Modeling And Intelligent Control Of Grain Drying Process Based On Equivalent Accumulated Temperature

Grain drying is a complex process of heat and mass transfer.The traditional drying model and drying process control method have some shortcomings,such as high limitation,high dependence on experience,single regulation index,etc.They are difficult to show good control effect in some complex cases.At present,the establishment of advanced control system with strong stability and good predictive performance is the research focus of the whole drying industry.Paddy is the main food of about 50%of the world's population.The loss of paddy drying after harvest in China is as high as 7%,so the control of paddy drying process is particularly important.Due to the special grain characteristics of paddy,the husk of paddy will hinder the outward transfer of water in the grain,so the drying process of paddy is complicated.In this paper,according to the characteristics of large hysteresis,nonlinear and strong coupling in the drying process of paddy,based on the comprehensive multi-factor stress test and the multi-factor coupling concept of effective drying accumulated temperature,the mathematical model of grain drying was reconstructed,and the research and application of paddy drying process modeling and intelligent control strategy were carried out.The main contents of this paper are as follows:1.Experimental study on drying characteristics and quality of paddy and establishment of accumulated temperature and quality chart.Based on the research of the theory of grain drying and the advanced control method,the regulation of grain drying was optimized,and the characteristics of paddy drying were studied by means of multivariate quadratic rotation orthogonal test.Based on the theory of effective drying accumulated temperature,the chart of drying accumulated temperature and quality of paddy was established,and the searching method of the chart was provided.2.The model of effective drying accumulated temperature of paddy was established.Based on the coupling of drying time and drying temperature,the relationship between the moisture ratio and the drying accumulated temperature was explored,seven kinds of common drying models were reconstructed,and finally the optimal mathematical model was obtained to describe the relationship between the moisture ratio and the drying accumulated temperature.3.An effective accumulated temperature model of paddy drying with tempering effect was established.In order to make the model more practical,an effective accumulated temperature model of paddy drying with tempering effect was established based on the results of multi-parameter coupled thin-layer drying test(in which the tempering time was considered).The influences of tempering time on paddy drying were analyzed by comparing the constants of models with and without tempering process.4.An intelligent control system for the continuous paddy drying was designed.Through the analysis and summary of artificial intelligence control methods,it is found that the BP neural network is suitable for the control core of this control system.An intelligent control system based on MATLAB and Lab VIEW was established,and the control rules of paddy drying are proposed.The whole drying system was controlled by the two-line control method of segmenting temperature control and grain discharging speed control so as to realize accurate control of the drying process of paddy.5.Verification test of intelligent control system for paddy drying.Based on the experimental data,the BP neural network model with multiple hidden layers was established,and the training method of neural network was introduced.A series of simulation tests under Lab VIEW environment were carried out.The test results showed that:for this drying system,the network structure of single hidden layer had the highest accuracy and the best fitting effect.The model with self-optimization function can predict the moisture content of grain more accurately and adjust the waiting time of grain drainage in time.The multi-hidden layer BP neural network controller showed excellent anti-interference ability,and the stability of the intelligent control system was also verified,which could be applied to practical drying operations.