| This article modifies the fixed multi-layer drying storage rack of the TGS-2 solar heat pump combined drying system into a multi-layer reciprocating conveyor belt dynamic storage device.Through experiments,its performance,feasibility in practice,and optimal process matching relationship are studied from the perspectives of drying efficiency and energy consumption.The influence of five factors,including hot air temperature,material thickness,medium flow rate,conveyor belt inclination angle,and conveyor belt operating speed,on drying rate and overall drying energy consumption was studied through single factor experiments.The results showed that during the drying stage before the moisture content of alfalfa grass reached 30%,the influence of hot air temperature,alfalfa grass thickness,medium flow rate,conveyor belt inclination angle,and conveyor belt operating speed on the drying rate was significant.When the moisture content of alfalfa grass decreased to below 30%,the influence of various factors on the drying rate was no longer significant.Use SPSS software to conduct a multiple factor analysis of variance on the results of orthogonal experiments,and obtain the significant relationship results and influence weights of each factor on the drying time and energy consumption under the influence of interaction.By comparing the energy consumption values of the solar heat pump combined heating mode and the heat pump separate heating mode,the energy-saving efficiency of the TGS-2 solar heat pump combined device is calculated to be 15.5%～25.6%.The effects of hot air temperature,conveyor belt inclination angle,medium flow rate,thickness,and conveyor belt operating speed on drying energy consumption were studied using SMER values.Study on the nutritional composition of alfalfa hay dried using different drying processes and hot air temperatures.Based on the analysis and research of nutritional composition,drying rate,and drying energy consumption,a staged variable temperature drying process flow for a solar heat pump dynamic drying device was proposed,and the optimal process parameters of the equipment were determined.A prediction model based on BP neural network was established to accurately predict drying time,moisture content,energy consumption,and energy conservation.And the established BP neural network was used to predict the drying time and energy consumption of the variable temperature drying process.Compared to constant temperature drying at45 ° C,the variable temperature drying process saves 18.29% of drying time;Compared to constant temperature drying at 65 ° C,it saves 21.01% of energy consumption.Based on the problems encountered in the experiment,equipment improvement plans were proposed for the overall drying equipment in terms of insulation,full utilization of hot air,and stable operation of conveyor belt equipment. |
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