Numerical Simulation And Digital Design Of Grain Drying Process Based On MATLAB

In industrial and agricultural production process, because of the generally largeyield, the grain drying operations after harvesting basically belong to the category ofdeep-bed drying. And in the drying areas, the study of thin layers drying of differenttypes of materials is relatively thorough, so generally when the study of deep-beddrying is carried out,the deep-bed will be divided into thin layers in a certain way. Inthis paper, differential thinking was adapted. Approximate partial differential modelof deep bed drying is established. It is easy to deduce the specific impact of variousdrying parameters to the drying process by non-physical tests. The laws of thevariation during the drying process is mastered. The study plays certain guiding roleson the occasion of improving product quality and controlling dryer in the dryingprocess. Thus, blindness is reduced and energy waste is effectively avoided.development and testing cycles can be more and more shorter. In this thesis, partialdifferential model of deep bed drying is regarded as the main object of study, thefollowing studies were carried out:(1) There are4equations in the partial differential model for cereals deep beddrying. It is mass balance equation, the heat balance equation, the heat transferequation and the drying rate equation. In the process of model differencing andrecursive calculation, a better understanding of the mechanism of grain drying isgained. The effect of every drying parameter and dryer design parameters on thedrying process is mastered. The laws of heat and mass exchange are summarized.(2) The calculation accuracy comparison of backward differentiation formulaand central difference formula is made when calculating partial differential equationsapproximately. The comparison on two computational stability and accuracy ofdifference schemes is made by graphical comparison method. The first-order centraldifference form was seemed as the analytical tool to simulate the prediction andcontrol system(3) The operator interface of deep bed drying predictive control system platformwas produced by using LabVIEW. The interface includes inputting areas combined environmental parameters section with corn characteristic parameters. Thecalculation parts thermodynamic parameters and simulation results display parts.(4) The abstract partial differential equations are solved through MATALB.Results matrixes are obtained of moisture content, grain temperature, outlet hot airtemperature and outlet hot air humidity at different times, different layers. Thesimulation results are displayed intuitively in LabVIEW graphical front panel by thetwo-dimensional and three-dimensional curves. It is easy for the operating personnelto understand real-time food situation inside the dryer. Appropriate actions can betaken to control the drying process inside the grain dryer.(5) The simulation operating results of drying process are reflected throughcharts by the system. The simulation results are compared with the actual deep beddrying tests to estimate the drying process energy consumption, calculate energyconsumption and thermal efficiency. Under different drying conditions, energyconsumption units can be compared. According to economic principles,the optimaldrying conditions can be selected.(6) Through mixed programming between MATLAB with LabVIEW, the actualdrying process is simulated. The performances of grain dryers and drying systemsare predicted, such as the length of time required for drying, distribution laws ofgrain moisture and temperature within the dry warehouse, distribution laws ontemperature and relative humidity of the heating air, etc. The influence of eachparameter on the dryer performance and drying process are analyzed. This can be thebasis for the improvement of grain dryers design. It is possible to improve andenhance the operational management and control method of grain drying systems.(7) The thin-layer drying test sets producing hot air is used to do the small corndeep-bed drying experiments. Air temperature and moisture content of each layerwas measured and recorded. Then the precipitation curves was drawn. Thesimulation results were compared with the actual dry precipitation curves to verifythe accuracy and credibility of simulation results for the PDE.The study designed the corn drying control system based on LabVIEW virtualinstrument technology. The system is based on the model of partial differential equations. The use of virtual instrument technology reduces the costs of operatingthe system and achieves the automation of drying process. The operation of thesystem is simple. The experiments process can be displayed in real time. The controlparameters can be modified online. The operation of the drying process and dataprocessing are simplified. The system with anti-interference ability has certainversatility and adaptability.