| In this thesis,the TGS-2 alfalfa solar-heat pump combined drying system is used as the research object,and the heating performance and drying characteristics of the drying system are respectively studied through no-load test and on-load drying test.Compared with the three modes of combined operation of solar energy and heat pump,the prediction model of alfalfa water content is established by two analysis methods,and the comparison and analysis are carried out,and improvement and optimization are carried out according to the simulation results.The no-load heating performance test is divided into the heat collection and temperature rise test of the solar subsystem,the constant temperature and temperature rise test of the heat pump subsystem,the solar-heat pump combined system.Taking temperature rise rate,power consumption per unit temperature rise and COP as test indexes,the test results show that compared with solar heating alone,the temperature rise rate of solar-heat pump combined heating increases by 99-112 times,and the power consumption per unit temperature rise decreases by 86.2%.(1)In the temperature rise test,the temperature rise rate of the former is 7.7%-17.6% lower than that of the latter;(2)In constant temperature heating test,COP of former increases by 6.5%-11.5% and power consumption decreases by 5.8%-10.7%;(3)In the heating test,COP of the former increases by 6.0%-10.4% and power consumption decreases by 6.6%-11.1%.Taking SMER and water content per unit of dehumidification energy consumption as test indexes,the dehumidification ability and drying effect of the system were studied by heat pump alone and solar heat pump combined drying test.The results show that:(1)The dehumidification performance of solar combined drying mode is better,and the SMER value is 19.8%-36.2% higher than that of heat pump single drying mode;(2)The drying rate and SMER of materials at each point in the drying chamber have great differences with the direction of air flow and perpendicular to the direction of air flow.The changing velocity and SMER of alfalfa moisture content at different positions along the horizontal direction of air flow increased with the closer distance to the air inlet and the air outlet,while the changing velocity and SMER rule of alfalfa moisture content at different positions perpendicular to the air flow direction were in the order of the third layer > the fourth layer >the first layer(bottom layer)> the second layer.According to the test data,nonlinear fitting was performed on the moisture content of alfalfa,and 7 influencing factors were obtained through the linear correlation analysis between each influencing factor and the moisture content of alfalfa.Fitting,although the fitting effect is poor,it can be seen from the fitting results that the law is close to the experimental law,which proves that the traditional drying model can only show the overall trend,and cannot be personalized according to the needs of users,so it is necessary to continue to explore other analysis method.Using BP neural network technology made a further analysis on the system ’s performance,based on the BP neural network of multi-factor single output and multiple factors output model:(1)to establish a single output two-stage model to predict water content of alfalfa,clover moisture content in dry indoor moisture content of each location accurately forecast,with less steps,error is small,the advantages of high fitting degree.(2)The multioutput BP neural network model of alfalfa water content and SMER was established,and the alfalfa water content and SMER were predicted,and the prediction of multiple indicators of drying performance was realized.(3)Predict the moisture content within 20 cm above and below the second layer of the drying rack with the worst drying effect.It is found that the drying effect can be improved after the drying rack is properly moved down,and a specific improvement plan is proposed to provide a reference for subsequent equipment improvement. |
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