Study On The Solar Drying Experiment And Moisture Content Model Simulation Of Alfalfa Bale

Using solar energy to deal with the drying of alfalfa bale can effectively solve the problems of high loss of nutrients, high energy consumption and high cost in the traditional forage drying process, which is beneficial to the development of local animal husbandry. The experiment was conducted to study the characteristics of solar hot air drying of alfalfa bale, and the established bale drying moisture simulation model by applying the artificial neural network theory, provide a reference for the optimization of alfalfa solar drying technology and system.This paper mainly studies the two aspects of the content:(1) Alfalfa bale drying experiment is hold on a set of solar alfalfa drying test bed. The drying characteristics and the heat and mass transfer of the solar drying process with high moisture content were analyzed, and the relationship among the bale factors, the medium factors and the drying characteristics were studied. The experiment shows that the moisture content of alfalfa bale is exponential with time, drying is mainly at constant rate and falling rate stage; density have great effects on the drying process; bale multi-layer drying process has obvious moisture gradient and temperature gradient, is not uniform; the drying medium temperature and humidity to bale water rate difference is very significant.(2) Introduces the basic theory of BP algorithm and the simulation model of moisture content in drying process was established with the experimental data, the maximum absolute error of test sample was 18.75% average percent error was 13.67%, model predictive error is large. The particle swarm optimization algorithm is introduced, on this basis, BP network parameters are optimized by particle swarm optimization algorithm, and the water content simulation model based on PSO-BP neural network is established. The results show that the network model has the advantages of high learning speed, high fitting degree and small error, and has good prediction performance for the moisture content of the alfalfa bale solar drying process.