CN109085752A - Aluminium electroloysis preference multi-objective optimization algorithm based on angle dominance relation - Google Patents
Aluminium electroloysis preference multi-objective optimization algorithm based on angle dominance relation Download PDFInfo
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- TXEYQDLBPFQVAA-UHFFFAOYSA-N tetrafluoromethane Chemical compound FC(F)(F)F TXEYQDLBPFQVAA-UHFFFAOYSA-N 0.000 description 4
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Abstract
The invention discloses a kind of aluminium electroloysis preference multi-objective optimization algorithm based on angle dominance relation, aluminum electrolysis process is modeled first with recurrent neural network, then policymaker sets expectation target value, preference multi-target quantum groups of individuals algorithm is recycled to optimize production process model, one group for obtaining each decision variable most meets the desired optimal solution of policymaker and the corresponding current efficiency of the optimal solution, tank voltage, perfluoro-compound discharge amount and ton aluminium energy consumption.Utilize variation, intersection and selection operation in differential evolution algorithm, preference optimizing is carried out to decision variable, the optimal value of technological parameter during aluminum electrolysis is determined with this, current efficiency can be effectively improved, reduce tank voltage, greenhouse gas emissions and ton aluminium energy consumption are reduced, while meeting decisionmaker's preference, is achieved energy-saving and emission reduction purposes.
Description
Technical field
The invention belongs to optimum control fields, and in particular to a kind of aluminium electroloysis preference multiple target based on angle dominance relation
Optimization algorithm.
Background technique
Environment-friendly type aluminum electrolysis process is but very challenging for a long time all by attention.In Aluminium Industry
In, final goal is on the basis of electrolytic cell even running, improves current efficiency, reduces tank voltage and reduce perfluorinated
Object, the discharge amount for reducing ton aluminium energy consumption.However, aluminium cell parameter is more, and non-linear, close coupling is showed between parameter
Property, larger difficulty is brought to aluminum electrolysis process model building, and recurrent neural network has very strong non-linear mapping capability,
Suitable for solving the problems, such as nonlinear system modeling, new thinking is provided for aluminum electrolysis process model building.For four targets,
Realization is then extremely difficult simultaneously, because target has the phenomenon that conflict between each other, can introduce the preference information of policymaker,
Expectation target is set, the weight between different target is adjusted flexibly, carries out variable optimization using preference A-PMDE optimization algorithm.A-
PMDE is to introduce preference A administration method on the basis of DE algorithm.DE is a kind of evolution algorithm of classics, and the algorithm is simple, transports
Calculation speed is fast, evolutionary process can be described directly with equation, thus is widely used in multiple fields.
Summary of the invention
The present invention is existing to solve by proposing a kind of aluminium electroloysis preference multi-objective optimization algorithm based on angle dominance relation
Have in technology during aluminum electrolysis because of huge energy consumption, low efficiency and serious dirt caused by can not obtaining optimal procedure parameters
The technical issues of contaminating environment.
The object of the present invention is achieved like this:
A kind of aluminium electroloysis preference multi-objective optimization algorithm based on angle dominance relation, includes the following steps:
S1: selection constitutes decision to current efficiency, tank voltage and the influential control parameter of perfluoro-compound discharge amount and becomes
Measure X=[x1,x2,···,xM], M is the number of selected control parameter；
S2: selected aluminium electrolytic industry scene acquires N group decision variable X1,X2,···,XNAnd its corresponding current efficiency
y1,y2,···,yN, tank voltage z1,z2,···,zNAnd perfluoro-compound discharge amount s1,s2,···,sNWith ton aluminium energy
Consume c1,c2,···,cNFor data sample, with each group of decision variable XiAs input, respectively with corresponding current efficiency yi、
Tank voltage ziAnd perfluoro-compound discharge amount siWith ton aluminium energy consumption ciAs output, sample is instructed using recurrent neural network
Practice, examine, establishes four aluminium cell production process models；
S3: A-PMDE algorithm, root are formed in conjunction with DE algorithm using the preference multiple target differential evolution algorithm dominated based on A
It is as a reference point according to the preset desired value of policymaker, the stringent partial ordering relation dominated based on A is established, it is resulting to step S2
Four production process models optimize, and obtain one group and most meet the desired decision variable X of policymakerbestAnd its corresponding electric current
Efficiency ybest, tank voltage zbestAnd perfluoro-compound discharge amount sbestWith ton aluminium energy consumption cbest；
S4: according to the resulting optimizing decision variable X of step S3bestIn control parameter come it is selected in rate-determining steps S2
Aluminium electrolytic industry scene, reaches the purpose of energy-saving and emission-reduction consumption reduction.
Preferably, in step S1, the control parameter includes potline current, blanking number, molecular proportion, aluminum yield, aluminum water
Flat, electrolyte level, bath temperature.
Preferably, in step S2, using current efficiency as output, aluminium cell production process model, input layer are established
Using 10 neuron nodes, hidden layer uses 15 neuron nodes, and output layer uses 1 neuron node, and input layer arrives
Transmission function is Tansig function between hidden layer, and hidden layer to the function between output layer is Purelin function, sample training
When the number of iterations be 1000.
Preferably, in step S2, using tank voltage as output, aluminium cell production process model is established, input layer is adopted
With 10 neuron nodes, hidden layer uses 15 neuron nodes, and output layer uses 1 neuron node, and input layer is to hidden
Hiding transmission function between layer is Logsig function, and hidden layer to the function between output layer is Purelin function, when sample training
The number of iterations be 1000.
Preferably, in step S2, using perfluoro-compound discharge amount as output, aluminium cell production process model is established,
Input layer uses 10 neuron nodes, and hidden layer uses 15 neuron nodes, and output layer uses 1 neuron node, defeated
Entering layer to transmission function between hidden layer is Logsig function, and hidden layer to the function between output layer is Purelin function, sample
The number of iterations when this training is 1000.
Preferably, in step S2, using ton aluminium energy consumption as output, aluminium cell production process model, input layer are established
Using 10 neuron nodes, hidden layer uses 15 neuron nodes, and output layer uses 1 neuron node, and input layer arrives
Transmission function is Tansig function between hidden layer, and hidden layer to the function between output layer is Purelin function, sample training
When the number of iterations be 1000.
Preferably, the A-PMDE algorithm in step S3 the following steps are included:
S31: the preference relation dominated according to A evaluates the fitness of each individual, and according to superiority and inferiority to individual optimal value with
Global optimum is replaced；
S32: individual gene information in Population Regeneration, including mutation operation, crossover operation and selection operation.
Preferably, step S31 the following steps are included:
S311: initialization system parameter, including population scale R, maximum number of iterations T generate n individual x at random1,
x2,···,xn, enable external archival collection Q for sky；
S312: policymaker sets preference angle [alpha] and preference intended reference point r (yp,zp,sp,cp), the preference target ginseng
Examination point includes the desired value of current efficiency, tank voltage, perfluoro-compound discharge amount and ton aluminium four targets of energy consumption；
S313: for each individual examination point x, its fitness and its angle with reference point reference line are calculated:
Wherein, fjIt (x) is fitness value of the individual x in jth target,
S314: being based on angle information on object space, preference zone is divided, if θ (r, x) < α, the individual are in
Preference zone；Otherwise it is in non-preference zone；
S315: judge any two individual xiWith xkBetween superiority and inferiority relationship, including following situations:
Work as xiWith xkWhen being in preference zone or non-preference zone simultaneously, if xiPareto dominates xk, then it is assumed that xiIt is more excellent,
If mutually Pareto is not dominated, then it is assumed that the two is suitable；
Work as xiIn preference zone, xkWhen in non-preference zone, if xiPareto dominates xkOr xiWith xkMutually not
Pareto is dominated, then it is assumed that xiBetter than xk, i.e. xiA dominates xk；
S316: determine the optimal base of individual because of pbesti, in system initialization, individual optimal base is because being set as the individual
Initial gene xi；After next iteration, based on the A dominance relation that S315 is proposed, to the new gene x of individualiWith pbestiIt carries out
Superiority and inferiority compares, and outstanding person saves as pbesti；
S317: updating external archival collection Q, is added to the individual that non-A is dominated between each other in population and achieves collection Q, deletes quilt
The particle of domination；
S318: randomly choosed in external archival collection Q using press mechanism and Tabu search algorithm an individual as it is global most
Excellent gene.
Preferably, step S32 the following steps are included:
S321: mutation operation is carried out to population, for each individual xi, randomly choosed in population other three it is different
Individual xr1,xr2,xr3, third individual is added to after the differential vector that any two of them body is formed is scaled by scale factor F
On, variation individual is generated with this, formula is as follows:
vi=xr1+F·(xr2-xr3), i ≠ r1≠r2≠r3
Wherein, r1,r2,r3For mutually different integer randomly selected from set { 1,2 ..., n }, and every carry out one
Secondary variation, these integers can all randomly select again；
S322: target individual xiWith its variation individual viCrossover operation is carried out, test individual u is generatedi；
S323: test individual uiIt will be with target individual xiSelection operation is carried out, to determine that it is next-generation which individual enters；
S324: judging whether current globally optimal solution meets condition or whether the number of iterations reaches maximum number of iterations T,
If it is, exporting current globally optimal solution, otherwise, the S321 that gos to step is computed repeatedly, until current global optimum
Solution meets condition or the number of iterations reaches maximum number of iterations T.
Aluminum electrolysis process is carried out by adopting the above-described technical solution, the invention firstly uses recurrent neural networks
Modeling, then policymaker set expectation target value, recycle preference multi-target quantum groups of individuals algorithm to production process model into
Row optimization, one group for obtaining each decision variable most meet the desired optimal solution of policymaker and the corresponding electric current effect of the optimal solution
Rate, tank voltage, perfluoro-compound discharge amount and ton aluminium energy consumption.Using variation in differential evolution algorithm (DE), intersect and selection operation,
Preference optimizing is carried out to decision variable, the optimal value of technological parameter during aluminum electrolysis is determined with this, electricity can be effectively improved
Efficiency is flowed, tank voltage is reduced, reduces greenhouse gas emissions and ton aluminium energy consumption, while meeting decisionmaker's preference, reaches energy conservation
The purpose of emission reduction.
Detailed description of the invention
Fig. 1 is flow chart of the method for the present invention；
Fig. 2 is CF4 forecasting of discharged quantity result figure；
Fig. 3 is CF4 forecasting of discharged quantity Error Graph
Fig. 4 is current efficiency prediction result figure；
Fig. 5 is current efficiency prediction-error image；
Fig. 6 is tank voltage forecasting of discharged quantity result figure；
Fig. 7 is tank voltage forecasting of discharged quantity Error Graph；
Fig. 8 is ton aluminium energy consumption prediction result figure；
Fig. 9 is ton aluminium energy consumption prediction-error image.
Specific embodiment
As shown in Figure 1, a kind of aluminium electroloysis preference multi-objective optimization algorithm based on angle dominance relation, including walk as follows
It is rapid:
S1: selection constitutes decision to current efficiency, tank voltage and the influential control parameter of perfluoro-compound discharge amount and becomes
Measure X=[x1,x2,···,xM], M is the number of selected control parameter.
The present embodiment is by statistics aluminum electrolysis in the process to current efficiency, tank voltage and perfluoro-compound discharge amount
With the influential original variable of ton aluminium energy consumption, and therefrom determine to current efficiency, tank voltage and perfluoro-compound discharge amount and ton
The big parameter of aluminium energy consumption is as decision variable X.
The present embodiment is obtained by counting to measurement parameter during actual industrial production to current efficiency, slot electricity
Pressure and perfluoro-compound discharge amount and the maximum variable of ton aluminium energy consumption are as follows: potline current x1, blanking number x2, molecular proportion x3, go out aluminium
Measure x4, the flat x of aluminum water5, electrolyte level x6, bath temperature x7Totally 7 variables.
S2: selected aluminium electrolytic industry scene acquires N group decision variable X1,X2,···,XNAnd its corresponding current efficiency
y1,y2,···,yN, tank voltage z1,z2,···,zNAnd perfluoro-compound discharge amount s1,s2,···,sNWith ton aluminium energy
Consume c1,c2,···,cNFor data sample, with each group of decision variable XiAs input, respectively with corresponding current efficiency yi、
Tank voltage ziAnd perfluoro-compound discharge amount siWith ton aluminium energy consumption ciAs output, sample is instructed using recurrent neural network
Practice, examine, establishes four aluminium cell production process models；Recurrent neural network in order to meet modeling requirement, in step S2
Including input layer, hidden layer and output layer.
Four aluminium cells production process model includes:
For the production process model constructed by the current efficiency, input layer uses 10 neuron nodes, hides
Layer uses 15 neuron nodes, and output layer uses 1 neuron node, and input layer is to transmission function between hidden layer
Tansig function, hidden layer to the function between output layer are Purelin function, and the number of iterations when sample training is 1000；
For the production process model constructed by the tank voltage, input layer uses 10 neuron nodes, hidden layer
Using 15 neuron nodes, output layer uses 1 neuron node, and input layer to transmission function between hidden layer is Logsig
Function, hidden layer to the function between output layer are Purelin function, and the number of iterations when sample training is 1000；
For the production process model constructed by the perfluoro-compound discharge amount, input layer uses 10 neuron sections
Point, hidden layer use 15 neuron nodes, and output layer uses 1 neuron node, and input layer is to transmitting letter between hidden layer
Number is Logsig function, and hidden layer to the function between output layer is Purelin function, and the number of iterations when sample training is
1000。
For the production process model constructed by the ton aluminium energy consumption, input layer uses 10 neuron nodes, hides
Layer uses 15 neuron nodes, and output layer uses 1 neuron node, and input layer is to transmission function between hidden layer
Tansig function, hidden layer to the function between output layer are Purelin function, and the number of iterations when sample training is 1000.
In the present embodiment, the 223# slot electrolytic cell in Chongqing Tiantai Aluminium Industry Co., Ltd. 170KA series electrolytic cell is acquired
Whole year production data in 2013 and 40 day datas before 2014 amount to 405 groups of data, wherein whole year production data in 2013
As modeling training sample, 40 groups of data in 2014 are as test sample.Data sample is as shown in table 1 below.
1 data sample of table
| Sample number | 1 | 2 | 3 | 4 | …… |
| x1 | 1683 | 1682 | 1686 | 1746 | …… |
| x2 | 624 | 716 | 625 | 743 | …… |
| x3 | 2.52 | 2.52 | 2.51 | 2.46 | …… |
| x4 | 1234 | 1230 | 1234 | 1235 | …… |
| x5 | 18.5 | 16.5 | 17.5 | 20 | …… |
| x6 | 14 | 14 | 15 | 16 | …… |
| x7 | 942 | 938 | 946 | 942 | …… |
| y1 | 94.65 | 94.66 | 94.43 | 93.22 | …… |
| y2 | 3721 | 3720 | 3725 | 3717 | …… |
| y3 | 4.25 | 4.84 | 4.01 | 4.15 | …… |
| y4 | 12354.3 | 12316.4 | 12283.1 | 12747.2 | …… |
In recurrent neural network design, since there are recursive signal, network state is changed with time, therefore
In addition to the number of hidden nodes, learning rate similarly affects the stability and accuracy of neural network model, is that neural network is set
Heavy difficult point in meter.
The setting of the number of nodes of hidden layer is obtained by trial and error procedure:
In formula, p is hidden neuron number of nodes, and n is input layer number, and m is output layer neuron number, k 1-10
Between constant.
Best learning rate value are as follows:
The setting parameter of recurrent neural network is as shown in table 2 below in this example.
Parameter is arranged in 2 recurrent neural network of table
| Objective function | Current efficiency | Tank voltage | Perfluoro-compound discharge amount | Ton aluminium energy consumption |
| The number of iterations | 1000 | 1000 | 1000 | 1000 |
| Hidden layer transmission function | Tansig | Logsig | Logsig | Tansig |
| Output layer transmission function | Purelin | Purline | Purelin | Purelin |
| Node in hidden layer | 13 | 12 | 12 | 13 |
It is carried out in the training process of neural network essentially according to following steps:
X is setk=[xk1,xk2,···,xkM] (k=1,2, N) and it is input vector, N is training sample
Number,
Weight when for the g times iteration between input layer M and hidden layer I
Vector, weighted vector when WJP (g) is the g times iteration between hidden layer J and output layer P is Yk(g)=[yk1(g),
yk2(g),···,ykP(g)] (k=1,2, N) be the g times iteration when network reality output, dk=[dk1,
dk2,···,dkP] (k=1,2, N) it is desired output；
S21: initialization, if the number of iterations g initial value be 0, be assigned to respectively WMI (0), (0) (0, the 1) section WJP with
Machine value；
S22: stochastic inputs sample Xk；
S23: to input sample Xk, the input signal and output signal of every layer of neuron of forward calculation recurrent neural network
S24: according to desired output dkWith reality output Yk(g), error E (g) is calculated；
Whether S25: error in judgement E (g) meet the requirements, and is such as unsatisfactory for, then enters step S26, such as meets, then enters step
S29；
S26: judging whether the number of iterations g+1 is greater than maximum number of iterations, such as larger than, then enters step S29, otherwise, into
Enter step S27；
S27: to input sample XkThe partial gradient of every layer of neuron of retrospectively calculate；
Network exports node layer error are as follows: e (k)=d (k)-y (k), e (k) are network desired output, and y (k) is that network is real
Border output.
By calculating output node layer error to the weight change rate of each layer are as follows:
Wherein βij(0)=0；I=1,2, n1；J=1,2, n0。
δi(0)=0；I=1,2, n1。
WhereinRespectively indicate the input and output of i-th of node of hidden layer；n0、n1Respectively output layer
And node in hidden layer；Respectively indicate associated layers, output layer, hidden layer weight.
S28: network weight modified computing formulae are as follows:
Wherein w (k) can be
W (k) can represent the weight of output layer, hidden layer or input layer in formula, and η is learning rate, enable g=g+1, jump to
Step S23；
S29: judging whether to complete all training samples, if it is, completing modeling, otherwise, continues to go to step
S22。
By above-mentioned cyclic process, recurrent neural networks prediction effect can be obtained as shown in Fig. 2,3,4,5,6,7,8,9.It is excellent
The foundation for changing model is the basis of aluminum electrolysis process optimization, and model accuracy directly affects optimum results.By to Fig. 2,3,
4,5,6,7,8,9 analyses are it is found that through recurrent neural network training, and the largest prediction error of current efficiency is 0.41%, tank voltage
Largest prediction error be 0.08%, carbon tetrafluoride CF4 forecasting of discharged quantity error -1.20%, ton aluminium energy consumption prediction error be
0.81%, model prediction accuracy is high, meets modeling demand.
S3: A-PMDE algorithm, root are formed in conjunction with DE algorithm using the preference multiple target differential evolution algorithm dominated based on A
According to the preset desired value of policymaker (reference point), the stringent partial ordering relation dominated based on A is established, resulting to step S2 four
A production process model optimizes, and obtains one group and most meets the desired decision variable X of policymakerbestAnd its corresponding electric current effect
Rate ybest, tank voltage zbestAnd perfluoro-compound discharge amount sbestWith ton aluminium energy consumption cbest；
On the basis of aluminum electrolysis process model, it is carried out within the scope of each decision variable using A-PMDE algorithm excellent
Change, each specific variation range of variable is as shown in table 3.
Each variable-value range of table 3
A-PMDE algorithm in step S3 the following steps are included:
S31: the preference relation dominated according to A evaluates the fitness of each individual, and according to superiority and inferiority to individual optimal value with
Global optimum is replaced；
Preferably, step S31 the following steps are included:
S311: initialization system parameter, including population scale R, maximum number of iterations T generate n individual x at random1,
x2,···,xn, enable external archival collection Q for sky；
S312: policymaker sets preference angle [alpha] and preference intended reference point r (yp,zp,sp,cp), the preference target ginseng
Examination point includes the desired value of current efficiency, tank voltage, perfluoro-compound discharge amount and ton aluminium four targets of energy consumption；
S313: for each individual examination point x, its fitness and its angle with reference point reference line are calculated:
Wherein, fjIt (x) is fitness value of the individual x in jth target,
S314: being based on angle information on object space, preference zone is divided, if θ (r, x) < α, the individual are in
Preference zone；Otherwise it is in non-preference zone；
S315: judge any two individual xi and xkBetween superiority and inferiority relationship, including following situations:
Work as xiWith xkWhen being in preference zone or non-preference zone simultaneously, if xiPareto dominates xk, then it is assumed that xiIt is more excellent,
If mutually Pareto is not dominated, then it is assumed that the two is suitable；
Work as xiIn preference zone, xkWhen in non-preference zone, if xiPareto dominates xkOr xiWith xkMutually not
Pareto is dominated, then it is assumed that xiBetter than xk, i.e. xiA dominates xk；
S316: determine the optimal base of individual because of pbesti, in system initialization, individual optimal base is because being set as the individual
Initial gene xi；After next iteration, based on the A dominance relation that S315 is proposed, to the new gene x of individualiWith pbestiIt carries out
Superiority and inferiority compares, and outstanding person saves as pbesti；
S317: updating external archival collection Q, is added to the individual that non-A is dominated between each other in population and achieves collection Q, deletes quilt
The particle of domination；
S318: randomly choosed in external archival collection Q using press mechanism and Tabu search algorithm an individual as it is global most
Excellent gene.
S32: individual gene information in Population Regeneration, including mutation operation, crossover operation and selection operation.
Preferably, step S32 the following steps are included:
S321: mutation operation is carried out to population, for each individual xi, randomly choosed in population other three it is different
Individual xr1,xr2,xr3, third individual is added to after the differential vector that any two of them body is formed is scaled by scale factor F
On, variation individual is generated with this, formula is as follows:
vi=xr1+F·(xr2-xr3), i ≠ r1≠r2≠r3
Wherein, r1,r2,r3For mutually different integer randomly selected from set { 1,2 ..., n }, and every carry out one
Secondary variation, these integers can all randomly select again；
S322: target individual xiWith its variation individual viCrossover operation is carried out, test individual u is generatedi；Intersected with binomial
For mode, setting crossover probability constant CR first, in M dimension variable per one-dimensional variable j, if [0,1] that generates with
Machine number is less than or equal to CR, then carries out crossover operation.This Crossover Strategy can be summarized as:
Wherein, randijFor [0,1] equally distributed random number, determine j-th of element of i-th of test individual by becoming
Different individual or target individual contribution.
S323: test individual uiIt will be with target individual xiSelection operation is carried out, to determine that it is next-generation which individual enters；
Using greedy selection strategy, for minimizing optimization, selection operation is as follows:
If the target function value of test individual is less than or equal to the target function value of respective objects individual, individual is tested
Enter the next generation instead of target individual.
S324: judging whether current globally optimal solution meets condition or whether the number of iterations reaches maximum number of iterations T,
If it is, exporting current globally optimal solution, otherwise, the S321 that gos to step is computed repeatedly, until current global optimum
Solution meets condition or the number of iterations reaches maximum number of iterations T.
Aluminum electrolysis process is optimized through the above steps can obtain 100 groups of optimal decision variables with it is corresponding defeated
It is worth out, chooses and be wherein listed in the table below in 4 for most reasonable 3 groups.
4 optimized producing parameter of table
| y1 | y2 | y3 | y4 | x1 | x2 | x3 | x4 | x5 | x6 | x7 |
| 99.24 | 3635 | 3.65 | 10835.15 | 1649 | 628 | 2.55 | 1210 | 16.5 | 14.5 | 942 |
| 98.13 | 3682 | 3.58 | 11527.21 | 1653 | 627 | 2.38 | 1200 | 17 | 15 | 924 |
| 95.37 | 3605 | 3.68 | 10478.52 | 1670 | 617 | 2.47 | 1090 | 17.5 | 15.5 | 935 |
S4: according to the resulting optimizing decision variable X of step S3bestIn control parameter come it is selected in rate-determining steps S2
Aluminium electrolytic industry scene, reaches the purpose of energy-saving and emission-reduction consumption reduction.
It is excellent by providing a kind of aluminium electroloysis preference multiple target based on angle dominance relation in above-described embodiment of the application
Change algorithm, aluminum electrolysis process is modeled first with recurrent neural network, then policymaker sets expectation target value,
Preference multi-target quantum groups of individuals algorithm is recycled to optimize production process model, one group for obtaining each decision variable is most full
The sufficient desired optimal solution of policymaker and the corresponding current efficiency of the optimal solution, tank voltage, perfluoro-compound discharge amount and ton aluminium energy
Consumption.Using variation, intersection and selection operation in differential evolution algorithm DE, preference optimizing is carried out to decision variable, aluminium is determined with this
The optimal value of technological parameter during electrolysis production can effectively improve current efficiency, reduce tank voltage, reduce greenhouse gas emission
Amount and ton aluminium energy consumption, while meeting decisionmaker's preference, achieve energy-saving and emission reduction purposes.
Finally, it is stated that preferred embodiment above is only used to illustrate the technical scheme of the present invention and not to limit it, although logical
It crosses above preferred embodiment the present invention is described in detail, however, those skilled in the art should understand that, can be
Various changes are made to it in form and in details, without departing from claims of the present invention limited range.
Claims (9)
1. a kind of aluminium electroloysis preference multi-objective optimization algorithm based on angle dominance relation, which comprises the steps of:
S1: selection constitutes decision variable X=to current efficiency, tank voltage and the influential control parameter of perfluoro-compound discharge amount
[x1,x2,…,xM], M is the number of selected control parameter；
S2: selected aluminium electrolytic industry scene acquires N group decision variable X1,X2,…,XNAnd its corresponding current efficiency y1,y2,…,
yN, tank voltage z1,z2,…,zNAnd perfluoro-compound discharge amount s1,s2,…,sNWith ton aluminium energy consumption c1,c2,…,cNFor data sample
This, with each group of decision variable XiAs input, respectively with corresponding current efficiency yi, tank voltage ziAnd perfluoro-compound discharge
Measure siWith ton aluminium energy consumption ciAs output, sample is trained using recurrent neural network, is examined, establishes four aluminium cells
Production process model；
S3: A-PMDE algorithm is formed in conjunction with DE algorithm using the preference multiple target differential evolution algorithm dominated based on A, according to certainly
The preset desired value of plan person is as a reference point, the stringent partial ordering relation that foundation is dominated based on A, and four resulting to step S2
Production process model optimizes, and obtains one group and most meets the desired decision variable X of policymakerbestAnd its corresponding current efficiency
ybest, tank voltage zbestAnd perfluoro-compound discharge amount sbestWith ton aluminium energy consumption cbest；
S4: according to the resulting optimizing decision variable X of step S3bestIn control parameter carry out selected aluminium electricity in rate-determining steps S2
Industry spot is solved, the purpose of energy-saving and emission-reduction consumption reduction is reached.
2. the aluminium electroloysis preference multi-objective optimization algorithm according to claim 1 based on angle dominance relation, feature exist
In in step S1, the control parameter includes that potline current, blanking number, molecular proportion, aluminum yield, aluminum water be flat, electrolyte water
Flat, bath temperature.
3. the aluminium electroloysis preference multi-objective optimization algorithm according to claim 1 based on angle dominance relation, feature exist
In in step S2, using current efficiency as output, establishing aluminium cell production process model, input layer uses 10 nerves
First node, hidden layer use 15 neuron nodes, and output layer uses 1 neuron node, and input layer is passed between hidden layer
Delivery function is Tansig function, and hidden layer to the function between output layer is Purelin function, the number of iterations when sample training
It is 1000.
4. the aluminium electroloysis preference multi-objective optimization algorithm according to claim 1 based on angle dominance relation, feature exist
In in step S2, using tank voltage as output, establishing aluminium cell production process model, input layer uses 10 neurons
Node, hidden layer use 15 neuron nodes, and output layer uses 1 neuron node, and input layer is transmitted between hidden layer
Function is Logsig function, and hidden layer to the function between output layer is Purelin function, and the number of iterations when sample training is
1000。
5. the aluminium electroloysis preference multi-objective optimization algorithm according to claim 1 based on angle dominance relation, feature exist
In in step S2, using perfluoro-compound discharge amount as output, establishing aluminium cell production process model, input layer uses 10
A neuron node, hidden layer use 15 neuron nodes, and output layer uses 1 neuron node, input layer to hidden layer
Between transmission function be Logsig function, hidden layer to the function between output layer is Purelin function, when sample training repeatedly
Generation number is 1000.
6. the aluminium electroloysis preference multi-objective optimization algorithm according to claim 1 based on angle dominance relation, feature exist
In in step S2, using ton aluminium energy consumption as output, establishing aluminium cell production process model, input layer uses 10 nerves
First node, hidden layer use 15 neuron nodes, and output layer uses 1 neuron node, and input layer is passed between hidden layer
Delivery function is Tansig function, and hidden layer to the function between output layer is Purelin function, the number of iterations when sample training
It is 1000.
7. the aluminium electroloysis preference multi-objective optimization algorithm according to claim 1 based on angle dominance relation, feature exist
In, A-PMDE algorithm in step S3 the following steps are included:
S31: the preference relation dominated according to A evaluates the fitness of each individual, and according to superiority and inferiority to individual optimal value and the overall situation
Optimal value is replaced；
S32: individual gene information in Population Regeneration, including mutation operation, crossover operation and selection operation.
8. the aluminium electroloysis preference multi-objective optimization algorithm according to claim 7 based on angle dominance relation, feature exist
In, step S31 the following steps are included:
S311: initialization system parameter, including population scale R, maximum number of iterations T generate n individual x at random1,x2,…,
xn, enable external archival collection Q for sky；
S312: policymaker sets preference angle [alpha] and preference intended reference point r (yp,zp,sp,cp), the preference intended reference point packet
Include the desired value of current efficiency, tank voltage, perfluoro-compound discharge amount and ton aluminium four targets of energy consumption；
S313: for each individual examination point x, its fitness and its angle with reference point reference line are calculated:
Wherein, fjIt (x) is fitness value of the individual x in jth target,
S314: being based on angle information on object space, divides preference zone, if θ (r, x) < α, which is in preference
Region；Otherwise it is in non-preference zone；
S315: judge any two individual xiWith xkBetween superiority and inferiority relationship, including following situations:
Work as xiWith xkWhen being in preference zone or non-preference zone simultaneously, if xiPareto dominates xk, then it is assumed that xiIt is more excellent, if phase
Mutually Pareto is not dominated, then it is assumed that the two is suitable；
Work as xiIn preference zone, xkWhen in non-preference zone, if xiPareto dominates xkOr xiWith xkMutual not Pareto branch
Match, then it is assumed that xiBetter than xk, i.e. xiA dominates xk；
S316: determine the optimal base of individual because of pbesti, in system initialization, individual optimal base is because being set as the initial of the individual
Gene xi；After next iteration, based on the A dominance relation that S315 is proposed, to the new gene x of individualiWith pbestiCarry out superiority and inferiority
Compare, outstanding person saves as pbesti；
S317: updating external archival collection Q, is added to the individual that non-A is dominated between each other in population and achieves collection Q, deletion is dominated
Particle；
S318: an individual is randomly choosed in external archival collection Q using press mechanism and Tabu search algorithm as global optimum's base
Cause.
9. the aluminium electroloysis preference multi-objective optimization algorithm according to claim 8 based on angle dominance relation, feature exist
In, step S32 the following steps are included:
S321: mutation operation is carried out to population, for each individual xi, other three different individuals are randomly choosed in population
xr1,xr2,xr3, it is added to after the differential vector that any two of them body is formed is scaled by scale factor F on third individual,
Variation individual is generated with this, formula is as follows:
vi=xr1+F·(xr2-xr3), i ≠ r1≠r2≠r3
Wherein, r1,r2,r3For mutually different integer randomly selected from set { 1,2 ..., n }, and every once become
Different, these integers can all randomly select again；
S322: target individual xiWith its variation individual viCrossover operation is carried out, test individual u is generatedi；
S323: test individual uiIt will be with target individual xiSelection operation is carried out, to determine that it is next-generation which individual enters；
S324: judging whether current globally optimal solution meets condition or whether the number of iterations reaches maximum number of iterations T, if
It is then to export current globally optimal solution, otherwise, the S321 that gos to step is computed repeatedly, until current globally optimal solution is full
Sufficient condition or the number of iterations reach maximum number of iterations T.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112239873A (en) * | 2019-07-19 | 2021-01-19 | 郑州轻冶科技股份有限公司 | Aluminum electrolysis process parameter optimization method and aluminum electrolysis cell set |
| EP3970905A1 (en) * | 2020-09-18 | 2022-03-23 | Bystronic Laser AG | Computer implemented method of and optimisation tool for refinement of laser cutting process parameters by means of an optimization tool |
| CN115081319A (en) * | 2022-06-10 | 2022-09-20 | 昆明理工大学 | Intelligent decision-making and multi-objective optimization method for aluminum electrolysis production process |
| CN115310353A (en) * | 2022-07-26 | 2022-11-08 | 明珠电气股份有限公司 | Power transformer design method based on rapid multi-objective optimization |
| CN116133785A (en) * | 2020-09-18 | 2023-05-16 | 百超激光有限公司 | Computer-implemented method and optimization tool for improving laser cutting process parameters by the optimization tool |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2587321A1 (en) * | 2011-10-25 | 2013-05-01 | Siemens Aktiengesellschaft | Wave filtering using differential evolution for dynamic positioning systems |
| CN103279620A (en) * | 2013-06-07 | 2013-09-04 | 山东大学 | Method for restoring sequence and path of unit and simultaneously performing optimization |
| CN104156584A (en) * | 2014-08-04 | 2014-11-19 | 中国船舶重工集团公司第七0九研究所 | Sensor target assignment method and system for multi-objective optimization differential evolution algorithm |
| CN105334824A (en) * | 2015-11-06 | 2016-02-17 | 重庆科技学院 | Aluminum electrolysis production optimization method based on NSGA-II algorithm |
| CN105631528A (en) * | 2015-09-22 | 2016-06-01 | 长沙理工大学 | NSGA-II and approximate dynamic programming-based multi-objective dynamic optimal power flow solving method |
| CN106295880A (en) * | 2016-08-10 | 2017-01-04 | 广东工业大学 | A kind of method and system of power system multi-objective reactive optimization |
| CN106502096A (en) * | 2016-11-14 | 2017-03-15 | 重庆科技学院 | Process decision parameter optimization method is adopted based on the oil field machine of preference multiple-objection optimization |
-
2018
- 2018-03-09 CN CN201810193063.1A patent/CN109085752B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2587321A1 (en) * | 2011-10-25 | 2013-05-01 | Siemens Aktiengesellschaft | Wave filtering using differential evolution for dynamic positioning systems |
| CN103279620A (en) * | 2013-06-07 | 2013-09-04 | 山东大学 | Method for restoring sequence and path of unit and simultaneously performing optimization |
| CN104156584A (en) * | 2014-08-04 | 2014-11-19 | 中国船舶重工集团公司第七0九研究所 | Sensor target assignment method and system for multi-objective optimization differential evolution algorithm |
| CN105631528A (en) * | 2015-09-22 | 2016-06-01 | 长沙理工大学 | NSGA-II and approximate dynamic programming-based multi-objective dynamic optimal power flow solving method |
| CN105334824A (en) * | 2015-11-06 | 2016-02-17 | 重庆科技学院 | Aluminum electrolysis production optimization method based on NSGA-II algorithm |
| CN106295880A (en) * | 2016-08-10 | 2017-01-04 | 广东工业大学 | A kind of method and system of power system multi-objective reactive optimization |
| CN106502096A (en) * | 2016-11-14 | 2017-03-15 | 重庆科技学院 | Process decision parameter optimization method is adopted based on the oil field machine of preference multiple-objection optimization |
Non-Patent Citations (4)
| Title |
|---|
| DHIAA HALBOOT MUHSEN: "A novel method for sizing of standalone photovoltaic system using multi-objective differential evolution algorithm and hybrid multi-criteria decision making methods", 《ENERGY》 * |
| JUN YI: "ar-MOEA: A Novel Preference-Based Dominance Relation for Evolutionary Multiobjective Optimization", 《IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION》 * |
| 周宇 等: "武器装备体系组合规划的高维多目标优化决策", 《系统工程理论与实践》 * |
| 王丽萍 等: "偏好多目标进化算法研究综述", 《计算机学报》 * |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112239873A (en) * | 2019-07-19 | 2021-01-19 | 郑州轻冶科技股份有限公司 | Aluminum electrolysis process parameter optimization method and aluminum electrolysis cell set |
| EP3970905A1 (en) * | 2020-09-18 | 2022-03-23 | Bystronic Laser AG | Computer implemented method of and optimisation tool for refinement of laser cutting process parameters by means of an optimization tool |
| WO2022058113A1 (en) * | 2020-09-18 | 2022-03-24 | Bystronic Laser Ag | Computer implemented method of and optimisation tool for refinement of laser cutting process parameters by means of an optimization tool |
| CN116133785A (en) * | 2020-09-18 | 2023-05-16 | 百超激光有限公司 | Computer-implemented method and optimization tool for improving laser cutting process parameters by the optimization tool |
| US11927927B2 (en) | 2020-09-18 | 2024-03-12 | Bystronic Laser Ag | Computer implemented method of and optimisation tool for refinement of laser cutting processing parameters by means of an optimization tool |
| CN115081319A (en) * | 2022-06-10 | 2022-09-20 | 昆明理工大学 | Intelligent decision-making and multi-objective optimization method for aluminum electrolysis production process |
| CN115081319B (en) * | 2022-06-10 | 2024-03-29 | 昆明理工大学 | Intelligent decision-making and multi-objective optimization method for aluminum electrolysis production process |
| CN115310353A (en) * | 2022-07-26 | 2022-11-08 | 明珠电气股份有限公司 | Power transformer design method based on rapid multi-objective optimization |
| CN115310353B (en) * | 2022-07-26 | 2024-02-20 | 明珠电气股份有限公司 | Power transformer design method based on rapid multi-objective optimization |
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