CN105334824A - Aluminum electrolysis production optimization method based on NSGA-II algorithm - Google Patents
Aluminum electrolysis production optimization method based on NSGA-II algorithm Download PDFInfo
- Publication number
- CN105334824A CN105334824A CN201510750359.5A CN201510750359A CN105334824A CN 105334824 A CN105334824 A CN 105334824A CN 201510750359 A CN201510750359 A CN 201510750359A CN 105334824 A CN105334824 A CN 105334824A
- Authority
- CN
- China
- Prior art keywords
- nsga
- algorithm
- aluminium
- population
- current efficiency
- 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.)
- Pending
Links
- 229910052782 aluminium Inorganic materials 0.000 title claims abstract description 66
- XAGFODPZIPBFFR-UHFFFAOYSA-N aluminium Chemical compound [Al] XAGFODPZIPBFFR-UHFFFAOYSA-N 0.000 title claims abstract description 65
- 238000000034 method Methods 0.000 title claims abstract description 47
- 238000005868 electrolysis reaction Methods 0.000 title claims abstract description 36
- 238000004422 calculation algorithm Methods 0.000 title claims abstract description 26
- 238000005457 optimization Methods 0.000 title claims abstract description 15
- 238000004519 manufacturing process Methods 0.000 title claims abstract description 12
- 238000013528 artificial neural network Methods 0.000 claims abstract description 13
- 239000004411 aluminium Substances 0.000 claims description 58
- 230000008569 process Effects 0.000 claims description 21
- 238000005265 energy consumption Methods 0.000 claims description 19
- 238000012549 training Methods 0.000 claims description 10
- 238000004364 calculation method Methods 0.000 claims description 9
- 230000006872 improvement Effects 0.000 claims description 9
- 230000035772 mutation Effects 0.000 claims description 6
- 239000003792 electrolyte Substances 0.000 claims description 3
- 238000010353 genetic engineering Methods 0.000 claims description 3
- 239000011159 matrix material Substances 0.000 claims description 3
- 239000005431 greenhouse gas Substances 0.000 abstract description 4
- 238000013507 mapping Methods 0.000 abstract description 3
- KRHYYFGTRYWZRS-UHFFFAOYSA-M Fluoride anion Chemical compound [F-] KRHYYFGTRYWZRS-UHFFFAOYSA-M 0.000 abstract 1
- 230000007423 decrease Effects 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000001537 neural effect Effects 0.000 description 2
- 210000002569 neuron Anatomy 0.000 description 2
- AZDRQVAHHNSJOQ-UHFFFAOYSA-N alumane Chemical compound [AlH3] AZDRQVAHHNSJOQ-UHFFFAOYSA-N 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 210000004027 cell Anatomy 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000005094 computer simulation Methods 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000009776 industrial production Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 238000003062 neural network model Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- TXEYQDLBPFQVAA-UHFFFAOYSA-N tetrafluoromethane Chemical compound FC(F)(F)F TXEYQDLBPFQVAA-UHFFFAOYSA-N 0.000 description 1
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
- G05B19/41865—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by job scheduling, process planning, material flow
-
- 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
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Landscapes
- Engineering & Computer Science (AREA)
- General Engineering & Computer Science (AREA)
- Manufacturing & Machinery (AREA)
- Quality & Reliability (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Electrolytic Production Of Non-Metals, Compounds, Apparatuses Therefor (AREA)
- Electrolytic Production Of Metals (AREA)
Abstract
The invention provides an aluminum electrolysis production optimization method based on NSGA-II algorithm. Firstly, a BP neural network is used for modeling an aluminum electrolysis production process; then, the NSGA-II algorithm is used for searching a mapping model, and a group of optimal solutions for each decision variable, and current efficiency, power consumption per ton aluminum and total fluoride emissions corresponding to the optimal solutions are obtained. The method of the invention determines the optimal value of technological parameters during the aluminum electrolysis production process, the current efficiency is effectively improved, greenhouse gas emissions are reduced, and the purposes of truly saving energy and reducing emissions can be realized.
Description
Technical field
The present invention relates to optimum control field, be specifically related to a kind of Aluminium Electrolysis optimization method based on NSGA-II algorithm.
Background technology
Environmentally friendly Aluminium Electrolysis process is all a challenging problem for a long time.In Aluminium Industry, final goal is on the basis of electrolytic tank even running, improve current efficiency, reduce ton aluminium energy consumption and reduce the discharge capacity of perfluoro-compound, but this target is very difficult to realize, reason is that aluminium cell parameter is more, present non-linear, strong coupling between parameter, bring larger difficulty to Aluminium Electrolysis process model building.BP neural network has very strong non-linear mapping capability, is applicable to solve nonlinear system modeling problem, for Aluminium Electrolysis process model building provides new thinking.NSGA-II algorithm is a kind of multi-objective Evolutionary Algorithm of classics, its fast operation, adaptable, there is higher performance when solving the multi-objective problem with complicated PS and be thus widely used in multiple field.
Summary of the invention
The application is by providing a kind of Aluminium Electrolysis optimization method based on NSGA-II algorithm, and to solve, the power consumption caused because obtaining optimal procedure parameters in Aluminium Electrolysis process in prior art is huge, efficiency is low and the technical matters of serious environment pollution.
For solving the problems of the technologies described above, the application is achieved by the following technical solutions:
Based on an Aluminium Electrolysis optimization method for NSGA-II algorithm, its key is, comprises the steps:
S1: to current efficiency, ton aluminium energy consumption and the influential original variable of perfluoro-compound discharge capacity in statistics Aluminium Electrolysis process, and therefrom determine to affect large parameter as decision variable X to current efficiency, ton aluminium energy consumption and perfluoro-compound discharge capacity;
S2: the sample of the decision variable X in acquisition time T and the current efficiency of correspondence, ton aluminium energy consumption and perfluoro-compound discharge capacity Y, obtains sample matrix, utilizes BP neural network to carry out training, checking, sets up Aluminium Electrolysis process model;
S3: utilize and be optimized production run model based on NSGA-II algorithm, obtains one group of optimum solution of each decision variable and corresponding current efficiency, ton aluminium energy consumption and the perfluoro-compound discharge capacity of this optimum solution; NSGA-II algorithm comprises the concrete steps that production run model is optimized:
S3: utilize and be optimized production run model based on NSGA-II algorithm, obtains one group of optimum solution of each decision variable and corresponding current efficiency, ton aluminium energy consumption and the perfluoro-compound discharge capacity of this optimum solution;
NSGA-II algorithm comprises the concrete steps that production run model is optimized:
S31: initialization population, Population Size is N;
S32: calculate the non-bad class value of each individuality, crowding distance and improvement sequence fitness value;
S33: counter r=1 is set, enters loop iteration;
S34: to every sub-population according to the non-bad class value of each individuality, crowding distance and improvement sequence fitness value, use roulette method to carry out setting Threshold selection and operate;
S35: use arithmetic crossover operator to carry out mutation operation, obtain N number of offspring;
S36: fitness value is calculated to each individuality after mutation operation;
S37: collect r generation and r+1 for all individualities, the scale of obtaining is the interim population of 2N;
S38: calculate the non-bad class value of each individuality in interim population, crowding distance and improvement sequence fitness value, use Stratified Strategy as required, select top n individual as optimum population from interim population, as the former generation of genetic manipulation of future generation;
S39: judge whether the new population generated meets termination condition, if so, then Output rusults, otherwise, by counter r=r+1, jump to step S34.
Further, X in step S2
k=[x
k1, x
k2..., x
kM] (k=1,2 ..., S) and be input vector, S is training sample number,
weighted vector when being the g time iteration between input layer M and hidden layer I, W
jPweighted vector when () is the g time iteration g between hidden layer J and output layer P, Y
k(g)=[y
k1(g), y
k2(g) ..., y
kP(g)] (k=1,2 ..., S) and the actual output of network when being the g time iteration, d
k=[d
k1, d
k2..., d
kP] (k=1,2 ..., S) and be desired output;
Set up Aluminium Electrolysis process model in step S2 specifically to comprise the steps:
S21: initialization, if iterations g initial value is 0, is assigned to W respectively
mI(0), W
jPthe random value that (0) one (0,1) is interval;
S22: stochastic inputs sample X
k;
S23: to input amendment X
k, the neuronic input signal of forward calculation BP neural network every layer and output signal;
S24: according to desired output d
ky is exported with reality
k(g), error of calculation E (g);
Whether S25: error in judgement E (g) meet the demands, and if do not met, then enters step S26, as met, then enters step S29;
S26: judging whether iterations g+1 is greater than maximum iteration time, as being greater than, then entering step S29, otherwise, enter step S27;
S27: to input amendment X
kthe neuronic partial gradient δ of backwards calculation every layer;
S28: calculate modified weight amount Δ W, and revise weights; Make g=g+1, jump to step S23;
S29: judged whether all training samples, if so, then completes modeling, otherwise, continue to jump to step S22.
As the preferred technical scheme of one, the decision variable in step S1 comprises: the gentle tank voltage of potline current, blanking number of times, molecular proportion, aluminum yield, aluminium level, electrolyte level, groove.
As the preferred technical scheme of one, maximum iteration time g is 800 times.
Compared with prior art, the technical scheme that the application provides, the technique effect had or advantage are: the method determines the optimal value of technological parameter in Aluminium Electrolysis process, effectively improve current efficiency, decrease greenhouse gas emissions, reduce a ton aluminium energy consumption, really reach the object of energy-saving and emission-reduction consumption reduction.
Accompanying drawing explanation
Fig. 1 is the Aluminium Electrolysis optimization method process flow diagram based on NSGA-II algorithm;
Fig. 2 is CF
4forecasting of discharged quantity result figure;
Fig. 3 is CF
4forecasting of discharged quantity Error Graph;
Fig. 4 is that current efficiency predicts the outcome figure;
Fig. 5 is current efficiency prediction-error image;
Fig. 6 is ton aluminium energy consumption prediction result figure;
Fig. 7 is ton aluminium energy consumption prediction-error image.
Embodiment
The embodiment of the present application is by providing a kind of Aluminium Electrolysis optimization method based on NSGA-II algorithm, and to solve, the power consumption caused because obtaining optimal procedure parameters in Aluminium Electrolysis process in prior art is huge, efficiency is low and the technical matters of serious environment pollution.
In order to better understand technique scheme, below in conjunction with Figure of description and concrete embodiment, technique scheme is described in detail.
Embodiment
Based on an Aluminium Electrolysis optimization method for NSGA-II algorithm, comprise the steps:
S1: to current efficiency, ton aluminium energy consumption and the influential original variable of perfluoro-compound discharge capacity in statistics Aluminium Electrolysis process, and therefrom determine to affect large parameter as decision variable X to current efficiency, ton aluminium energy consumption and perfluoro-compound discharge capacity;
Obtain current efficiency y by carrying out statistics to measurement parameter in actual industrial production process
1with greenhouse gas emissions y
2the variable had the greatest impact is: potline current x
1, blanking number of times x
2, molecular proportion x
3, aluminum yield x
4, the horizontal x of aluminium
5, electrolyte level x
6, groove temperature x
7, tank voltage x
8totally 8 variablees.
S2: the sample of the decision variable X in acquisition time T and the current efficiency of correspondence, ton aluminium energy consumption and perfluoro-compound discharge capacity Y, obtains sample matrix, utilizes BP neural network to carry out training, checking, sets up Aluminium Electrolysis process model;
In the present embodiment, the 223# groove whole year production data in 2013 in collection Chongqing Tiantai Aluminium Industry Co., Ltd. 170KA series electrolytic tank and 40 day data before 2014, amount to 405 groups of data, data sample is as shown in table 1 below.
Table 1 data sample
X is set
k=[x
k1, x
k2..., x
kM] (k=1,2 ..., S) and be input vector, N is training sample number,
weighted vector when being the g time iteration between input layer M and hidden layer I, W
jPweighted vector when () is the g time iteration g between hidden layer J and output layer P, Y
k(g)=[y
k1(g), y
k2(g) ..., y
kP(g)] (k=1,2 ..., S) and the actual output of network when being the g time iteration, d
k=[d
k1, d
k2..., d
kP] (k=1,2 ..., S) and be desired output, iterations g gets 800;
Set up Aluminium Electrolysis process model in step S2 specifically to comprise the steps:
S21: initialization, if iterations g initial value is 0, is assigned to W respectively
mI(0), W
jPthe random value that (0) one (0,1) is interval;
S22: stochastic inputs sample X
k;
S23: to input amendment X
k, the neuronic input signal of forward calculation BP neural network every layer and output signal;
S24: according to desired output d
ky is exported with reality
k(g), error of calculation E (g);
Whether S25: error in judgement E (g) meet the demands, and if do not met, then enters step S26, as met, then enters step S29;
S26: judging whether iterations g+1 is greater than maximum iteration time, as being greater than, then entering step S29, otherwise, enter step S27;
S27: to input amendment X
kthe neuronic partial gradient δ of backwards calculation every layer;
S28: calculate modified weight amount Δ W, and revise weights, computing formula is:
in formula, η is learning efficiency; Make g=g+1, jump to step S23;
S29: judged whether all training samples, if so, then completes modeling, otherwise, continue to jump to step S22.
In neural network design, the number of hidden nodes number be the key determining neural network model quality, be also the difficult point in neural network design, adopt method of trial and error to determine the nodes of hidden layer here.
In formula, p is hidden neuron nodes, and n is input layer number, and m is output layer neuron number, and k is the constant between 1-10.The parameters of BP neural network is as shown in table 2 below.
The neural parameters of table 2BP
By said process, BP neural network prediction effect can be obtained as shown in Fig. 1,2,3,4,5,6.The basis of Aluminium Electrolysis process optimization is the foundation of Optimized model, and model accuracy directly affects optimum results.By analyzing known to Fig. 1,2,3,4,5,6, through BP neural metwork training, carbon tetrafluoride CF
4forecasting of discharged quantity error is 2.3%, and the largest prediction error of current efficiency is-3%, and ton aluminium energy consumption predicated error is-4.9%, model prediction accuracy is high, meets modeling demand.
On the basis of Aluminium Electrolysis process model, NSGA-II algorithm is utilized to be optimized it within the scope of each decision variable, obtain one group of optimum solution of each decision variable and corresponding current efficiency, ton aluminium energy consumption and the perfluoro-compound discharge capacity of this optimum solution, the concrete variation range of each decision variable is as shown in table 3.
The each variable-value scope of table 3
NSGA-II algorithm comprises the concrete steps that production run model is optimized:
S31: initialization population, Population Size is N;
S32: calculate the non-bad class value of each individuality, crowding distance and improvement sequence fitness value;
S33: counter r=1 is set, enters loop iteration;
S34: to every sub-population according to the non-bad class value of each individuality, crowding distance and improvement sequence fitness value, use roulette method to carry out setting Threshold selection and operate;
S35: use arithmetic crossover operator to carry out mutation operation, obtain N number of offspring;
S36: fitness value is calculated to each individuality after mutation operation;
S37: collect r generation and r+1 for all individualities, the scale of obtaining is the interim population of 2N;
S38: calculate the non-bad class value of each individuality in interim population, crowding distance and improvement sequence fitness value, use Stratified Strategy as required, select top n individual as optimum population from interim population, as the former generation of genetic manipulation of future generation;
S39: judge whether the new population generated meets termination condition, if so, then Output rusults, otherwise, by counter r=r+1, jump to step S34.
By above-mentioned steps Aluminium Electrolysis process is optimized to the decision variable and corresponding output valve that can obtain 100 groups of optimums, choose wherein the most rational 3 groups be listed in the table below in 4.
Table 4 optimized producing parameter
Contrast wherein optimal operating parameter and the annual mean value recorded in 2013 is known, current efficiency improves 3.95%, ton aluminium energy consumption reduces 1207.23KWh/t-A1, CF
4discharge capacity reduces 0.28.
In above-described embodiment of the application, by providing a kind of Aluminium Electrolysis optimization method based on NSGA-II algorithm, first, BP neural network is utilized to carry out modeling to Aluminium Electrolysis process, then, utilize and based on NSGA-II algorithm, mapping model is searched for, obtain one group of optimum solution of each decision variable and current efficiency corresponding to this optimum solution, the power consumption of ton aluminium and perfluoro-compound discharge capacity.The method determines the optimal value of technological parameter in Aluminium Electrolysis process, effectively improves current efficiency, decreases greenhouse gas emissions, really reaches the object of energy-saving and emission-reduction.
It should be noted that; above-mentioned explanation is not limitation of the present invention; the present invention is also not limited in above-mentioned citing, the change that those skilled in the art make in essential scope of the present invention, modification, interpolation or replacement, also should belong to protection scope of the present invention.
Claims (4)
1., based on an Aluminium Electrolysis optimization method for NSGA-II algorithm, it is characterized in that, comprise the steps:
S1: to current efficiency, ton aluminium energy consumption and the influential original variable of perfluoro-compound discharge capacity in statistics Aluminium Electrolysis process, and therefrom determine to affect large parameter as decision variable X to current efficiency, ton aluminium energy consumption and perfluoro-compound discharge capacity;
S2: the sample of the decision variable X in acquisition time T and the current efficiency of correspondence, ton aluminium energy consumption and perfluoro-compound discharge capacity Y, obtains sample matrix, utilizes BP neural network to carry out training, checking, sets up Aluminium Electrolysis process model;
S3: utilize and be optimized production run model based on NSGA-II algorithm, obtains one group of optimum solution of each decision variable and corresponding current efficiency, ton aluminium energy consumption and the perfluoro-compound discharge capacity of this optimum solution;
NSGA-II algorithm comprises the concrete steps that production run model is optimized:
S31: initialization population, Population Size is N;
S32: calculate the non-bad class value of each individuality, crowding distance and improvement sequence fitness value;
S33: counter r=1 is set, enters loop iteration;
S34: to every sub-population according to the non-bad class value of each individuality, crowding distance and improvement sequence fitness value, use roulette method to carry out setting Threshold selection and operate;
S35: use arithmetic crossover operator to carry out mutation operation, obtain N number of offspring;
S36: fitness value is calculated to each individuality after mutation operation;
S37: collect r generation and r+1 for all individualities, the scale of obtaining is the interim population of 2N;
S38: calculate the non-bad class value of each individuality in interim population, crowding distance and improvement sequence fitness value, use Stratified Strategy as required, select top n individual as optimum population from interim population, as the former generation of genetic manipulation of future generation;
S39: judge whether the new population generated meets termination condition, if so, then Output rusults, otherwise, by counter r=r+1, jump to step S34.
2. the Aluminium Electrolysis optimization method based on NSGA-II algorithm according to claim 1, is characterized in that, X in step S2
k=[x
k1, x
k2..., x
kM] (k=1,2 ..., S) and be input vector, S is training sample number,
Set up Aluminium Electrolysis process model in step S2 specifically to comprise the steps:
S21: initialization, if iterations g initial value is 0, is assigned to W respectively
mI(0), W
jPthe random value that (0) one (0,1) is interval;
S22: stochastic inputs sample X
k;
S23: to input amendment X
k, the neuronic input signal of forward calculation BP neural network every layer and output signal;
S24: according to desired output d
ky is exported with reality
k(g), error of calculation E (g);
Whether S25: error in judgement E (g) meet the demands, and if do not met, then enters step S26, as met, then enters step S29;
S26: judging whether iterations g+1 is greater than maximum iteration time, as being greater than, then entering step S29, otherwise, enter step S27;
S27: to input amendment X
kthe neuronic partial gradient δ of backwards calculation every layer;
S28: calculate modified weight amount Δ W, and revise weights; Make g=g+1, jump to step S23;
S29: judged whether all training samples, if so, then completes modeling, otherwise, continue to jump to step S22.
3. the Aluminium Electrolysis optimization method based on NSGA-II algorithm according to claim 1, it is characterized in that, the decision variable in step S1 comprises: the gentle tank voltage of potline current, blanking number of times, molecular proportion, aluminum yield, aluminium level, electrolyte level, groove.
4. the Aluminium Electrolysis optimization method based on NSGA-II algorithm according to claim 2, is characterized in that, maximum iteration time g is 800 times.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510750359.5A CN105334824A (en) | 2015-11-06 | 2015-11-06 | Aluminum electrolysis production optimization method based on NSGA-II algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510750359.5A CN105334824A (en) | 2015-11-06 | 2015-11-06 | Aluminum electrolysis production optimization method based on NSGA-II algorithm |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN105334824A true CN105334824A (en) | 2016-02-17 |
Family
ID=55285433
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510750359.5A Pending CN105334824A (en) | 2015-11-06 | 2015-11-06 | Aluminum electrolysis production optimization method based on NSGA-II algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105334824A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106444378A (en) * | 2016-10-10 | 2017-02-22 | 重庆科技学院 | Plant culture method and system based on IoT (Internet of things) big data analysis |
| CN108038330A (en) * | 2017-12-26 | 2018-05-15 | 重庆科技学院 | Aluminium electroloysis work consumption model building method based on SUKFNN algorithms |
| CN109085752A (en) * | 2018-03-09 | 2018-12-25 | 重庆科技学院 | Aluminium electroloysis preference multi-objective optimization algorithm based on angle dominance relation |
| CN115323440A (en) * | 2022-09-30 | 2022-11-11 | 湖南力得尔智能科技股份有限公司 | Aluminum electrolysis holographic closed-loop control system based on AI neural network deep self-learning |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR100899023B1 (en) * | 2006-12-07 | 2009-05-21 | 한국전자통신연구원 | Optimizing method of radio resources using goal programming-applied genetic algorithm, and recorded media recording computer program readable on computer using thereof |
| CN103198354A (en) * | 2013-02-28 | 2013-07-10 | 重庆科技学院 | Optimization method of achieving oil field pumping unit oil-extraction energy conservation and production increasing with back propagation (BP) neural network and non-dominated sorting genetic algorithm (NSGA) 2 |
| CN103345559A (en) * | 2013-07-10 | 2013-10-09 | 重庆科技学院 | Dynamic evolution modeling method for aluminum electrolysis process electrolytic bath technology energy consumption |
| CN103628094A (en) * | 2013-11-07 | 2014-03-12 | 中国铝业股份有限公司 | Method for online measuring current efficiency of electrolytic cell on line and realization device of method |
| CN104573869A (en) * | 2015-01-16 | 2015-04-29 | 河海大学常州校区 | Optimization method and system for achieving dredging operations based on BP neural network and NSGA-II |
-
2015
- 2015-11-06 CN CN201510750359.5A patent/CN105334824A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR100899023B1 (en) * | 2006-12-07 | 2009-05-21 | 한국전자통신연구원 | Optimizing method of radio resources using goal programming-applied genetic algorithm, and recorded media recording computer program readable on computer using thereof |
| CN103198354A (en) * | 2013-02-28 | 2013-07-10 | 重庆科技学院 | Optimization method of achieving oil field pumping unit oil-extraction energy conservation and production increasing with back propagation (BP) neural network and non-dominated sorting genetic algorithm (NSGA) 2 |
| CN103345559A (en) * | 2013-07-10 | 2013-10-09 | 重庆科技学院 | Dynamic evolution modeling method for aluminum electrolysis process electrolytic bath technology energy consumption |
| CN103628094A (en) * | 2013-11-07 | 2014-03-12 | 中国铝业股份有限公司 | Method for online measuring current efficiency of electrolytic cell on line and realization device of method |
| CN104573869A (en) * | 2015-01-16 | 2015-04-29 | 河海大学常州校区 | Optimization method and system for achieving dredging operations based on BP neural network and NSGA-II |
Non-Patent Citations (2)
| Title |
|---|
| 姚立忠: "复杂工业过程动态演化建模与决策参数稳健优化", 《中国优秀硕士学位论文全文数据库工程科技I辑》 * |
| 陈婕等: "NSGA-Ⅱ算法的改进策略研究", 《计算机工程与应用》 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106444378A (en) * | 2016-10-10 | 2017-02-22 | 重庆科技学院 | Plant culture method and system based on IoT (Internet of things) big data analysis |
| CN106444378B (en) * | 2016-10-10 | 2019-07-12 | 重庆科技学院 | Plant cultivating method and system based on Internet of Things big data analysis |
| CN108038330A (en) * | 2017-12-26 | 2018-05-15 | 重庆科技学院 | Aluminium electroloysis work consumption model building method based on SUKFNN algorithms |
| CN108038330B (en) * | 2017-12-26 | 2022-02-08 | 重庆科技学院 | Aluminum electrolysis power consumption model construction method based on SUKFNN algorithm |
| CN109085752A (en) * | 2018-03-09 | 2018-12-25 | 重庆科技学院 | Aluminium electroloysis preference multi-objective optimization algorithm based on angle dominance relation |
| CN109085752B (en) * | 2018-03-09 | 2020-09-29 | 重庆科技学院 | Aluminum electrolysis preference multi-objective optimization algorithm based on angle domination relationship |
| CN115323440A (en) * | 2022-09-30 | 2022-11-11 | 湖南力得尔智能科技股份有限公司 | Aluminum electrolysis holographic closed-loop control system based on AI neural network deep self-learning |
| CN115323440B (en) * | 2022-09-30 | 2023-04-07 | 湖南力得尔智能科技股份有限公司 | Aluminum electrolysis holographic closed-loop control system based on AI neural network deep self-learning |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105302973A (en) | MOEA/D algorithm based aluminum electrolysis production optimization method | |
| CN105447567A (en) | BP neural network and MPSO algorithm-based aluminium electrolysis energy-saving and emission-reduction control method | |
| CN105302976A (en) | Aluminum electrolysis production optimization method based on SPEA2 algorithm | |
| CN109146121A (en) | The power predicating method stopped in the case of limited production based on PSO-BP model | |
| CN105404926B (en) | Aluminum electrolysis production technique optimization method based on BP neural network Yu MBFO algorithms | |
| CN105334824A (en) | Aluminum electrolysis production optimization method based on NSGA-II algorithm | |
| CN108846526A (en) | A kind of CO2 emissions prediction technique | |
| CN109085752B (en) | Aluminum electrolysis preference multi-objective optimization algorithm based on angle domination relationship | |
| CN104865827B (en) | Oil pumping unit oil extraction optimization method based on multi-working-condition model | |
| CN107506865A (en) | A kind of load forecasting method and system based on LSSVM optimizations | |
| CN105574586B (en) | General-purpose aircraft boat material needing forecasting method based on MPSO-BP networks | |
| CN108445756B (en) | Aluminum electrolysis energy-saving emission-reduction intelligent control method based on AR domination relationship | |
| CN115809810A (en) | Equipment transformation model selection method based on low-carbon economy and terminal | |
| CN105426959B (en) | Aluminium electroloysis energy-saving and emission-reduction method based on BP neural network Yu adaptive M BFO algorithms | |
| CN114429248A (en) | Transformer apparent power prediction method | |
| Zhao et al. | Analysis of energy consumption prediction model based on genetic algorithm and wavelet neural network | |
| CN114740713B (en) | Multi-objective optimization control method for wet flue gas desulfurization process | |
| CN109086469B (en) | Aluminum electrolysis modeling and optimizing method based on recurrent neural network and preference information | |
| CN108984813B (en) | Aluminum electrolysis modeling and optimizing method based on recurrent neural network and angle preference | |
| Wei et al. | Scale-Based product family optimization design based on the improved AMPSO in selecting optimum strategy | |
| CN108363303B (en) | Differential evolution aluminum electrolysis multi-objective optimization method based on AR preference information | |
| CN115081319B (en) | Intelligent decision-making and multi-objective optimization method for aluminum electrolysis production process | |
| CN105426960A (en) | BP neural network and MBFO algorithm-based aluminum electrolysis energy conservation and emission reduction control method | |
| CN103135444A (en) | Steel production energy consumption immune prediction control model | |
| Bai et al. | Predictive model of energy cost in steelmaking process based on BP neural network |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20160217 |