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 Plates Rice Moisture Content Measuring Device

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 32583.69mm2。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。

Abstract:

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 32583.69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.