Problems in Process Control Research
3.1 Insufficient integration of drying technology and control technology
The drying process is a typical multivariable, large inertia, highly nonlinear and complex system, making it difficult to establish an ideal mathematical model that conforms to the actual drying process; And building a model requires a lot of effort, sometimes even impossible. Usually, in order to facilitate research, the modeling conditions need to be simplified. The simplified equation cannot accurately reflect the drying process, and simplification often brings errors. Some models, such as heat and mass transfer models, drying process optimization control models, fuzzy control and intelligent control models, have shortcomings. At the same time, the research on drying theory is limited to the circle of diffusion theory, and the characteristic function of the material itself has not been found, which also brings difficulties to the establishment of the model. Even if some drying processes can establish mathematical models, their structures are often complex and difficult to design and implement effective control. At present, research mainly focuses on control based on one-dimensional mathematical models, often only controlling a specific parameter, resulting in unsatisfactory control effects and inability to achieve multi-objective intelligent control. Without a good mathematical model, other indirect methods have to be sought when implementing control, which to some extent affects the accuracy and effectiveness of control. The combination of drying technology research and control technology research is not good enough, resulting in incomplete reflection of the role of drying machine control in realizing the ideal efficiency of drying machines and improving product quality.
3.2 There is limited research on drying process control methods and control effects
3.2.1 Insufficient control variables in process control
The drying process control system is based on conventional single variable technology, and the control objective is mainly limited to the smooth operation of a certain variable or a few variables, ensuring smooth production and fewer accidents. With the increasing trend of large-scale, integrated, continuous, and complex grain drying industry, higher requirements have been put forward for the quality of process control. A good control system should not only protect the stability of the system and the safety of the entire production, meet certain constraints, but also bring certain economic and social benefits. In grain drying, once the temperature and humidity of the hot air in a certain drying section change, it not only directly affects the temperature and moisture content of the grain in that drying section, but also indirectly affects the temperature and moisture content of the grain in the next section and even the outlet of the drying tower. If the speed of the grain discharge motor slows down or accelerates, not only will the moisture content of the grain at the outlet of the drying tower change, but the temperature and moisture content of the grain in each drying section will also change accordingly. In this complex series of changes, there will inevitably be delays, coupling, time-varying, and a series of nonlinear processes. If only the deviation and rate of change of the controlled variable are used as inputs to the control system, it is difficult to ensure its control effect when internal or external disturbances increase in the system. The classic fuzzy control system often simplifies the research problem to a single input single output single variable fuzzy controller, which has great limitations in application. The input of the controller is only the deviation and variation of the controlled variable, essentially equivalent to a variable parameter single input PD regulator. Therefore, the complexity of the drying process determines that there is more than one control quantity and the controlled quantity, and there is a complex relationship between them. The ideal values of each controlled quantity will also have mutual constraints, making it difficult to find an ideal control plan.
3.2.2 Few advanced control applications and single centralized methods
Although the exploration of how to apply intelligent control to drying processes has been ongoing for decades, there has been little research on the design methods of advanced control systems for grain drying, and more research has focused on certain methods. During the "15th Five Year Plan" period, the State Grain Administration spent a large amount of funds to solve the online testing and automatic control of moisture during the grain drying process, and conducted research and development work on some projects in conjunction with some grain depots. However, most design units adopted fuzzy control methods. Browsing domestic dissertations, it can also be seen that many of them use neural networks to establish mathematical models of drying towers, use fuzzy thinking to comprehensively evaluate the performance of drying machines, and optimize the design of drying machines; There is no report on the application of model predictive control. Although advanced control methods have many advantages, a single method also has some shortcomings. Fuzzy control is built on the basis of proficient operation experience, and requires system self-learning and continuous parameter correction to gradually approach the target value. However, there are many factors that affect the moisture content of grain during drying, making it difficult to find the experienced parameters of skilled operators. Without using a mathematical model method that accurately reflects the control quantity of the dryer for automatic control design, it is difficult to ensure the quality of grain after drying. Although adaptive control can solve uncertain problems to a certain extent, the algorithm is complex, computationally intensive, and has poor adaptability to unmodeled dynamics and disturbances. The robustness of the system still needs to be further addressed, and its application is limited. Developing a friendly graphical interface based ^ system is one of the development directions for drying process control, but due to the long search time during problem solving, the ability of the ^ system for online control is relatively poor. In the form of neural network modeling, networks based on BP algorithm have the disadvantages of long training time and frequent non convergence; Although using radial basis functions to approximate the drying process can greatly improve convergence speed and enable the network to converge globally, it is difficult to determine its central coordinates. Most existing nonlinear model predictive control methods can only be used for slower process control, which is unfavorable for drying process control with high real-time requirements. Therefore, a single application of a certain control strategy cannot better leverage the advantages of process control.
3.3 Detection over control, low accuracy and stability of moisture sensors
The detection and control instruments of grain drying parameters are directly related to the quality and economic benefits of drying. There are not many applications of automatic control for domestic grain dryers. Some dryers are equipped with air temperature digital display, over temperature alarm, and grain discharge speed display devices, but they cannot be automatically controlled. The domestic grain moisture detector only measures and displays grain moisture without forming a real-time and online control system that is compatible with grain drying equipment, making it impossible to achieve automatic control of the grain drying process. It is difficult to achieve online and rapid measurement of grain moisture testing. Currently, drying equipment used in China cannot achieve automatic control of the grain drying process due to the lack of a standardized dynamic process moisture detection method. The accuracy and stability issues of online moisture testing sensors have not been well resolved together, and have not truly matured to the stage of reliable detection, which has affected the accuracy of the process method.
4 Development directions
4.1 Improvement of drying process model
Continue to conduct in-depth research on the internal heat and mass transfer laws of materials during the drying process; Establishing a mathematical model that can reflect the state of the drying process can help improve the automatic control of the drying process. At the same time, an intelligent model of the drying process can be established, replacing mathematical models with intelligent models. The intelligent control system can approximate the real system and effectively control it. If artificial neural network technology is used to establish mathematical models, it can map multiple independent variables to multiple dependent variables, making it particularly suitable for complex grain drying processes.
4.2 Combination penetration of multiple control methods
It is difficult to fully leverage the advantages of a single advanced control technology. An inevitable trend is for various control strategies to penetrate each other, complement each other's strengths and weaknesses, and combine them into a composite control strategy. The composite control strategy combining multiple control strategies overcomes the shortcomings of individual strategies and has better characteristics, which can better meet the requirements of different applications. It is the future development direction. Research has shown that replacing the inference method of fuzzy mathematics with neural networks will greatly improve the online control ability of the ^ system; The neural network ^ system that combines artificial neural networks with ^ systems is a beneficial attempt for problem solving; The combination of neural networks and traditional control theory gives control systems a considerable degree of intelligence. Therefore, the composite control strategy will promote the research on neural network control that remains in the stages of mathematical simulation and laboratory research to be used for practical system control. Fuzzy PID explicit control, fuzzy variable structure control, adaptive fuzzy control, fuzzy predictive control, fuzzy neural network control, ^ fuzzy control and other composite controls are emerging, and it is believed that there will be greater development and widespread application.
4.3 In depth research on control strategies
The design of drying process systems can no longer rely solely on traditional control theories and techniques based on quantitative mathematical models. It is necessary to further develop advanced process control systems, study advanced process control laws, and transplant and transform existing control theories and methods into the field of process control. These aspects are also receiving increasing attention from the control community. Further strengthen research on control theory, such as focusing on the three major mechanisms of predictive control: predictive models, feedback correction methods, and strategies for solving optimization, and conducting comprehensive research and breakthroughs; There is an urgent need to develop a real-time model predictive control method in the drying process control, which can reduce online calculation time while ensuring drying quality; Emphasize interdisciplinary research, draw on other effective control methods, solve existing problems in process control, continuously improve, develop, and innovate existing drying process control algorithms; Further improve the reliability of the automatic control system for drying quality and establish control algorithms with adaptive capabilities.
