Corn Drying Process Digital Simulation, Control And Expert System Technology

Grain drying is an important part of processing and storage after the harvest, and the drying process has a great impact on grain quality. In recent years, as China's national economic development and people's material living standards, the demand of processing quality of harvested grain has become much higher; therefore, the configuration of the drying process and parameters of a reasonable and intelligent control technology has proved to be the pivotal method to improve the quality of dry grain. Domestic and foreign scholars have also done a lot of research work on this aspect.This study is some parts of the national high-tech Research and Development Program (863 Project) - "continuous drying control technology of food and distributed intelligent control system" (2006AA10Z256). Based on theoretical analysis and experimental study, aiming at the drying process of the cross-flow combination dryer, the paper studies the factors influencing the quality of drying corn, establishes mathematical model of drying process and the drying process simulation system. It predicts and simulates the state parameters in the maize cross-flow drying process and provides the frame of reference for the automation and intelligence of the control process. Combining extension theory technology, this paper also sets up the reasoning expert system based on extension theory, which deduces and optimizes the processing control parameters and ensure the dry corn's quality characteristics. Based on the model simulation system and expert system, it develops the intelligent control system on maize variable-temperature drying process, and applied to practice. The paper's full text is as follows:(1)Using partial differential equations model and seed corn dynamic vitality model, the article establishes the corn cross-flow drying simulation system, and according to the test verification, it can predict and reflect the changing law of the cross-flow drying process parameters. The system interface is intuitive, and it can also reflect the grain and hot air characteristic parameters during every dryer section. Through the simulation, the system is capable of timely responding to fluctuations of interference signals and inputs, at the same time ensures that the stability of water and germination rate in the exit place.(2)Stress crack is one of the form of damage grain quality, and impact on the quality of corn, grain defected than full grain easily broken and moldy, the paper through the thin layer drying experiments, the drying temperature, drying time and the raw grain moisture impact on the crack rate. The prediction model of crack rate using BP artificial neural network to establish. By verifying that: the model can forecast the rate of crack in drying process.(3)The quality of dried grain has been growing more attention. In quality parameters, germination rate and crack rate are two important indicators of grain quality, the impact factors include air temperature, drying time and the raw grain moisture. The paper used neural network model and mathematical model to predict the rate of grain germination rate crack rate, real-time control of air temperature and adjust the grain velocity, stabilize the moisture, improve the quality of grain drying.(4)The extension theory is an emerging discipline which has a wide application prospect in the field of artificial intelligence. This paper introduces the representation methods of basic theory and knowledge of extension theory and the extension reasoning principles, realizes the data processing of extension theory through using database technology.(5)There is a detailed description of the design process of corn drying process parameters reasoning expert system. Using graphical programming language (G language), database technology and extension theory, it establishes drying parameters expert system which takes Access database as a platform to build an evidence cases and problems database. The system knowledge is expressed by primitive form, achieves case-based reasoning and provides timely optimal process parameters for the drying process.( 6 ) By using virtual instruments, artificial neural networks, artificial intelligence (AI), distributed serial communication techniques; It builds the twin goals of corn drying intelligent measurement and control system based on predictive control. The system consists of industrial control computer, AI monitoring and control instrumentation (measurement temperature, moisture and control of air temperature, etc.), programmable logic controller (PLC), transducer, and so on.; through combining an online model prediction and expert system, intelligent control technology, it decreases the fluctuations of the water and germination rate in the exit place caused by the changing drying condition. The system's software is compiled by using MATLAB and the LabVIEW platform; now the system has been applied in production.