Intelligent control system for ABS plastic particle production

Abstract

The invention relates to the technical field of intelligent production control, in particular to an intelligent control system for ABS plastic particle production. Through combined detection of the high-precision weighing sensor and the near-infrared spectrometer, real-time quantitative analysis of ternary components of styrene, butadiene and acrylonitrile is realized, and a dual quality assurance mechanism is constructed in combination with dielectric constant moisture content monitoring; when formula deviation or abnormal moisture content is detected, the system automatically triggers a material supplementing program and outputs an alarm signal, so that the raw material quality fluctuation correction response time is shortened, and the product performance dispersion caused by raw material fluctuation is remarkably reduced; on the basis of a three-dimensional heat transfer control equation constructed by a free radical polymerization kinetic model, online estimation of the reaction conversion rate is realized; when the conversion rate is detected to be abnormal, the system can automatically adjust the concentration of the initiator and the reaction temperature, so that the reaction period is shortened, and meanwhile, the problem of molecular weight distribution broadening caused by over-reaction is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent production control, in particular to an intelligent control system for producing ABS plastic pellets. Background Art

[0002] In the production process of ABS (acrylonitrile-butadiene-styrene) plastic pellets, traditional control systems generally have three major technical bottlenecks, which directly restrict the stability of product quality and the improvement of production efficiency: Existing technologies often rely on manual spot checks or single-parameter sensors to determine raw material proportions. For example, weighing sensors monitor total mass alone, but are unable to achieve real-time quantitative analysis of the three components: styrene, butadiene, and acrylonitrile. This extensive control approach makes it difficult to identify fluctuations in raw material quality and lacks a dynamic compensation mechanism for moisture content fluctuations, severely limiting the yield rate for high-end applications such as automotive interiors and electronic components.

[0003] Traditional reactor control typically employs open-loop control or a single temperature-pressure coupling model, ignoring the kinetics of free radical polymerization reactions. For example, the estimated error in reaction conversion can reach 5% to 8%. Furthermore, the lack of real-time monitoring of key parameters such as stirring torque results in response delays exceeding 5 minutes to abnormal operating conditions, which can easily lead to over- or under-reaction problems.

[0004] In view of the above problems, it is necessary to propose an intelligent control system for ABS plastic pellet production. Summary of the Invention

[0005] The purpose of the present invention is to solve the problems existing in the background technology and to provide an intelligent control system for the production of ABS plastic pellets.

[0006] The purpose of the present invention can be achieved through the following technical solutions: An intelligent control system for ABS plastic pellet production includes a raw material pretreatment intelligent proportioning module, a reactor multi-parameter coordinated control module, a melt gear pump-pelletizer linkage module, and a feedback and execution module.

[0007] The intelligent proportioning module for raw material preprocessing accesses a high-precision weighing sensor and near-infrared spectrometer to obtain, in real time, the standard mass ratio of the pre-set ternary components of styrene, butadiene, and acrylonitrile in the plastic pellet production raw materials. It detects anomalies in the mass ratio of the plastic pellet production raw materials and issues a formula deviation alarm signal when a deviation is detected. It also dynamically compensates for moisture content fluctuations in the plastic pellet production raw materials using the dielectric constant and issues a moisture content deviation alarm signal when an abnormal moisture content is detected.

[0008] Obtain the standard mass proportions M1, M2 and M3 of the ternary components of styrene, butadiene and acrylonitrile in the preset plastic pellet production raw materials.

[0009] As a preferred embodiment of the present invention, the absorbance of the plastic pellet production raw material in a preset band is collected by a near-infrared spectrometer to analyze the concentration of styrene, butadiene, and acrylonitrile in the plastic pellet production raw material. The specific process is as follows: Obtain the absorbance A(λ), B(λ), and C(λ) of the plastic pellet production raw materials at wavelengths of 5000-6200nm, 4500-5000nm, and 4000-4500nm, and input them into the preset formula: ; Determine the concentrations C1, C2, and C3 of the ternary components of styrene, butadiene, and acrylonitrile in the raw materials for plastic pellet production. k is the index of the preset Gaussian kernel used to detect concentrations, with k = 1, 2, ..., n; n is the total number of preset Gaussian kernels used to detect concentrations; βk is a set of preset PLS regression coefficients, and αj is the preset Gaussian kernel width. λk is the reference wavelength corresponding to each Gaussian kernel.

[0010] When the formula deviation characteristic value When it is greater than the preset threshold, it is determined that a recipe deviation is detected and a recipe deviation alarm signal is output.

[0011] As a preferred embodiment of the present invention, abnormal fluctuations in moisture content are monitored using a dielectric sensor, and the specific process is as follows: Get the moisture content W of the raw materials for plastic pellet production through the preset formula: Calculate total feed mass .

[0012] in, is the total mass of the current plastic pellet production raw materials, where γ is the preset ABS water absorption expansion coefficient, the specific value is γ=0.25; It is the preset standard moisture content.

[0013] As a preferred embodiment of the present invention, when the water content of W is detected to be different from the preset standard water content, The absolute value of the difference If the water content is greater than the preset threshold, it is determined that the water content is abnormal and a water content deviation alarm signal is output.

[0014] The generated formula deviation alarm signal or moisture content deviation alarm signal is sent to the feedback and execution module.

[0015] The reactor reaction rate monitoring module estimates the reaction conversion rate in the reactor based on the free radical polymerization kinetic model, and determines that the reactor reaction rate is abnormal when the reaction conversion rate is lower than the preset threshold.

[0016] The real-time temperature T(t), pressure P(t), and stirring torque τ(t) in each reactor are collected to construct the temperature control equation describing the three-dimensional heat transfer of the reactor: The temperature governing equations include the core dynamic equations: ; Among them is the free radical polymerization reaction rate, where f is the preset initiator efficiency, and is the concentration of the initiator input, is the free radical polymerization concentration in the reactor collected by the sensor. is the chain growth rate constant. kt is the preset influencing factor. X is the reaction conversion rate to be solved.

[0017] When it is detected that the reaction conversion rate X is lower than the preset threshold, a low efficiency signal is generated and sent to the feedback and execution module. The real-time temperature T(t), pressure P(t) and stirring torque τ(t) collected in each reactor are also sent to the feedback and execution module.

[0018] The melt gear pump-granulator linkage module controls the melt gear pump and granulator in the reactor to ensure dynamic matching of melt pressure and granulator speed.

[0019] Establish the melt gear pump-granulator linkage control strategy model: ; Among them is the gear pump pressure transfer function corresponding to the complex frequency variable s, which describes the gear pump speed when the complex frequency variable is s To melt pressure The dynamic relationship of , where s is the complex frequency variable, that is, the frequency variable in Laplace transform; Among them is the gain coefficient, which indicates the proportional relationship between the pressure change and the speed change when the melt is in an unstable state. Its specific value is Where η is the melt viscosity, 、 and are the gear radius, tooth height and tooth backlash of the melt gear pump respectively; Among them It is the preset time constant, which indicates the system response speed. The larger the value, the slower the melt pressure responds to the speed change. is the hysteresis operator, which describes the dead time effect caused by mechanical transmission delay, where Represents the inherent delay of mechanical transmission due to gear clearance. is the particle size transfer function of the granulator corresponding to the complex frequency variable s, where is the particle diameter corresponding to the complex frequency variable s, is the pelletizer speed corresponding to the complex frequency variable s; Among them is the preset particle size gain coefficient, which indicates the contribution of the increase in rotation speed to the reduction in particle size, specifically a preset negative value; is the natural angular frequency, which indicates the response speed of the granulator to change the speed. The specific value is ; Among them is the tool shaft stiffness, is the rotor inertia of the pelletizer cutter head. The damping ratio of the pelletizer to suppress particle size oscillation is a dimensionless parameter with a specific value of Where c is the preset hydraulic damping coefficient.

[0020] As a preferred embodiment of the present invention, pressure-speed dynamic feedforward compensation is performed based on the melt gear pump-granulator linkage control strategy model, and the specific process is as follows: Gear pump speed adjustment value for: ; Among them is the compensation term, which represents the compensation for the interference of the pelletizer speed change on the pressure. The specific value is ; Among them is the preset pressure standard value, ΔP is the pressure change value at the previous moment; is the time compensation value, . It is the delayed speed compensation item.

[0021] Adjust the speed of the granulator for: ; Among them is the preset particle size gain coefficient, where is the preset standard particle size, is the actual particle size, and β is the preset pressure-particle size coupling coefficient.

[0022] The calculated gear pump speed adjustment value and the speed adjustment value of the granulator Sent to the feedback and execution module.

[0023] The feedback and execution module performs feedback and control of plastic pellet production based on the received data and signals, performs multi-parameter coordinated control of the reactor and executes the ingredient ratio optimization plan based on the collected signals, and performs delayed speed compensation adjustment based on the acquired gear pump speed and pelletizer speed adjustment values.

[0024] When the formula deviation alarm signal and the moisture content deviation alarm signal are identified at the same time, the feeder is controlled to add material to compensate for the formula deviation and moisture content anomaly.

[0025] As a preferred embodiment of the present invention, the production inspection personnel are reminded to check the temperature, pressure and stirring torque in the reactor according to the collected low efficiency signal.

[0026] As a preferred embodiment of the present invention, according to the collected gear pump speed adjustment value and the speed adjustment value of the granulator Perform delayed speed compensation adjustment.

[0027] Adjust the gear pump speed to Sent to the melt gear pump, and the gear pump speed of the melt gear pump is adjusted based on the current speed through PID control , A positive value increases the speed, and a negative value decreases the speed; Adjust the speed of the granulator to Sent to the granulator, and the PID control is used to adjust the speed of the granulator's blade based on the current speed , A positive value increases the speed, and a negative value decreases the speed.

[0028] Compared with the prior art, the present invention has the following beneficial effects: 1. Through the combined detection of high-precision weighing sensors and near-infrared spectrometers, real-time quantitative analysis of the three components of styrene, butadiene, and acrylonitrile is achieved. Combined with dielectric constant and moisture content monitoring, a dual quality assurance mechanism is established. When formula deviation or abnormal moisture content is detected, the system automatically triggers the refill program and outputs an alarm signal, shortening the response time to correct raw material quality fluctuations and significantly reducing product performance dispersion caused by raw material fluctuations. 2. The present invention constructs a three-dimensional heat transfer control equation based on a free radical polymerization kinetic model, integrating multi-parameter coupled analysis of temperature T(t), pressure P(t), and stirring torque τ(t) to achieve online estimation of the reaction conversion rate X. When an abnormal conversion rate is detected, the system can automatically adjust the initiator concentration and reaction temperature to shorten the reaction cycle while avoiding the problem of broadening the molecular weight distribution caused by overreaction; 3. This invention achieves decoupled control of pressure and speed through complex frequency domain transfer functions and pelletizing particle size transfer functions. The feedforward compensation algorithm improves the ability to suppress melt pressure fluctuations, optimizes the particle size distribution coefficient, and effectively solves the problem of pelletizing particle size deviations caused by mechanical delays in traditional systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings: Figure 1 This is a system block diagram of the present invention. DETAILED DESCRIPTION

[0030] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0031] See also Figure 1 As shown, an intelligent control system for ABS plastic pellet production includes a raw material pretreatment intelligent proportioning module, a reactor multi-parameter coordinated control module, a melt gear pump-pelletizer linkage module and a feedback and execution module.

[0032] The intelligent proportioning module for raw material preprocessing accesses a high-precision weighing sensor and near-infrared spectrometer to obtain, in real time, the standard mass ratio of the pre-set ternary components of styrene, butadiene, and acrylonitrile in the plastic pellet production raw materials. It detects anomalies in the mass ratio of the plastic pellet production raw materials and issues a formula deviation alarm signal when a deviation is detected. It also dynamically compensates for moisture content fluctuations in the plastic pellet production raw materials using the dielectric constant and issues a moisture content deviation alarm signal when an abnormal moisture content is detected.

[0033] Obtain the standard mass proportions M1, M2 and M3 of the ternary components of styrene, butadiene and acrylonitrile in the preset plastic pellet production raw materials.

[0034] Furthermore, the absorbance of the plastic pellet production raw materials in a preset band is collected by a near-infrared spectrometer to analyze the concentration of styrene, butadiene, and acrylonitrile in the plastic pellet production raw materials. The specific process is as follows: Obtain the absorbance A(λ), B(λ), and C(λ) of the plastic pellet production raw materials at wavelengths of 5000-6200nm, 4500-5000nm, and 4000-4500nm, and input them into the preset formula: ; Determine the concentrations C1, C2, and C3 of the ternary components of styrene, butadiene, and acrylonitrile in the raw materials for plastic pellet production. k is the index of the preset Gaussian kernel used to detect concentrations, with k = 1, 2, ..., n; n is the total number of preset Gaussian kernels used to detect concentrations; βk is a set of preset PLS regression coefficients, and αj is the preset Gaussian kernel width. λk is the reference wavelength corresponding to each Gaussian kernel.

[0035] It should be noted that in near-infrared (NIR) spectroscopy analysis of ABS plastic pellets, the characteristic absorption bands used to detect the three key components, styrene, butadiene, and acrylonitrile, are primarily located in the combined frequency and harmonic frequency regions of the NIR spectrum. The primary characteristic band for styrene corresponds to the wavelength range of 5950-6170 nm, reflecting the absorption of the C-H bond vibration on the benzene ring in the NIR region of this band; the primary characteristic band for butadiene, from 4670 to 4950 nm, reflects the absorption mode of the conjugated diene structure within butadiene in the NIR region of this band; and the primary characteristic band for acrylonitrile, from 4425 to 4525 nm, reflects the absorption peak of the nitrile group in the NIR region of this band. However, in actual spectral analysis, due to the absorption peak width, spectral overlap and interference between components, and between components and background (such as moisture, other polymers, and fillers), a continuous wavelength range is established for spectral data collection.

[0036] When the formula deviation characteristic value When it is greater than the preset threshold, it is determined that a recipe deviation is detected and a recipe deviation alarm signal is output.

[0037] Furthermore, dielectric sensors are used to monitor abnormal fluctuations in moisture content. The specific process is as follows: Get the moisture content W of the raw materials for plastic pellet production through the preset formula: Calculate total feed mass .

[0038] in, is the total mass of the current plastic pellet production raw materials, where γ is the preset ABS water absorption expansion coefficient, the specific value is γ=0.25; It is the preset standard moisture content.

[0039] Furthermore, when it is detected that W is different from the preset standard moisture content, The absolute value of the difference If the water content is greater than the preset threshold, it is determined that the water content is abnormal and a water content deviation alarm signal is output.

[0040] The generated formula deviation alarm signal or moisture content deviation alarm signal is sent to the feedback and execution module.

[0041] The reactor reaction rate monitoring module estimates the reaction conversion rate in the reactor based on the free radical polymerization kinetic model, and determines that the reactor reaction rate is abnormal when the reaction conversion rate is lower than the preset threshold.

[0042] The real-time temperature T(t), pressure P(t), and stirring torque τ(t) in each reactor are collected to construct the temperature control equation describing the three-dimensional heat transfer of the reactor: The temperature governing equations include the core dynamic equations: ; Among them is the free radical polymerization reaction rate, where f is the preset initiator efficiency, and is the concentration of the initiator input, is the free radical polymerization concentration in the reactor collected by the sensor. is the chain growth rate constant. kt is the preset influencing factor. X is the reaction conversion rate to be solved.

[0043] When it is detected that the reaction conversion rate X is lower than the preset threshold, a low efficiency signal is generated and sent to the feedback and execution module. The real-time temperature T(t), pressure P(t) and stirring torque τ(t) collected in each reactor are also sent to the feedback and execution module.

[0044] The melt gear pump-granulator linkage module controls the melt gear pump and granulator in the reactor to ensure dynamic matching of melt pressure and granulator speed.

[0045] Establish the melt gear pump-granulator linkage control strategy model: ; Among them is the gear pump pressure transfer function corresponding to the complex frequency variable s, which describes the gear pump speed when the complex frequency variable is s To melt pressure The dynamic relationship of , where s is the complex frequency variable, that is, the frequency variable in Laplace transform; Among them is the gain coefficient, which indicates the proportional relationship between the pressure change and the speed change when the melt is in an unstable state. Its specific value is Where η is the melt viscosity, 、 and are the gear radius, tooth height and tooth backlash of the melt gear pump respectively; Among them It is the preset time constant, which indicates the system response speed. The larger the value, the slower the melt pressure responds to the speed change. is the hysteresis operator that describes the dead time effect due to mechanical transmission delay, where represents the inherent delay of mechanical transmission due to gear clearance.

[0046] Among them is the particle size transfer function of the granulator corresponding to the complex frequency variable s, where is the particle diameter corresponding to the complex frequency variable s, is the pelletizer speed corresponding to the complex frequency variable s; Among them is the preset particle size gain coefficient, which indicates the contribution of the increase in rotation speed to the reduction in particle size, specifically a preset negative value; is the natural angular frequency, which indicates the response speed of the granulator to change the speed. The specific value is ; Among them is the tool shaft stiffness, is the rotor inertia of the pelletizer cutter head. The damping ratio of the pelletizer to suppress particle size oscillation is a dimensionless parameter with a specific value of Where c is the preset hydraulic damping coefficient.

[0047] It should be noted that the mismatch between melt pressure and pelletizing speed will affect the cutting efficiency of the melt, resulting in wire drawing of the particles. In severe cases, it will cause melt retention and mold blocking, and damage the pelletizer blade.

[0048] Furthermore, based on the melt gear pump-granulator linkage control strategy model, pressure-speed dynamic feedforward compensation is performed. The specific process is as follows: Gear pump speed adjustment value for: ; Among them is the compensation term, which represents the compensation for the interference of the pelletizer speed change on the pressure. The specific value is ; is the preset pressure standard value, and ΔP is the pressure change value at the previous moment; Among them is the time compensation value, . It is the delayed speed compensation item.

[0049] Adjust the speed of the granulator for: ; Among them is the preset particle size gain coefficient, where is the preset standard particle size, is the actual particle size, and β is the preset pressure-particle size coupling coefficient.

[0050] The calculated gear pump speed adjustment value and the speed adjustment value of the granulator Sent to the feedback and execution module.

[0051] The feedback and execution module performs feedback and control of plastic pellet production based on the received data and signals, performs multi-parameter coordinated control of the reactor and executes the ingredient ratio optimization plan based on the collected signals, and performs delayed speed compensation adjustment based on the acquired gear pump speed and pelletizer speed adjustment values.

[0052] When the formula deviation alarm signal and the moisture content deviation alarm signal are identified at the same time, the feeder is controlled to add material to compensate for the formula deviation and moisture content anomaly.

[0053] Furthermore, the collected low-efficiency signal reminds the production inspection personnel to check the temperature, pressure and stirring torque in the reactor.

[0054] Furthermore, according to the collected gear pump speed adjustment value and the speed adjustment value of the granulator Perform delayed speed compensation adjustment.

[0055] Adjust the gear pump speed to Sent to the melt gear pump, and the gear pump speed of the melt gear pump is adjusted based on the current speed through PID control , A positive value increases the speed, and a negative value decreases the speed; Adjust the speed of the granulator to Sent to the granulator, and the PID control is used to adjust the speed of the granulator's blade based on the current speed , A positive value increases the speed, and a negative value decreases the speed.

[0056] It should be understood that the terms “include” and “comprising” used in the specification and claims of the present disclosure indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0057] It should also be understood that the terms used in this disclosure are for the purpose of describing particular embodiments only and are not intended to limit the disclosure. As used in this disclosure and the claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise. It should also be further understood that the term "and / or" used in this disclosure and the claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations; The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. An intelligent control system for ABS plastic pellet production, comprising a raw material pretreatment intelligent proportioning module, a reactor multi-parameter coordinated control module, and a melt gear pump-pelletizer linkage module, characterized in that: The intelligent proportioning module for raw material preprocessing accesses a high-precision weighing sensor and a near-infrared spectrometer to obtain, in real time, the standard mass ratio of the preset ternary components of styrene, butadiene, and acrylonitrile in all raw materials used for plastic pellet production. It also detects anomalies in the mass ratio of the plastic pellet production raw materials and outputs a formula deviation alarm signal when a formula deviation is detected. It also dynamically compensates for moisture content fluctuations in the plastic pellet production raw materials through the dielectric constant and outputs a moisture content deviation alarm signal when an abnormal moisture content is detected. The reactor reaction rate monitoring module estimates the reaction conversion rate in the reactor based on the free radical polymerization kinetic model and determines that the reactor reaction rate is abnormal when the reaction conversion rate is lower than the preset threshold; The melt gear pump-granulator linkage module controls the melt gear pump and granulator in the reactor to ensure the dynamic matching of melt pressure and granulation speed. A melt gear pump-granulator linkage control strategy model is established to perform collaborative simulation of the melt gear pump and granulator.

2. An intelligent control system for ABS plastic pellet production according to claim 1, characterized in that: It also includes feedback and execution modules; The feedback and execution module performs feedback and control of plastic pellet production based on the received data and signals, performs multi-parameter coordinated control of the reactor and executes the ingredient ratio optimization plan based on the collected signals, and performs delayed speed compensation adjustment based on the acquired gear pump speed and pelletizer speed adjustment values.

3. The intelligent control system for ABS plastic pellet production according to claim 1, characterized in that: The specific process of abnormal detection of the mass ratio of raw materials for plastic pellet production is as follows: Obtaining the standard mass ratios M1, M2 and M3 of the ternary components of styrene, butadiene and acrylonitrile in the preset raw materials for producing plastic pellets; The absorbance of the plastic pellet production raw materials in the preset band is collected by near-infrared spectrometer to analyze the concentration of styrene, butadiene, and acrylonitrile in the plastic pellet production raw materials; Obtain the absorbance A(λ), B(λ), and C(λ) of the plastic pellet production raw materials at wavelengths of 5000-6200nm, 4500-5000nm, and 4000-4500nm, and input them into the preset formula: ； Solve the concentrations C1, C2 and C3 of the ternary components of styrene, butadiene and acrylonitrile in the raw materials for plastic pellet production; where k is the preset Gaussian kernel index for detecting concentration, k=1, 2, ..., n; n is the total number of preset Gaussian kernels for detecting concentration; βk is a set of preset PLS regression coefficients, αj is the preset Gaussian kernel width; λk corresponds to the reference wavelength of each Gaussian kernel; when the formula deviation characteristic value When it is greater than the preset threshold, it is determined that a recipe deviation is detected and a recipe deviation alarm signal is output.

4. The intelligent control system for ABS plastic pellet production according to claim 1, characterized in that: Abnormal fluctuations in moisture content are monitored using dielectric sensors. The specific process is as follows: the moisture content W of the raw materials for plastic pellet production is obtained, and the preset formula is used: Calculate total feed mass ; in, is the total mass of the current plastic pellet production raw materials, where γ is the preset ABS water absorption expansion coefficient, is the preset standard moisture content; when W is detected to be consistent with the preset standard moisture content The absolute value of the difference If the water content is greater than the preset threshold, it is determined that the water content is abnormal and a water content deviation alarm signal is output.

5. The intelligent control system for ABS plastic pellet production according to claim 1, characterized in that: The specific process of estimating the reaction conversion rate in the reactor is as follows: collecting the real-time temperature T(t), pressure P(t), and stirring torque τ(t) in each reactor, and constructing the temperature control equation that describes the three-dimensional heat transfer of the reactor: The temperature governing equations include the core dynamic equations: ； Among them is the free radical polymerization reaction rate, f is the preset initiator efficiency, is the concentration of the initiator input, is the free radical polymerization concentration in the reactor collected by the sensor; is the chain growth rate constant; kt is the preset influencing factor; X is the reaction conversion rate to be solved; when it is detected that the reaction conversion rate X is lower than the preset threshold, a low efficiency signal is generated and sent to the feedback and execution module, and the real-time temperature T(t), pressure P(t) and stirring torque τ(t) in each reactor are collected and sent to the feedback and execution module.

6. The intelligent control system for ABS plastic pellet production according to claim 1, characterized in that: The specific control strategy model of melt gear pump-granulator linkage is: ； Among them is the gear pump pressure transfer function corresponding to the complex frequency variable s, which describes the gear pump speed when the complex frequency variable is s To melt pressure The dynamic relationship of , where s is the complex frequency variable, that is, the frequency variable in Laplace transform; Among them is the gain coefficient, which indicates the proportional relationship between the pressure change and the speed change when the melt is in an unstable state. Its specific value is ; Where η is the melt viscosity, 、 and are respectively the gear radius, tooth height and tooth backlash of the melt gear pump; is the preset time constant, which indicates the system response speed. The larger the value, the slower the melt pressure responds to the speed change. ; is the hysteresis operator, which describes the dead time effect caused by mechanical transmission delay, where represents the inherent delay of mechanical transmission due to gear clearance; Among them is the particle size transfer function of the granulator corresponding to the complex frequency variable s, where is the particle diameter corresponding to the complex frequency variable s, is the pelletizer speed corresponding to the complex frequency variable s; is the preset particle size gain coefficient, which indicates the contribution of the increase in rotation speed to the reduction in particle size, specifically a preset negative value; is the natural angular frequency, which indicates the response speed of the granulator to change the speed. The specific value is Among them is the tool shaft stiffness, is the rotor inertia of the granulator cutter head; Among them is the pelletizing damping ratio of the pelletizer to suppress particle size oscillation, which is a dimensionless parameter with a specific value of Where c is the preset hydraulic damping coefficient; pressure-speed dynamic feedforward compensation is performed based on the melt gear pump-granulator linkage control strategy model.

7. An intelligent control system for ABS plastic pellet production according to claim 6, characterized in that: The specific process of pressure feedforward compensation based on the melt gear pump-granulator linkage control strategy model is as follows: Among them is the compensation term, which represents the compensation for the interference of the pelletizer speed change on the pressure. The specific value is ; is the preset pressure standard value, ΔP is the pressure change value at the previous moment; is the time compensation value, ; It is the delayed speed compensation item.

8. The intelligent control system for ABS plastic pellet production according to claim 6, characterized in that: The specific process of speed feedforward compensation based on the melt gear pump-granulator linkage control strategy model is as follows: Adjust the speed of the granulator for: ; is the preset particle size gain coefficient, where is the preset standard particle size, Among them is the actual particle size, β is the preset pressure-particle size coupling coefficient; the calculated gear pump speed adjustment value and the speed adjustment value of the granulator Sent to the feedback and execution module.

9. The intelligent control system for ABS plastic pellet production according to claim 2, characterized in that: The specific process of feedback and control of plastic pellet production is as follows: When the formula deviation alarm signal and the moisture content deviation alarm signal are recognized at the same time, the feeder is controlled to add material to compensate for the formula deviation and moisture content abnormality; According to the collected low efficiency signals, the production inspection personnel are reminded to check the temperature, pressure and stirring torque in the reactor; Adjust the gear pump speed according to the collected value and the speed adjustment value of the granulator Adjust the delayed speed compensation; adjust the gear pump speed to Sent to the melt gear pump, and the gear pump speed of the melt gear pump is adjusted based on the current speed through PID control ; A positive value indicates an increase in speed, and a negative value indicates a decrease in speed. Sent to the granulator, and the PID control is used to adjust the speed of the granulator's blade based on the current speed ; A positive value increases the speed, and a negative value decreases the speed.

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