翅片式双重极板水稻含水率检测装置优化设计与试验  

Optimization Design and Experiment on Finned Double PlatesRice Moisture Content Measuring Device

作　　者：万霖[1,2] 唐宏宇 马广宇 车刚 邹丹丹[2] 孙文胜 WAN Lin;TANG Hongyu;MA Guangyu;CHE Gang;ZOU Dandan;SUN Wensheng(College of Engineering,Heilongjiang Bayi Agricultural University,Daqing 163319,China;Heilongjiang Provincial Key Laboratory of Intelligent Agricultural Machinery Equipment,Daqing 163319,China)

机构地区：[1]黑龙江八一农垦大学工程学院,大庆163319 [2]黑龙江省农机智能装备重点实验室,大庆163319

出　　处：《农业机械学报》2021年第2期320-328,共9页Transactions of the Chinese Society for Agricultural Machinery

基　　金：“十二五”国家科技支撑计划项目(2014BAD06B00);黑龙江省应用技术研究与开发计划重大项目(GA15B402);全国基层农机推广补助项目(JCTG1904);黑龙江省省属高等学校科研项目(PTJH202002)。

摘　　要：为提高水稻含水率在线检测准确度,以平行板电容器为研究对象,采用翅片式双重极板检测方式对水稻含水率的检测装置进行优化试验。以极板厚度、极板间距和相对面积为试验因素,采用二次回归正交组合试验方法进行电容比灵敏度影响试验,获得最优极板结构参数组合为极板厚度2.98 mm、极板间距101.60 mm、相对面积32583.69 mm^(2)。应用Matlab软件建立非线性自回归神经网络NARX的水稻含水率预测与校正模型,通过对比分析确定了模型结构的参数以及优化算法。分析表明:基于量化共轭梯度算法的神经网络NARX水稻含水率预测模型为最佳,模型的隐含层为1层,神经元数量为5,滞后阶数为3,含水率预测值与105℃恒重法实测值的误差范围在±0.5%以内。测试含水率最大相对偏差为0.65%,最小相对偏差为0.26%,平均相对偏差为0.44%。与静态电容式水分仪测试结果相比,本文水稻含水率检测装置的测试偏差浮动较小,检测性能满足水稻干燥生产实际要求。In order to improve the accuracy of on-line detection technology of rice moisture content,the parallel plate capacitor was taken as the research object,and the fin type double plate detection method was adopted to optimize the detection value.Taking the plate thickness,plate spacing and relative area as experimental factors,the capacitance ratio sensitivity test was carried out by quadratic regression orthogonal combination test method.The optimal plate structure parameters were obtained as follows:plate thickness was 2.98 mm,plate spacing was 101.60 mm,and relative area was 32583.69 mm^(2).The prediction and correction model of moisture content based on nonlinear autoregressive neural network NARX was established by Matlab software.The parameters of model structure and optimization algorithm were determined by comparative analysis.The error analysis showed that the NARX prediction model based on the quantitative conjugate gradient algorithm was the best.The hidden layer of the model was 1 layer,the number of neurons was 5,and the lag order was 3.Compared with the 105℃constant weight method,the calibration error range was within±0.5%,the maximum deviation was 0.65%,the minimum deviation was 0.26%,and the average deviation was 0.44%.Compared with the static capacitance water meter,the deviation fluctuation of on-line test of rice drying production was small,which met the requirements of rice drying production.

关 键 词：水稻 含水率 双重极板电容 神经网络 优化设计 试验 

分 类 号：S226.6[农业科学—农业机械化工程] S237[农业科学—农业工程]