| Cowpea is rich in high-quality protein that promotes digestion and absorption,as well as a variety of vitamins and trace elements.The B vitamins contained in it can help consumers to promote digestion and increase appetite.At the same time,the phospholipids rich in cowpea can promote the secretion of insulin,which is a high-quality food suitable for diabetic patients.The moisture content of fresh cowpeas is as high as90%or more.In the process of transportation,storage and sale,it is easy to cause a large amount of nutrition loss or even deterioration of quality due to the influence of different environmental temperature and humidity,resulting in economic losses.Dry storage of fresh cowpeas after picking can greatly extend the shelf life and bring more economic benefits.And the dried cowpea has a unique flavor and is very popular in the market.This paper explores the drying characteristics,process optimization,average convective heat and mass transfer model,and energy consumption efficiency evaluation of cowpea drying process around hot air drying.The conclusions are as follows:(1)The characteristics of hot air drying and the model analysis showed that the hot air temperature and the number of bedding layer had a great effect on the hot air drying rate and the total drying time of cowpea,while the wind speed had a little effect on the drying rate and the total drying time of cowpea.The Avhad and Marchetti model was determined to be the optimal prediction model with R~2≥0.9947,RMSE≤0.02525 and SSE≤0.0102,which indicated that the model had better prediction effect on moisture content in the process of hot air drying of cowpea.At the same time,BP artificial neural network was used to predict the moisture content change of cowpea during hot air drying.The fitting degree of the final training results was greater than 0.999 in the process of training,verification and testing,indicating that the prediction effect was good.The two models were verified by the verification test,and it was clear that the BP artificial neural network model had a higher fitting degree with the experimental value,indicating that BP artificial neural network could better predict and describe the trend of moisture content in the hot air drying process of cowpea.(2)The process optimization of hot air drying test and combined hot air and vacuum drying test of cowpea was carried out.The results showed that the sensitivity degree of influence factors on rehydration ratio and unit energy consumption in hot air drying was as follows:hot air temperature>hot air speed>number of layings;The relationship of sensitivity to color difference is:hot air temperature>layer-number>hot air speed;The sensitivity of various influencing factors to rehydration ratio and unit energy consumption in hot-air and vacuum combined drying is as follows:hot air temperature>water content of transfer point>vacuum temperature;The sensitivity to color difference value is as follows:hot air temperature>vacuum temperature>water content of conversion point.The process parameters were optimized and the results showed that the optimal process parameters were as follows:hot air temperature 50℃,water content of transfer point 48%,vacuum temperature 69℃.The energy consumption per unit of the verification test is30.986k J/kg,the color difference value is 21.35 and the rehydration ratio is 1.682.(3)The relationship between hot air temperature,hot air speed and number of layers on convective heat transfer and mass transfer coefficient during hot air drying of cowpea was analyzed.The results showed that the influence degree of each factor on convective heat transfer coefficient was as follows:hot air temperature>number of layers>hot air speed;The influence degree of mass transfer coefficient is as follows:hot air temperature>hot air speed>number of layers.The prediction models of convective heat transfer and mass transfer coefficient for hot air drying of cowpea were established respectively.R~2 of the two models were close to 1,C.V.%was less than 6%,and the missing items were not significant,indicating that the two models had good fitting degree,high accuracy and high reliability.(4)Exergy transfer and energy efficiency of hot air drying exergy of cowpea were studied,and the results showed the following rules:The higher the hot air temperature was,the lower the average thermal exergy was,and the higher the average flowing exergy was.When temperature and layer-number were fixed,the average thermal exergy efficiency and average flow exergy efficiency were negatively correlated with the wind speed of hot air.Similarly,the average thermal exergic efficiency and the average flow exergic efficiency also decreased with the increase of the number of coating layers.According to the test results,the three groups with the highest overall exergic efficiency were analyzed:70℃,1.5m/s and 2 layers;60℃,1.0m/s,2 layers;60℃,1.5m/s and 1layer,the average thermal exergic efficiency and average flow exergic efficiency of the three groups were 47.05%and 54.39%,respectively;57.43%,47.71%;Overall exergic efficiency was 58.15%and 45.04%,and the highest exergic efficiency was in the second group.According to the energy consumption analysis of the drying system,the energy efficiency evaluation is optimal when the test conditions are 60℃,1.0m/s and 2 layers,and the energy consumption is 4.487k W·h,which is consistent with the previous comprehensive exergy efficiency analysis result. |
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