The Design And Implementation Of Grain Dryers Data Monitoring System

China is a large grain producer. The safty storage of grain relates to establishing resources-conserving and sustainable development society as well as some relevant issues.The harvested grain must be passed the dry processing,in order to reach the safety moisture for long-term storage. But neither processing nor monitoring of machinery drying has reached the level of completely automatism in our country at this moment. A working status data monitoring system is designed for grain drying process . Meanwhile, to raise the automation level of drying process, a control algorithm is proposed to improve the consistency of moisture after drying , which would decrease the grain moisture and the possibility of moldy in grain storage to reach the National security storage standard.The monitoring system is designed to be used in corn drying by a 5HSH800 drying tower (DT).Using corn drying as an example, according to the variation of corn drying temperature and the relevance of motor speed and corn moisture out of machine, an intelligent control algorithm is designed by combining with BP neural network technology to forecast the motor speed based on the drying tower working state so that the moisture of dried corn would reach 14%.First of all, The relationship between the effect of grain drying and grain moisture out of machine is understood through vast references and the actual measurement provided by grain storage units. According to the principle of BP neural network nonlinear function approximation,the grain drying process can be regarded as a black box to predict the motor speed, in another word, to make the dried grain be able to reach uniformity moisture through controlling the motor speed.Secondly,according to the requirement of application,a crop drying tower data monitoring system is designed. And detection objects in this system include grain temperature, grain moisture, drying air temperture and motor speed. Hot air temperture, grain temperture and moisture are collected by BUS MODE through 89C52 MCU and then sent to PC through a wireless network.Finally,the working data of drying process in drying tower is collected in the field through the monitoring system designed in this paper. The field test data is used as input information for BP neural network algorithm, the monitoring system is designed to forecast the required motor speed. Through contrasting the result of forecasted value and real value of DT motor speed, the workability of this BP neural network algorithm is verified.