A temperature control system and method during the production process of plastic particles

Abstract

The present invention discloses a temperature control system and method during the production process of plastic particles, belonging to the technical field of temperature control. The method specifically includes: analyzing the characteristics of plastic raw materials to obtain the key temperature nodes during the production process of plastic particles, establishing a temperature prediction model based on the characteristics of plastic raw materials and the structural parameters of plastic particle production equipment, setting a time interval to correct the temperature prediction model, calculating the dynamic adjustment amount of the temperature of plastic particles according to different production stages, monitoring the temperature of plastic materials in real time during the production process, and performing real-time feedback control. After the plastic particles are formed, they enter the cooling stage, and the temperature and flow rate of the cooling medium are controlled; by setting different temperature control targets at different stages of the production process, and the temperature control at each stage is dynamically adjusted based on real-time data, thereby achieving precise temperature control and effectively reducing energy consumption.

Description

Technical Field

[0001] The present invention belongs to the technical field of temperature control, and specifically relates to a temperature control system and method during the production process of plastic particles. Background Art

[0002] During the production process of plastic particles, temperature control is a key factor affecting product quality and production efficiency. Plastic particles are usually produced through processes such as melting extrusion, cooling, and pelletizing, and these processes all have strict requirements for temperature. Temperature fluctuations will not only affect the appearance and physical properties of products, but may also cause a series of problems such as equipment failures and increased energy consumption. Therefore, it is particularly important to implement precise temperature control during the production process.

[0003] Currently, most of the temperature control technologies in plastic particle production lines use simple temperature control devices, such as constant temperature heaters, cooling water circulation systems, and temperature sensors. These devices are used to maintain the temperature stability of each link during the production process. However, the existing technologies have certain limitations in many aspects: 1) Large temperature fluctuations, difficult to achieve precise control: Most of the temperature control systems in the existing technologies can only be adjusted within a certain predetermined temperature range, and cannot be adjusted in real time according to the actual changes during the production process; 2) Slow response speed, unable to adapt to complex changes: During the production process, temperature is affected by many factors, such as raw material characteristics, production speed, and external environmental temperature; 3) Low energy efficiency, serious waste: Traditional temperature control systems usually use heating and cooling devices with fixed power at each stage, and cannot be dynamically adjusted according to actual needs, resulting in waste of energy.

[0004] Based on these problems, a temperature control method that can control temperature more precisely and flexibly and can optimize the entire production process is particularly important. Summary of the Invention

[0005] In view of the deficiencies of the existing technology, the present invention proposes a temperature control system and method during the production process of plastic particles. By real-time monitoring and adjusting the temperature at each stage of the production process and combining a dynamic feedback mechanism, it can effectively avoid the influence of temperature fluctuations on the quality of plastic particles and improve the stability and efficiency of the production process.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A temperature control method during the production process of plastic particles, comprising:

[0008] Analyze the characteristics of plastic raw materials to obtain the key temperature nodes during the production process of plastic particles; [[ID=IO]]

[0009] Based on the characteristics of plastic raw materials and the structural parameters of plastic particle production equipment, establish a temperature prediction model;

[0010] Set a time interval to correct the temperature prediction model, and calculate the dynamic adjustment amount of the temperature of plastic particles according to different production stages;

[0011] During the production process, the temperature of the plastic material is monitored in real time and feedback control is carried out in real time;

[0012] After the plastic particles are formed, they enter the cooling stage, and the temperature and flow rate of the cooling medium are controlled.

[0013] Specifically, the temperature prediction model has the following specific formula:

[0014]

[0015] Among them, ρ represents the density of the plastic material, C p represents the specific heat capacity of the plastic material, represents the partial derivative function, T represents the temperature, represents the velocity vector of the plastic material, λ represents the thermal conductivity of the plastic material, ΔT represents the change in temperature, represents the temperature gradient, Q qxt represents the external heat source term, Q flow represents the viscous dissipation heat generated by the flow of the plastic material, Q chem represents the heat generated by chemical reactions, Q rad represents the thermal radiation term.

[0016] Specifically, the correction of the temperature prediction model by the set time interval includes:

[0017] Set a time interval Δt, collect the temperature data at key positions during the production process of plastic particles, including but not limited to different sections of the extruder screw, multiple points on the inner wall of the barrel, inside the mold cavity, etc., including M monitoring points, and filter the collected temperature data;

[0018] Based on the temperature data at the key positions collected and the predicted temperature values calculated by the temperature prediction model, construct a temperature error function E, and the specific formula is:

[0019]

[0020] Among them, E represents the temperature error function, T filt (m,t) represents the filtered temperature value of the m-th monitoring point at the actual time t, represents the predicted temperature value calculated by the temperature prediction model for the m-th monitoring point at the actual time t;

[0021] Based on the temperature error function E, adjust and optimize the key parameters in the temperature prediction model.

[0022] Specifically, calculating the dynamic adjustment amount of the temperature of plastic particles according to different production stages includes:

[0023] During the production process of plastic particles, extracting characteristic parameters related to each production stage;

[0024] Using the extracted characteristic parameters to construct a production stage state evaluation model, taking the characteristic parameters as input variables and the state evaluation index of the production stage as output variables;

[0025] According to the output result of the production stage state evaluation model, adopting an adaptive adjustment strategy to dynamically determine the temperature adjustment amount.

[0026] Specifically, during the production process, real-time monitoring and real-time feedback control of the temperature of plastic materials include:

[0027] Adopting the sliding window method to calculate the change rate of the temperature adjustment amount, performing linear fitting on the temperature adjustment amount data in the past period of time, and obtaining the slope of the fitting line as the change rate of the temperature adjustment amount;

[0028] Adopting a neural network hybrid control strategy to determine the control output of heating. The specific formula is:

[0029]

[0030] Among them, u(t) represents the output signal of the feedback control, x j represents the input variable, ω ij represents the connection weight from the input layer to the hidden layer, b i represents the bias of the hidden layer neurons, f() represents the activation function of the hidden layer neurons, represents the connection weight from the hidden layer to the output layer, g() represents the activation function of the output layer, n represents the number of hidden layer neurons, and J represents the number of input variables.

[0031] Specifically, after the plastic particles are formed and enter the cooling stage, controlling the temperature and flow rate of the cooling medium includes:

[0032] Setting the initial temperature of the cooling medium according to the size of the plastic particles and the required cooling rate;

[0033] Performing thermal property analysis on the produced plastic particles, establishing a plastic particle cooling model based on the thermophysical properties of the cooling medium and the plastic particles, as well as the geometric structure of the cooling system, and calculating the temperature of the plastic particles after cooling at time t. The specific formula is:

[0034]

[0035] Among them, ψ(t) represents the temperature of the plastic particles at the cooling time t, ψ0 represents the initial temperature, that is, the temperature after the plastic particles are molded, ψ ι represents the temperature corresponding to the ι-th boundary condition, ι represents the ι-th boundary condition, τ represents the time parameter, t c represents the characteristic cooling time, represents the equivalent thermal diffusivity, cos() represents the cosine function, e represents the exponential function, and t represents time;

[0036] When the temperature of the monitored plastic particles reaches the preset end temperature of cooling, it is determined that the cooling process ends.

[0037] Specifically, the characteristic analysis of the plastic raw material includes: determination of the melting point range, thermal decomposition temperature, specific heat capacity, and thermal conductivity parameters of the raw material.

[0038] A temperature control system in the production process of plastic particles, used to implement the temperature control method in the production process of plastic particles, includes: a characteristic analysis module, a temperature prediction module, a dynamic adjustment module, a feedback control module, and a cooling control module;

[0039] The characteristic analysis module is used to perform characteristic analysis on the plastic raw material to obtain the key temperature nodes in the production process of plastic particles;

[0040] The temperature prediction module is used to establish a temperature prediction model based on the characteristics of the plastic raw material and the structural parameters of the plastic particle production equipment;

[0041] The dynamic adjustment module is used to set a time interval to correct the temperature prediction model and calculate the dynamic adjustment amount of the temperature of the plastic particles according to different production stages;

[0042] The feedback control module is used to monitor the temperature of the plastic material in real time during the production process and perform real-time feedback control;

[0043] The cooling control module is used to control the temperature and flow rate of the cooling medium after the plastic particles are molded and enter the cooling stage.

[0044] Specifically, the dynamic adjustment module includes: an error function unit, a state evaluation unit, and an adjustment amount determination unit;

[0045] The error function unit is used to construct a temperature error function to adjust and optimize the key parameters in the temperature prediction model;

[0046] The state evaluation unit is used to extract the characteristic parameters related to each production stage and construct a production stage state evaluation model;

[0047] The adjustment amount determination unit is configured to dynamically determine the temperature adjustment amount by using an adaptive adjustment strategy according to the output result of the production stage state evaluation model.

[0048] Specifically, the feedback control module includes: a change amount calculation unit and a feedback control unit;

[0049] The change amount calculation unit is configured to perform linear fitting on the temperature adjustment amount data over a past period of time, and obtain the slope of the fitting line as the change rate of the temperature adjustment amount;

[0050] The feedback control unit is configured to determine the control output of heating by using a neural network hybrid control strategy.

[0051] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0052] 1. The present invention proposes a temperature control method in the production process of plastic particles. By setting different temperature control targets at different stages of the production process, such as plasticization, extrusion, cooling, etc., and dynamically adjusting the temperature control according to real-time data at each stage, precise temperature control can be achieved.

[0053] 2. The present invention proposes a temperature control method in the production process of plastic particles. By adjusting the power of heating and cooling equipment according to the real-time requirements in the production process, the problems of overheating and overcooling in the traditional method are avoided, thereby effectively reducing energy consumption.

[0054] 3. The present invention proposes a temperature control method in the production process of plastic particles. By introducing a dynamic feedback control mechanism, it can quickly respond to the temperature changes in the production process of plastic particles, adjust the working states of relevant equipment, ensure that the temperature in the production process of plastic particles always remains within the optimal control range, and adapt to complex factors such as raw material fluctuations, production speed changes, and external environmental temperature changes, thereby improving the stability of production. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a flowchart of a temperature control method in the production process of plastic particles provided by the present invention;

[0056] Figure 2 It is an architecture diagram of a temperature control system in the production process of plastic particles provided by the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0057] The present application will be described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present application, but do not limit the present application in any form. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made. These all fall within the protection scope of the present application.

[0058] In order to make the purpose, technical solution and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0059] It should be noted that if there is no conflict, the various features in the embodiments of the present application can be combined with each other, and all are within the protection scope of the present application. In addition, although the functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order from the module division in the device or the flowchart. In addition, the terms "first", "second", "third", etc. used in the present application do not limit the data and execution order, but only distinguish the same items or similar items with basically the same functions and effects.

[0060] Unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs. The terms used in this specification in the description of the present application are only for the purpose of describing specific embodiments and are not used to limit the present application. The term "and / or" used in this specification includes any and all combinations of one or more of the related listed items.

[0061] Embodiment 1

[0062] Please refer to Figure 1 , an embodiment provided by the present invention: a temperature control method in the production process of plastic particles, including the following specific steps:

[0063] Step S1: Analyze the characteristics of the plastic raw materials to obtain the key temperature nodes in the production process of plastic particles;

[0064] Analyzing the characteristics of the plastic raw materials includes: accurately measuring parameters such as the melting point range, thermal decomposition temperature, specific heat capacity, and thermal conductivity of the raw materials. Through these parameters, combined with the final performance indicators required for the plastic particles, determine the key temperature nodes in the entire production process, such as the melting temperature, extrusion temperature, molding temperature, etc.;

[0065] Step S2: Establish a temperature prediction model based on the characteristics of the plastic raw materials and the structural parameters of the plastic particle production equipment;

[0066] The structural parameters of the plastic production equipment include: the pitch, diameter, and length changes of the extruder screw, the wall thickness and thermal properties of the barrel, the cavity structure of the mold, the layout of the cooling channels, etc. By performing high-precision 3D modeling on the plastic particle production equipment, the geometric shape, dimensions, and internal structure information of the equipment can be accurately obtained using laser scanning or computer-aided design technology;

[0067] The temperature prediction model in step S2 has the following specific formula:

[0068]

[0069] Among them, ρ represents the density of the plastic material, considering its variation relationship with temperature and pressure, which is obtained by fitting experimental data or calculating using the equation of state. C p represents the specific heat capacity of the plastic material, considering its temperature and pressure dependence. Data under different conditions are obtained through experimental methods such as DSC, and are represented using polynomial fitting or piecewise functions. represents the partial derivative function, T represents temperature, represents the velocity vector of the plastic material, which is obtained by simulating the flow field of the material in the equipment using computational fluid dynamics (CFD). The effects of factors such as screw rotation, plunger pushing, and viscous resistance of the material on the material flow are considered. λ represents the thermal conductivity of the plastic material, which is corrected according to the microstructure and temperature conditions of the material. For composite materials or filled plastics, the effective medium theory model is used to calculate the thermal conductivity. ΔT represents the change in temperature, represents the temperature gradient, Q qxt represents the external heat source term, Q flow represents the viscous dissipation heat generated by the plastic material flow, Q chem represents the heat generated by chemical reactions, Q rad represents the thermal radiation term;

[0070] The principle of the above formula: The first term on the left side of the formula represents the rate of change of the internal energy of the plastic material per unit volume with time, reflecting the change in the heat stored inside the plastic material due to the change in temperature over time, that is, the rate of heat accumulation or heat consumption. The second term considers the influence of the plastic material flow on the temperature distribution, that is, the convective heat transfer term. The flow of the plastic material carries heat and transfers it in space. The dot product of the velocity vector and the temperature gradient represents the direction and rate of heat transfer caused by the material flow. This term plays an important role in describing the dynamic heat transfer process of the material in the equipment, especially in the conveying section and metering section of the extruder screw, where the material flow velocity is relatively fast and the convective heat transfer has a significant influence on the temperature distribution; The first term on the right side is the heat conduction term, which describes the heat conduction process within the material due to the temperature gradient, that is, the transfer of heat from the high-temperature region to the low-temperature region. The thermal conductivity λ determines the rate of heat conduction, and the external heat source term Q qxt considers the direct impact of equipment heating on the material temperature. By accurately calculating the power input and heat distribution of the heating element, it can accurately simulate the contribution of external heating to the temperature rise of the material during the production process. The viscous dissipation heat Q flow reflects the heat generated due to viscous resistance during the flow of the material. This part of the heat will cause the material temperature to rise, especially in the high-shear region. The chemical reaction heat term Q chem considers the influence of chemical reactions during the production of plastic particles on the temperature. Different chemical reactions will release or absorb heat, and the generation or consumption of this heat will change the material temperature. By accurately calculating the chemical reaction heat and incorporating it into the temperature prediction model, it can more realistically simulate the thermal changes during the production process. The thermal radiation term Q rad considers the thermal radiation exchange between the material and the surrounding environment. During high-temperature production processes, thermal radiation is a non-negligible heat transfer method. Especially when there is a large temperature difference between the material and the inner wall of the equipment and between different parts of the material, thermal radiation will affect the temperature distribution. This method of establishing a temperature prediction model can more accurately predict the temperature changes during the production process by considering various physical phenomena and complex factors in the production of plastic particles in detail, providing strong support for achieving precise temperature control, thereby improving the production quality and production efficiency of plastic particles, reducing the scrap rate and energy consumption.

[0071] Step S3: Set a time interval to correct the temperature prediction model, and calculate the dynamic adjustment amount of the plastic particle temperature according to different production stages;

[0072] The specific steps of Step S3 are as follows:

[0073] Step S301: Set a time interval Δt, collect the temperature data at key positions during the production of plastic particles, including but not limited to different sections of the extruder screw, multiple points on the inner wall of the barrel, inside the mold cavity, etc., including M monitoring points, and perform filtering processing on the collected temperature data;

[0074] The filtering processing is used to remove noise interference;

[0075] Step S302: Based on the temperature data at the key positions collected and the predicted temperature values calculated by the temperature prediction model, construct a temperature error function E. The specific formula is:

[0076]

[0077] where E represents the temperature error function, T filt(m, t) represents the filtered temperature value of the m-th monitoring point at the actual time t, which represents the predicted temperature value calculated by the temperature prediction model of the m-th monitoring point at the actual time t;

[0078] This error function comprehensively considers the temperature prediction errors of multiple monitoring points over a period of time, and can more comprehensively reflect the deviation between the temperature prediction model and the actual situation;

[0079] Step S303: Based on the temperature error function E, adjust and optimize the key parameters in the temperature prediction model;

[0080] For example, thermal conductivity, specific heat capacity, etc. Taking the thermal conductivity λ as an example, its update formula is as follows: where λ new represents the updated thermal conductivity, μ represents the learning rate, represents the partial derivative of the temperature error function E with respect to the thermal conductivity λ. Through iterative calculation, the parameters are continuously adjusted to make the temperature error function E gradually decrease until the preset convergence condition (such as the error change is less than a certain threshold) is reached, so as to realize the optimization of the temperature prediction model parameters and make the model more accurately reflect the heat transfer characteristics and temperature change laws in the current production process;

[0081] Step S304: During the production process of plastic particles, extract the characteristic parameters related to each production stage;

[0082] For example, during the raw material melting stage, monitor the screw torque, motor current, and the feeding rate of the raw material; during the extrusion molding stage, monitor the screw speed of the extruder, the head pressure, etc. Collect and analyze these parameters in real time, and extract their characteristic values, such as calculating the average value of the screw torque, the root mean square value of the motor current, the standard deviation of the fluctuation of the feeding rate, the fluctuation frequency of the screw speed, and the change slope of the head pressure, etc., as the basis for judging the state of the production stage and adjusting the target temperature;

[0083] Step S305: Use the extracted characteristic parameters to construct a production stage state evaluation model, with the characteristic parameters as input variables and the state evaluation indicators of the production stage as output variables;

[0084] In this embodiment, the state evaluation indicators include: melting degree, mixing uniformity, extrusion stability, etc.;

[0085] For example, for the evaluation of the melting degree in the raw material melting stage, the following fuzzy rules are defined: 1) If the average value of the screw torque is large, the root mean square value of the motor current is large, and the standard deviation of the fluctuation of the feeding rate is small, then the melting degree is high; 2) If the average value of the screw torque is medium, the root mean square value of the motor current is medium, and the standard deviation of the fluctuation of the feeding rate is medium, then the melting degree is medium; 3) If the average value of the screw torque is small, the root mean square value of the motor current is small, and the standard deviation of the fluctuation of the feeding rate is large, then the melting degree is low;

[0086] According to the production stage state evaluation model, the state evaluation value of the current production stage is obtained. This value is between 0 and 1, and the closer it is to 1, the closer the ideal state of this stage is to being achieved.

[0087] Step S306: According to the output result of the production stage state evaluation model, an adaptive adjustment strategy is adopted to dynamically determine the temperature adjustment amount.

[0088] In this embodiment, taking the extrusion molding stage as an example, let the current extrusion temperature target value be T target (t), the change slope of the head pressure be k p , and the fluctuation frequency of the screw speed be f n , then the calculation formula for the temperature adjustment amount is: ΔTT = α1×k p +α2×f n +α3×(1 - S), where ΔTT represents the temperature adjustment amount, α1, α2, and α3 represent the weight coefficients determined according to experience, S represents the extrusion stage state evaluation value. When the change slope of the head pressure is large, the fluctuation frequency of the screw speed is high, or the extrusion stage state evaluation value is low, the target temperature is increased or decreased accordingly to optimize the temperature conditions during the extrusion molding process and ensure the fluidity and molding performance of the plastic melt.

[0089] Step S4: During the production process, the temperature of the plastic material is monitored in real time and feedback control is carried out in real time;

[0090] The specific steps of Step S4 are as follows:

[0091] Step S401: The sliding window method is used to calculate the change rate of the temperature adjustment amount. The temperature adjustment amount data over a past period of time is linearly fitted, and the slope of the fitted straight line is used as the change rate of the temperature adjustment amount;

[0092] Step S402: A neural network hybrid control strategy is used to determine the control output of heating. The specific formula is:

[0093]

[0094] where u(t) represents the output signal of the feedback control, such as heating power, heating time, etc., x jDenote the input variables, including the temperature adjustment amount, the rate of change of the temperature adjustment amount, and other possible parameters, such as the operating parameters of the production equipment, the real-time characteristic parameters of the raw materials, etc., ω ij Denote the connection weights from the input layer to the hidden layer, b i Denote the biases of the hidden layer neurons, providing an additional learnable parameter for the hidden layer neurons, which helps the neural network better fit complex non-linear relationships, improve its expressive ability and generalization ability, enabling the control strategy to adapt to a wider range of production conditions. f() represents the activation function of the hidden layer neurons. Denote the connection weights from the hidden layer to the output layer, g() represents the activation function of the output layer, n represents the number of hidden layer neurons, and J represents the number of input variables.

[0095] The neural network here is a feedforward neural network FNN;

[0096] Principle of the above formula: The activation function introduces non-linear characteristics, enabling the neural network to handle complex non-linear system control problems. The activation function f() of the hidden layer performs non-linear transformation on the input signal, increasing the expressive ability of the network; the activation function g() of the output layer adjusts the final result according to the specific requirements of the control output. For example, a linear activation function is suitable for continuous control quantity output (such as the adjustment of heating power).

[0097] Step S5: After the plastic particles are molded, enter the cooling stage, and control the temperature and flow rate of the cooling medium.

[0098] The specific steps of Step S5 are as follows:

[0099] Step S501: Set the initial temperature of the cooling medium according to the size of the plastic particles and the required cooling rate.

[0100] For example, for a kind of heat-sensitive plastic particles, a cooling medium with a lower temperature and a higher flow rate may be required to achieve rapid and uniform cooling, while avoiding excessive temperature gradients that cause internal stress.

[0101] Step S502: Conduct a thermal characteristic analysis on the produced plastic particles. Based on the thermophysical properties of the cooling medium and the plastic particles, as well as the geometric structure of the cooling system, establish a plastic particle cooling model and calculate the temperature of the plastic particles after cooling at time t. The specific formula is:

[0102]

[0103] Among them, ψ(t) represents the temperature of the plastic particles at the cooling time t, ψ0 represents the initial temperature, that is, the temperature of the plastic particles after molding, ψ ιrepresents the temperature corresponding to the ι-th boundary condition, which is related to the temperature of the cooling medium and the heat exchange situation. ι represents the ι-th boundary condition, τ represents the time parameter, which is related to the flow characteristics of the cooling medium and the heat transfer process, and t c represents the characteristic cooling time represents the equivalent thermal diffusivity, which comprehensively considers the thermal diffusivity, thermal conductivity of the plastic particles and the interaction with the cooling medium. cos() represents the cosine function, e represents the exponential function, and t represents time;

[0104] Explanation and principle of the above formula: ψ ι reflects the temperature values corresponding to the complex heat exchange relationship between the plastic particles and the cooling medium under different boundary conditions. For example, when the flow rate of the cooling medium changes or the temperature distribution of the cooling medium is uneven, there will be different ψ ι values, and factors such as the specific structure of the cooling system, the physical properties of the cooling medium, and the position of the particles during the cooling process need to be comprehensively considered;

[0105] τ is closely related to the flow characteristics of the cooling medium, including factors such as the flow rate of the cooling medium, the change frequency of the flow direction, and the residence time in the cooling channel, reflecting the dynamic characteristics of heat transfer between the cooling medium and the plastic particles. A smaller τ value may indicate that the cooling medium is updated faster and the heat exchange efficiency is higher, but it may also lead to uneven temperature distribution. A larger τ value may mean that the heat exchange is relatively slow and stable;

[0106] Characteristic cooling time t c comprehensively considers factors such as the geometric shape, size, thermophysical properties of the material of the plastic particles, and the overall performance of the cooling system. It is a parameter describing the overall time scale of the cooling process. Under different particles and cooling conditions, the t c value is different. For example, for plastic particles with larger sizes or lower thermal conductivities, the t c value will be relatively larger, indicating that the cooling process is relatively slow;

[0107] The boundary conditions mainly include the following aspects: the temperature of the cooling medium, the flow rate of the cooling medium, the flow direction of the cooling medium, the ambient temperature, etc.;

[0108] considers the thermal diffusivity and thermal conductivity of the plastic particles themselves, and also considers factors such as the thermal resistance between the particles and the cooling medium, contact heat conduction, and heat transfer changes caused by the surface state of the particles (such as roughness, whether there is a coating, etc.), reflecting the comprehensive efficiency of heat diffusion inside the particles and heat exchange with the external cooling medium in a complex cooling environment;

[0109] This formula is based on the Fourier heat conduction analysis of the cooling process of plastic particles, taking into account the complex heat exchange boundary conditions between the cooling medium and the particles and their dynamic changes over time. The infinite series part in the formula represents the contributions of different frequency heat conduction modes to the temperature change, and the superposition of these modes is used to accurately describe the variation of temperature with time during the cooling process. Compared with the traditional cooling stage control formula, this formula comprehensively considers the dynamic characteristics of the cooling medium, the complex and variable heat exchange relationship between the particles and the cooling medium, and the comprehensive influence of the thermophysical properties of the particles themselves in actual production, rather than simply based on assumptions such as steady-state heat conduction or average heat transfer coefficient. This enables it to more accurately predict the temperature change of plastic particles during the cooling stage, providing theoretical support for more precise temperature control, helping to improve the product quality of plastic particles, reduce defects such as internal stress and deformation caused by uneven cooling, and at the same time optimize the energy consumption and production efficiency of the cooling process.

[0110] Step S503: When the monitored temperature of the plastic particles reaches the preset cooling end temperature, it is determined that the cooling process ends.

[0111] Embodiment 2

[0112] Please refer to Figure 2 , another embodiment provided by the present invention: A temperature control system during the production process of plastic particles, comprising: a characteristic analysis module, a temperature prediction module, a dynamic adjustment module, a feedback control module, and a cooling control module;

[0113] The characteristic analysis module is used to analyze the characteristics of the plastic raw material to obtain the key temperature nodes during the production process of plastic particles;

[0114] The temperature prediction module is used to establish a temperature prediction model based on the characteristics of the plastic raw material and the structural parameters of the plastic particle production equipment;

[0115] The dynamic adjustment module is used to correct the temperature prediction model at set time intervals and calculate the dynamic adjustment amount of the temperature of the plastic particles according to different production stages;

[0116] The feedback control module is used to monitor the temperature of the plastic material in real time during the production process and perform real-time feedback control;

[0117] The cooling control module is used to control the temperature and flow rate of the cooling medium after the plastic particles are formed and enter the cooling stage.

[0118] The dynamic adjustment module includes: an error function unit, a state evaluation unit, and an adjustment amount determination unit;

[0119] The error function unit is used to construct a temperature error function and adjust and optimize the key parameters in the temperature prediction model;

[0120] The state evaluation unit is used to extract the characteristic parameters related to each production stage and construct a production stage state evaluation model;

[0121] The adjustment amount determination unit is used to dynamically determine the temperature adjustment amount by adopting an adaptive adjustment strategy according to the output result of the production stage state evaluation model.

[0122] The feedback control module includes: a change amount calculation unit and a feedback control unit;

[0123] The change amount calculation unit is used to perform linear fitting on the temperature adjustment amount data in the past period of time to obtain the slope of the fitting straight line as the change rate of the temperature adjustment amount;

[0124] The feedback control unit is used to determine the control output of heating by adopting a neural network hybrid control strategy.

[0125] In addition, the parts of the above technical solutions provided in the embodiments of the present application that are consistent with the corresponding technical solutions in the prior art in terms of implementation principles are not described in detail to avoid excessive elaboration.

[0126] As described above in the specific embodiments, the purpose, technical solutions and beneficial effects of the present invention are further described in detail. It should be understood that the above are only the specific embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

Claims

1. A temperature control method during the production process of plastic particles, characterized in that, including: analyze the characteristics of plastic raw materials to obtain the key temperature nodes during the production process of plastic particles; establish a temperature prediction model based on the characteristics of plastic raw materials and the structural parameters of plastic particle production equipment; set a time interval to correct the temperature prediction model, and calculate the dynamic adjustment amount of the temperature of plastic particles according to different production stages; during the production process, monitor the temperature of plastic materials in real time and perform real-time feedback control; after the plastic particles are formed, enter the cooling stage, and control the temperature and flow rate of the cooling medium; the temperature prediction model, the specific formula is: where ρ represents the density of the plastic material, C p represents the specific heat capacity of the plastic material, represents the partial derivative function, T represents the temperature, represents the velocity vector of the plastic material, λ represents the thermal conductivity of the plastic material, ΔT represents the change in temperature, ▽T represents the temperature gradient, Q qxt represents the external heat source term, Q flow represents the viscous dissipation heat generated by the flow of the plastic material, Q chem represents the heat generated by the chemical reaction, Q rad represents the thermal radiation term, t represents the time; after the plastic particles are formed, enter the cooling stage, and control the temperature and flow rate of the cooling medium, including: set the initial temperature of the cooling medium according to the size of the plastic particles and the required cooling rate; conduct a thermal characteristic analysis on the produced plastic particles, establish a plastic particle cooling model based on the thermophysical properties of the cooling medium and plastic particles, and the geometric structure of the cooling system, and calculate the temperature of the plastic particles after cooling at time t, the specific formula is: Among them, ψ(t) represents the temperature of the plastic particles at the cooling time t, ψ0 represents the initial temperature, that is, the temperature after the plastic particles are molded, ψ ι represents the temperature corresponding to the ι-th boundary condition, ι represents the ι-th boundary condition, τ represents the time parameter, t c represents the characteristic cooling time, represents the equivalent thermal diffusivity, cos() represents the cosine function, e represents the exponential function, and t represents the time; when the temperature of the monitored plastic particles reaches the preset cooling end temperature, it is determined that the cooling process ends.

2. The temperature control method during the production process of plastic particles as described in claim 1, wherein the setting of the time interval to correct the temperature prediction model includes: set a time interval Δt, collect the temperature data at key positions during the production process of plastic particles, including M monitoring points, and filter the collected temperature data; based on the temperature data at the key positions collected and the predicted temperature value calculated by the temperature prediction model, construct a temperature error function E, the specific formula is: where E represents the temperature error function, and T filt (m, t) represents the filtered temperature value of the m-th monitoring point at the actual time t, represents the predicted temperature value calculated by the temperature prediction model of the m-th monitoring point at the actual time t; based on the temperature error function E, adjust and optimize the key parameters in the temperature prediction model.

3. The temperature control method during the production process of plastic particles according to claim 2, wherein, the calculation of the dynamic adjustment amount of the temperature of plastic particles according to different production stages includes: during the production process of plastic particles, extract the characteristic parameters related to each production stage; use the extracted characteristic parameters to construct a production stage state evaluation model, take the characteristic parameters as input variables, and the state evaluation index of the production stage as the output variable; according to the output result of the production stage state evaluation model, adopt an adaptive adjustment strategy to dynamically determine the temperature adjustment amount.

4. A temperature control method during the production process of plastic particles according to claim 3, characterized in that during the production process, monitor the temperature of plastic materials in real time and perform real-time feedback control, including: adopt the sliding window method to calculate the change rate of the temperature adjustment amount, perform linear fitting on the temperature adjustment amount data in the past period of time, and obtain the slope of the fitting line as the change rate of the temperature adjustment amount; adopt a neural network hybrid control strategy to determine the control output of heating, the specific formula is: where, u(t) represents the output signal of the feedback control, x j represents the input variable, ω ij represents the connection weight from the input layer to the hidden layer, b i represents the bias of the hidden layer neurons, f() represents the activation function of the hidden layer neurons, represents the connection weight from the hidden layer to the output layer, g() represents the activation function of the output layer, n represents the number of hidden layer neurons, and J represents the number of input variables.

5. The temperature control method during the production process of plastic particles as described in claim 4, characterized in that the analysis of the characteristics of plastic raw materials includes: determination of the melting point range, thermal decomposition temperature, specific heat capacity, and thermal conductivity parameters of the raw materials.

6. A temperature control system in the production process of plastic particles, which is used to implement the temperature control method in the production process of plastic particles according to any one of claims 1-5, and is characterized in that, including: a characteristic analysis module, a temperature prediction module, a dynamic adjustment module, a feedback control module, and a cooling control module; the characteristic analysis module is used to analyze the characteristics of plastic raw materials to obtain the key temperature nodes during the production process of plastic particles; the temperature prediction module is used to establish a temperature prediction model based on the characteristics of plastic raw materials and the structural parameters of plastic particle production equipment; The dynamic adjustment module is used to correct the temperature prediction model at set time intervals and calculate the dynamic adjustment amount of the plastic particle temperature according to different production stages; The feedback control module is used to monitor the temperature of the plastic material in real time during the production process and perform real-time feedback control; The cooling control module is used to control the temperature and flow rate of the cooling medium when the plastic particles enter the cooling stage after molding.

7. The temperature control system during the production process of plastic particles as claimed in claim 6, wherein, The dynamic adjustment module includes: an error function unit, a state evaluation unit, and an adjustment amount determination unit; The error function unit is used to construct a temperature error function and adjust and optimize the key parameters in the temperature prediction model; The state evaluation unit is used to extract the characteristic parameters related to each production stage and construct a production stage state evaluation model; The adjustment amount determination unit is used to dynamically determine the temperature adjustment amount by adopting an adaptive adjustment strategy according to the output result of the production stage state evaluation model.

8. A temperature control system during the production process of plastic particles according to claim 7, wherein, The feedback control module includes: a change amount calculation unit and a feedback control unit; The change amount calculation unit is used to perform linear fitting on the temperature adjustment amount data in the past period of time and obtain the slope of the fitting line as the change rate of the temperature adjustment amount; The feedback control unit is used to determine the control output of heating by adopting a neural network hybrid control strategy.

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