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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- 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
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- 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
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
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- B29C45/00—Injection moulding, i.e. forcing the required volume of moulding material through a nozzle into a closed mould; Apparatus therefor
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- B29C45/00—Injection moulding, i.e. forcing the required volume of moulding material through a nozzle into a closed mould; Apparatus therefor
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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
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- 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 invention relates to the technical field of valve opening control, in particular to a method for controlling the opening of a feeding valve and a discharging valve of a material dryer based on a mixed frog leaping algorithm.
Background
In the injection molding production engineering, plastic particles often carry moisture, which directly affects the quality of plastic products, and the moisture can be vaporized during high-temperature molding injection, so that various defects of hollow bubbles, surface silver wires, insufficient injection molding, cavities and the like are easily formed in injection molding products, the mechanical property and the appearance quality of the plastic products are affected, and when the conditions are serious, the moisture can even promote the degradation of plastics, and the performance is greatly reduced. Therefore, the injection molding machine is generally provided with a material dryer as a raw material auxiliary input device, wherein the automatic scheduling of the feeding and discharging valves of the material dryer is a very critical part of an automatic feeding system of the injection molding machine, the feeding and discharging speeds and the duration time are adjusted by controlling the opening degrees of the feeding valves and the discharging valves, so that the absolute drying of materials is ensured, the heating energy consumption of the material dryer is optimized, and the resources of the automatic feeding system of the injection molding machine are saved.
In the prior art, the opening control of the feeding and discharging valve of the material dryer is generally manually adjusted according to historical experience, the degree of the manual judgment is high, time and labor are wasted, and the control cannot be effectively carried out on the optimization of the heating energy consumption of the material dryer and the saving of the automatic feeding system resources of the injection molding machine.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a material dryer feeding and discharging valve opening control method based on a mixed frog-leaping algorithm, which can automatically generate opening control parameters of a material dryer feeding and discharging valve, and the automatically generated parameters can ensure absolute drying of materials, optimize heating energy consumption of the material dryer and save occupied capacity of an automatic feeding system of an injection molding machine.
In order to achieve the purpose, the invention adopts the following technical scheme that:
the method for controlling the opening degree of the material inlet and outlet valve of the material dryer based on the mixed frog leaping algorithm comprises the following steps:
s1, obtaining basic information data about the opening control of the feeding and discharging valve, wherein the basic information data comprises:
the volume of the injection molding machine is Ci; the buffer storage amount of the injection molding machine, namely the buffer volume is Cc;
the volume of the material dryer is Cd; the daily average power of the material dryer is Pavg;
the expected maximum energy consumption of the material dryer is EcostMax;
the relation among the actual energy consumption Ecost of the material dryer, the feeding and discharging duration and the opening of the feeding and discharging valve is as follows:
Ecost＝Pavg*H+Tin*(Kin*XcosTin+YcosTin)+Tout*(Kout*XcosTout+YcosTout)；
h is the working duration of the material dryer, Tout is the discharging duration, Tin is the feeding duration, Kin is the opening degree of the feeding valve, Kout is the opening degree of the discharging valve, and XcosTin, YcosTin, XcosTout and YcosTout are coefficients of the relational expression;
the proportional relation between the opening Kin of the feeding valve and the feeding speed Sin is 1: Xin, namely the feeding speed Sin is Kin x Xin;
the proportional relation between the opening Kout of the discharge valve and the discharge speed Sout is 1: Xout, namely the discharge speed Sout is Kout Xout;
the plastic particles sucked into the material dryer are required to be heated to chi ℃ and the constant temperature is kept for Tm;
the minimum energy consumption of the material dryer is EcostMin, and
EcostMin＝Pavg*Tm*(Tin*Sin/Cd)＝Pavg*Tm*(Tin*Kin*Xin/Cd)；
the manufacturing process of the product is Tp, the net weight of the product is Q, namely the production cycle of the product is that plastic particles with the net weight of Q can be produced within the time of one manufacturing process Tp;
s2, determining a decision variable set, and establishing a target function and a constraint function set related to the decision variable set; the decision variable group is an opening control parameter of the material dryer;
s3, obtaining the value of the decision variable group which minimizes the value of the objective function and meets the constraint function group by using a mixed frog-leaping algorithm, namely an optimal solution;
and S4, controlling the material inlet and outlet valve of the material dryer by using the optimal solution.
In step S1, the basic information data are different according to different production requirements, and in the actual operation process, the production manager sets or adjusts the corresponding basic information data according to the production requirements; meanwhile, aiming at a plurality of different production requirements, the production manager arranges different basic information data respectively corresponding to the plurality of different production requirements according to the production time sequence.
In step S2, the decision variable group includes 4 decision variables, which are respectively: opening degree Kin of a feeding valve, opening degree Kout of a discharging valve, feeding duration time Tin and discharging duration time Tout;
the objective function, i.e. fitness function, is:
the set of constraint functions includes 5 constraint functions, which are:
the constraint function 1 is Kin Xin Tin-Kout Xout Tout < Cd;
the constraint function 2 is Kout Xout Tout < Ci-Cc;
the constraint function 3 is Sout × Tp > Q, i.e., Kout × Xout × Tp > Q;
the constraint function 4 is Kin x in Tin > Kout x Xout Tout;
the constraint function 5 is that EcostMin is less than or equal to Ecost is less than or equal to EcostMax, i.e.
6. The method for controlling the opening degree of the feeding and discharging valve of the material dryer based on the mixed frog leap algorithm is characterized in that in the step S3, the mixed frog leap algorithm comprises the following specific steps:
s31, setting algorithm parameters and initializing the algorithm parameters, wherein the algorithm parameters comprise: the number FrogCount of the solution of the decision variable group, the sub-population number GroupCount, the sub-population frog number FrogCountInGroup, the total iteration times InteratingTimes, the group optimization times upper limit MaxUpdateTimesInGroup, the experiment times TestTimes, the maximum leapfrog step, and the fuzzy interval BrtterFrogRange used when a more optimal solution is taken;
preliminarily setting a certain number of solutions which accord with a constraint function group according to empirical data of injection molding production feeding, wherein the solutions are values of decision variables (Kin, Kout, Tin and Tout); the certain number is equal to the number FrogCount of solutions of the set decision variable group;
s32, calculating the fitness of each solution, namely each frog, and arranging the solutions according to the descending order of the fitness; the fitness is a value F (Kin, Kout, Tin and Tout) obtained by substituting the frog, namely the solution, into an objective function, the higher the value F (Kin, Kout, Tin and Tout), the worse the fitness, and the lower the value F (Kin, Kout, Tin and Tout), the better the fitness;
s33, dividing the sub-populations, dispersing the solutions into each solution space randomly, namely dispersing the frogs into each sub-population, and arranging the frogs in each sub-population according to the descending order of the fitness;
s34, group optimization, namely, in-group optimization of a sub-population: carrying out fuzzy evolution based on the maximum leapfrog step length MaxLeapStep and randomly generating a new solution, wherein the fuzzy evolution is based on the existing solution in the group, and modifying the values of 4 decision variables in the decision variable group according to the maximum leapfrog step length MaxLeapStep; calculating the fitness difference between the solution with the worst fitness in the current group and the new solution, judging whether the absolute value of the fitness difference is larger than the set better solution, using a fuzzy interval BrtterFrogRange to judge whether the new solution is a better solution, wherein,
if the absolute value of the fitness difference is not greater than the set BrtterFrogRange, the new solution is considered as a quasi-optimal solution, and the next step is carried out;
if the absolute value of the fitness difference is larger than the set BrtterFrogRange, the new solution is considered not to be the quasi-optimal solution, and the step S34 is executed once again, namely the in-group optimization is carried out once again; and the total number of times of in-group optimization is required to be not more than the set upper limit of the number of times of in-group optimization MaxUpdateTimesInGroup, if the total number of times of in-group optimization is more than the MaxUpdateTimesInGroup, the new solution finally generated in the in-group optimization process is not subjected to fitness comparison and judgment, namely the new solution is directly considered as the quasi-optimal solution, and the next step is carried out;
s35, updating the solution with the worst fitness in the current group by using the quasi-optimal solution obtained by the in-group optimization, and recording the updating times;
s36, judging whether the updating times reach the total iteration times InterationTimes, if not, continuing to perform in-group optimizing and updating, namely skipping to execute the step S34;
if yes, calculating the fitness of each frog, namely each solution, and judging whether the minimum fitness in the current solution space meets a termination condition, namely whether the minimum fitness in the current solution space is smaller than a set threshold epsilon, if not, continuing to calculate, re-dividing the sub-population, and skipping to execute the step S33; if the minimum fitness is smaller than the set threshold epsilon, outputting the solution corresponding to the minimum fitness in the current solution space, wherein the solution corresponding to the output minimum fitness is the optimal solution, and terminating the algorithm.
In step S4, after the optimal solution is obtained, the operation record of obtaining the optimal solution is stored in the database, and when the mixed frog leap algorithm is executed next time, the optimal solution stored in the database is called as the solution preliminarily set in the initialization process;
the database is a corresponding relation table between static input parameters, namely basic information data, and adjustment control parameters, namely optimal solutions.
The invention has the advantages that:
(1) the algorithm of the invention can automatically generate the opening control parameter of the material inlet and outlet valve of the material dryer according to the basic information data, and the material dryer controls the opening of the material inlet and outlet valve according to the parameter, thereby realizing the automatic processing of the material dryer.
(2) The production manager can set or adjust different basic information data according to actual production requirements; meanwhile, aiming at a plurality of different production requirements, a production manager arranges different basic information data respectively corresponding to the plurality of different production requirements according to the production time sequence, so that the material dryer can automatically adjust the opening control of the feeding and discharging valve according to different production requirements.
(3) The invention can make the heating energy consumption of the material dryer reach the lowest and the material quantity in the material dryer reach the maximum when the decision variable group, namely the setting of the opening control parameter of the charging and discharging valve, accords with the actual situation and meets the set objective function.
(4) The method uses the optimal solution of the mixed frog-leaping algorithm, effectively ensures the correctness and optimality of the obtained opening control parameters of the material inlet and outlet valves of the material dryer, and simultaneously uses a fuzzy evolution mode to solve the quasi-optimal solution in the mixed frog-leaping algorithm, thereby ensuring the high solving speed.
(5) The invention also saves the operation record into the database after obtaining the optimal solution, and calls the optimal solution saved in the database as the solution preliminarily set in the initialization process when executing the mixed frog-leaping algorithm next time, thereby forming a process knowledge base to help the working personnel to update and adjust the target function and the algorithm parameter, and avoiding the scheduling method which cannot obtain the integral optimal due to the occurrence of local optimization.
Drawings
Fig. 1 is a flow chart of a method for controlling the opening of a material inlet and outlet valve of a material dryer based on a mixed frog leaping algorithm.
Fig. 2 is a flow chart of the mixed frog leaping algorithm of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the method for controlling the opening of the material inlet and outlet valve of the material dryer based on the mixed frog leaping algorithm comprises the following steps:
s1, acquiring basic information data related to the opening control of the feeding and discharging valve, wherein the basic information data are process parameters; the basic information data are different according to different production requirements, and in the actual operation process, a production manager sets or adjusts the corresponding basic information data according to the production requirements; meanwhile, when aiming at a plurality of different production requirements, a production manager arranges different basic information data respectively corresponding to the plurality of different production requirements according to the production time sequence, so that the material dryer can automatically adjust the opening control of the feeding and discharging valve according to the different production requirements.
The basic information data includes:
the volume of the injection molding machine is Ci; the buffer storage amount of the injection molding machine, namely the buffer volume is Cc;
the volume of the material dryer is Cd; the daily average power of the dryer is Pavg;
the maximum energy consumption of the material dryer expected by a customer is EcostMax;
the relation among the actual energy consumption Ecost of the material dryer, the feeding and discharging duration and the opening of the feeding and discharging valve is as follows:
ecost is Pavg H + Tin (Kin XcosTin + YcosTin) + Tout (Kout XcosTout + YcosTout), wherein H is the working time of the material dryer, Tout is the discharge duration, Tin is the feeding duration, Kin is the opening of the feeding valve, Kout is the opening of the discharge valve, XcosTin, YcosTin, XcosTout and YcosTout are coefficients of the relational expression;
the proportional relation between the opening Kin of the feeding valve and the feeding speed Sin is 1: Xin, namely the feeding speed Sin is Kin x Xin;
the proportional relation between the opening Kout of the discharge valve and the discharge speed Sout is 1: Xout, namely the discharge speed Sout is Kout Xout;
in production, plastic particles sucked into a material dryer are required to be heated to chi ℃ and kept at a constant temperature for Tm;
the minimum energy consumption of the material dryer is EcostMin, and the minimum energy consumption is as follows:
EcostMin＝Pavg*Tm*(Tin*Sin/Cd)＝Pavg*Tm*(Tin*Kin*Xin/Cd)；
in production, according to the production cycle of the product, i.e. plastic particles, the product process, i.e. plastic particle process, is Tp, and the product net weight, i.e. plastic particle net weight, is Q, i.e. plastic particles with net weight Q are required to be produced within the time of one process Tp.
In the invention, the actual energy consumption Ecost of the material dryer and the minimum energy consumption EcostMin of the material dryer which are calculated according to the formula are the energy consumption under single scheduling.
In the present embodiment, the first and second electrodes are,
the adopted injection molding machine is Haitian MA1200, the buffer capacity of plastic particles is 25kg, namely Ci is 25 kg; the buffer amount of the current injection molding machine is Cc, the initialization Cc is 0, and in actual production, Cc is obtained by sensor feedback.
The adopted dryer is an 80 kg-grade injection molding machine hopper dryer, namely Cd is 80 kg; and its rated power is 30 KW.
The injection-molded product is a V302DAB COVER injection molding part, the main raw material is plastic particles PP + EPDM-T20AIP-2015LOP2B-S1020, the diameter is about 2-5 mm, and the volume is about 65-125 mm3Density of about 0.0009g/mm3The mass is 0.0000585-0.0001125 kg; in this embodiment, the relevant parameters of the plastic particles take the maximum values.
The customer expects the material dryer to consume no more than 120 degrees of electricity per hour, i.e., 120 kwh, i.e., 43200 kws, and in this embodiment, the operation duration H of the material taking dryer is the maximum value per day, i.e., H24H 86400s, so the customer expects the maximum energy consumption EcostMax 432000KWs of the material dryer.
The main energy consumption of the material dryer is heating and constant temperature maintenance, and the daily average power Pavg of the material dryer is about 4 KW.
Partial heat can be lost when the valve is opened by the material dryer, the loss linear coefficient is related to the opening degree of the valve, and after actual measurement, the coefficients of the relational expressions among the actual energy consumption Ecost of the material dryer, the feeding and discharging duration and the opening degree of the feeding and discharging valve are respectively as follows: XcosTin ═ 9.84, yconstin ═ 0.64, XcosTout ═ 0.68, and YcosTout ═ 0.11;
therefore, the actual energy consumption Ecost of the material dryer is:
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)；
aiming at different scenes, the coefficients XcosTin, YcosTin, XcosTout and YcosTout 1 of the relational expression between the actual energy consumption Ecost of the material dryer and the opening degree of the material inlet and outlet duration and the material inlet and outlet valve are different.
The opening degree of the feeding and discharging valve refers to the cross-sectional area of the valve, and can be known according to the standard volume of plastic particles:
the opening degree is 0.000025m2When in use, the feeding speed and the discharging speed are both 0.0001125kg/s,
the opening degree is 0.0012m2When in use, the feeding speed and the discharging speed are both 0.0054 kg/s;
therefore, the proportional relationship Xin between the inlet valve opening Kin and the inlet feed speed Sin is 0.0001125/0.000025-0.0054/0.0012-4.5, i.e., the inlet feed speed Sin-Kin-Xin-4.5;
since the particles will expand after heating, the proportional relationship Xout between the discharging valve opening Kout and the feeding speed Sout is slightly smaller, and Xout is 3.3-4.1, in this embodiment, the maximum value of Xout is 4.1.
In production, plastic particles sucked into a dryer are required to be heated to 110 ℃ and kept at a constant temperature for 1.5 hours, namely Tm 5400 s.
The minimum energy consumption EcostMin of the material dryer is 4 × 5400 (Tin × Kin 4.5/75) ═ 1296 × Tin × Kin.
In production, according to the production tact time of the product, 489g of the product is produced every 45 seconds, i.e. the product process Tp is 45s, and the product net weight Q is 0.489 kg.
S2, determining a decision variable set, and establishing a target function and a constraint function set related to the decision variable set; the decision variable group is an opening control parameter of the material dryer;
the decision variable group includes 4 decision variables, which are respectively: opening degree Kin of a feeding valve, opening degree Kout of a discharging valve, feeding duration time Tin and discharging duration time Tout;
the objective function, i.e. fitness function, is:
the satisfaction of the objective function enables the heating energy consumption of the material dryer to be minimum, the quantity of the materials in the material dryer to be maximum, and the constant temperature keeping time to be enough for drying all the materials in the material dryer;
the set of constraint functions includes 5 constraint functions, which are:
constraining the quantity of the materials in the material dryer, wherein the quantity of the materials in the material dryer is required to be smaller than the volume of the material dryer, and a constraint function 1 is Kin Xin Tin-Kout Xout Tout < Cd;
constraining the discharge amount of the material dryer, wherein the discharge amount of the material dryer is required to be smaller than the capacity of the injection molding machine, and a constraint function 2 is Kout Xout Tout < Ci-Cc;
constraining the discharging speed of the material dryer to meet the minimum material pulling requirement of the injection molding machine, wherein a constraint function 3 is Sout Tp > Q, namely Kout Xout Tp > Q;
constraining the feeding amount and the discharging amount of the material dryer, wherein the feeding amount of the material dryer is required to be larger than the discharging amount, and the constraint function 4 is Kin Xin Tin > Kout Xout Tout;
constraining the actual energy consumption Ecost of the material dryer, wherein the actual energy consumption Ecost of the material dryer is not less than the lowest energy consumption EcostMin of the material dryer and not more than the maximum energy consumption EcostMax expected by the customer, and the constraint function 5 is that the EcostMin is not less than the Ecost and not more than the EcostMax, namely
Pavg*Tm*(Tin*Kin*Xin/Cd)≤
Pavg*H+Tin*(Kin*XcosTin+YcosTin)+Tout*(Kout*XcosTout+YcosTout)≤EcostMax
In the present embodiment, the first and second electrodes are,
in the decision variable set: the opening Kin of the feeding valve is more than 0, the opening Kout of the discharging valve is more than 0, the feeding duration time Tin is more than 0, and the discharging duration time Tout is more than 0.
The objective function, i.e. fitness function, is:
constraint function 1 is Kin 4.5 Tin-Kout 4.1 Tout < 80.
The constraint function 2 is Kout 4.1 Tout < 25-Cc.
The constraint function 3 is Kout 4.1 42 > 0.489.
The constraint function 4 is Kin × 4.5 × Tin > Kout × 4.1 × Tout.
The constraint function 5 is
1296 times Tin is 345600+ time (Kin 9.84+0.64) + Tout (Kout 0.68+0.11) 432000, and in this example, the duration of the inlet and outlet valves of the extraction dryer is the maximum value per day, i.e. 24h, 86400s, only for constraint function 5, so that 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, obtaining the value of the decision variable group (Kin, Kout, Tin, Tout) which minimizes the value of the objective function and satisfies the constraint function group by using a mixed frog-leaping algorithm, namely the optimal solution.
As shown in fig. 2, in step S3, the operation flow of the mixed frog-leaping algorithm is specifically as follows:
s31, setting algorithm parameters and initializing the algorithm parameters, wherein the algorithm parameters comprise: the number FrogCount of the solution of the decision variable group, the sub-population number GroupCount, the sub-population frog number FrogCountInGroup, the total iteration times InteratingTimes, the group optimization times upper limit MaxUpdateTimesInGroup, the experiment times TestTimes, the maximum leapfrog step, and the fuzzy interval BrtterFrogRange used when a more optimal solution is taken;
preliminarily setting a certain number of solutions which accord with a constraint function group, namely values of decision variables (Kin, Kout, Tin and Tout) according to empirical data of injection molding production feeding; taking the opening degree of a feeding valve, the opening degree of a discharging valve, the feeding duration and the discharging duration which are set in the common production as solutions which are preliminarily set and accord with a constraint function group;
the certain number is equal to the number FrogCount of solutions of the set decision variable set.
S32, calculating the fitness of each frog, and arranging the fitness in a descending order; each frog is a value of a decision variable (Kin, Kout, Tin, Tout) which is a solution of a constraint function group; the fitness is a value F (Kin, Kout, Tin, Tout) obtained by substituting the frog, namely the solution (Kin, Kout, Tin, Tout), into an objective function, the fitness is worse when the value F (Kin, Kout, Tin, Tout) is larger, and the fitness is better when the value F (Kin, Kout, Tin, Tout) is smaller.
And S33, dividing the sub-populations, randomly dispersing the solutions into each solution space, namely dispersing the frogs into each sub-population, and arranging the frogs in each sub-population according to the descending order of the fitness. Wherein, the number of frogs in each sub-population can be set to be equal or unequal.
S34, group optimization, namely, in-group optimization of a sub-population: carrying out fuzzy evolution based on the maximum leapfrog step length MaxLeapStep and randomly generating a new solution, wherein the fuzzy evolution is based on the existing solution in the group, and modifying the values of 4 decision variables in the decision variable group according to the maximum leapfrog step length MaxLeapStep, and specifically, the modification mode of the fuzzy evolution can be referred to in the prior art; calculating the fitness difference between the solution with the worst fitness in the current group and the new solution, judging whether the absolute value of the fitness difference is larger than the set better solution, using a fuzzy interval BrtterFrogRange to judge whether the new solution is a better solution, wherein,
if the absolute value of the fitness difference is not greater than the set BrtterFrogRange, the new solution is considered as a quasi-optimal solution, and the next step is carried out;
if the absolute value of the fitness difference is larger than the set BrtterFrogRange, the new solution is considered not to be the quasi-optimal solution, and the step S34 is executed once again, namely the in-group optimization is carried out once again; and the total number of times of in-group optimization is required to be not more than the set upper limit of the number of times of in-group optimization MaxUpdateTimesInGroup, if the total number of times of in-group optimization is more than the MaxUpdateTimesInGroup, the new solution finally generated in the in-group optimization process is not subjected to fitness comparison and judgment, namely the new solution is directly considered as the quasi-optimal solution, and the next step is carried out;
s35, updating the solution with the worst fitness in the current group by using the quasi-optimal solution obtained by the in-group optimization, and recording the updating times;
s36, judging whether the updating times reach the total iteration times InterationTimes, if not, continuing to perform in-group optimizing and updating, namely skipping to execute the step S34;
if yes, calculating the fitness of each frog, namely each solution, and judging whether the minimum fitness in the current solution space meets a termination condition, namely whether the minimum fitness in the current solution space is smaller than a set threshold epsilon, if not, continuing to calculate, re-dividing the sub-population, and skipping to execute the step S33; if the minimum fitness is smaller than the set threshold epsilon, outputting the solution corresponding to the minimum fitness in the current solution space, wherein the solution corresponding to the output minimum fitness is the optimal solution, and terminating the algorithm. In this embodiment, the threshold value ∈ is set to 0.0001.
And S4, controlling the material inlet and outlet valve of the material dryer by using the output optimal solution. Meanwhile, the operation record of the optimal solution is saved in a database, and when the mixed frog-leaping algorithm is executed next time, the optimal solution saved in the database is called as a solution preliminarily set in the initialization process, so that a process knowledge base is formed, a worker is helped to update and adjust the target function and the algorithm parameters, and the scheduling method which is locally optimal and cannot obtain the overall optimal is avoided.
In step S4, the database is a correspondence table between static input parameters, i.e., basic information data, and adjustment control parameters, i.e., an optimal solution; equivalently, under the setting of corresponding parameters of each product, each raw material and each dryer, the optimal solution obtained by the frog leaping algorithm at each production time forms an operation record to be stored in a database.
Aiming at the same production requirement, namely, the diameter of the same PP particle is about 2-5 mm, the moisture content of the packaged material is 0.89% after being taken out of a warehouse, the particle is required to be heated to 110 ℃, the drying time is kept for 1.5 hours, the weight of the product is 489g, the process is 45 seconds, the operation preparation time is removed, the mold changing time is omitted, 850 continuous white classes are used, 800 continuous night classes are used, the total material requirement is 806.85kg, 25kg of packaged material is used, and 33 packages of water and materials are added;
in the embodiment, the opening control parameter of the material dryer, which is set according to the traditional method, namely according to manual experience, and the drying condition of the material and the energy consumption condition of the material dryer under the control parameter are provided; wherein,
the feeding valve is opened with the maximum opening when the material level in the charging barrel is lower than the lower limit of the material level, and the maximum area of the opening of the feeding valve is 0.003 square meter;
when the buffer memory in the injection molding machine is short of materials, the discharge valve is automatically opened to the maximum opening degree or manually controlled, the opening degree of the discharge valve is generally the same as that of the feed valve, namely the maximum area of the opening degree of the discharge valve is also 0.003 square meter;
33 bags, 3 bags are fed each time, the total feeding is carried out 11 times a day, the feeding valve is opened 11 times, each operation is carried out for 5 minutes, the cumulative operation time is 6600 seconds, namely 1.83 hours, in addition, the maximum opening of the feeding valve is 0.003 square meter, the feeding time is 825/(0.003 x 4.5)/3600 which is 16.97 hours, and therefore, the cumulative opening time of the feeding valve is 18.803 hours;
the discharging is automatic pulling or manual transferring of the injection molding machine, the manual transferring time is about consistent with the accumulated operation time during the charging, 6600 seconds are 1.83 hours, in addition, the maximum opening of the discharging valve is 0.003 square meter, the discharging time is 825/(0.003 x 4.1)/3600 is 18.63 hours, and therefore the opening time of the accumulated discharging valve is 20.46 hours;
judging the drying condition of the material by using the moisture detection content of the dried material, wherein the moisture detection content of the dried material is 0.09-0.48% under the opening control parameter;
the energy consumption condition of the material dryer is measured by using an intelligent electric meter, and the measurement result is 399470.742144 kilowatt seconds, namely 110.96409504 degrees.
In the embodiment, the opening control parameter of the material dryer set according to the method of the invention, and the drying condition of the material and the energy consumption condition of the material dryer under the control parameter are also provided; wherein,
after centralized feeding, the algorithm is executed once every 5 minutes, 288 times of accumulated automatic scheduling are carried out, the opening area of the feeding valve is automatically adjusted, the automatic adjustment range is 0-0.0026 square meter, and the average opening of the feeding valve is 0.0024 square meter;
after centralized feeding, the algorithm is executed once every 5 minutes, 288 times of accumulated automatic scheduling are carried out, the opening area of the discharge valve is automatically adjusted, the automatic adjustment range is 0-0.0028 square meter, and the average opening of the discharge valve is 0.0027 square meter;
cumulative feed valve open time was 21.22 hours;
the opening time of the discharge valve is accumulated to be 20.7 hours;
judging the drying condition of the material by using the moisture detection content of the dried material, wherein the moisture detection content of the dried material is 0.06% -0.14% under the opening control parameter;
the energy consumption condition of the material dryer is measured by using an intelligent electric meter, and the measurement result is 399172.762656 kilowatt seconds, namely 110.88132296 degrees.
For the same production requirement, comparing and analyzing the opening control parameter of the material dryer set by the traditional method and the drying condition of the material and the energy consumption condition of the material dryer under the control parameter, and comparing and analyzing the opening control parameter of the material dryer set by the method of the invention and the drying condition of the material and the energy consumption condition of the material dryer under the control parameter, it can be known that: the method can realize automatic adjustment and control of the opening of the material dryer and can meet production requirements; compared with the traditional method, the method for controlling the moisture content detection of the dried material in the material dryer is lower, and the material drying condition is better.
The invention is not to be considered as limited to the specific embodiments shown and described, but is to be understood to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the invention as defined by the appended claims.
Claims (5)
1. A method for controlling the opening degree of a material inlet and outlet valve of a material dryer based on a mixed frog leaping algorithm is characterized by comprising the following steps of:
s1, obtaining basic information data about the opening control of the feeding and discharging valve, wherein the basic information data comprises:
the volume of the injection molding machine is Ci; the buffer storage amount of the injection molding machine, namely the buffer volume is Cc;
the volume of the material dryer is Cd; the daily average power of the material dryer is Pavg;
the expected maximum energy consumption of the material dryer is EcostMax;
the relation among the actual energy consumption Ecost of the material dryer, the feeding and discharging duration and the opening of the feeding and discharging valve is as follows:
Ecost＝Pavg*H+Tin*(Kin*XcosTin+YcosTin)+Tout*(Kout*XcosTout+YcosTout)；
h is the working duration of the material dryer, Tout is the discharging duration, Tin is the feeding duration, Kin is the opening degree of the feeding valve, Kout is the opening degree of the discharging valve, and XcosTin, YcosTin, XcosTout and YcosTout are coefficients of the relational expression;
the proportional relation between the opening Kin of the feeding valve and the feeding speed Sin is 1: Xin, namely the feeding speed Sin is Kin x Xin;
the proportional relation between the opening Kout of the discharge valve and the discharge speed Sout is 1: Xout, namely the discharge speed Sout is Kout Xout;
the plastic particles sucked into the material dryer are required to be heated to chi ℃ and the constant temperature is kept for Tm;
the minimum energy consumption of the material dryer is EcostMin, and
EcostMin＝Pavg*Tm*(Tin*Sin/Cd)＝Pavg*Tm*(Tin*Kin*Xin/Cd)；
the manufacturing process of the product is Tp, the net weight of the product is Q, namely the production cycle of the product is that plastic particles with the net weight of Q can be produced within the time of one manufacturing process Tp;
s2, determining a decision variable set, and establishing a target function and a constraint function set related to the decision variable set; the decision variable group is an opening control parameter of the material dryer;
s3, obtaining the value of the decision variable group which minimizes the value of the objective function and meets the constraint function group by using a mixed frog-leaping algorithm, namely an optimal solution;
and S4, controlling the material inlet and outlet valve of the material dryer by using the optimal solution.
2. The method for controlling the opening of the feeding and discharging valve of the material dryer based on the mixed frog-leaping algorithm as claimed in claim 1, wherein in step S1, the basic information data are different for different production requirements, and in the actual operation process, the production manager sets or adjusts the corresponding basic information data according to the production requirements; meanwhile, aiming at a plurality of different production requirements, the production manager arranges different basic information data respectively corresponding to the plurality of different production requirements according to the production time sequence.
3. The method for controlling the opening degree of the feeding and discharging valve of the material dryer based on the mixed frog-leaping algorithm as claimed in claim 1, wherein in step S2, the decision variable group comprises 4 decision variables, which are respectively: opening degree Kin of a feeding valve, opening degree Kout of a discharging valve, feeding duration time Tin and discharging duration time Tout;
the objective function, i.e. fitness function, is:
the set of constraint functions includes 5 constraint functions, which are:
the constraint function 1 is Kin Xin Tin-Kout Xout Tout < Cd;
the constraint function 2 is Kout Xout Tout < Ci-Cc;
the constraint function 3 is Sout × Tp > Q, i.e., Kout × Xout × Tp > Q;
the constraint function 4 is Kin x in Tin > Kout x Xout Tout;
the constraint function 5 is that EcostMin is less than or equal to Ecost is less than or equal to EcostMax, i.e.
4. The method for controlling the opening degree of the feeding and discharging valve of the material dryer based on the mixed frog leap algorithm is characterized in that in the step S3, the mixed frog leap algorithm comprises the following specific steps:
s31, setting algorithm parameters and initializing the algorithm parameters, wherein the algorithm parameters comprise: the number FrogCount of the solution of the decision variable group, the sub-population number GroupCount, the sub-population frog number FrogCountInGroup, the total iteration times InteratingTimes, the group optimization times upper limit MaxUpdateTimesInGroup, the experiment times TestTimes, the maximum leapfrog step, and the fuzzy interval BrtterFrogRange used when a more optimal solution is taken;
preliminarily setting a certain number of solutions which accord with a constraint function group according to empirical data of injection molding production feeding, wherein the solutions are values of decision variables (Kin, Kout, Tin and Tout); the certain number is equal to the number FrogCount of solutions of the set decision variable group;
s32, calculating the fitness of each solution, namely each frog, and arranging the solutions according to the descending order of the fitness; the fitness is a value F (Kin, Kout, Tin and Tout) obtained by substituting the frog, namely the solution, into an objective function, the higher the value F (Kin, Kout, Tin and Tout), the worse the fitness, and the lower the value F (Kin, Kout, Tin and Tout), the better the fitness;
s33, dividing the sub-populations, dispersing the solutions into each solution space randomly, namely dispersing the frogs into each sub-population, and arranging the frogs in each sub-population according to the descending order of the fitness;
s34, group optimization, namely, in-group optimization of a sub-population: carrying out fuzzy evolution based on the maximum leapfrog step length MaxLeapStep and randomly generating a new solution, wherein the fuzzy evolution is based on the existing solution in the group, and modifying the values of 4 decision variables in the decision variable group according to the maximum leapfrog step length MaxLeapStep; calculating the fitness difference between the solution with the worst fitness in the current group and the new solution, judging whether the absolute value of the fitness difference is larger than the set better solution, using a fuzzy interval BrtterFrogRange to judge whether the new solution is a better solution, wherein,
if the absolute value of the fitness difference is not greater than the set BrtterFrogRange, the new solution is considered as a quasi-optimal solution, and the next step is carried out;
if the absolute value of the fitness difference is larger than the set BrtterFrogRange, the new solution is considered not to be the quasi-optimal solution, and the step S34 is executed once again, namely the in-group optimization is carried out once again; and the total number of times of in-group optimization is required to be not more than the set upper limit of the number of times of in-group optimization MaxUpdateTimesInGroup, if the total number of times of in-group optimization is more than the MaxUpdateTimesInGroup, the new solution finally generated in the in-group optimization process is not subjected to fitness comparison and judgment, namely the new solution is directly considered as the quasi-optimal solution, and the next step is carried out;
s35, updating the solution with the worst fitness in the current group by using the quasi-optimal solution obtained by the in-group optimization, and recording the updating times;
s36, judging whether the updating times reach the total iteration times InterationTimes, if not, continuing to perform in-group optimizing and updating, namely skipping to execute the step S34;
if yes, calculating the fitness of each frog, namely each solution, and judging whether the minimum fitness in the current solution space meets a termination condition, namely whether the minimum fitness in the current solution space is smaller than a set threshold epsilon, if not, continuing to calculate, re-dividing the sub-population, and skipping to execute the step S33; if the minimum fitness is smaller than the set threshold epsilon, outputting the solution corresponding to the minimum fitness in the current solution space, wherein the solution corresponding to the output minimum fitness is the optimal solution, and terminating the algorithm.
5. The method for controlling the opening degree of the feeding and discharging valve of the material drying machine based on the mixed frog-leaping algorithm as claimed in claim 1, wherein in step S4, after the optimal solution is obtained, the operation record for obtaining the optimal solution is stored in the database, and when the mixed frog-leaping algorithm is executed next time, the optimal solution stored in the database is called as the solution preliminarily set in the initialization process;
the database is a corresponding relation table between static input parameters, namely basic information data, and adjustment control parameters, namely optimal solutions.
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