CN110014550A - Opening control method for material inlet and outlet valves of material dryer based on mixed frog leaping algorithm - Google Patents
Opening control method for material inlet and outlet valves of material dryer based on mixed frog leaping algorithm Download PDFInfo
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- 238000007796 conventional method Methods 0.000 description 3
- 238000005259 measurement Methods 0.000 description 3
- 238000005457 optimization Methods 0.000 description 3
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
- B29B—PREPARATION OR PRETREATMENT OF THE MATERIAL TO BE SHAPED; MAKING GRANULES OR PREFORMS; RECOVERY OF PLASTICS OR OTHER CONSTITUENTS OF WASTE MATERIAL CONTAINING PLASTICS
- B29B13/00—Conditioning or physical treatment of the material to be shaped
- B29B13/06—Conditioning or physical treatment of the material to be shaped by drying
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
- B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
- B29C45/00—Injection moulding, i.e. forcing the required volume of moulding material through a nozzle into a closed mould; Apparatus therefor
- B29C45/17—Component parts, details or accessories; Auxiliary operations
- B29C45/18—Feeding the material into the injection moulding apparatus, i.e. feeding the non-plastified material into the injection unit
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
- B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
- B29C45/00—Injection moulding, i.e. forcing the required volume of moulding material through a nozzle into a closed mould; Apparatus therefor
- B29C45/17—Component parts, details or accessories; Auxiliary operations
- B29C45/76—Measuring, controlling or regulating
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- G—PHYSICS
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
- B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
- B29C45/00—Injection moulding, i.e. forcing the required volume of moulding material through a nozzle into a closed mould; Apparatus therefor
- B29C45/17—Component parts, details or accessories; Auxiliary operations
- B29C45/76—Measuring, controlling or regulating
- B29C2045/7606—Controlling or regulating the display unit
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
- B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
- B29C2945/00—Indexing scheme relating to injection moulding, i.e. forcing the required volume of moulding material through a nozzle into a closed mould
- B29C2945/76—Measuring, controlling or regulating
- B29C2945/76494—Controlled parameter
- B29C2945/76545—Flow rate
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Abstract
The invention discloses a method for controlling the opening of a material inlet and outlet valve of a material dryer based on a mixed frog leaping algorithm, which comprises the following steps: acquiring basic information data about the control of the opening degree of a feeding and discharging valve; determining a decision variable set, and establishing a target function and a constraint function set related to the decision variable set; obtaining a value of a decision variable group which enables the value of the objective function to be minimum and meets the constraint function group by using a mixed frog-leaping algorithm, namely an optimal solution; and controlling the material inlet and outlet valve of the material dryer by using the optimal solution. The opening control parameters of the material inlet and outlet valves of the material dryer can be automatically generated, absolute drying of materials can be guaranteed through the automatically generated parameters, heating energy consumption of the material dryer is optimized, and occupied capacity of an automatic feeding system of the injection molding machine is saved.
Description
Technical field
The present invention relates to valve opening control technology fields, are based especially on the material drying machine disengaging of shuffled frog leaping algorithm
Expect valve opening control method.
Background technique
In injection molding production engineering, plastic pellet often carries moisture, this directly influences the quality of plastic products, water
Divide the meeting steam in high-temperature molding injection molding, not so as to cause injecting products hollow bubble easy to form, surface filamentary silver, injection molding
The various defects such as full, hole, and then affect the mechanical mechanics property and presentation quality of plastic products, when situation is serious, moisture
Even it can promote plastic degradation, performance is greatly lowered.Therefore injection molding machine generally can all be equipped with material drying machine as former material
Expect auxiliary input device, wherein the Automatic dispatching of the charging/discharging valve of material drying machine is that injection molding machine automatic feeding system closes very much
A part of key adjusts speed and the duration of feed and discharging by the aperture control to inlet valve and outlet valve, with
Guarantee that material is absolutely dried, optimizes the heating energy consumption of material drying machine, save the resource of injection molding machine automatic feeding system.
The aperture of the charging/discharging valve of material drying machine is controlled in the prior art, it is usually manual according to historical experience
Parameter regulation is carried out, the degree that this mode relies on artificial judgment is larger, and it is time-consuming and laborious, and adding for optimization material drying machine
Heat consumption energy can not effective control with saving injection molding machine automatic feeding system resource.
Summary of the invention
In order to overcome above-mentioned defect in the prior art, the present invention provide the material drying machine based on shuffled frog leaping algorithm into
Discharge valve opening control method, can automatically generate the aperture control parameter of material drying machine charging/discharging valve, and this is automatically generated
Parameter can guarantee that material is absolutely dried, optimize material drying machine heating energy consumption, and save injection molding machine Automatic-feeding system
The occupancy capacity of system.
To achieve the above object, the present invention uses following technical scheme, comprising:
Material drying machine charging/discharging valve aperture control method based on shuffled frog leaping algorithm, comprising the following steps:
S1, obtains the essential information data controlled about charging/discharging valve aperture, and the essential information data include:
The volume of injection molding machine is Ci；It is Cc that the buffer memory of injection molding machine, which caches volume,；
The volume of material drying machine is Cd；The per day power of material drying machine is Pavg；
The maximum energy consumption of desired material drying machine is EcostMax；
Relational expression between the actual energy consumption Ecost and input and output material duration, charging/discharging valve aperture of material drying machine are as follows:
Ecost=Pavg*H+Tin* (Kin*XcosTin+YcosTin)+Tout* (Kout*XcosTout+
YcosTout)；
Wherein, H is the operating time of material drying machine, and Tout is the discharging duration, and Tin is feed duration, Kin
To feed valve opening, Kout is discharging valve opening, and XcosTin, YcosTin, XcosTout, YcosTout are the relational expression
Coefficient；
Feeding the proportionate relationship between valve opening Kin and pan feeding speed Sin is 1:Xin, i.e. pan feeding speed Sin is Kin*
Xin；
Proportionate relationship between discharging valve opening Kout and discharging speed Sout is 1:Xout, i.e. discharging speed Sout is
Kout*Xout；
It is required that the plastic pellet of sucking material drying machine is heated to χ degrees Celsius, and thermostatic hold-time is Tm；
The minimum energy dissipation of material drying machine is EcostMin, and
EcostMin=Pavg*Tm* (Tin*Sin/Cd)=Pavg*Tm* (Tin*Kin*Xin/Cd)；
Product processing procedure is Tp, and product net weight is Q, i.e., the productive temp of product is the energy output within the time of a processing procedure Tp
Net weight is the plastic pellet of Q；
S2 determines decision variable group, and establishes objective function relevant to decision variable group and constraint group of functions；It is described
Decision variable group is the aperture control parameter of material drying machine；
S3 is obtained keeping the value of objective function minimum and is met the decision variable of constraint function group using shuffled frog leaping algorithm
The value of group, i.e. optimal solution；
S4 controls material drying machine charging/discharging valve using the optimal solution.
In step S1, for different production requirements, the essential information data are also different, actual mechanical process
In, production management person sets or adjusts its corresponding essential information data according to production requirement；Meanwhile for multiple and different lifes
It produces and requires, production management person is produced the corresponding different essential information data of this multiple and different production requirement institute by it
Time sequencing arrangement.
In step S2, the decision variable group includes 4 decision decision variables, is respectively as follows: charging valve opening Kin, discharging
Valve opening Kout, feed duration Tin, discharging duration T out；
Objective function, that is, the fitness function are as follows:
The constraint function group includes 5 constraint functions, is respectively as follows:
Constraint function 1 is Kin*Xin*Tin-Kout*Xout*Tout < Cd；
Constraint function 2 is Kout*Xout*Tout < Ci-Cc；
Constraint function 3 is Sout*Tp > Q, i.e. Kout*Xout*Tp > Q；
Constraint function 4 is Kin*Xin*Tin > Kout*Xout*Tout；
Constraint function 5 is EcostMin≤Ecost≤EcostMax, i.e.,
6. the material drying machine charging/discharging valve aperture controlling party according to claim 1 based on shuffled frog leaping algorithm
Method, which is characterized in that in step S3, the shuffled frog leaping algorithm comprising the following specific steps
S31, set algorithm parameter simultaneously initialize it, and the algorithm parameter includes: of the solution of decision variable group
Number FrogCount, sub- population quantity GroupCount, sub- population frog quantity FrogCountInGroup, total the number of iterations
Optimizing maximum number of times MaxUpdateTimesInGroup, experiment number TestTimes, maximum in InteratingTimes, group
The step-length that leapfrogs MaxLeapStep, fuzzy interval BrtterFrogRange is used when taking more excellent solution；
According to the empirical data of injection molding production feed, a certain number of solutions for meeting constraint function group of initial setting are described
Solution is the value of decision variable (Kin, Kout, Tin, Tout)；The certain amount is equal to set decision variable group
The number FrogCount of solution；
S32, calculates the fitness of each i.e. each frog of solution, and it is arranged according to the descending of fitness；
The fitness is that the frog i.e. solution is substituted into obtained value F (Kin, Kout, Tin, Tout) after objective function, and value F
(Kin, Kout, Tin, Tout) more big then fitness is poorer, and the smaller then fitness of value F (Kin, Kout, Tin, Tout) is better；
S33, sub- population dividing, random is distributed to solution in each solution space, i.e., frog is distributed to each sub- population
In, and the frog in each sub- population is arranged according to the descending of fitness；
S34, organize in optimizing in an optimizing i.e. sub- population: based on maximum leapfrog step-length MaxLeapStep obscured into
Change and generate new explanation at random, it is described it is fuzzy evolve as based on existing solution in organizing, the foundation maximum step-length MaxLeapStep that leapfrogs is repaired
Change the value of 4 decision variables in decision variable group；Calculate the adaptation between fitness in current organize worst solution and the new explanation
Difference is spent, judges whether the absolute value of the fitness difference is greater than and set uses fuzzy interval when taking more excellent solution
BrtterFrogRange, to judge whether the new explanation is a more excellent solution, wherein
If the absolute value of the fitness difference is not more than set BrtterFrogRange, then it is assumed that the new explanation is
One quasi-optimal solution, into next step；
If the absolute value of the fitness difference is greater than set BrtterFrogRange, then it is assumed that the new explanation is not
Quasi-optimal solution re-executes a step S34 and re-starts optimizing in primary organize；And the total degree of optimizing cannot in requirement group
Beyond optimizing maximum number of times MaxUpdateTimesInGroup in set group, if the total degree of optimizing exceeds in organizing
When MaxUpdateTimesInGroup, then this time organizes the new explanation ultimately generated in interior searching process and relatively sentence without fitness
It is disconnected, i.e., optimal solution subject to the new explanation is directly thought, into next step；
S35 updates the worst solution of the fitness in current group using the quasi-optimal solution that optimizing in organizing obtains, and records more
New number；
S36, judges whether update times reach total the number of iterations InteratingTimes, if not up to, continuing
The interior optimizing of group simultaneously updates, that is, jumps and execute step S34；
If reaching, each frog i.e. fitness of each solution is calculated, and judge that the minimum in current solution space is suitable
Whether response meets termination condition, i.e., whether the minimum fitness in current solution space is less than the threshold epsilon of setting, if being unsatisfactory for i.e.
Not less than the threshold epsilon of setting, then continue to calculate, repartition sub- population, jumps and execute step S33；It is less than setting if meeting
Threshold epsilon, then export solution corresponding to the minimum fitness in current solution space, solution corresponding to the minimum fitness of the output
As optimal solution, algorithm terminate.
In step S4, after obtaining optimal solution, also the operation record for seeking optimal solution is saved into database, and next time is again
When executing the shuffled frog leaping algorithm, solution of the optimal solution saved in database as initial setting in initialization procedure is called；
The database is static input parameter, that is, essential information data and adjusts between control parameter, that is, optimal solution
Mapping table.
The present invention has the advantages that
(1) algorithm of the invention can automatically generate the aperture control of material drying machine charging/discharging valve according to essential information data
Parameter processed, and material drying machine is controlled according to the aperture that this parameter carries out charging/discharging valve, realizes the automation of material drying machine
Processing.
(2) production management person can require setting according to actual production or adjust different essential information data；Meanwhile needle
To multiple and different production requirements, production management person is by the corresponding different essential information numbers of this multiple and different production requirement
It is arranged according to according to the time sequencing of its production, to facilitate material drying machine that can automatically adjust for different production requirements
The aperture of charging/discharging valve controls.
(3) present invention tallies with the actual situation for the setting of decision variable group, that is, charging/discharging valve aperture control parameter, and
When meeting set objective function, material drying machine heating energy consumption can be made to reach minimum, make the material number in material drying machine
Amount reaches most.
(4) present invention uses shuffled frog leaping algorithm optimal solution, and obtained material drying machine input and output material is effectively guaranteed
The correctness and optimality of the aperture control parameter of valve, while in shuffled frog leaping algorithm, ask quasi- in the way of fuzzy evolution
Optimal solution guarantees that solving speed is fast.
(5) present invention saves operation record into database also after obtaining optimal solution, and next time executes the mixing again
Leapfrog algorithm when, the solution of the optimal solution that saves as initial setting in initialization procedure in database is called, thus formation process
Knowledge base avoids the occurrence of local optimum and causes to help staff to be updated adjusting to objective function and algorithm parameter
It cannot get the dispatching method of total optimization.
Detailed description of the invention
Fig. 1 is the method stream of the material drying machine charging/discharging valve aperture control method of the invention based on shuffled frog leaping algorithm
Cheng Tu.
Fig. 2 is the algorithm flow chart of shuffled frog leaping algorithm of the invention.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
As shown in Figure 1, the material drying machine charging/discharging valve aperture control method based on shuffled frog leaping algorithm, including following step
It is rapid:
S1, obtains the essential information data in relation to the control of charging/discharging valve aperture, and the essential information data are technique ginseng
Number；And it is directed to different production requirements, the essential information data are also different, in the actual operation process, production management
Member sets or adjusts its corresponding essential information data according to production requirement；It is raw meanwhile when for multiple and different production requirements
Produce the time sequencing that administrator produces the corresponding different essential information data of this multiple and different production requirement according to it
It is arranged, to facilitate material drying machine can be for the aperture control of different production requirement automatic adjustment charging/discharging valves.
The essential information data include:
The volume of injection molding machine is Ci；It is Cc that the buffer memory of injection molding machine, which caches volume,；
The volume of material drying machine is Cd；The per day power of dryer is Pavg；
Client it is expected that the maximum energy consumption of material drying machine is EcostMax；
Relational expression between the actual energy consumption Ecost and input and output material duration, charging/discharging valve aperture of material drying machine are as follows:
Ecost=Pavg*H+Tin* (Kin*XcosTin+YcosTin)+Tout* (Kout*XcosTout+
YcosTout), wherein H is the operating time of material drying machine, and Tout is the discharging duration, and Tin is feed duration,
Kin is charging valve opening, and Kout is discharging valve opening, and XcosTin, YcosTin, XcosTout, YcosTout are the relationship
The coefficient of formula；
Feeding the proportionate relationship between valve opening Kin and pan feeding speed Sin is 1:Xin, i.e. pan feeding speed Sin is Kin*
Xin；
Proportionate relationship between discharging valve opening Kout and discharging speed Sout is 1:Xout, i.e. discharging speed Sout is
Kout*Xout；
In production, it is desirable that the plastic pellet for sucking material drying machine is heated to χ degrees Celsius, and thermostatic hold-time is
Tm；
The minimum energy dissipation of material drying machine is EcostMin, and minimum energy dissipation:
EcostMin=Pavg*Tm* (Tin*Sin/Cd)=Pavg*Tm* (Tin*Kin*Xin/Cd)；
In production, according to product, that is, plastic pellet productive temp, product processing procedure, that is, plastic pellet processing procedure is Tp, and product is net
I.e. plastic pellet net weight is Q again, that is, requires the plastic pellet that energy output net weight is Q within the time of a processing procedure Tp.
In the present invention, according to the minimum of the actual energy consumption Ecost of formula material drying machine calculated and material drying machine
Energy consumption be EcostMin be single dispatch under energy consumption.
In the present embodiment,
Used injection molding machine is sea day MA1200, and plastic pellet buffer memory capacity is 25kg, i.e. Ci=25kg；Current injection molding
The buffer memory of machine is Cc, initializes Cc=0, and when actual production, and Cc is obtained by sensor feedback.
Used dryer is 80kg grades of injection molding machine Hopper Dryers, i.e. Cd=80kg；And its rated power is 30KW.
The product being molded is V302DAB COVER moulding, and main raw material are plastic pellet PP+EPDM-T20AIP-
2015LOP2B-S1020, diameter about 2~5mm, volume about 65~125mm3, density about 0.0009g/mm3, quality is
0.0000585~0.0001125kg；In the present embodiment, the relevant parameter of plastic pellet is maximized.
Client it is expected that the electricity that material drying machine consumes per hour is no more than 120 degree, i.e. 120 kilowatt hours, i.e., 43200 kilowatts
Second, and in the present embodiment, taking the operating time H of material drying machine is daily maximum value, i.e. H=24h=86400s is therefore, objective
It is expected the maximum energy consumption EcostMax=432000KWs of material drying machine in family.
The main energy consumption of material drying machine is to heat and constant temperature is kept, and per day power P avg is about 4KW.
Material drying machine can be lost partial heat when opening valve, and loss linear coefficient is related with the aperture of valve, and
After actual measurement, relationship between the actual energy consumption Ecost and input and output material duration, charging/discharging valve aperture of material drying machine
The coefficient of formula is respectively as follows: XcosTin=9.84, YcosTin=0.64, XcosTout=0.68, YcosTout=0.11；
Therefore, the actual energy consumption Ecost of material drying machine are as follows:
Ecost=Pavg*H+Tin* (Kin*XcosTin+YcosTin)+Tout* (Kout*XcosTout+YcosTout)
=4*86400+Tin* (Kin*9.84+0.64)+Tout* (Kout*0.68+0.11)
=345600+Tin* (Kin*9.84+0.64)+Tout* (Kout*0.68+0.11)；
For different scenes, the actual energy consumption Ecost of material drying machine and input and output material duration, charging/discharging valve aperture
Between coefficient XcosTin, YcosTin, XcosTout, YcosTout 1 of relational expression be different.
The aperture of charging/discharging valve refers to the cross-sectional area of valve, and according to known to plastic pellet normal volume:
Aperture is 0.000025m2When, the speed of charging and discharging is 0.0001125kg/s,
Aperture is 0.0012m2When, the speed of charging and discharging is 0.0054kg/s；
Therefore, the proportionate relationship Xin=0.0001125/0.000025 between valve opening Kin and pan feeding speed Sin is fed
=0.0054/0.0012=4.5, i.e. pan feeding speed Sin=Kin*Xin=Kin*4.5；
Due to that can be expanded after heating particulates, therefore the proportionate relationship between the valve opening Kout and pan feeding speed Sout that discharges
Xout can be smaller, and Xout=3.3~4.1, and in the present embodiment, it is 4.1 that Xout, which is maximized,.
In production, it is desirable that the plastic pellet for sucking dryer is heated to 110 degrees Celsius, and constant temperature holding 1.5 hours, i.e.,
Tm=5400s.
Minimum energy dissipation EcostMin=4*5400* (Tin*Kin*4.5/75)=1296*Tin*Kin of material drying machine.
In production, according to production beat, the product of one net weight 489g of every 45 seconds outputs, i.e. product processing procedure Tp=
45s, product net weight Q=0.489kg.
S2 determines decision variable group, and establishes objective function relevant to decision variable group and constraint group of functions；It is described
Decision variable group is the aperture control parameter of material drying machine；
The decision variable group include 4 decision variables, be respectively as follows: charging valve opening Kin, discharging valve opening Kout, into
Expect duration T in, discharging duration T out；
Objective function, that is, the fitness function are as follows:
The satisfaction of the objective function makes material drying machine heating energy consumption reach minimum, makes the material number in material drying machine
Amount reaches at most, and makes all materials in the enough material drying dryers of thermostatic hold-time；
The constraint function group includes 5 constraint functions, is respectively as follows:
Material quantity in material drying machine is constrained, it is desirable that material quantity is less than drying materials in material drying machine
The volume of machine, and constraint function 1 is Kin*Xin*Tin-Kout*Xout*Tout < Cd；
The load of material drying machine is constrained, it is desirable that the load of material drying machine is less than the capacity of injection molding machine,
And constraint function 2 is Kout*Xout*Tout < Ci-Cc；
The discharging speed of material drying machine is constrained, it is desirable that it is minimum that the discharging speed of material drying machine meets injection molding machine
Material demand is drawn, and constraint function 3 is Sout*Tp > Q, i.e. Kout*Xout*Tp > Q；
The inlet amount and load of material drying machine are constrained, it is desirable that the inlet amount of material drying machine is greater than discharging
Amount, and constraint function 4 is Kin*Xin*Tin > Kout*Xout*Tout；
The actual energy consumption Ecost of material drying machine is constrained, it is desirable that the actual energy consumption Ecost of material drying machine is not
Maximum energy consumption EcostMax, and constraint function 5 it is expected less than the minimum energy consumption EcostMin of material drying machine and no more than client
For EcostMin≤Ecost≤EcostMax, i.e.,
Pavg*Tm*(Tin*Kin*Xin/Cd)≤
Pavg*H+Tin*(Kin*XcosTin+YcosTin)+Tout*(Kout*XcosTout+YcosTout)≤
EcostMax
In the present embodiment,
In decision variable group: charging valve opening Kin > 0, discharging valve opening Kout > 0, feed duration Tin > 0, going out
Expect duration T out > 0.
Objective function, that is, fitness function are as follows:
Constraint function 1 is Kin*4.5*Tin-Kout*4.1*Tout < 80.
Constraint function 2 is Kout*4.1*Tout < 25-Cc.
Constraint function 3 is Kout*4.1*42 > 0.489.
Constraint function 4 is Kin*4.5*Tin > Kout*4.1*Tout.
Constraint function 5 is
1296*Tin*Kin≤345600+Tin*(Kin*9.84+0.64)+Tout*(Kout*0.68+0.11)≤
432000, and in the present embodiment, only for constraint function 5, it is every for taking the duration of the charging/discharging valve of material drying machine
Its maximum value, i.e., for 24 hours, 86400s, therefore constraint function 5 is Kin*9.84+0.64+Kout*0.68+0.11≤1, i.e. Kin*
9.84+Kout*0.68≤0.25；
S3 is obtained keeping the value of objective function minimum and is met the decision variable of constraint function group using shuffled frog leaping algorithm
The value of group (Kin, Kout, Tin, Tout), i.e. optimal solution.
As shown in Figure 2, in step S3, the operation process of the shuffled frog leaping algorithm is specific as follows shown:
S31, set algorithm parameter simultaneously initialize it, and the algorithm parameter includes: of the solution of decision variable group
Number FrogCount, sub- population quantity GroupCount, sub- population frog quantity FrogCountInGroup, total the number of iterations
Optimizing maximum number of times MaxUpdateTimesInGroup, experiment number TestTimes, maximum in InteratingTimes, group
The step-length that leapfrogs MaxLeapStep, fuzzy interval BrtterFrogRange is used when taking more excellent solution；
According to the empirical data of injection molding production feed, a certain number of solutions for meeting constraint function group of initial setting are determined
The value of plan variable (Kin, Kout, Tin, Tout)；Set charging valve opening when by usual production, discharging valve opening, into
Expect the solution for meeting constraint function group of duration, discharging duration as initial setting；
The certain amount is equal to the number FrogCount of the solution of set decision variable group.
S32, calculates the fitness of each frog, and it is arranged according to the descending of fitness；Described each
Frog is a solution i.e. value of decision variable (Kin, Kout, Tin, Tout) for meeting constraint function group；The fitness
For will the frog i.e. the solution (Kin, Kout, Tin, Tout) substitute into objective function after obtain value F (Kin, Kout, Tin,
Tout), and the more big then fitness of value F (Kin, Kout, Tin, Tout) is poorer, and value F (Kin, Kout, Tin, Tout) is smaller, fits
Response is better.
S33, sub- population dividing, random is distributed to solution in each solution space, i.e., frog is distributed to each sub- population
In, and the frog in each sub- population is arranged according to the descending of fitness.Wherein, the number of the frog in each sub- population
Amount can be set to equal, may be set to be unequal.
S34, organize in optimizing in an optimizing i.e. sub- population: based on maximum leapfrog step-length MaxLeapStep obscured into
Change and generate new explanation at random, it is described it is fuzzy evolve as based on existing solution in organizing, the foundation maximum step-length MaxLeapStep that leapfrogs is repaired
Change the value of 4 decision variables in decision variable group, specifically, the fuzzy modification mode evolved can be found in and the prior art；Meter
Calculate the fitness difference between fitness in current organize worst solution and the new explanation, judge the fitness difference absolute value whether
Fuzzy interval BrtterFrogRange is used when taking more excellent solution greater than set, to judge whether the new explanation is one more excellent
Solution, wherein
If the absolute value of the fitness difference is not more than set BrtterFrogRange, then it is assumed that the new explanation is
One quasi-optimal solution, into next step；
If the absolute value of the fitness difference is greater than set BrtterFrogRange, then it is assumed that the new explanation is not
Quasi-optimal solution re-executes a step S34 and re-starts optimizing in primary organize；And the total degree of optimizing cannot in requirement group
Beyond optimizing maximum number of times MaxUpdateTimesInGroup in set group, if the total degree of optimizing exceeds in organizing
When MaxUpdateTimesInGroup, then this time organizes the new explanation ultimately generated in interior searching process and relatively sentence without fitness
It is disconnected, i.e., optimal solution subject to the new explanation is directly thought, into next step；
S35 updates the worst solution of the fitness in current group using the quasi-optimal solution that optimizing in organizing obtains, and records more
New number；
S36, judges whether update times reach total the number of iterations InteratingTimes, if not up to, continuing
The interior optimizing of group simultaneously updates, that is, jumps and execute step S34；
If reaching, each frog i.e. fitness of each solution is calculated, and judge that the minimum in current solution space is suitable
Whether response meets termination condition, i.e., whether the minimum fitness in current solution space is less than the threshold epsilon of setting, if being unsatisfactory for i.e.
Not less than the threshold epsilon of setting, then continue to calculate, repartition sub- population, jumps and execute step S33；It is less than setting if meeting
Threshold epsilon, then export solution corresponding to the minimum fitness in current solution space, solution corresponding to the minimum fitness of the output
As optimal solution, algorithm terminate.In the present embodiment, threshold epsilon=0.0001 of setting.
S4 controls material drying machine charging/discharging valve using the optimal solution of output.Meanwhile the operation for seeking optimal solution being recorded
It saves into database, and next time when executing the shuffled frog leaping algorithm again, calls the optimal solution saved in database as initial
The solution of initial setting during change, thus formation process knowledge base, with help staff to objective function and algorithm parameter into
Row, which updates, to be adjusted, and is avoided the occurrence of local optimum and is led to the dispatching method that cannot get total optimization.
In step S4, the database is static input parameter, that is, essential information data and adjusting control parameter, that is, optimal solution
Between mapping table；It is equivalent to, by each product, every kind of raw material, each dryer, in the setting that it corresponds to parameter
Under, in the optimal solution that each production time, the algorithm that leapfrogs find out, formed in an operation record deposit database.
For same production requirement, that is, identical PP particle, diameter about 2~5mm, material packet warehouse-out inspection moisture content are
0.89%, it is desirable that particle is heated to 110 degree, warms time holding 1.5 hours, 489 grams of product weight, processing procedure 45 seconds, removes
Operation time simultaneously ignores mould replacing time, continuous day shift 850, continuous night shift 800, and total material requirement is 806.85kg, material
Every packet 25kg is wrapped, in addition saliva material needs 33 packets altogether；
In the present embodiment, provide according to conventional method i.e. according to the aperture control of material drying machine set by artificial experience
The drying situation of material and the energy consumption condition of material drying machine under parameter processed and the control parameter；Wherein,
Material position is lower than the opening inlet valve of material position lower limit then maximum opening in barrel, and the maximum area for feeding valve opening is
0.003 square metre；
Caching lacks the opening outlet valve or manual control of material then automatic maximum opening in injection molding machine, the valve opening that usually discharges with
Charging valve opening is identical, i.e. the maximum area of discharging valve opening is also 0.003 square metre；
33 construction materials contracts, each feeding 3 wrap, and one day feeding 11 times altogether, inlet valve is opened 11 times, every time operation 5 minutes, add up
Operating time is 6600 seconds i.e. 1.83 hours, in addition, inlet valve maximum opening is 0.003 square metre, feed time 825/
(0.003*4.5)/3600=16.97 hours, therefore, the opening time for adding up inlet valve was 18.803 hours；
Discharging be injection molding machine pull automatically or by hand transfer, manual transfer time about with cumulative operating time when feeding
Unanimously, 6600 seconds i.e. 1.83 hours, in addition, outlet valve maximum opening is 0.003 square metre, discharging time is 825/ (0.003*
4.1)/3600=18.63 hours, therefore, the opening time for adding up outlet valve was 20.46 hours；
The drying situation of material is determined using the water content detection content of the material after drying, is controlled and is joined in above-mentioned aperture
Under several, the water content detection content of the material after drying is 0.09%~0.48%；
The energy consumption condition of material drying machine is measured using intelligent electric meter, and measurement result is 399470.742144 kilowatts
Second, i.e., 110.96409504 degree.
In the present embodiment, the aperture control parameter of material drying machine set by method according to the present invention is additionally provided, with
And the drying situation of material and the energy consumption condition of material drying machine under the control parameter；Wherein,
It after concentrating feed, executes within algorithm every 5 minutes once, adds up Automatic dispatching 288 times, the open or close range of inlet valve is automatic
The range for adjusting, and automatically adjusting is between 0~0.0026 square metre, and the average aperture of inlet valve is 0.0024 square meter；
It after concentrating feed, executes within algorithm every 5 minutes once, adds up Automatic dispatching 288 times, the open or close range of outlet valve is automatic
It adjusts, and automatically adjusting range is between 0~0.0028 square metre, the average aperture of outlet valve is 0.0027 square meter；
The opening time of accumulative inlet valve is 21.22 hours；
The opening time of accumulative outlet valve is 20.7 hours；
The drying situation of material is determined using the water content detection content of the material after drying, is controlled and is joined in above-mentioned aperture
Under several, the water content detection content of the material after drying is 0.06%~0.14%；
The energy consumption condition of material drying machine is measured using intelligent electric meter, and measurement result is 399172.762656 kilowatts
Second, i.e., 110.88132296 degree.
For the same production requirement, the aperture control parameter of material drying machine set by comparative analysis conventional method with
And the drying situation of material and the energy consumption condition of material drying machine under the control parameter, it is dried with material set by the method for the present invention
The drying situation of material and the energy consumption condition of material drying machine under the aperture control parameter and the control parameter of dry machine, it is known that:
The method of the present invention, which can be realized, automatically adjusts control to the aperture of material drying machine, and can complete production requirement；And this
The water content detection content that inventive method controls the material after material drying machine is dried is lower compared to conventional method, and material dries
Dry better off.
The above is only the preferred embodiments of the invention, are not intended to limit the invention creation, all in the present invention
Made any modifications, equivalent replacements, and improvements etc., should be included in the guarantor of the invention within the spirit and principle of creation
Within the scope of shield.
Claims (5)
1. the material drying machine charging/discharging valve aperture control method based on shuffled frog leaping algorithm, which is characterized in that including following step
It is rapid:
S1, obtains the essential information data controlled about charging/discharging valve aperture, and the essential information data include:
The volume of injection molding machine is Ci；It is Cc that the buffer memory of injection molding machine, which caches volume,；
The volume of material drying machine is Cd；The per day power of material drying machine is Pavg；
The maximum energy consumption of desired material drying machine is EcostMax；
Relational expression between the actual energy consumption Ecost and input and output material duration, charging/discharging valve aperture of material drying machine are as follows:
Ecost=Pavg*H+Tin* (Kin*XcosTin+YcosTin)+Tout* (Kout*XcosTout+YcosTout)；
Wherein, H is the operating time of material drying machine, and Tout is the discharging duration, and Tin is feed duration, Kin be into
Expect valve opening, Kout is discharging valve opening, and XcosTin, YcosTin, XcosTout, YcosTout are that the relational expression is
Number；
Feeding the proportionate relationship between valve opening Kin and pan feeding speed Sin is 1:Xin, i.e. pan feeding speed Sin is Kin*Xin；
Proportionate relationship between discharging valve opening Kout and discharging speed Sout is 1:Xout, i.e. discharging speed Sout is Kout*
Xout；
It is required that the plastic pellet of sucking material drying machine is heated to χ degrees Celsius, and thermostatic hold-time is Tm；
The minimum energy dissipation of material drying machine is EcostMin, and
EcostMin=Pavg*Tm* (Tin*Sin/Cd)=Pavg*Tm* (Tin*Kin*Xin/Cd)；
Product processing procedure is Tp, and product net weight is Q, i.e., the productive temp of product is the energy output net weight within the time of a processing procedure Tp
For the plastic pellet of Q；
S2 determines decision variable group, and establishes objective function relevant to decision variable group and constraint group of functions；The decision
Set of variables is the aperture control parameter of material drying machine；
S3 is obtained keeping the value of objective function minimum and is met the decision variable group of constraint function group using shuffled frog leaping algorithm
Value, i.e. optimal solution；
S4 controls material drying machine charging/discharging valve using the optimal solution.
2. the material drying machine charging/discharging valve aperture control method according to claim 1 based on shuffled frog leaping algorithm,
It is characterized in that, in step S1, for different production requirements, the essential information data are also different, actual mechanical process
In, production management person sets or adjusts its corresponding essential information data according to production requirement；Meanwhile for multiple and different lifes
It produces and requires, production management person is produced the corresponding different essential information data of this multiple and different production requirement institute by it
Time sequencing arrangement.
3. the material drying machine charging/discharging valve aperture control method according to claim 1 based on shuffled frog leaping algorithm,
It is characterized in that, in step S2, the decision variable group includes 4 decision decision variables, is respectively as follows: charging valve opening Kin, discharging
Valve opening Kout, feed duration Tin, discharging duration T out；
Objective function, that is, the fitness function are as follows:
The constraint function group includes 5 constraint functions, is respectively as follows:
Constraint function 1 is Kin*Xin*Tin-Kout*Xout*Tout < Cd；
Constraint function 2 is Kout*Xout*Tout < Ci-Cc；
Constraint function 3 is Sout*Tp > Q, i.e. Kout*Xout*Tp > Q；
Constraint function 4 is Kin*Xin*Tin > Kout*Xout*Tout；
Constraint function 5 is EcostMin≤Ecost≤EcostMax, i.e.,
4. the material drying machine charging/discharging valve aperture control method according to claim 1 based on shuffled frog leaping algorithm,
Be characterized in that, in step S3, the shuffled frog leaping algorithm comprising the following specific steps
S31, set algorithm parameter simultaneously initialize it, and the algorithm parameter includes: the number of the solution of decision variable group
FrogCount, sub- population quantity GroupCount, sub- population frog quantity FrogCountInGroup, total the number of iterations
Optimizing maximum number of times MaxUpdateTimesInGroup, experiment number TestTimes, maximum in InteratingTimes, group
The step-length that leapfrogs MaxLeapStep, fuzzy interval BrtterFrogRange is used when taking more excellent solution；
The empirical data of feed, a certain number of solutions for meeting constraint function group of initial setting are produced according to injection molding, the solution is
For the value of decision variable (Kin, Kout, Tin, Tout)；The certain amount is equal to the solution of set decision variable group
Number FrogCount；
S32, calculates the fitness of each i.e. each frog of solution, and it is arranged according to the descending of fitness；It is described
Fitness be the frog i.e. solution is substituted into obtained value F (Kin, Kout, Tin, Tout) after objective function, and value F (Kin,
Kout, Tin, Tout) more big then fitness is poorer, and the smaller then fitness of value F (Kin, Kout, Tin, Tout) is better；
S33, sub- population dividing, random is distributed to solution in each solution space, i.e., frog is distributed in each sub- population, and
Frog in each sub- population is arranged according to the descending of fitness；
S34, organize in optimizing in an optimizing i.e. sub- population: fuzzy evolve simultaneously is carried out based on the maximum step-length MaxLeapStep that leapfrogs
It is random to generate new explanation, it is described it is fuzzy evolve as based on existing solution in organizing, determine according to the maximum step-length MaxLeapStep modification that leapfrogs
The value of 4 decision variables in plan set of variables；The fitness calculated between fitness in current organize worst solution and the new explanation is poor
Value judges whether the absolute value of the fitness difference is greater than and set uses fuzzy interval when taking more excellent solution
BrtterFrogRange, to judge whether the new explanation is a more excellent solution, wherein
If the absolute value of the fitness difference is not more than set BrtterFrogRange, then it is assumed that the new explanation is one
Quasi-optimal solution, into next step；
If the absolute value of the fitness difference is greater than set BrtterFrogRange, then it is assumed that the new explanation be not it is quasi- most
Excellent solution re-executes a step S34 and re-starts optimizing in primary organize；And the total degree of optimizing cannot exceed in requirement group
Optimizing maximum number of times MaxUpdateTimesInGroup in set group, if the total degree of optimizing exceeds in organizing
When MaxUpdateTimesInGroup, then this time organizes the new explanation ultimately generated in interior searching process and relatively sentence without fitness
It is disconnected, i.e., optimal solution subject to the new explanation is directly thought, into next step；
S35 updates the worst solution of the fitness in current group using the quasi-optimal solution that optimizing in organizing obtains, and records update time
Number；
S36, judges whether update times reach total the number of iterations InteratingTimes, if not up to, continuing in group
Optimizing simultaneously updates, that is, jumps and execute step S34；
If reaching, each frog i.e. fitness of each solution is calculated, and judge the minimum fitness in current solution space
Whether termination condition is met, i.e., whether the minimum fitness in current solution space is less than the threshold epsilon of setting, if being unsatisfactory for i.e. not small
In the threshold epsilon of setting, then continue to calculate, repartition sub- population, jumps and execute step S33；If meeting the threshold i.e. less than setting
Value ε, then export solution corresponding to the minimum fitness in current solution space, and solution corresponding to the minimum fitness of the output is
Optimal solution, algorithm terminate.
5. the material drying machine charging/discharging valve aperture control method according to claim 1 based on shuffled frog leaping algorithm,
It is characterized in that, in step S4, after obtaining optimal solution, also saves the operation record for seeking optimal solution into database, and next time
When executing the shuffled frog leaping algorithm again, solution of the optimal solution saved in database as initial setting in initialization procedure is called；
The database is the correspondence between static input parameter, that is, essential information data and adjusting control parameter, that is, optimal solution
Relation table.
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