CN-121995734-A - Multi-component spraying fluidized bed composite coating PID control method based on particle swarm algorithm

CN121995734ACN 121995734 ACN121995734 ACN 121995734ACN-121995734-A

Abstract

The invention belongs to the technical field of industrial automatic control, and in particular relates to a multi-spray fluidized bed composite envelope PID control method based on a particle swarm algorithm, which comprises the following steps of S1, obtaining operation parameters of fluidized bed equipment and measured material temperature with hysteresis characteristics, constructing a state space model through an extended Kalman filtering algorithm, and deducing and outputting real material temperature and transient evaporation rate without hysteresis; S2, according to the real material temperature and the transient evaporation rate and by combining with the physical characteristics of the coating material, the material evaporation hysteresis coefficient and the transient thermal shock intensity are calculated and obtained respectively, S3, the comprehensive dynamic penalty weight is calculated in parallel by utilizing the material evaporation hysteresis coefficient and the transient thermal shock intensity, and the target fitness function of the particle swarm algorithm is reconstructed by utilizing the comprehensive dynamic penalty weight. The invention effectively overcomes the thermal inertia hysteresis of the temperature sensor and realizes the control decoupling and self-adaptive matching of the characteristics of multiple materials.

Inventors

LIU ZHIGUANG
DONG JINGJING
GUO HUA
ZHANG MIN
YOU LUNCHENG
CHEN QI
WANG CONGHUI

Assignees

山东农业大学

Dates

Publication Date: 20260508
Application Date: 20260410

Claims (10)

1. The multi-component spray fluidized bed composite coating PID control method based on the particle swarm algorithm is characterized by comprising the following steps: Acquiring operation parameters of fluidized bed equipment and measured material temperature with hysteresis characteristics, constructing a state space model through an extended Kalman filtering algorithm, and deducing and outputting real material temperature and transient evaporation rate without hysteresis; According to the real material temperature and the transient evaporation rate, combining the physical characteristics of the coating material, and respectively calculating to obtain the material evaporation hysteresis coefficient and the transient thermal shock strength; Calculating comprehensive dynamic punishment weights in parallel by using the material evaporation lag coefficient and the transient thermal shock intensity, and reconstructing a target fitness function of a particle swarm algorithm by using the comprehensive dynamic punishment weights; And substituting the operation parameters into the reconstructed target fitness function to trigger a particle swarm optimization algorithm to perform iterative search, obtaining an optimal control parameter set, and overwriting the optimal control parameter set into a control closed-loop module at the bottom layer to perform closed-loop control execution and parameter dynamic issuing, so as to realize the composite envelope proportional-integral-derivative control of the multi-component spray fluidized bed based on the particle swarm algorithm.
2. The particle swarm algorithm-based multi-component spray fluidized bed composite coating PID control method of claim 1, wherein the method is characterized in that according to the real material temperature and the transient evaporation rate, the physical characteristics of the coating material are combined, and the evaporation hysteresis coefficient and the transient thermal shock strength of the material are respectively calculated and obtained, and the method comprises the following steps: based on the kinematic viscosity of the mixed solution in the current pipeline, the temperature of the real material and the physical boiling point constant of the specific solvent, the evaporation hysteresis coefficient of the material is obtained through calculation according to the following relation: in the formula, Representing the coefficient of evaporation hysteresis of the material, Representing the real-time dynamic viscosity of the mixed solution at the current formulation ratio, Represents the base dynamic viscosity of the neat solvent at standard atmospheric pressure, An absolute thermodynamic temperature value representing the temperature of the real material, Representing the absolute thermodynamic temperature value of the physical boiling constant of the particular solvent.
3. The particle swarm algorithm-based multi-component spray fluidized bed composite coating PID control method according to claim 2, wherein the method is characterized in that according to the real material temperature and the transient evaporation rate, the physical characteristics of the coating material are combined, and the evaporation hysteresis coefficient and the transient thermal shock strength of the material are respectively calculated and obtained, and the method further comprises the steps of: Based on the transient evaporation rate, the vaporization latent heat constant of the solvent and the comprehensive specific heat capacity of the solid phase material in the fluidized bed, the transient thermal shock strength is obtained through calculation according to the following relation: in the formula, Representing the intensity of the transient thermal shock, Representing the rate of evaporation in the transient state, Representing the control sampling cycle time of the upper computer, Representing the latent heat of vaporization constant of the solvent, Represents the comprehensive specific heat capacity of the solid phase material in the fluidized bed, Representing the cumulative total mass of material in the current bed, Representing the maximum temperature fluctuation tolerance boundary value allowed in the process specification.
4. The particle swarm algorithm-based multi-component spray fluidized bed composite coating PID control method of claim 3, wherein the parallel calculation of the comprehensive dynamic penalty weight by using the material evaporation hysteresis coefficient and the transient thermal shock intensity comprises the following steps: Multiplying a preset pre-base distribution coefficient by a material evaporation hysteresis coefficient to obtain a pre-product result, multiplying a preset post-base distribution coefficient by a transient thermal shock strength to obtain a post-product result, and adding the pre-product result and the post-product result to obtain the comprehensive dynamic penalty weight.
5. The method for controlling the composite coating PID of the multi-component spray fluidized bed based on the particle swarm algorithm according to claim 4, wherein the reconstructing the target fitness function of the particle swarm algorithm by using the comprehensive dynamic punishment weight comprises the steps of calculating the target fitness function within the length of an evaluation window based on a conventionally set temperature error weight coefficient, a process target set temperature, a real material temperature estimated value, a control increment amplitude output to an executing mechanism by a controller and the comprehensive dynamic punishment weight by the following relation: in the formula, Representing a function of the degree of fitness of the object, Representing the length of the evaluation window, Representing the sequence number of the current sampling period, Representing a conventionally set temperature error weight coefficient, Representing the set temperature of the process target, Represents the first A lag-free real material temperature estimate for the sub-sampling period, Representing the controller at the first The sub-sampling period is output to the control increment amplitude of the actuator, Represents the first The subsampled period is synchronously calculated as a comprehensive dynamic penalty weight.
6. The particle swarm algorithm-based multi-component spray fluidized bed composite envelope PID control method of claim 5, wherein obtaining the operating parameters of the fluidized bed equipment and the measured material temperature with hysteresis characteristics comprises: The method comprises the steps of synchronously collecting the air inlet temperature, the air inlet quantity, the current pipeline spray rate and the measured material temperature with hysteresis characteristics of the bottom equipment of the fluidized bed by using a programmable logic controller in a fixed sampling period.
7. The particle swarm algorithm-based multi-component spray fluidized bed composite envelope PID control method of claim 6, wherein constructing a state space model by an extended Kalman filtering algorithm, deducing and outputting a real material temperature and a transient evaporation rate without hysteresis, comprises: the real material temperature and the transient evaporation rate are constructed into two-dimensional column vectors of a state space model, and the measured material temperature is used as an observation vector; And deducing a priori state estimation value through a prediction equation of an extended Kalman filtering algorithm, calculating a Kalman gain matrix, and carrying out state updating by combining the measured material temperature to output a real material temperature without hysteresis and a real-time transient evaporation rate.
8. The particle swarm algorithm-based multi-component spray fluidized bed composite envelope PID control method of claim 7, wherein substituting the operation parameters into the reconstructed target fitness function triggers the particle swarm optimization algorithm to perform iterative search, and obtains an optimal control parameter set, comprising: Calling real-time proportioning information in a background formula database, and substituting the operation parameters into the reconstructed target fitness function; triggering an iterative program of a particle swarm optimization algorithm, carrying out repeated iterative updating in a constraint boundary, and searching an optimal control parameter set which enables a target fitness function to take a minimum value, wherein the optimal control parameter set comprises a proportional control parameter, an integral control parameter and a differential control parameter.
9. The particle swarm algorithm-based multi-component spray fluidized bed composite envelope PID control method of claim 8, wherein the step of overwriting the optimal control parameter set to the control closed loop module of the bottom layer for closed loop control execution and parameter dynamic issuing comprises the steps of: The proportional control parameter, the integral control parameter and the differential control parameter are rewritten into a control closed loop module; The control closed loop module calculates an adjusting signal according to the real temperature deviation, drives the proportional integral electric adjusting valve to change the flux of the steam heat exchanger, and drives the frequency converter to change the output rotating speed of the centrifugal fan.
10. The particle swarm optimization-based multi-component spray fluidized bed composite coating PID control method according to claim 9, wherein the real-time dynamic viscosity is obtained by interpolation of a pre-stored fluid viscosity empirical data table according to the mass flow ratio of each feed pump by an upper computer system, and the accumulated total mass of the materials in the current bed is obtained by adding the initial bed charge mass to the liquid spraying solid content accumulated by the system integration.

Description

Multi-component spraying fluidized bed composite coating PID control method based on particle swarm algorithm Technical Field The invention belongs to the technical field of industrial automatic control, and particularly relates to a multi-component spraying fluidized bed composite coating PID control method based on a particle swarm algorithm. Background In the production flow of the agricultural sustained-release fertilizer, the adoption of polymer materials such as full-bio-based degradable polyhydroxyalkanoate, polycaprolactone, polybutylene succinate, polylactic acid, poly (adipic acid)/butylene terephthalate and the like for carrying out fluidized bed composite coating on urea particles represents the core industrialization development direction. The physical mechanism of the top jet fluidized bed coating process is that the polymer solution sprayed into the fluidized bed is quickly and uniformly spread and volatilized on the surface of urea particles to form a film by precisely adjusting key operation variables such as the air inlet temperature, the fluidization air quantity and the like. The heat and mass transfer in the physical process are extremely complex, the temperature of the core material must be kept constant through a high-precision bottom control system, the precise control of the temperature of the material in the fluidized bed is the core for guaranteeing the film forming quality of the coating, the coating surface can be subjected to microscopic cracking and polymer crosslinking fracture due to the overhigh temperature, the solid particles can be partially subjected to overwet adhesion and abnormal material agglomeration due to the overlow temperature, and the spreading of the high polymer material solution on the surface of the urea particles and the solvent volatilization film forming effect can be directly influenced by the uncontrolled fluctuation of the temperature field. At present, a fixed proportional integral derivative control algorithm is often adopted in the industry to adjust the air inlet temperature and the air quantity of a fan, however, as a fluidized bed belongs to a typical nonlinear large hysteresis system, a conventional fixed parameter controller is difficult to meet the high-precision process requirement, and further the temperature field is extremely easy to generate out-of-control fluctuation. In order to further improve the temperature control precision, a particle swarm optimization algorithm is generally introduced in the industry to perform online self-adaptive optimization on each parameter of the controller. Under the specific working condition of continuous composite spraying, the system needs to alternately or mixedly spray polymer material solutions with different physical properties. The physical binding force of different materials to solvent molecules is different, and the dynamic kinematic viscosity of the solution is obviously different. The high-viscosity material solution causes slow volatilization of the solvent and causes slow and continuous reduction of the temperature of the fluidized bed, and the extremely volatile material solution can absorb a large amount of latent heat of vaporization in a very short time, so that the temperature in the fluidized bed is reduced drastically in a transient state. The traditional parameter optimizing algorithm adopts a fixed fitness function structure, only calculates the absolute deviation between a temperature set value and an actual measured value from a numerical level, and does not incorporate the physical characteristics of specific materials causing the deviation into a core evaluation system at all. The serious lack of the physical evaluation dimension causes serious control misjudgment of an optimizing algorithm when facing transient high-intensity heat absorption mutation, further calculates extreme adjustment parameters, promotes a heating executing mechanism to generate severe power overshoot, and finally causes the fluid material sprayed subsequently to be solidified due to abnormal height Wen Guozao so as not to be uniformly spread into a film. Disclosure of Invention The invention provides a particle swarm algorithm-based composite coating PID control method for a multi-component spray fluidized bed, which aims to solve the technical problems that a bottom layer adjusting system is seriously controlled with lag and local thermal impact damage is caused by excessive compensation of heating power due to the difference of physical evaporation characteristics of multi-component materials in the fluidized bed. The invention provides a particle swarm algorithm-based multi-component spray fluidized bed composite coating PID control method, which comprises the following steps: Acquiring operation parameters of fluidized bed equipment and measured material temperature with hysteresis characteristics, constructing a state space model through an extended Kalman filtering algorithm, and deducing and out