Control method of centralized feeding system of full-automatic PVC (polyvinyl chloride) mixing line

Abstract

The invention relates to the technical field of plastic production, in particular to a control method of a PVC (polyvinyl chloride) full-automatic mixing line centralized feeding system, which comprises the following steps: acquiring each sub-domain and a melt plasticizing value of each sub-domain at each acquisition moment; the ideal melt plasticizing value of each sub-domain and the simulated melt plasticizing value of each sub-domain at the next acquisition moment are obtained respectively; obtaining the fitness of each parameter vector; in the process of obtaining the optimal parameter vector at the current collection moment, the convergence factor during each iteration passes through the convergence factor during the adjacent iteration and the closeness degree of the fitness of all parameter vectors during the adjacent iteration, and the difference of the parameter vectors between each iteration and the adjacent iteration is combined to obtain the optimal parameter vector at the current collection moment. Comparing the simulated temperature at the next acquisition moment with the preset ideal temperature for updating; and controlling the pressure of the die head of the extruder based on the obtained optimal parameter vector. The invention aims to improve the control precision of the pressure of the extruder die head.

Description

Technical Field

[0001] The present application relates to the technical field of plastic production, and in particular to a control method for a centralized feeding system of a PVC fully automatic compounding line. Background Art

[0002] The centralized feeding system of the PVC automatic compounding line consists of an auxiliary material pouring layer, a metering layer, and a mixing layer. The PVC mixed material particles in the centralized feeding system of the PVC automatic compounding line vary in size. The PVC mixed materials of different particle sizes have different melting states in the extruder, which causes fluctuations in the extruder die head pressure and affects the molding of PVC pellets.

[0003] To ensure the production quality of PVC pellets, the PID (Proportional Integral Derivative) algorithm is currently used to control the screw speed in the barrel with the die pressure as the feedback signal to ensure constant die pressure. However, the centralized feeding system of the PVC fully automatic compounding line needs to adapt to different production conditions and material characteristics. The fixed PID algorithm parameters are difficult to accurately control the die pressure, resulting in a decrease in the stability of the extrusion molding quality of PVC pellets. Summary of the invention

[0004] In view of the above, it is necessary to provide a control method for a centralized feeding system of a PVC fully automatic compounding line, which improves the control accuracy of the pressure of the extruder die head compared with the traditional control method of a centralized feeding system of a PVC fully automatic compounding line.

[0005] The control method of the centralized feeding system of a PVC fully automatic compounding line of the present application adopts the following technical scheme: An embodiment of the present application provides a control method for a centralized feeding system of a PVC fully automatic compounding line, the method comprising the following steps: Divide the melting section and melt conveying section of the extruder barrel into sub-areas, obtain the actual temperature of each sub-area at each sampling time, and obtain the ideal pressure of the extruder die head; Based on the actual temperature, isothermal flow field modeling is performed on each subdomain, and the melt plasticization value of each subdomain at each sampling time is obtained by analyzing the pressure distribution and flow velocity distribution in the flow field and combining the actual temperature of each subdomain; Through the transfer function of the frequency converter in the feeding system, the simulated temperature and the simulated pressure of each subdomain and the extruder die head at the next acquisition moment of the current acquisition moment are simulated under the control of any parameter vector including a proportional parameter, an integral parameter and a differential parameter; based on the preset ideal temperature of each subdomain and the simulated temperature, the ideal melt plasticizing value and the simulated melt plasticizing value of each subdomain are respectively obtained by using the same calculation method as the melt plasticizing value; Based on the difference between the simulated pressure and the ideal pressure, the difference between the simulated melt plasticization value and the ideal melt plasticization value of each subdomain, and comparing the distribution of the simulated melt plasticization value and the ideal melt plasticization value of all subdomains, the fitness of any parameter vector is obtained; In the process of using the whale optimization algorithm to obtain the optimal parameter vector at the current acquisition moment, the convergence factor at each iteration is updated by comparing the simulated temperature at the next acquisition moment with the preset ideal temperature through the convergence factor at the adjacent iteration and the fitness of all parameter vectors at the adjacent iterations, combined with the difference between the parameter vectors of each iteration and its adjacent iterations; The optimal parameter vector obtained based on the whale optimization algorithm is used to control the pressure of the extruder die head.

[0006] In one embodiment, the process of obtaining the melt plasticization value is: The flow field area obtained by modeling is meshed to obtain the velocity tensor and pressure tensor of each grid unit at each acquisition time; the flow data and pressure data of each grid unit at each acquisition time are respectively obtained through the velocity tensor and the pressure tensor; Based on the pressure distribution and velocity distribution of all grid cells in each subdomain at each acquisition time, and combined with the actual temperature of each subdomain, the melt plasticization value of each subdomain at each acquisition time is obtained.

[0007] In one embodiment, the further acquisition process of the melt plasticization value is: Calculate the discrete degree of velocity data of all grid cells in each subdomain at each acquisition time; The mean value of the velocity data and the mean value of the pressure data of all grid cells in each subdomain at each acquisition time are recorded as the velocity mean and the pressure mean, respectively; Analyze the distribution differences of pressure data of all grid cells in each subdomain at each acquisition time; The melt plasticization value is positively correlated with the mean flow rate, the actual temperature, and the discrete degree, and is negatively correlated with the mean pressure and the distribution difference.

[0008] In one embodiment, the specific analysis method of the distribution difference is: The minimum and maximum values ​​of the pressure data of all grid cells in each subdomain at each acquisition time are taken as the endpoints of the pressure interval of each subdomain at each acquisition time; Each pressure interval is equally divided into a plurality of quantization intervals, and all pressure data in any quantization interval are assigned the minimum value of the pressure data in any quantization interval; Combining a preset number of continuous quantization intervals into each sub-interval; All the assigned pressure data in each sub-interval are used as the input of the maximum inter-class variance algorithm, and the maximum inter-class variance of each sub-interval is output; The distribution difference is reflected by the sum of all the maximum inter-class variances corresponding to each subdomain.

[0009] In one embodiment, the process of obtaining the fitness is as follows: The simulated melt plasticization value and the ideal melt plasticization value of each subdomain and its adjacent subdomain are used to form a simulated plasticization state vector and an ideal plasticization state vector of each subdomain, respectively, and the similarity between the simulated plasticization state vector and the ideal plasticization state vector of each subdomain is calculated; The difference between the simulated pressure and the ideal pressure is recorded as a pressure difference; The difference between the simulated melt plasticization value and the ideal melt plasticization value of each subdomain is recorded as the plasticization difference; The fitness is positively correlated with the pressure difference, the plasticization difference, and the serial number of each subdomain, and negatively correlated with the similarity; wherein each subdomain is numbered in sequence from far to near according to the distance between the subdomain and the extruder die head.

[0010] In one embodiment, the further acquisition process of the fitness is: calculating the product of the plasticization difference of each subdomain and its serial number; the fitness is positively correlated with the pressure difference and the product, and negatively correlated with the similarity.

[0011] In one embodiment, the fitness expression is: ; Wherein, F represents the fitness of any parameter vector; P represents the pressure difference of the extruder die head; L represents the number of subdomains of the extruder barrel; i represents the sequence number of the subdomain; represents the plasticization difference of the i-th subdomain; represents the similarity of the ith subdomain; exp() represents an exponential function with a natural constant as the base.

[0012] In one embodiment, the method for updating the convergence factor at each iteration is: Calculate the distance between the parameter vector with the minimum fitness in each iteration and the parameter vector with the minimum fitness in the adjacent iteration, recorded as vector distance; Calculating the accumulated value of the difference between the simulated temperature and the preset ideal temperature of all sub-domains; The minimum value of the fitness of all parameter vectors at each iteration is counted and recorded as the minimum fitness value; Calculating the difference between the fitness of each parameter vector and the minimum fitness value in adjacent iterations of each iteration; The convergence factor in each iteration is positively correlated with the convergence factor in the adjacent iteration, the vector distance, the accumulated value, and the difference value.

[0013] In one embodiment, the further acquisition process of the convergence factor in each iteration is: Calculating a normalized value of a fusion result of the vector distance, the accumulated value, and the difference; The convergence factor at each iteration is the product of the convergence factor at the adjacent iteration and the normalized value.

[0014] In one embodiment, the optimal parameter vector obtained based on the whale optimization algorithm controls the pressure of the extruder die head, including: The components in the optimal parameter vector are used as control parameters of the PID algorithm, the difference between the pressure of the extruder die head at the current moment and the ideal pressure is calculated, and the pressure of the extruder die head is controlled in combination with the PID control algorithm.

[0015] This application has at least the following beneficial effects: The present application obtains the flow rate and pressure distribution of the PVC mixture in the cylinder by performing isothermal flow field modeling on different subdomains respectively. The beneficial effect is that by analyzing the changes of the rheological properties and thermal performance parameters of the mixture in the subdomain with temperature, the accuracy of the flow field model establishment is improved, thereby improving the accuracy of the subsequent acquisition of the plasticization effect of the mixture melt; Furthermore, according to the flow rate and pressure distribution of the PVC mixture in the extruder barrel, the melt plasticization value of the subdomain is calculated. The beneficial effect is that by analyzing the physical properties of the PVC mixture melt and its extrusion characteristics in the barrel, and utilizing the difference in pressure distribution in the barrel subdomain, the plasticization state of the melt in each subdomain can be accurately obtained; Furthermore, by simulating the degree to which the future plasticization state of the melt deviates from the ideal plasticization state under the control of each parameter vector, the fitness of each parameter vector is obtained to evaluate the possibility of each parameter vector as the optimal parameter vector, so as to facilitate the subsequent selection of the most suitable parameter vector to accurately control the pressure of the extruder die head; Furthermore, the convergence factor is calculated based on the fitness of the parameter vector, which can improve the accuracy of the optimization of the parameter vector. The whale optimization algorithm is used to obtain the optimal parameter vector. The various components in the optimal parameter vector are used as the control parameters of the PID algorithm, which can optimize the speed control of the extruder screw and achieve more precise control of the pressure of the extruder die head. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 A flow chart of the steps of a control method for a centralized feeding system of a PVC fully automatic compounding line provided in this application; Figure 2 This is a schematic diagram of the sensor installation location and sub-domain division; Figure 3 Schematic diagram of the process for obtaining the melt plasticizing value. DETAILED DESCRIPTION

[0018] In the description of the embodiments of the present application, words such as "exemplary", "or", "for example" and the like are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary", "or", "for example" and the like is intended to present related concepts in a concrete manner.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art in the present application. The terms used in the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. It should be understood that, unless otherwise specified, " / " means or.

[0020] It should also be noted that the terms "first" and "second" in the present application are used to distinguish similar objects rather than to describe a specific order or sequence.

[0021] The specific scheme of the control method of the centralized feeding system of a PVC fully automatic compounding line provided by the present application is described in detail below with reference to the accompanying drawings.

[0022] An embodiment of the present application provides a control method for a centralized feeding system of a PVC fully automatic compounding line. Specifically, the following control method for a centralized feeding system of a PVC fully automatic compounding line is provided. Figure 1 , the method comprises the following steps: Step S1, dividing the melting section and melt conveying section of the extruder barrel into sub-areas, and obtaining the actual temperature of each sub-area at each sampling time.

[0023] The barrel of the extruder is divided into three functional sections: solid conveying section, melting section and melt conveying section. Since the PVC mixture is in the initial stage of the melting process in the solid conveying section of the barrel, the subsequent flow field simulation error of the two-phase flow in this functional section is large, and the state of the PVC mixture in the solid conveying section has little effect on the quality of the extruded pellets. Therefore, the PVC mixture flow field in the solid conveying section is not simulated.

[0024] Furthermore, U temperature sensors are evenly installed on the melting section and melt conveying section of the barrel, and the area between every two adjacent temperature sensors is recorded as a subdomain of the barrel, and a total of L subdomains are obtained, and the subdomains are numbered according to the distance from the extruder die head. The subdomain farthest from the extruder die head is the first subdomain, and the subdomain closest to the extruder die head is the Lth subdomain. A pressure sensor is installed at the die head of the extruder.

[0025] In this embodiment, the acquisition frequency of the temperature sensor and the pressure sensor is once per second, and the values ​​of L and U are 9 and 10 respectively. The acquisition frequency, L and U values ​​are preset manually and can be set by the implementer. This application does not impose any special restrictions.

[0026] Sensor installation location and sub-domain division diagram as shown below: Figure 2 As shown, Figure 2 1 represents the feed port, 2 represents the motor, 3 represents the barrel, 4 represents the die head, 5 represents the solid conveying section, 6 represents the melting section and the melt conveying section, 7 represents the temperature sensor, and 8 represents the pressure sensor.

[0027] POLYFLOW is a CFD software that uses the finite element method and is dedicated to the flow simulation of viscoelastic materials. It integrates a variety of rheological models, thermodynamic models, and chemical reaction models, and has the ability to handle complex boundary conditions. It can better simulate the extrusion molding process of PVC mixtures in the extruder.

[0028] Due to the temperature differences between different subdomains, the simulation model is simplified to the case that the PVC mixture in each subdomain has the same temperature, and the average value of the temperature data measured at both ends of each subdomain is taken as the actual temperature of each subdomain. The isothermal flow field modeling is performed for each subdomain using the POLYFLOW software.

[0029] Due to the different temperatures of different subdomains, there are differences in their flow field modeling. Before using POLYFLOW software to establish a finite element model of the flow field of the PVC mixture in the extruder and performing finite element meshing, it is necessary to first obtain the changes in the rheological properties and thermal performance parameters of the PVC mixture with temperature. The Cross model and the Arrhenius model are used in this application to represent the changes in the rheological properties and thermal performance parameters of the PVC mixture with temperature in any subdomain. Take the jth acquisition moment as an example.

[0030] The constitutive equation of the PVC mixture flow field is: ; Wherein, τ and D represent the pressure tensor and flow rate tensor of the grid unit, respectively, which are output by the simulation software during the simulation process; η represents the viscosity of the PVC mixture.

[0031] The relationship between the viscosity of PVC mixture and shear rate and temperature is: ; Wherein, η represents the viscosity of the PVC mixture; and Represent the Cross model and Arrhenius model respectively, and the expressions are as follows: ; ; In the formula, represents zero shear rate viscosity; λ represents natural time; γ represents the shear rate of the grid unit, which is output by the simulation software during the simulation process; m represents the Cross model index; exp[ ] represents an exponential function with a natural constant as the base; b represents the temperature sensitivity coefficient; represents the reference temperature; T represents the actual temperature of any sub-domain at the jth acquisition moment.

[0032] In this embodiment, the value of zero shear rate viscosity is 8000 Pa﹒ s, the value of natural time is 0.12 s; the value of Cross model index is 0.75, and the value of temperature sensitivity coefficient is 0.0025K -1 The reference temperature is 573K. The implementers can set the values ​​of zero shear rate viscosity, natural time, Cross model index, temperature sensitivity coefficient and reference temperature according to actual conditions, and this application does not impose any special restrictions.

[0033] In this application, based on the structural parameters of the conveying elements such as the screw in the extruder, and the above-mentioned rheological properties and thermal performance parameters of the PVC mixture, the POLYFLOW software is used to establish a finite element model of the flow field of the PVC mixture in the extruder, and after finite element mesh division and boundary condition preprocessing, the flow rate and pressure distribution of the PVC mixture melt in the extruder barrel are obtained.

[0034] The flow boundary conditions of the PVC mixing flow field are set to zero pressure at the flow inlet and flow outlet, and no slip on the inner surface of the cylinder. The finite element mesh adopts a tetrahedral mesh, and the minimum mesh size is set to 2mm; the PVC mixing flow field simulation model adopts the RNG k-epsilon model. The process of establishing the PVC mixing flow field simulation model is well known to those skilled in the art and will not be repeated in this application.

[0035] The 2-norm of the velocity tensor of each grid cell at the j-th acquisition moment is used as the velocity data of each grid cell at the j-th acquisition moment, and the 2-norm of the pressure tensor of each grid cell at the j-th acquisition moment is used as the pressure data of each grid cell at the j-th acquisition moment.

[0036] Step S2, based on the actual temperature, isothermal flow field modeling is performed on each subdomain, and the melt plasticization value of each subdomain at each sampling time is obtained by analyzing the pressure distribution and flow velocity distribution in the flow field and combining the actual temperature of each subdomain.

[0037] Affected by the difference in the size of PVC mixed material particles in the centralized feeding system of the PVC fully automatic compounding line, there are differences in the melting process of the PVC mixed material in the extruder barrel. The smaller the mixed material particles, the more heat is generated by heat conduction and interaction between the mixed materials, the higher the temperature in the barrel, the higher the melting state of the PVC mixed material, and the better the melt plasticization effect; on the contrary, the larger the mixed material particles, the lower the temperature in the barrel, the worse the PVC mixed material melt plasticization effect, and at the same time, the pressure of the extruder die head fluctuates greatly, affecting the extrusion molding quality of PVC pellets.

[0038] Considering the physical properties of the PVC mixed melt, the higher the temperature, the better the flow performance. Although the pressure distribution at different positions in the barrel fluctuates, the fluctuation is small; the lower the temperature, the worse the flow performance. The PVC mixed melt generates high pressure on the screw rib thrust surface, and the pressure on the back of the screw rib is lower. The pressure distribution at different positions in the barrel is obviously different. Take the jth acquisition moment as an example.

[0039] In order to obtain the difference in pressure distribution in each subdomain of the cylinder, the minimum and maximum values ​​of the pressure data of all grid cells in each subdomain at the jth acquisition time are counted, and the minimum and maximum values ​​are used as interval endpoints to form the pressure interval of each subdomain. Each pressure interval is equally divided into a preset number of quantization intervals, and all pressure data in any quantization interval are assigned the minimum value of the pressure data in any quantization interval.

[0040] Furthermore, multiple continuous quantization intervals in each pressure interval are combined into a subinterval, and all the assigned pressure data in each subinterval are used as the input of the maximum inter-class variance algorithm, and the maximum inter-class variance of each subinterval is output. The larger the maximum inter-class variance, the greater the difference in pressure distribution of the pressure data in each subinterval, the greater the pressure difference at different extrusion points in the cylinder, the worse the flow performance of the PVC mixture, and the worse the plasticization effect of the mixture melt. Among them, the maximum inter-class variance algorithm is a well-known technology and will not be repeated in this application.

[0041] In this embodiment, the value of the preset number is 256. The value of the preset number is preset manually and can be set by the implementer. This application does not impose any special restrictions.

[0042] In this embodiment, 32 continuous quantization intervals are combined into one sub-interval, and there are 8 sub-intervals in each pressure interval. The number of continuous quantization intervals that make up the sub-interval can be set by the implementer, and this application does not impose any special restrictions.

[0043] Furthermore, the pressure distribution and velocity distribution of all grid cells in each subdomain are analyzed, and the melt plasticization value of each subdomain is obtained in combination with the actual temperature of each subdomain, which is used to reflect the plasticization effect of the PVC mixture when it forms a melt in each subdomain. The larger the melt plasticization value, the better the plasticization effect. However, when the melt plasticization value is too large, the risk of decomposition of the PVC mixture is greater, which is not conducive to subsequent extrusion molding. The expression is: ; In the formula, represents the melt plasticization value of the i-th subdomain; , They represent the mean value of velocity data and the mean value of pressure data of all grid cells in the i-th subdomain respectively; represents the actual temperature of the ith subdomain; Indicates the discrete degree of velocity data of all grid cells in the i-th subdomain; It represents the sum of the maximum inter-class variances of all sub-intervals within the pressure interval of the i-th sub-domain.

[0044] In this embodiment, the degree of discreteness is the standard deviation. As other implementation methods, on the basis of being able to measure the uneven distribution of flow rate data, the implementer may use other existing technologies for measurement, such as variance, coefficient of variation, etc., and this application does not impose any special restrictions.

[0045] It should be noted that temperature, flow rate and pressure are important indicators that reflect the plasticization state of the PVC mixture melt. The higher the actual temperature and the average flow rate of the melt, the better the melt plasticization effect; at the same time, the more uniform the melt flow rate, the smaller the calculated discreteness, which reflects that the melt flow rate changes in different pressure areas of the screw transmission are smaller, the flow performance of the melt is worse, and the melt plasticization value is smaller; in addition, the smaller the melt pressure, the smaller the extrusion stress of the mixture during screw transmission, the better the melt flow performance, and the larger the calculated melt plasticization value; It reflects the difference in pressure distribution at different positions in the cylinder. The greater the difference in pressure distribution, the greater the sum of the maximum inter-class variance, the worse the flow performance of the PVC mixture in the cylinder, the worse the melt plasticization effect, and the smaller the calculated melt plasticization value. The schematic diagram of the melt plasticization value acquisition process is shown in the figure. Figure 3 shown.

[0046] The melt plasticization value of each subdomain at each sampling moment is obtained by adopting the same acquisition method as the melt plasticization value of the i-th subdomain at the j-th sampling moment.

[0047] Step S3, obtaining the ideal pressure of the extruder die head, and simulating the simulated temperature and simulated pressure of each subdomain and the extruder die head at the next collection time of the current collection time under the control of any parameter vector including proportional parameters, integral parameters and differential parameters through the transfer function of the frequency converter in the feeding system; based on the preset ideal temperature and the simulated temperature of each subdomain, the same calculation method as the melt plasticizing value is adopted to obtain the ideal melt plasticizing value and the simulated melt plasticizing value of each subdomain respectively.

[0048] In order to ensure that the pressure of the extruder die head remains constant and avoid defects such as depression and dragging in the extruded PVC pellets, it is necessary to analyze whether the melting state of the PVC mixture in each sub-domain is deviated according to the melt plasticization value of each sub-domain of the extruder barrel. When deviation occurs, the screw speed should be adjusted in time to ensure that the die head pressure remains stable during melt transmission.

[0049] The heating power of each subdomain is constant, so each subdomain has a preset ideal temperature. However, due to the differences in particle size of the pellets and the complexity of the mixing and stirring process, the actual temperature of the melt in each subdomain is different from the preset ideal temperature. Therefore, the parameters of the pellets in the extruder at the preset ideal temperature are used as evaluation criteria, and the screw speed is adjusted to improve the control accuracy of the die head pressure.

[0050] In this embodiment, the preset ideal temperatures of the first subdomain to the Lth subdomain are 126°C, 130°C, 132°C, 134°C, 136°C, 138°C, 140°C, 142°C, and 142°C, respectively. The preset ideal temperature of each subdomain can be set by the implementer according to actual conditions.

[0051] When the actual temperature of each sub-domain is the preset ideal temperature, the pressure data collected by the pressure sensor is used as the ideal pressure of the die head.

[0052] For the inverter used to control the screw speed in the centralized feeding system of the PVC fully automatic compounding line, the PID algorithm is used to control the inverter, thereby achieving the purpose of maintaining the die head pressure stable. Three random numbers are selected in the open interval (0,100) as the proportional parameter, integral parameter and differential parameter of the PID algorithm, and a parameter vector is formed. The difference between the die head pressure and the ideal pressure is used as the input of the PID algorithm, and the control instruction is output. The inverter is used to control the extruder screw speed according to the control instruction to keep the extruder die head pressure stable.

[0053] Through the transfer function of the frequency converter used to control the screw speed, the simulated temperature and pressure of each subdomain and the extruder die head at the next collection time of the current collection time are simulated under the control of any parameter vector. Among them, the calculation of the transfer function and the calculation of the simulated temperature and pressure by the transfer function are well-known technologies and will not be repeated in this application.

[0054] Based on the preset ideal temperature of each subdomain, the same acquisition method as the melt plasticization value of the ith subdomain at the jth acquisition moment is adopted, and the actual temperature is replaced with the preset ideal temperature to obtain the melt plasticization value of each subdomain, which is recorded as the ideal melt plasticization value; based on the simulated temperature of each subdomain at the next acquisition moment of the current acquisition moment, the same acquisition method as the melt plasticization value of the ith subdomain at the jth acquisition moment is adopted, and the actual temperature is replaced with the simulated temperature to obtain the melt plasticization value of each subdomain at the next acquisition moment, which is recorded as the simulated melt plasticization value.

[0055] When the melt plasticization value is less than the ideal melt plasticization value, the plasticization effect of the melt in the subdomain is poor; when the melt plasticization value exceeds the ideal melt plasticization value, the risk of decomposition of the PVC mixture in the subdomain is greater. Both situations will affect the quality of subsequent PVC pellet extrusion molding.

[0056] Step S4, based on the difference between the simulated pressure and the ideal pressure, the difference between the simulated melt plasticization value and the ideal melt plasticization value of each subdomain, and comparing the distribution of the simulated melt plasticization value and the ideal melt plasticization value of all subdomains, obtain the fitness of any parameter vector.

[0057] In order to select a suitable parameter vector, W parameter vectors are obtained as the initial population of the whale optimization algorithm. The whale optimization algorithm is used to iteratively process multiple parameter vectors, and finally the optimal parameter vector is output. Each component in the optimal parameter vector is used as the control parameter of the PID algorithm. During the iteration process, it is necessary to measure the quality of each parameter vector. The specific process is: The simulated melt plasticizing value and the ideal melt plasticizing value of each subdomain and its adjacent subdomain are respectively used to form a simulated plasticizing state vector and an ideal plasticizing state vector of each subdomain.

[0058] In this embodiment, the value of W is 50. The value of W is preset manually and can be set by the implementer. This application does not impose any special restrictions.

[0059] In this embodiment, the simulated melt plasticizing value and the ideal melt plasticizing value of each subdomain and its adjacent subsequent subdomain are respectively used to form the simulated plasticizing state vector and the ideal plasticizing state vector of each subdomain. For the Lth subdomain, the two components of the simulated plasticizing state vector of the Lth subdomain are both the simulated melt plasticizing value of the Lth subdomain, and the two components of the ideal plasticizing state vector of the Lth subdomain are both the ideal melt plasticizing value of the Lth subdomain. As another embodiment, the simulated melt plasticizing value and the ideal melt plasticizing value of each subdomain and its adjacent previous subdomain are respectively used to form the simulated plasticizing state vector and the ideal plasticizing state vector of each subdomain. For the 1st subdomain, the two components of the simulated plasticizing state vector of the 1st subdomain are both the simulated melt plasticizing value of the 1st subdomain, and the two components of the ideal plasticizing state vector of the 1st subdomain are both the ideal melt plasticizing value of the 1st subdomain.

[0060] The difference between the simulated pressure and the ideal pressure of the extruder die head, as well as the difference between the simulated melt plasticization value and the ideal melt plasticization value of each subdomain are analyzed, and the fitness of any parameter vector is obtained in combination with the similarity between the simulated plasticization state vector and the ideal plasticization state vector of each subdomain, which is used to reflect the extrusion molding quality of PVC pellets when the speed of the screw in the barrel is controlled by the parameter vector. The smaller the fitness, the better the pellet extrusion molding quality. The specific process is as follows: The similarity between the simulated plasticization state vector and the ideal plasticization state vector of each subdomain is calculated, and the difference between the simulated pressure and the ideal pressure is recorded as the pressure difference; the difference between the simulated melt plasticization value and the ideal melt plasticization value of each subdomain is recorded as the plasticization difference; the fitness of any parameter vector is positively correlated with the pressure difference, the plasticization difference, and the sequence number of each subdomain, and negatively correlated with the similarity.

[0061] The fitness expression of any parameter vector is: ; Wherein, F represents the fitness of any parameter vector; P represents the pressure difference of the extruder die head; L represents the number of subdomains of the extruder barrel; i represents the sequence number of the subdomain; represents the plasticization difference of the i-th subdomain; represents the similarity of the ith subdomain; exp() represents an exponential function with a natural constant as the base, the purpose of which is to convert Mapped to positive values.

[0062] In this embodiment, in the process of calculating fitness, the differences involved are all the squares of the differences. As other implementation methods, on the basis of being able to measure the difference between the simulated pressure and the ideal pressure, and the difference between the simulated melt plasticization value and the ideal melt plasticization value, the implementer may adopt other calculation methods, such as the absolute value of the difference, the ratio, etc., and this application does not impose any special restrictions.

[0063] In this embodiment, the similarity between the simulated plasticization state vector and the ideal plasticization state vector is cosine similarity. As other implementation methods, on the basis of the measurable similarity between the simulated plasticization state vector and the ideal plasticization state vector, the implementer may adopt other existing technologies for measurement, such as the reciprocal of the Euclidean distance, the reciprocal of the DTW distance between sequences composed of vectors arranged in time sequence, etc. This application does not impose any special restrictions.

[0064] It should be noted that: on the one hand, the die pressure directly affects the extrusion molding quality of PVC pellets. The greater the difference between the simulated pressure and the ideal pressure, the greater the calculated fitness, reflecting the worse the extrusion molding quality of the pellets; on the other hand, the ideal plasticizing state of the mixture in different subdomains is different, and its ideal melt plasticizing value changes according to a certain gradient; the greater the similarity between the simulated plasticizing state vector of the subdomain and the ideal plasticizing state vector, the closer the change trend of the plasticizing states of the two is, and the smaller the calculated fitness; at the same time, the greater the difference between the simulated melt plasticizing value and the ideal melt plasticizing value of the subdomain, the greater the fitness, and the larger the serial number of the subdomain, the closer it is to the die head, and the greater the impact on the extrusion molding of PVC pellets, so a larger calculation weight is set; the parameter vector with a smaller fitness is more suitable as the optimal parameter vector.

[0065] Step S5, in the process of using the whale optimization algorithm to obtain the optimal parameter vector at the current acquisition moment, the convergence factor of each iteration is obtained by the convergence factor of its adjacent iteration, and the degree of proximity of the fitness of all parameter vectors in its adjacent iterations, and combined with the difference in parameter vectors between each iteration and its adjacent iterations, the simulated temperature at the next acquisition moment is compared with the preset ideal temperature for update; based on the optimal parameter vector obtained by the whale optimization algorithm, the pressure of the extruder die head is controlled.

[0066] Considering the complexity of the PVC mixing flow field in the cylinder, the fitness distribution of different parameter vectors has a large randomness. Therefore, the update method of the convergence factor in each iteration is: Calculate the distance between the parameter vector with the minimum fitness in each iteration and the parameter vector with the minimum fitness in the adjacent iteration, recorded as the vector distance; calculate the cumulative value of the difference between the simulated temperature and the preset ideal temperature of all subdomains; count the minimum value of the fitness of all parameter vectors in each iteration, recorded as the fitness minimum value; calculate the difference between the fitness of each parameter vector in the adjacent iterations of each iteration and the fitness minimum value, recorded as the fitness difference; calculate the normalized value of the fusion result of the vector distance, the cumulative value and the difference; the convergence factor in each iteration is the product of the convergence factor in its adjacent iterations and the normalized value.

[0067] In this embodiment, the value of the convergence factor in the first iteration is 2. The value of the convergence factor in the first iteration can be set by the implementer according to actual conditions, and this application does not impose any special restrictions.

[0068] In this embodiment, the distance between parameter vectors is the Euclidean distance. As other implementation methods, on the basis of being able to measure the distance between parameter vectors, the implementer may adopt other existing technologies for measurement, such as Manhattan distance, cosine distance, etc., and this application does not impose any special restrictions.

[0069] In this embodiment, the Sigmoid function is used to normalize the fusion result. As other implementation methods, on the basis of being able to normalize the fusion result, the implementer can adopt other existing technologies, such as decimal calibration normalization method, hyperbolic tangent function, etc., and this application does not make any special restrictions.

[0070] It should be noted that fusion refers to combining multiple independent variables in a way that enhances the overall effect, such as additive relationship, synthetic relationship, etc. The implementer can limit it according to the actual situation, and this application does not impose any special restrictions.

[0071] In this embodiment, the expression of the fusion result is: ; Wherein, B represents the fusion result; d represents the distance between the parameter vector with the minimum fitness at the t-th iteration and the parameter vector with the minimum fitness at the t-1-th iteration; represents the accumulated value; W represents the number of parameter vectors in the whale optimization algorithm population; represents the adaptation difference corresponding to the wth parameter vector at the t-1th iteration.

[0072] In another embodiment, the expression of the fusion result is: ; Wherein, B represents the fusion result; d represents the distance between the parameter vector with the minimum fitness at the t-th iteration and the parameter vector with the minimum fitness at the t-1-th iteration; represents the accumulated value; W represents the number of parameter vectors in the whale optimization algorithm population; represents the adaptation difference corresponding to the wth parameter vector at the t-1th iteration.

[0073] It should be noted that: on the one hand, if the distance d between the parameter vectors with the minimum fitness in two adjacent iterations is smaller, and the difference between the fitness value of each parameter vector in the population and the minimum fitness value is The smaller it is, the closer the parameter vector with the lowest current fitness is to the optimal value, and the faster the convergence factor decreases, which improves the accuracy of optimization. On the other hand, the greater the difference between the simulated temperature of the subdomain and the preset ideal temperature, the larger the range of screw speed adjustment needs to be, and the optimization range of the parameter vector needs to be expanded, and the slower the convergence factor decreases, so as to avoid falling into the local optimum.

[0074] The process of using the whale optimization algorithm to obtain the optimal parameter vector at the current moment is as follows: the initial population is used as the input of the whale optimization algorithm, and the optimal parameter vector is output based on the calculated fitness and convergence factor, wherein the maximum number of iterations is M. Each component in the optimal parameter vector is used as the control parameter of the PID algorithm, and the difference between the pressure of the extruder die head at the current moment and the ideal pressure is used as the input of the PID algorithm, and a control instruction is output. The frequency converter is used to control the extruder screw speed according to the control instruction, so that the extruder die head pressure remains stable, thereby improving the quality of PVC pellet extrusion molding.

[0075] In this embodiment, the value of M is 50. The value of M is preset manually and can be set by the implementer. This application does not impose any special restrictions.

[0076] In summary, the present application obtains the flow rate and pressure distribution of the PVC mixture in the cylinder by performing isothermal flow field modeling on different subdomains respectively. The beneficial effect is that by analyzing the changes of the rheological properties and thermal performance parameters of the mixture in the subdomain with temperature, the accuracy of the flow field model establishment is improved, thereby improving the accuracy of the subsequent acquisition of the plasticization effect of the mixture melt; Furthermore, according to the flow rate and pressure distribution of the PVC mixture in the extruder barrel, the melt plasticization value of the subdomain is calculated. The beneficial effect is that by analyzing the physical properties of the PVC mixture melt and its extrusion characteristics in the barrel, and utilizing the difference in pressure distribution in the barrel subdomain, the plasticization state of the melt in each subdomain can be accurately obtained; Furthermore, by simulating the degree to which the future plasticization state of the melt deviates from the ideal plasticization state under the control of each parameter vector, the fitness of each parameter vector is obtained to evaluate the possibility of each parameter vector as the optimal parameter vector, so as to facilitate the subsequent selection of the most suitable parameter vector to accurately control the pressure of the extruder die head; Furthermore, the convergence factor is calculated based on the fitness of the parameter vector, which can improve the accuracy of the optimization of the parameter vector. The whale optimization algorithm is used to obtain the optimal parameter vector. The various components in the optimal parameter vector are used as the control parameters of the PID algorithm, which can optimize the speed control of the extruder screw and achieve more precise control of the pressure of the extruder die head.

[0077] The flowchart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to the embodiment of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. In the description corresponding to the flowchart and the block diagram in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in a different order from the order disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified functions or actions, or may be implemented by a combination of dedicated hardware and computer instructions.

[0078] It is obvious to those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and that the present application can be implemented in other specific forms without departing from the basic features of the present application. Therefore, no matter from which point of view, the above embodiments of the present application should be regarded as exemplary and non-restrictive.

Claims

1. A control method for a centralized feeding system of a PVC fully automatic compounding line, characterized in that: The method comprises the following steps: Divide the melting section and melt conveying section of the extruder barrel into sub-areas, obtain the actual temperature of each sub-area at each sampling time, and obtain the ideal pressure of the extruder die head; Based on the actual temperature, isothermal flow field modeling is performed on each subdomain, and the melt plasticization value of each subdomain at each sampling time is obtained by analyzing the pressure distribution and flow velocity distribution in the flow field and combining the actual temperature of each subdomain; Through the transfer function of the frequency converter in the feeding system, the simulated temperature and the simulated pressure of each subdomain and the extruder die head at the next acquisition moment of the current acquisition moment are simulated under the control of any parameter vector including a proportional parameter, an integral parameter and a differential parameter; based on the preset ideal temperature of each subdomain and the simulated temperature, the ideal melt plasticizing value and the simulated melt plasticizing value of each subdomain are respectively obtained by using the same calculation method as the melt plasticizing value; Based on the difference between the simulated pressure and the ideal pressure, the difference between the simulated melt plasticization value and the ideal melt plasticization value of each subdomain, and comparing the distribution of the simulated melt plasticization value and the ideal melt plasticization value of all subdomains, the fitness of any parameter vector is obtained; In the process of using the whale optimization algorithm to obtain the optimal parameter vector at the current acquisition moment, the convergence factor at each iteration is updated by comparing the simulated temperature at the next acquisition moment with the preset ideal temperature through the convergence factor at the adjacent iteration and the fitness of all parameter vectors at the adjacent iterations, combined with the difference between the parameter vectors of each iteration and its adjacent iterations; The optimal parameter vector obtained based on the whale optimization algorithm is used to control the pressure of the extruder die head.

2. The control method of a centralized feeding system for a PVC fully automatic compounding line according to claim 1, characterized in that: The process of obtaining the melt plasticization value is as follows: The flow field area obtained by modeling is meshed to obtain the velocity tensor and pressure tensor of each grid unit at each acquisition time; the flow data and pressure data of each grid unit at each acquisition time are respectively obtained through the velocity tensor and the pressure tensor; Based on the pressure distribution and velocity distribution of all grid cells in each subdomain at each acquisition time, and combined with the actual temperature of each subdomain, the melt plasticization value of each subdomain at each acquisition time is obtained.

3. The control method of a centralized feeding system for a PVC fully automatic compounding line according to claim 2, characterized in that: The further acquisition process of the melt plasticization value is as follows: Calculate the discrete degree of velocity data of all grid cells in each subdomain at each acquisition time; The mean value of the velocity data and the mean value of the pressure data of all grid cells in each subdomain at each acquisition time are recorded as the velocity mean and the pressure mean, respectively; Analyze the distribution differences of pressure data of all grid cells in each subdomain at each acquisition time; The melt plasticization value is positively correlated with the mean flow rate, the actual temperature, and the discrete degree, and is negatively correlated with the mean pressure and the distribution difference.

4. The control method of a centralized feeding system for a PVC fully automatic compounding line according to claim 3, characterized in that: The specific analysis method of the distribution difference is: The minimum and maximum values ​​of the pressure data of all grid cells in each subdomain at each acquisition time are taken as the endpoints of the pressure interval of each subdomain at each acquisition time; Each pressure interval is equally divided into a plurality of quantization intervals, and all pressure data in any quantization interval are assigned the minimum value of the pressure data in any quantization interval; Combining a preset number of continuous quantization intervals into each sub-interval; All the assigned pressure data in each sub-interval are used as the input of the maximum inter-class variance algorithm, and the maximum inter-class variance of each sub-interval is output; The distribution difference is reflected by the sum of all the maximum inter-class variances corresponding to each subdomain.

5. The control method of a centralized feeding system for a PVC fully automatic compounding line according to claim 1, characterized in that: The process of obtaining the fitness is as follows: The simulated melt plasticization value and the ideal melt plasticization value of each subdomain and its adjacent subdomain are used to form a simulated plasticization state vector and an ideal plasticization state vector of each subdomain, respectively, and the similarity between the simulated plasticization state vector and the ideal plasticization state vector of each subdomain is calculated; The difference between the simulated pressure and the ideal pressure is recorded as a pressure difference; The difference between the simulated melt plasticization value and the ideal melt plasticization value of each subdomain is recorded as the plasticization difference; The fitness is positively correlated with the pressure difference, the plasticization difference, and the serial number of each subdomain, and negatively correlated with the similarity; wherein each subdomain is numbered in sequence from far to near according to the distance between the subdomain and the extruder die head.

6. The control method of a centralized feeding system for a PVC fully automatic compounding line according to claim 5, characterized in that: The further acquisition process of the fitness is: calculating the product of the plasticization difference and the serial number of each subdomain; the fitness is positively correlated with the pressure difference and the product, and negatively correlated with the similarity.

7. The control method of a centralized feeding system for a PVC fully automatic compounding line according to claim 5, characterized in that: The fitness expression is: ; Wherein, F represents the fitness of any parameter vector; P represents the pressure difference of the extruder die head; L represents the number of subdomains of the extruder barrel; i represents the sequence number of the subdomain; represents the plasticization difference of the i-th subdomain; represents the similarity of the ith subdomain; exp() represents an exponential function with a natural constant as the base.

8. The control method of a centralized feeding system for a PVC fully automatic compounding line according to claim 1, characterized in that: The update method of the convergence factor at each iteration is: Calculate the distance between the parameter vector with the minimum fitness in each iteration and the parameter vector with the minimum fitness in the adjacent iteration, recorded as vector distance; Calculating the accumulated value of the difference between the simulated temperature and the preset ideal temperature of all sub-domains; The minimum value of the fitness of all parameter vectors at each iteration is counted and recorded as the minimum fitness value; Calculating the difference between the fitness of each parameter vector and the minimum fitness value in adjacent iterations of each iteration; The convergence factor in each iteration is positively correlated with the convergence factor in the adjacent iteration, the vector distance, the accumulated value, and the difference value.

9. The control method of a centralized feeding system for a PVC fully automatic compounding line according to claim 8, characterized in that: The further acquisition process of the convergence factor at each iteration is as follows: Calculating a normalized value of a fusion result of the vector distance, the accumulated value, and the difference; The convergence factor at each iteration is the product of the convergence factor at the adjacent iteration and the normalized value.

10. The control method of a centralized feeding system for a PVC fully automatic compounding line according to claim 1, characterized in that: The optimal parameter vector obtained based on the whale optimization algorithm controls the pressure of the extruder die head, including: The components in the optimal parameter vector are used as control parameters of the PID algorithm, the difference between the pressure of the extruder die head at the current moment and the ideal pressure is calculated, and the pressure of the extruder die head is controlled in combination with the PID control algorithm.

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