Abstract: Based on the analysis of the characteristics of advanced control, this paper summarizes the development and current situation of advanced control methods in the grain drying process, points out the existing problems in the drying process control, and proposes the development direction of grain drying process control.
Keywords: drying; Advanced control; Adaptive control; Model predictive control^ Control; Fuzzy control; Neural network control
The basic goal of grain drying is to achieve the ideal drying quality of grains with lower drying costs and energy consumption while maintaining the stability of the drying process. The process of grain drying is a typical nonlinear, multivariable, large hysteresis, and parameter correlated coupling non-stationary heat and mass transfer process. Grain itself is a complex biochemical substance. To achieve the above goals, it is necessary to continuously adjust the drying parameters and control the working process of the dryer during the drying process. The automatic control of the drying process is an effective means to achieve high-quality, efficient, low consumption, and safe operation of the dryer. The automatic control of the drying process and the automatic control of the grain dryer are of great significance in ensuring uniform and consistent moisture content of the discharged grains, improving the quality of dried grains, reducing the labor intensity of operators, and fully utilizing the production capacity of the dryer. According to the development goals set by the National Grain Administration in the "Tenth Five Year Plan for Scientific and Technological Development of the Grain Industry", online monitoring and automatic control of the grain drying process have become a key issue in improving the efficiency of China's grain drying processing technology and an important path to achieve the "Tenth Five Year Plan". With the increasing investment in grain depot construction in China, the grain processing industry is increasingly in line with international standards. The automation of grain drying will lay the foundation for China's grain to enter the international circulation market.
1. The characteristics of advanced control
The research on automatic control of grain drying process began in the 1960s. At that time, traditional control methods such as feedforward control, feedback control, feedback feedforward control, and adaptive control were used. Traditional control theory uses difference equations or transfer functions to express the knowledge and existing information of the drying process system into analytical expressions. However, when using and designing a grain dryer control system using the above control methods, many difficulties will be encountered. The reasons are: (1) the grain drying process is complex, time-varying, and nonlinear; (2) Some drying process variables (such as grain quality and color) cannot be directly measured, while the measurement of some variables (such as grain moisture content) may be discontinuous, incomplete, or unreliable; (3) The process model of a dryer is an approximation of the actual process and requires a significant amount of calculation time; (4) It is almost impossible to use an appropriate model to represent a nonlinear, lagging, and time-varying complex system like the drying process; (5) There is an interaction effect between the controlled variables and the control variables of the grain dryer; (6) The operating conditions of grain dryers are complex, with a wide range of disturbance variables that are difficult to control.
Obviously, to overcome the above difficulties, it is necessary to continuously improve the traditional control methods of grain dryers, while exploring new and more effective control methods. In the 1970s, the advancement of the electronics industry, especially the development of computer technology, enabled the widespread dissemination of the concept of advanced control. The goal of advanced control is to solve complex industrial process control problems that are not effectively controlled or even uncontrollable by conventional control methods. In recent years, modern control and artificial intelligence have made significant progress, laying a strong theoretical foundation for the implementation of advanced control systems; The popularization of distributed control systems (DCS) and the rapid advancement of computer network technology provide a powerful hardware and software platform for the application of advanced control. In short, the needs of industrial development, control theory, and the development of computer and network technology have strongly promoted the development of advanced control.
With the rapid development of computer technology, artificial intelligence control theory has begun to be applied in the control of drying machines, significantly improving the performance of drying machine control systems. Traditional control methods are not suitable for controlling grain dryers due to their large lag and non-linear relationship with the grain drying process. The advancement of artificial intelligence technology is widely applied in the field of engineering, and advanced control theories and methods are applied to the automation control of grain drying processes. The control methods are continuously improved, and the control effect is enhanced. After the 1990s, process control began to develop towards intelligence, and the theory of intelligent control was increasingly combined with drying technology. Artificial neural networks were used to simulate and control the drying process^ The system is applied to grain quality prediction, drying process control, and management consulting.
Advanced control systems closely related to control theory, instruments, computers, computer communication, and network technologies have the following characteristics:
(1) The theoretical basis of advanced control systems is mainly model-based control strategies, such as model predictive control, which fully utilize the input and output information of industrial processes to establish system models, without relying on in-depth research on reaction mechanisms. Recently, knowledge-based control, such as ^ control and fuzzy logic control, is becoming an important development direction in advanced control.
(2) Advanced control systems are commonly used to handle complex and variable process control problems, such as large time delays, multivariable coupling, and various constraints between controlled variables and control variables. The advanced control strategy adopted is a dynamic coordinated constraint control based on conventional single loop control, which can make the control system adapt to the dynamic characteristics and operational requirements of actual industrial production processes.
(3) The implementation of advanced control systems requires high-performance computers as support platforms. Due to the complexity of advanced controller control algorithms and the influence of computer hardware, advanced control algorithms for complex systems are usually implemented on the upper computer. With the continuous enhancement of DCS functions and the development of advanced control technologies, some advanced control strategies can be implemented on DCS along with the basic control loop. The latter approach can effectively enhance the reliability, operability, and maintainability of advanced control.
2. Development status of advanced control in drying process
Advanced control strategy is the core content of advanced control systems. Currently, there are many types of advanced control strategies, and the main advanced control strategies in the drying process include predictive control, fuzzy logic control, neural control, adaptive control, and ^ system.
2.1 Model based control
2.1.1 Adaptive Control
The basic principle of adaptive control is to adjust the control parameters at any time based on the changes in drying process parameters and external interference, so as to keep the dryer in an ideal working state. Adaptive control has the advantages of being applicable to various grain dryers, not requiring any data about the characteristics of the dryer itself, having no special requirements for environmental and grain conditions, having a fast response speed to interference by the controller, and being able to automatically adjust the parameters in the control model with changes in external conditions. Nybrant (1985) from Sweden applied self correction technology to the control of a cross flow grain dryer. The exhaust temperature of the dryer is used as the output variable, and the grain discharge rate is used as the controlled variable. The Automatic Regression Moving Average (ARMA) model is selected to represent the dynamic characteristics of the cross flow dryer. A confirmatory experiment was conducted on a cross flow dryer in the laboratory, and the standard deviation of control error was 0.13 ℃ during the last 50 samples. The results indicate that the adaptive controller can accurately control the exhaust temperature. Liu Jianjun [5] (2003) conducted research on the HTJ-200 dryer, quantitatively analyzing the system through online sample collection and intelligent optimization algorithms, establishing a process intelligence model determined by real-time detection data, and then calling the artificial intelligence model through intelligent optimization algorithms to obtain the control rules of the system. The control program provided the control quantity, which was then converted into D/A and output to the executing component. Li Xiaobin et al. (1998) studied the advanced control system of vacuum freeze-drying equipment. Based on the process requirements of different freeze-drying materials, two adaptive and self-tuning control methods, DRA algorithm and critical proportion method, were adopted to solve the problem of temperature lag in the main control parameter of the controlled object.
2.1.2 Model Predictive Control
The research field of process control theory is model predictive control, which is an optimization control algorithm based on model, rolling implementation, and combined with feedback correction. It is particularly effective for controlling nonlinear and large lag processes.
Forbes，Jacobson，Rhodes， A model-based drying controller was designed with Sullivan [24] (1984) and Eltigani, whose control behavior is based on a process model and a so-called counterfeit inlet grain moisture content. The drying rate parameters are updated intermittently based on the difference between the predicted values of the model and the measured moisture content at the sensor outlet. The difference between Forbes and Eltigani controllers lies in the different types of process models used in the control algorithms. Liu Qiang from the University of Michigan [25] (2001) proposed a model predictive controller for a cross flow dryer. The simulation test was conducted on a Zimmerman VT-1210 tower cross flow grain dryer, and the controller established using Labview was able to operate successfully, achieving control of corn moisture content at the outlet within 0.7% of the set point. The controller can effectively compensate for a significant range of changes in the moisture content of the grain entering the dryer, as well as large step changes in the hot air temperature.
In the research of model predictive control, a lot of work focuses on the establishment and solution of process models, and considers the issue of drying quality in the models. P. from France Dufour et al. (2003) extended model predictive control to system models using partial differential equations (PDES), enabling the large-scale application of PDES equations. They proposed a global model aimed at reducing the online computation time caused by PDE models based on optimization task solutions. Develop a universal MPC framework that combines IMC structures that are widely used in practice. Two feedback loops are used in the IMC MPC structure to correct process performance and simulation errors caused by model-based online optimizers. Helge Didriksen from Denmark [29] (2002) developed a dynamic first-order rule model for describing mass, energy, and momentum conversion in a drum dryer and applied it to predictive control in sugar beet drying. The results indicate that the model has good predictive ability with changes in operating variables and disturbances. By simulating and comparing model predictive control with traditional feedback control, model predictive control showed better performance. I.C. Treela from France, G. Trystram and F. Courtois [27] designed a nonlinear predictive optimization control algorithm for batch drying processes in 1997 and tested it on a pilot scale dryer. Experiments have shown that the algorithm can handle important disturbances and failures. This control algorithm can be conveniently applied to other batch processes, such as freezing, sterilization, or fermentation. Some scholars use neural networks to model predictive control processes. Jay [32] (1996) first applied neural network models for predictive control of drying processes. J. from France A. Hernandez Perez et al. (2004) proposed a mass and heat transfer prediction model based on artificial neural networks. The model takes product shrinkage as a function of moisture and applies two independent feedforward networks with a hidden layer containing three neural cells, which can predict mass and heat transfer. In data device verification, simulation and experimental kinematic testing are consistent. The developed model can be used for online state estimation and control of the drying process.
2.2 Intelligent Control
Intelligent control is an emerging theory and technology, which is an advanced stage in the development of traditional control. This is a control theory characterized by no model, which is closer to the thinking mode of the human brain. It is mainly used to solve the control of complex systems that are difficult to solve with traditional methods. The design of the controller breaks free from the constraints of the system model, and the algorithm is simple and robust. At present, intelligent control technologies such as ^ control, neural control, and fuzzy control are becoming an important development direction for advanced control.
2.2.1 ^ Control
^System technology can integrate mathematical algorithms with the operational experience of control engineers, making the most of existing knowledge and achieving control effects that traditional control methods cannot achieve^ The control system operates in a continuous real-time environment, utilizing real-time information processing to monitor the dynamic characteristics of the system and provide appropriate control effects. Combining system technology with grain drying process control for grain production, management, and monitoring can improve the production efficiency and efficiency of grain. Liu Mingshan [12] (2001) developed a fuzzy control system for grain drying, and compared the simulation results with the measured data, which were basically consistent. Liu Shurong [13] (2001) combined system technology with drying process control to design a fuzzy system for controlling the drying process of high moisture grains. He Yuchun [14] (2001) optimized the drying parameters during the drying process through intelligent control, and found the common points of energy consumption, efficiency, and quality in the design of drying equipment and the drying process. The dryer dried grains along the common line, ensuring that the equipment remained in ideal operation throughout the drying process; At the same time, interconnect temperature measurement and control technology with network technology to establish a simple and effective temperature based network measurement and control system.
2.2.2 Neural Network Control
Neural networks can provide effective methods for modeling complex nonlinear processes, which can be used in the design of process soft sensing and control systems. There are two main applications of neural networks in the drying process: modeling and control of the drying process.
J. from France- L. Dirion (1996) [6] developed a neural controller for adjusting the temperature of a semi batch experimental reactor. The basic experiment formed a learning database for neural networks, which can provide excellent set point tracking and interference elimination. Liu Yaqiu [9] (2000) developed an adaptive PID controller based on a single neuron, designed a neural network model for wood drying kilns, described the input-output characteristics of the drying kilns using the BP algorithm, and learned and trained the model. Through experiments and simulations, it was proven that the conclusions obtained meet the requirements of error indicators. Zhang Jili [10] (2003) combined fuzzy control technology with neural network technology to design an online detection and intelligent predictive control system for grain drying process parameters. The range of variation in grain moisture content at the outlet of the dryer under intelligent control is smaller than that under manual control, with the former ranging from 13.6% to 14.4% and the latter from 12.4% to 14.2%; The fluctuation frequency of export grain moisture content under intelligent control is smaller than that under manual control. The former has a fluctuation period of about 20 hours, while the latter has a period of about 8 hours. Wang Pin [11] (2003) used an improved BP network algorithm to establish a neural network model for the drying tower. Through the neural network model, a neural network controller was established to achieve intelligent control of grain moisture drying in the arch drying tower system, improving the quality and efficiency of grain drying.
Liu Yongzhong [8] (1999) applied the theory of artificial neural network systems to predict the characteristics of freeze-drying processes. The drying process characteristic parameters such as drying time, sublimation drying time, drying product productivity, and sublimation interface temperature were used as the output parameters of the network model. The predicted results of the network were compared with the calculations of the mathematical model, and the predicted results were in good agreement with the calculated results. Zheng Wenli [7] (2000) used artificial neural networks to intelligently simulate the weight changes of freeze-dried materials during the freeze-drying process: learned the orthogonal experimental results of freeze-drying process conditions, and used the learned network to predict and optimize the process conditions.
2.2.3 Fuzzy control
Fuzzy control is a rule-based control that directly adopts linguistic control rules based on the control experience or relevant knowledge of on-site operators. In design, there is no need to establish a mathematical model of the controlled object, so the control mechanism and strategy are easy to accept and understand.
At present, the main application of fuzzy control method in drying process control both domestically and internationally. Zhang Qin et al. (1994) conducted a study on fuzzy control of a continuous cross flow grain dryer. By adjusting the power of the heater and the speed of the unloading agitator to control the operation of the dryer, the success rate of the experimental control was verified to be 86.4%. Li Junming et al. (1996) based on the hot air temperature of the drying tower, developed fuzzy control rules for a skilled operator in corn drying production through sensory system observation and experience. They used fuzzy control to adjust the speed of the displacement motor and proposed that the self-organizing fuzzy controller of the cross flow corn dryer should adopt an open-loop fuzzy control system to solve the problem of large lag in the corn drying process. Li Yede and Li Yegang [17] (2001) designed a fuzzy intelligent controller with 89c51 microcontroller as the core. Through online drying experiments on wheat in a parallel flow dryer, it was proven that the system has short response time, small overshoot, and high control accuracy. However, fluctuations in grain moisture at the inlet can affect the drying process.
Many graduate students in China are engaged in research on fuzzy control of grain dryers. Meng Xianpei from Northeastern University [18] (2003) used fuzzy set theory and optimization algorithms to establish an intelligent model and fuzzy rules for the grain drying system in the intelligent modeling and control of grain drying towers, and designed a fuzzy controller for the system. Tang Xiaojian from Harbin Institute of Technology [20] (2003) studied a multivariable fuzzy control method for a mixed flow grain drying tower based on the TS model, conducted control simulation on the system, and compared it with manual control methods and traditional fuzzy control methods. Cao Yanming from South China Agricultural University [21] (2000) developed an automatic control system for a rice cycle dryer based on the characteristics of the high humidity rice cycle slow drying process, using a design method of fuzzy control to simulate human thinking. Su Yufeng from Northwest Light Industry College [23] (2002) used a fuzzy algorithm based on the actual operating experience of workers to control the freeze-drying system using a microcontroller, improving the automation level of the equipment.
