Study On Control Method Of High-frequency Vacuum Combined Wood Drying

Today’s global forest resources dwindling, how to improve the forest resource utilization has become one of the difficulties faced by the wood processing industry. Wood drying is an important link in wood processing, with perfect drying equipment and advanced drying technology; it can not only save timber resources, improve the quality of the wood, but also have an energy conservation and environmental protection effect.High-frequency vacuum combined wood drying is a new union wood drying technology which is using less time, less power and lower pollution to the environment. This technology has the respective advantages of high frequency drying and steam drying, it’s not only reducing the possibility of cracking occurs or burning because of the local temperature is too high in the wood drying process, but also shorten the drying time. At the same time, it’s also ensured the drying quality of the wood. So in the wood drying area, this technology has broad application value and development prospects.Wood drying process has the characteristics of nonlinear, time-varying and strong couplings, o it’s difficult to using the traditional theoretical to establish a precise mathematical model for hitting the effects of automation-control. This paper is using the Genetic Algorithms and genetic algorithm that is the main process of SAGA algorithm to establish the temperature control model and drying baseline model for High-frequency vacuum combined wood drying, which is based on BP neural network. Simulation results show the following conclusions: temperature control model and drying baseline model are both having higher prediction accuracy which is based on the BP neural network that is optimized by the SAGA algorithm. Comparing with the BP neural network that is optimized by the GA algorithm, SAGA algorithm is more appropriate for describing the complicated process of wood drying, and it’s also the essential preparation of designing the precise control system and optimized drying baseline model.The wood drying model has been built through the theoretical analysis of High-frequency vacuum combined wood drying. And this paper also has designed wood drying fuzzy controller and fuzzy Neural Network Controller. The two control methods were simulated in MATLAB/SIMULINK environment and got the result:Fuzzy Neural Network Control could get better control effect in the wood drying process, for instance, the temperature could rise faster, for the field of wood drying from manual, semi-automatic control to automatic control forward has played a role in promoting.