Reserch On Property And Mathematical Model Of The Corn Low Temperature Deep-bed Drying

This paper was financially supported by National Food Authority NonprofitResearch Industry Research Projects, Grain Drying Speed Control Model andIntelligent System (No.20133001), and Public Service Sectors (Agriculture) ResearchProjects, Suitable for Small Farmers in Different Regions Stored Grain Research andDemonstration Facility (No.201003077).China is a populous agricultural country to ensure food security is a basic policyof our country. Grain drying is an important part of the grain postproduction, andgrain drying is important to ensure the safety of our food. Corn is the main crop innorthern China, the North’s climatic conditions decision to corn after harvest must bedried to safe moisture before they can enter the warehouse store, so the northernregion always attached great importance to the development of of corn dryingtechnology.Corn drying process is a typical multi-variable, nonlinear, time-varying and largelag in the process in industrial production, thereby increasing the difficulty of thedrying process to achieve automatic control. Over the years, the corn drying processcontrol model building, implementation, applications, and improve product automaticcontrol has been the focus of China’s grain industry.With the advancement of technology, some of the more advanced control theoryand technology began to be used in industrial control. This study is based on currentgrain drying mathematical model to calculate the presence of imprecise and actualresults out of the larger issues such as the status quo, the grain drying test rig tomanufacture as the object of study, Based on the test and simulate the drying process of corn, the variation of the corn temperature and humidity, moisture is analyzed, andestablished a new mathematical model of corn deep bed drying to achieve moistureforecast, aimed at achieving intelligent detection and automatic control of the dryingprocess.Using computer simulation and process analysis, optimization and control has avery important role in the study of modern engineering. By using computer simulationand other means can help us have a more in-depth understanding for the dryingprocess. By using mathematical formulas and other methods to appear the heat andmass variation of drying process, while research on the virtual platform helps reducethe time of pre-design, development and research institutes to spend, but also can savemanpower and material resources.In this proposed model-predictive control solutions, we will use the corndeep-bed drying test sets self-made, include the SHT11digital temperature andhumidity sensors, the temperature heating modules, the frequency regulator, theRS485to RS232module, the STC89C52SCM and PC and other hardware devices,meanwhile we use computer software development platform LabVIEW and RS485fieldbus technology to design cryogenic deep-bed drying corn moisture predictionmodel and control system software.The main contents are the following aspects.1. For the lack of the current deep bed drying study, using a new type ofdeep-bed drying mode, maize the variation of grain temperature, humidity, moisturein deep bed drying process, providing guidance for the deep-bed drying corn research.2. For the specific case of the current water testing methods and dryingequipment, to build a new multi-point temperature and humidity distributed onlinecollection of hot air drying test rig. The system consists of multi-line detection SHT11digital temperature and humidity sensors to test temperature and humidity of the corn,combined with real-time electronic weighing scales to calculate grain moisturecontent, control drying time, data acquisition using STC89C52SCM single agency lower machine system, based on485fieldbus enables data transfer to a PC, the longdistance, using LabVIEW write PC collection data processing systems andhuman-machine interface.3. China’s mechanical ventilation of grain storage standards commonly usedCAE equation, Wuwen Fu is our research group’s teacher， he improved CAE model,we propose a new condensed CAE model to separate desorption and adsorptionequation, and can achieve humidity, moisture mutual down, increase the range ofapplications, this article will be applied to the drying process to be verified andcorrected.4. By doing a lot of deep-bed drying experiment, collecting large amounts ofdata, using computer simulation technology, to build mathematical models among airtemperature, wind speed, material thickness, drying time and moisture content toachieve the drying process moisture predicted and experimental verification.