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基于BP神經(jīng)網(wǎng)絡(luò)的旁熱式輻射與對流糧食干燥過程模型
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國家糧食公益性行業(yè)科研專項(201413006)


Model of Drying Process for Combined Side-heat Infrared Radiation and Convection Grain Dryer Based on BP Neural Network
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    摘要:

    針對旁熱式輻射與對流糧食干燥機(jī)的干燥特點,建立了一種糧食干燥機(jī)干燥過程的BP神經(jīng)網(wǎng)絡(luò)預(yù)測模型。該模型采用了3層神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu)(8-10-1),模型輸入為糧食干燥機(jī)的8個變量,模型輸出為出口糧食水分比或干燥速率。通過編寫Matlab建模程序,基于實際干燥實驗的樣本數(shù)據(jù)訓(xùn)練與測試網(wǎng)絡(luò),實現(xiàn)了紅外輻射與對流聯(lián)合干燥的動力學(xué)模型,并給出了相應(yīng)的模型數(shù)學(xué)表達(dá)式,模型預(yù)測的出口水分比與干燥速率的R2分別為0.9989和0.9980,均方根誤差分別為0.009和0.0041,預(yù)測結(jié)果與實際測量數(shù)據(jù)擬合較好;另外,結(jié)合實驗干燥條件對模型干燥性能的預(yù)測結(jié)果進(jìn)行了分析與總結(jié),并依據(jù)同樣方法建立了順逆流糧食干燥過程的出口糧食水分比預(yù)測模型,對比了2種干燥方式的干燥性能。仿真預(yù)測表明用BP神經(jīng)網(wǎng)絡(luò)方法建模簡單,具有自適應(yīng)性、靈活性和自學(xué)習(xí)性等特點,相比于其他糧食干燥的經(jīng)驗數(shù)學(xué)模型,能綜合考慮多種影響因素,可為紅外輻射與對流聯(lián)合干燥過程提供一種新的建模方法。

    Abstract:

    The drying mechanism of combined side-heat infrared radiation and convection (IRC) grain dryer is more complicated compared with that of the traditional convection drying. In order to explore the model of uncertain system like the grain drying and application of BP artificial neural network method, a new intelligent prediction model for the combined side-heated IRC dryer used to predict the outlet core moisture content ratio and drying rate is developed based on BP neural network algorithm. The model which has three layer neural network structures (8-10-1) is trained and tested based on the train data set and test data set by programming the model in Matlab. The model inputs are the eight influence variables of grain dryer, and the model output is the outlet grain moisture ratio of the dryer or the drying rate. The corresponding mathematical expressions of moisture ratio and drying rate model are also given, and the determination coefficients (R2) of model prediction are 0.9989 and 0.9980, and the root mean square errors (RMSE) are 0.009 and 0.0041, respectively. The predicted results are fitted well with the measured data, and the prediction accuracy is high. In addition, combined with the experimental drying conditions, the prediction results of the model are analyzed and summarized. According to the same method, the prediction model of outlet moisture ratio for the counter-current grain drying is also successfully established. By the comparison of predicted performance curves for two types of drying process, it is proved that the combined side-heat IRC drying has faster drying rate and less time to dry to the target moisture value than those of the conventional hot air convection drying. It can be used to predict the drying performance of different drying processes and to realize the comparison of different drying processes. In addition, compared with other grain drying mathematical models, various influence factors of grain drying can be comprehensively considered, which can provide a new modeling method for the complex system like the drying of combined side-heat IRC.

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代愛妮,周曉光,劉相東,劉景云,張馳.基于BP神經(jīng)網(wǎng)絡(luò)的旁熱式輻射與對流糧食干燥過程模型[J].農(nóng)業(yè)機(jī)械學(xué)報,2017,48(3):351-360. DAI Aini, ZHOU Xiaoguang, LIU Xiangdong, LIU Jingyun, ZHANG Chi. Model of Drying Process for Combined Side-heat Infrared Radiation and Convection Grain Dryer Based on BP Neural Network[J]. Transactions of the Chinese Society for Agricultural Machinery,2017,48(3):351-360.

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  • 收稿日期:2016-07-03
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  • 在線發(fā)布日期: 2017-03-10
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