CN112017733A - Particle swarm algorithm-based high polymer slurry parameter identification method - Google Patents

Particle swarm algorithm-based high polymer slurry parameter identification method Download PDF

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CN112017733A
CN112017733A CN202010855134.7A CN202010855134A CN112017733A CN 112017733 A CN112017733 A CN 112017733A CN 202010855134 A CN202010855134 A CN 202010855134A CN 112017733 A CN112017733 A CN 112017733A
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李晓龙
姚文艺
贾赫扬
钟燕辉
张蓓
申震洲
李阳
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Abstract

The invention relates to the field of chemical grouting, and relates to a particle swarm algorithm-based high polymer slurry parameter identification method, which comprises the following steps: pre-treating; initializing the speed, position, adaptive value, individual historical optimal position, group historical optimal adaptive value, group historical optimal position and current iteration number of each particle; performing iterative solution by a particle swarm algorithm; judging whether the operation of the preset times is executed or whether the group history optimal adaptive value meets the precision requirement; and identifying to obtain the chemical reaction kinetic parameters. The method can quickly and efficiently identify the chemical reaction kinetic parameters of the high polymer slurry by utilizing the particle swarm algorithm, and the obtained parameters are more accurate.

Description

Particle swarm algorithm-based high polymer slurry parameter identification method
Technical Field
The invention belongs to the field of chemical grouting, and particularly relates to a particle swarm algorithm-based high polymer slurry parameter identification method.
Background
Grouting is an academic branch with strong specialty in geotechnical engineering and is widely applied to the treatment of various geotechnical engineering problems. The principle is that special equipment is used for injecting grout (usually cement mortar, chemical grout and the like) into rock-soil bodies needing to be reinforced or stopped leaking, and the properties of the grout are used for exerting effects on surrounding rock-soil bodies in the modes of permeation, compaction, splitting, filling and the like, so that the originally broken and loose surrounding rock is changed into a stone-forming body with good integrity and high strength, or the filling effect of a grouting material is used for blocking cracks of the surrounding rock, and the aim of seepage prevention and reinforcement is fulfilled.
In recent years, the novel high polymer grouting material has the advantages of early strength, water resistance, light weight, durability, environmental protection and the like, is widely applied to underground engineering such as mines, tunnels and the like, water damage prevention, foundation reinforcement, road maintenance and the like, and obtains great social and economic benefits. The method is characterized in that the chemical reaction process of the high polymer slurry is accurately solved, so that the chemical reaction kinetic parameters of the slurry are determined: pre-factors and activation energy.
The traditional method for determining the chemical reaction kinetic parameters is to determine through tests, develop a polymerization reaction test of polymer slurry, draw a slurry temperature (T) -time (T) change curve, calculate ln (K) (K is a chemical reaction rate constant) by using the curve, and further calculate pre-exponential factors and activation energy according to characteristics such as slope and intercept of the ln (K) and 1/T relation curve. CN110470559A discloses a method and a device for acquiring kinetic parameters of solid high-temperature high-pressure chemical reaction, wherein the acquisition method comprises the following steps: the electric furnace is heated to a specified temperature, high-pressure reaction gas is introduced, the solid sample is sent into a hearth of the electric furnace to react, the mass of the sample is measured in real time, and the kinetic parameters of the solid high-temperature high-pressure chemical reaction are obtained by differentiating the mass curve of the sample. The method also calculates the chemical reaction kinetic parameters by testing and collecting test data and combining with a sample mass curve, and has larger errors.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a particle swarm algorithm-based high polymer slurry parameter identification method so as to solve the problems of complicated process and large error in the prior art for identifying the chemical reaction kinetic parameters of the high polymer slurry.
In order to solve the technical problems, the invention adopts the following technical scheme:
the invention provides a particle swarm algorithm-based high polymer slurry parameter identification method, which comprises the following steps:
s10, preprocessing: determining the number n of particles, the dimension d of the particles, the maximum iteration number T and a first learning factor c of the particle swarm1A second learning factor c2Convergence accuracy;
s20, initializing the speed, position, adaptive value, individual historical optimal position, population historical optimal adaptive value, population historical optimal position and current iteration number of each particle: assigning an initial velocity value and an initial position to each particle; calculating an initial adaptive value corresponding to the initial position of each particle; assigning the individual historical optimal position and the corresponding individual historical optimal adaptive value to each particle; giving the optimal position of the group history of the whole particle swarm and the corresponding optimal adaptive value of the group history; assigning an initial value to the current iteration times;
s30, performing iterative solution by using a particle swarm algorithm: updating the position of each particle, calculating an adaptive value corresponding to a new position of each particle, determining whether to update the individual historical optimal adaptive value and the corresponding individual historical optimal position as well as the group historical optimal adaptive value and the corresponding group historical optimal position according to the magnitude relation between the new adaptive value and the individual historical optimal adaptive value and the group historical optimal adaptive value, and updating the speed of each particle;
s40, judging whether the operation is executed for a preset number of times or whether the optimal adaptive value of the group history meets the precision requirement, if so, executing a step S50; if not, go to step S30;
s50, outputting an optimal value: and outputting component values corresponding to the historical optimal positions of the groups, namely the chemical reaction kinetic parameters obtained by identification.
Further, the step S20 specifically includes:
s21: taking a chemical reaction kinetic parameter value feasible domain as a solving space, and randomly generating the initial velocity value of each particle in the population in the solving space in the form of vi＝(vA,vE) I is 1: n, said vARepresenting said initial velocity value v of each particleiVelocity component on the horizontal axis, said vERepresenting said initial velocity value v of each particleiA velocity component on the longitudinal axis;
s22: taking a chemical reaction kinetic parameter value feasible domain as a solving space, and randomly generating the initial speed value and the initial position of each particle in the population in the solving space, wherein the form of the initial position is xi＝(AOH,EOH) I is 1: n, wherein A isOHRepresents the distance of each particle from the origin on the horizontal axis, EOHRepresents the distance of each particle from the origin on the vertical axis;
s23: calculating the initial fitness value f for each particle using expression (1)i；
The expression (1) is:
Figure BDA0002646162240000041
in the expression (1), the
Figure BDA0002646162240000042
Is a measured temperature value, the
Figure BDA0002646162240000043
For calculating the temperature value, k is a temperature recording point serial number, m is the total number of the temperature recording points, and i is 1: n.
S24: the initial position x of each particleiAnd corresponding said initial adaptation value fiAs the individual historical optimal position p for each particle, respectivelyiAnd corresponding historical optimal adaptive value f of the individualpiAn initial value of (d);
s25: the individual historical optimum adaptation value f at each particlepiSelecting the minimum value from the initial values as the optimal adaptive value f of the group historygOf the respective individual historical optimal position piTaking the minimum value in the initial values as the optimal position p of the group historygAn initial value;
s26: and setting the current iteration time t to be 1.
Further, in the step S23, the step
Figure BDA0002646162240000046
Obtained by numerically solving the chemical reaction rate equation (2) and the heat balance equation (3) of the high polymer slurry
Figure BDA0002646162240000045
The temperature is measured in the slurry chemical reaction process;
the chemical reaction rate equation (2) of the high polymer slurry is as follows:
Figure BDA0002646162240000044
the heat balance equation (3) is:
Figure BDA0002646162240000051
in the chemical reaction rate equation (2) and the heat balance equation (3) of the high polymer slurry, theAOHIs a pre-exponential factor, said EOHFor activation energy, the R isgIs a gas constant, T is an absolute temperature, XOHFor the conversion of the gelling reaction, cNCO,0C to cOH,0Initial molar concentrations of isocyanate and polyol, respectively, in the reaction mixture, ppFor mixing the density of the slurry, CpFor the specific heat of the mixed slurry, the (Δ H)OHIs the heat of reaction.
Further, the step S30 specifically includes:
s31: updating the position of each particle according to the expression (4);
the expression (4) is:
Figure BDA0002646162240000052
s32: calculating an adaptive value f corresponding to the current position of each particle by using the expression (1)i t；
S33: comparing the adaptive value f corresponding to the current position of the particlei tAnd the individual history optimal adaptation value fpiIf f isi t<fpiThen updating the individual historical optimal adaptive value fpiIs fpi＝fi tThe individual history optimal position piWith the current position
Figure BDA0002646162240000053
Replacement;
if the individual history optimal adaptive value fpiUpdating, further comparing it with the historical optimal adaptive value f of the populationgIf f is large or smallpi<fgThen updating the group history optimal adaptive value fg＝fpiCorresponding to said population history optimal position pgThe individual historical optimal position p is replaced correspondinglyi；
S34: updating the speed of each particle according to an expression (5);
the expression (5) is:
Figure BDA0002646162240000061
in the expression (5), t represents the current iteration number, and t-1 represents the last iteration number of the current iteration number; said r1Representing a first random factor, said r2Represents a second random factor, and the value range is [0,1 ]](ii) a W is an inertia weight used for controlling the speed and balancing the overall and local searching capability of the algorithm; c is mentioned1First learning factor of bit algorithm, c2For the second learning factor of the algorithm, two identical non-negative constants are usually taken, and the value range is [0,4 ]](ii) a Said p isiFor individual historical optimal positions of particles, the pgHistorical optimal positions of particle groups are obtained;
in the expression (5), the inertia weight w can be calculated by an expression (6);
the expression (6) is:
w＝(T-t)×1.0/T (6)
further, the step S40 specifically includes:
when T < T and fg>When the operation is finished, step S30 is executed, while t is t + 1;
if T > T or fgIf not, executing the step S50;
wherein said is a set convergence accuracy.
Compared with the prior art, the particle swarm algorithm-based high polymer slurry parameter identification method provided by the invention at least has the following beneficial effects:
by utilizing the particle swarm algorithm, the chemical reaction kinetic parameters of the high polymer slurry can be rapidly and efficiently identified, and the obtained parameters are more accurate.
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In order to illustrate the solution of the present application more clearly, the drawings that are needed in the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and that other drawings can be obtained by those skilled in the art without inventive effort.
FIG. 1 is a schematic general flow chart of a particle swarm algorithm-based method for identifying parameters of polymer slurry provided by the present invention;
FIG. 2 is a comparison graph of theoretical temperature and calculated temperature obtained based on identification parameters in a first embodiment of the particle swarm optimization-based polymer slurry parameter identification method provided by the invention;
FIG. 3 is a comparison graph of the measured temperature and the calculated temperature based on the identification parameters of the second embodiment of the particle swarm optimization-based polymer slurry parameter identification method provided by the present invention;
Detailed Description
To facilitate an understanding of the present application, the present application will now be described more fully with reference to the accompanying drawings. Preferred embodiments of the present application are shown in the drawings. This application may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the description of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.
Example one
In the embodiment, theoretical data are calculated by adopting a non-aqueous reaction polymer chemical reaction kinetic parameter identification method based on a particle swarm algorithm to obtain polymer chemical reaction kinetic parameters, and the effectiveness of the method is verified. Firstly, setting the concentration c of the slurry component in a calculation modelOH,0Is 4000mol/m3，cNCO,0Is 4000mol/m3The density of the powder is 1100kg/m3Heat of reaction 7.49X 104J·mol-1The specific heat is 1800J/(kg. K).
S10, preprocessing: the number of particles n in the group of particles was determined to be 60,the particle dimension d is 2, the maximum number of iterations T is 50, and a first learning factor c10.5, second learning factor c20.5, convergence accuracy 0.00001;
s20, initializing the speed, position, adaptive value, individual historical optimal position, population historical optimal adaptive value, population historical optimal position and current iteration number of each particle: assigning an initial velocity value and an initial position to each particle; calculating an initial adaptive value corresponding to the initial position of each particle; giving each particle individual historical optimal position and corresponding individual historical optimal adaptive value; giving a group history optimal position and a corresponding group history optimal adaptive value of the whole particle swarm; assigning an initial value to the current iteration times; the S20 concrete steps are as follows:
s21: taking a chemical reaction kinetic parameter value feasible domain as a solving space, and randomly generating an initial velocity value of each particle in a population in the solving space in the form of vi＝(vA,vE) N, said velocity component representing said initial velocity value of each particle on the horizontal axis, said velocity component representing said initial velocity value of each particle on the vertical axis;
s22: taking a chemical reaction kinetic parameter value feasible domain as a solving space, and randomly generating an initial position of each particle in the population in the solving space in the form of xi＝(AOH,EOH) N, said representing the distance of each particle from the origin on the horizontal axis, said representing the distance of each particle from the origin on the vertical axis;
s23: calculating an initial fitness value f for each particle using expression (1)i；
Expression (1) is:
Figure BDA0002646162240000091
in the first embodiment, in order to verify the accuracy of the present invention, in the expression (1),
Figure BDA0002646162240000092
in order to be the theoretical temperature value,
Figure BDA0002646162240000093
for calculating the temperature value, k is the serial number of the temperature recording point, and m is the total number of the temperature recording points. Calculating the initial particle temperature value through the expressions (2) and (3):
expression (2) is:
Figure BDA0002646162240000094
expression (3) is:
Figure BDA0002646162240000095
wherein A isOHIs a pre-exponential factor, EOHFor activation energy, RgIs the gas constant, T is the absolute temperature, XOHConversion for the gelling reaction, cNCO,0、cOH,0Initial molar concentrations, p, of isocyanate and polyol, respectively, in the reaction mixturepFor mixing the density of the slurry, CpSpecific heat of the mixed slurry (. DELTA.H)OHHeat of gelation reaction. Thereafter, each particle is given an initial velocity vi。
S24: assigning an individual historical optimal position and a corresponding individual historical optimal adaptation value to each particle: initial position x of each particleiAnd corresponding initial adaptation value fiAs individual historical optimal positions p for each particleiAnd corresponding individual historical optimum adaptation values fpiAn initial value of (d);
s25: giving the optimal position of the group history of the whole particle swarm and the corresponding optimal adaptive value of the group history: individual historical optimum adaptation value f at each particlepiSelecting the minimum value from the initial values as the optimal adaptive value f of the group historygInitial value of (a), corresponding individual historical optimal position piInitial value as group history optimal position pgAn initial value;
s26: and setting the current iteration time t to be 1.
S30, performing iterative solution by using a particle swarm algorithm: updating the position of each particle, calculating an adaptive value corresponding to a new position of each particle, determining whether to update the individual historical optimal adaptive value and the corresponding individual historical optimal position and the group historical optimal adaptive value and the corresponding group historical optimal position according to the magnitude relation between the new adaptive value and the individual historical optimal adaptive value and the group historical optimal adaptive value, and updating the speed of each particle; the method comprises the following specific steps:
s31: updating the position of each particle according to the expression (4);
expression (4) is:
Figure BDA0002646162240000101
s32: calculating an adaptive value f corresponding to the current position of each particle by using an expression (1)i t；
S33: performing a comparison and replacement operation for each particle:
comparing the adaptive value f corresponding to the current position of the particlei tAnd individual history optimal adaptation value fpiIf f isi t<fpiThen updating the individual historical optimal adaptive value fpiIs fpi＝fi tIndividual historical optimal position piBy current position
Figure BDA0002646162240000102
Replacement;
if the individual history is the optimal adaptive value fpiUpdating, and further comparing it with the optimal adaptive value f of the group historygIf f is large or smallpi<fgThen updating the group history optimal adaptive value fg＝fpiCorresponding group history optimal position pgThe individual historical optimal position p is correspondingly replacedi；
S34: updating the speed of each particle according to an expression (5);
expression (5) is:
Figure BDA0002646162240000111
in the expression (5), t represents the current iteration number, and t-1 represents the last iteration number of the current iteration number; r is1Representing a first random factor, r2Represents a second random factor, and the value range is [0,1 ]](ii) a w is the inertial weight used to control the speed, balancing the overall and local search capabilities of the algorithm; c. C1Is the first learning factor, c, of the algorithm2For the second learning factor of the algorithm, two identical non-negative constants are usually taken, and the value range is [0,4 ]]；piFor individual historical optimal positions of particles, pgAnd (4) historical optimal positions for the groups.
In expression (5), preferably, the inertial weight w may be calculated by expression (6);
expression (6) is:
w＝(T-t)×1.0/T (6)
s40, judging whether the operation is executed for the preset times or whether the optimal adaptive value of the group history meets the precision requirement, if so, executing a step S50; if not, step S30 is executed.
S50, outputting an optimal value: and outputting component values corresponding to the historical optimal positions of the groups, namely the chemical reaction kinetic parameters obtained by identification.
Table 1 shows the chemical reaction kinetic parameters and the errors thereof finally obtained in the first embodiment of the particle swarm optimization-based polymer slurry parameter identification method provided by the present invention, as follows:
Figure BDA0002646162240000112
Figure BDA0002646162240000121
TABLE 1
As shown in table 1, the error between the theoretical parameter value and the particle swarm inversion parameter value is not more than 1%, and it can be seen from fig. 2 that the theoretical temperature and the calculated temperature in the polymer slurry parameter identification method based on the particle swarm algorithm provided by the invention are almost the same, which shows that the chemical reaction kinetic parameters obtained by the polymer slurry parameter identification method based on the particle swarm algorithm provided by the invention are more accurate, and the accuracy and the reliability of the polymer slurry parameter identification method based on the particle swarm algorithm provided by the invention are fully proved.
Example two
In this embodiment, the non-aqueous reaction polymer chemical reaction kinetic parameter identification method based on the particle swarm optimization is adopted to calculate the test data to obtain the polymer chemical reaction kinetic parameter. Firstly, setting the concentration c of the slurry component in a calculation modelOH,0Is 2110mol/m3，cNCO,0Is 4600mol/m3Density of 1000kg/m3Heat of reaction 7.49X 104J·mol-1The specific heat is 1800J/(kg. K).
The steps of the method for identifying the chemical reaction kinetic parameters of the non-aqueous reaction polymer based on the particle swarm optimization in the embodiment are the same as the steps of the method for identifying the chemical reaction kinetic parameters of the non-aqueous reaction polymer based on the particle swarm optimization in the first embodiment, and are not repeated herein.
Table 2 shows the chemical reaction kinetic parameters finally obtained in the second embodiment of the particle swarm algorithm-based polymer slurry parameter identification method provided by the present invention, as follows:
Figure BDA0002646162240000131
TABLE 2
As can be seen from table 2, the chemical reaction kinetic parameters finally obtained in the second embodiment of the particle swarm algorithm-based polymer slurry parameter identification method provided by the present invention are within the parameter value range, and as can be seen from fig. 3, the actual temperature and the calculated temperature in the particle swarm algorithm-based polymer slurry parameter identification method provided by the present invention are almost the same, which indicates that the chemical reaction kinetic parameters obtained by the particle swarm algorithm-based polymer slurry parameter identification method provided by the present invention are relatively accurate.
It is to be understood that the above-described embodiments are merely illustrative of some, but not restrictive, of the broad invention, and that the appended drawings illustrate preferred embodiments of the invention and do not limit the scope of the invention. This application is capable of embodiments in many different forms and is provided for the purpose of enabling a thorough understanding of the disclosure of the application. Although the present application has been described in detail with reference to the foregoing embodiments, it will be apparent to one skilled in the art that the present application may be practiced without modification or with equivalents of some of the features described in the foregoing embodiments. All equivalent structures made by using the contents of the specification and the drawings of the present application are directly or indirectly applied to other related technical fields and are within the protection scope of the present application.

Claims (5)

1. A particle swarm algorithm-based high polymer slurry parameter identification method is characterized by comprising the following steps:
s10, preprocessing: determining the number n of particles, the dimension d of the particles, the maximum iteration number T and a first learning factor c of the particle swarm1A second learning factor c2Convergence accuracy;
s20, initializing the speed, position, adaptive value, individual historical optimal position, population historical optimal adaptive value, population historical optimal position and current iteration number of each particle: assigning an initial velocity value and an initial position to each particle; calculating an initial adaptive value corresponding to the initial position of each particle; assigning the individual historical optimal position and the corresponding individual historical optimal adaptive value to each particle; giving the optimal position of the group history of the whole particle swarm and the corresponding optimal adaptive value of the group history; assigning an initial value to the current iteration times;
s30, performing iterative solution by using a particle swarm algorithm: updating the position of each particle, calculating an adaptive value corresponding to a new position of each particle, determining whether to update the individual historical optimal adaptive value and the corresponding individual historical optimal position as well as the group historical optimal adaptive value and the corresponding group historical optimal position according to the magnitude relation between the new adaptive value and the individual historical optimal adaptive value and the group historical optimal adaptive value, and updating the speed of each particle;
s40, judging whether the operation is executed for a preset number of times or whether the optimal adaptive value of the group history meets the precision requirement, if so, executing a step S50; if not, go to step S30;
s50, outputting an optimal value: and outputting component values corresponding to the historical optimal positions of the groups, namely the chemical reaction kinetic parameters obtained by identification.
2. The particle swarm algorithm-based polymer slurry parameter identification method according to claim 1, wherein the step S20 specifically comprises:
s21: taking a chemical reaction kinetic parameter value-taking feasible domain as a solving space, and randomly generating the initial velocity value v and the initial velocity value v of each particle in the population in the solving spacei＝(vA,vE) I is 1: n, said vARepresenting said initial velocity value v of each particleiVelocity component on the horizontal axis, said vERepresenting said initial velocity value v of each particleiA velocity component on the longitudinal axis;
s22: taking a chemical reaction kinetic parameter value feasible domain as a solving space, and randomly generating the initial position of each particle in the population in the solving space, wherein the initial position is in the form of xi＝(AOH,EOH) I is 1: n, wherein A isOHRepresents the distance of each particle from the origin on the horizontal axis, EOHRepresents the distance of each particle from the origin on the vertical axis;
s23: calculating the initial fitness value f for each particle using expression (1)i；
The expression (1) is:
Figure FDA0002646162230000021
in the expression (1), the
Figure FDA0002646162230000022
Is a measured temperature value, the
Figure FDA0002646162230000023
For calculating the temperature value, k is a temperature recording point serial number, m is the total number of the temperature recording points, and i is 1: n.
S24: the initial position x of each particleiAnd corresponding said initial adaptation value fiAs the individual historical optimal position p for each particle, respectivelyiAnd corresponding historical optimal adaptive value f of the individualpiAn initial value of (d);
s25: the individual historical optimum adaptation value f at each particlepiSelecting the minimum value from the initial values as the optimal adaptive value f of the group historygOf the respective individual historical optimal position piTaking the minimum value in the initial values as the optimal position p of the group historygAn initial value;
s26: and setting the current iteration time t to be 1.
3. The particle swarm optimization-based method for identifying the parameters of the polymer slurry as claimed in claim 2, wherein in step S23, the polymer slurry is subjected to a particle swarm optimization
Figure FDA0002646162230000024
Obtained by numerically solving the chemical reaction rate equation (2) and the heat balance equation (3) of the high polymer slurry
Figure FDA0002646162230000025
The temperature is measured in the slurry chemical reaction process;
the chemical reaction rate equation (2) of the high polymer slurry is as follows:
Figure FDA0002646162230000031
the heat balance equation (3) is:
Figure FDA0002646162230000032
in the chemical reaction rate equation (2) and the heat balance equation (3), A isOHIs a pre-exponential factor, said EOHFor activation energy, the R isgIs a gas constant, T is an absolute temperature, XOHFor the conversion of the gelling reaction, cNCO,0C to cOH,0Initial molar concentrations of isocyanate and polyol, respectively, in the reaction mixture, ppFor mixing the density of the slurry, CpFor the specific heat of the mixed slurry, the (Δ H)OHIs the heat of reaction.
4. The particle swarm algorithm-based polymer slurry parameter identification method according to claim 2, wherein the step S30 specifically comprises:
s31: updating the position of each particle according to the expression (4);
the expression (4) is:
Figure FDA0002646162230000033
s32: calculating an adaptive value f corresponding to the current position of each particle by using the expression (1)i t；
S33: comparing the adaptive value f corresponding to the current position of the particlei tAnd the individual history optimal adaptation value fpiIf f isi t<fpiThen, it is furtherNew historical optimal adaptation value f of said individualpiIs fpi＝fi tThe individual history optimal position piWith the current position
Figure FDA0002646162230000034
Replacement;
if the individual history optimal adaptive value fpiUpdating, further comparing it with the historical optimal adaptive value f of the populationgIf f is large or smallpi<fgThen updating the group history optimal adaptive value fg＝fpiCorresponding to said population history optimal position pgThe individual historical optimal position p is replaced correspondinglyi；
S34: updating the speed of each particle according to an expression (5);
the expression (5) is:
Figure FDA0002646162230000041
in the expression (5), t represents the current iteration number, and t-1 represents the last iteration number of the current iteration number; said r1Representing a first random factor, said r2Represents a second random factor, and the value range is [0,1 ]](ii) a W is an inertia weight used for controlling the speed and balancing the overall and local searching capability of the algorithm; c is mentioned1Is a first learning factor of the algorithm, said c2For the second learning factor of the algorithm, two identical non-negative constants are usually taken, and the value range is [0,4 ]](ii) a Said p isiFor individual historical optimal positions of particles, the pgHistorical optimal positions of particle groups are obtained;
in the expression (5), the inertial weight w is calculated by an expression (6);
the expression (6) is:
w＝(T-t)×1.0/T (6)。
5. the particle swarm algorithm-based polymer slurry parameter identification method according to claim 4, wherein the step S40 specifically comprises:
when T < T and fg>When the operation is finished, step S30 is executed, while t is t + 1;
if T > T or fgIf not, executing the step S50;
wherein said is a set convergence accuracy.
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