摘要
为了研究环境温度、环境湿度、烟气温度(风温)对玉米热风干燥过程中能火用效率、可持续性和可提高势变化规律的影响,选取影响换热器换热性能的关键因素包括环境温度、湿度及烟气温度作为输入因素,换热器的火用效率、比火用损、可持续性指标及可提高势作为输出目标,构建了3-9-4的BP神经网络模型,并利用MatLab软件进行神经网络模型的建立及验证。基于经典的热力学第一、二定律分析了新型工业化玉米干燥机换热器的热力学特性,结果表明:所构建的BP神经网络模型经过31次迭代之后达到最佳逼近误差(0.0013001),R值为0.99998,表明模型训练精度较高。增加一组验证试验,结果表明:试验各指标预测值与实测值MSE值均低于10%,表明模型精度较高;玉米干燥机换热器的火用效率变化范围为8.32%~13.96%,SI值变化区间为1.08~1.16,可提高势随比火用损的增加而增大,其变化区间分别为643.35~1114.24kW和17.59~29.04kW。
The influences of ambient temperature,ambient humidity and flue gas temperature(air temperature)on exergic and energy efficiency,sustainability and improving potentiality change in hot air drying process of corn were studied,the key factors affecting the heat transfer performance of heat exchanger,including ambient temperature and humidity and flue gas temperature,were selected as input factors,exergic efficiency ratio of heat exchanger exergic loss sustainability index and potentiality of improvement were selected as output targets to construct a 3-9-4 BP neural network model,and the neural network model was established and verified by Matlab software.In addition,based on the first and second laws of thermodynamics,this paper analyzed the thermodynamic characteristics of the heat exchanger for the new industrialized corn dryer.The results showed that:after 31 iterations,the constructed BP neural network model reached the optimal approximation error(0.0013001),and the R value was 0.99998,indicating that the training accuracy of the model was high;A set of verification tests were added,and the results showed that the predicted and measured MSE values of each indicator in the test were both lower than 10%,indicating a higher accuracy of the model;exergy efficiency of the heat exchanger for the corn dryer presented in this paper ranged from 8.32%to 13.96%,and the SI value ranged from 1.08 to 1.16.The improved potential increased with the increase of specific exergy loss,and the variation ranges were 643.35-1114.24kw and 17.59-29.04kw,respectively.
作者
曾治亨
黎斌
李成杰
黄隽盈
欧文妍
李长友
Zeng Zhiheng;Li Bin;Li Chengjie;Huang Junying;Ou Wenyan;Li Changyou(College of Engineering,South China Agricultural University,Guangzhou 510642,China)
出处
《农机化研究》
北大核心
2021年第9期237-244,共8页
Journal of Agricultural Mechanization Research
基金
国家自然科学基金项目(31671783,31371871)
广东省科技计划项目(2014B020207001)。
