteration ADP algorithms | |
| Nourani et al. | Estimation of suspended sediment load using artificial intelligence-based ensemble model | |
| KR102236678B1 (en) | Method and device for forecasting flood based on data analyzing | |
| CN107255920A (en) | PID control method and apparatus and system based on network optimization algorithm | |
| CN113850692A (en) | Urban water supply system gate pump group optimal scheduling method based on deep learning | |
| CN114037163A (en) | Sewage treatment effluent quality early warning method based on dynamic weight PSO (particle swarm optimization) optimization BP (Back propagation) neural network | |
| Chidthong et al. | Developing a hybrid multi‐model for peak flood forecasting | |
| CN117035201B (en) | Multi-target scheduling rule making method and system for plain river network water engineering cluster | |
| Long et al. | Meta-heuristic algorithms applied to the optimization of type-1 and type 2 TSK fuzzy logic systems for sea water level prediction | |
| CN108280791B (en) | Sewage deep sea optimized discharge response relation determination method based on tidal current change | |
| CN108304654B (en) | Sewage deep sea optimized discharge method based on tidal current change | |
| CN108287972B (en) | Method for calculating deep sea optimized discharge amount of sewage based on tidal current change | |
| CN114119277A (en) | Artificial intelligent neural network-based flocculation dosing decision analysis method | |
| Mao et al. | Coating thickness modeling and prediction for hot-dip galvanized steel strip based on GA-BP neural network | |
| CN105512383A (en) | Dredging process regulation and control parameter screening method based on BP neural network | |
| CN105302976A (en) | Aluminum electrolysis production optimization method based on SPEA2 algorithm | |
| CN105334824A (en) | Aluminum electrolysis production optimization method based on NSGA-II algorithm |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20220317 Address after: No.37, Xingang No.2 Road, Binhai New Area, Tianjin, 300456 Patentee after: Tianjin Research Institute for Water Transport Engineering, M.O.T Patentee after: TIWTE ENVIRONMENTAL TECHNOLOGY DEVELOPMENT (TIANJIN) CO.,LTD. Address before: No.2618, No.2 Road, Tanggu, Binhai New Area, Tianjin Patentee before: Tianjin Research Institute for Water Transport Engineering, M.O.T |
|
| TR01 | Transfer of patent right | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20210727 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |