基于含水率变化的气体射流冲击干燥过程温度自适应控制系统  

Temperature adaptive control system for air-impingement drying process based on moisture content change

作　　者：杨涛庆 郑霞[1,2,3] 肖红伟 单春会[5] 张继凯[1,2,3] YANG Taoqing;ZHENG Xia;XIAO Hongwei;SHAN Chunhui;ZHANG Jikai(College of Mechanical and Electrical Engineering,Shihezi University,Shihezi 832000,China;Key Laboratory of Northwest Agricultural Equipment,Ministry of Agriculture and Rural Affairs,Shihezi 832003,China;Xinjiang Production and Construction Corps Key Laboratory of Modern Agricultural Machinery,Shihezi 832003,China;College of Engineering,China Agricultural University,Beijing 100083,China;College of Food,Shihezi University,Shihezi 832003,China)

机构地区：[1]石河子大学机械电气工程学院,石河子832000 [2]农业农村部西北农业装备重点实验室,石河子832003 [3]现代农业机械兵团重点实验室,石河子832003 [4]中国农业大学工学院,北京100083 [5]石河子大学食品学院,石河子832003

出　　处：《农业工程学报》2024年第2期52-62,共11页Transactions of the Chinese Society of Agricultural Engineering

基　　金：石河子大学成果转化与技术推广计划项目(CGZH201808)。

摘　　要：为了给变温干燥工艺提供新的技术支持,实现基于含水率变化的干燥温度自适应控制,该研究设计了具有物料含水率在线检测功能的温度自适应控制系统。采用卷积神经网络建立了以质量检测值、气流冲击速度、称重传感器弹性基体温度、气流冲击距离为输入,物料真实质量为输出的含水率在线检测模型。进行了含水率在线检测模型验证试验。结果表明,该模型满足变温干燥工艺中含水率在线检测的精度要求,5组含水率在线检测模型验证试验的决定系数R2和均方根误差RMSE依次为0.993 4和1.20%。该文设计了改进神经网络-PID(improved neural network-PID,INN-PID)控制器来实现变温干燥工艺中的温度控制。在MATLAB软件中以单位阶跃信号为输入对PID、神经网络-PID(neural network-PID,NN-PID)和INN-PID控制器的动态性能进行仿真。对3种控制器分别进行了50~55℃的干燥温度控制试验。结果表明,在仿真试验中,INN-PID控制器的控制稳定性和调节时间均显著优于另外两种控制器;干燥温度控制试验结果与仿真结果存在近似相同的规律,INN-PID控制器的峰值时间是208.00 s,调节时间是120.59 s,最大超调量是4.87%,满足变温干燥过程中温度控制的要求。该研究在气体射流冲击干燥机中搭建了温度自适应控制系统,进行了基于含水率变化的温度自适应控制试验。结果表明,该系统可以对基于含水率变化的变温干燥工艺中的干燥温度进行快速且有效的调节。该研究对提高干燥设备的自动化水平以及开发新的变温干燥工艺具有重要意义,对其他领域的多信息融合检测和控制策略研究提供参考。In order to provide new technical support for the variable temperature drying process and realize the adaptive control of drying temperature based on the change of moisture content,the study designed a temperature adaptive control system with the function of online detection of material moisture content.A moisture content online detection model with weight detection value,air velocity,the temperature of load sensor elastic substrate,airflow impingement distance as inputs,and the real material weight as outputs was established by using a convolutional neural network.A validation test of the moisture content online detection model was carried out.The results showed that the model meets the accuracy requirements of online moisture content detection in the variable temperature drying process,and the coefficient of determination R2 and root mean square error(RMSE)of the five groups of model validation tests were 0.9934 and 1.20%in that order.In this reasearch,an improved neural network-PID(INN-PID)controller was designed to realize temperature control in the variable temperature drying process.The dynamic performance of PID,neural network-PID(NN-PID),and INN-PID controllers was simulated in MATLAB software with unit step signal as input.The three controllers were tested for drying temperature control at 50-55℃.The results showed that the control stability and regulation time of the INN-PID controller were significantly better than the other two controllers in the simulation test,the drying temperature control results had approximately the same law with the simulation results,and the peak time of the INN-PID controller was 208.00 s,the regulation time was 120.59 s,and the maximum overshooting was 4.87%,which meets the requirements of temperature control in the variable-temperature drying process.In this paper,a temperature adaptive control system was built in the air-impingement dryer,and the temperature adaptive control test based on the moisture content change was carried out.The results showed that the system coul

关 键 词：含水率 在线检测 变温干燥 自适应 控制器 

分 类 号：TS205.1[轻工技术与工程—食品科学] TP273[轻工技术与工程—食品科学与工程]