大容积电烤箱内传热过程的反向传播神经网络控制算法  

Thermal Uniformity Control in Electronic Oven Guided byBack Propagation Neural Network

作　　者：姚青 唐巍峰 郑鑫 王锐 梁文龙 刘玉贤 褚雯霄 YAO Qing;TANG Weifeng;ZHENG Xin;WANG Rui;LIANG Wenlong;LIU Yuxian;CHU Wenxiao(Key Laboratory of Healthy&Intelligent Kitchen System Integration of Zhejiang Province,Ningbo,Zhejiang 315336,China;Ningbo Fotile Kitchen Ware Company,Ningbo,Zhejiang 315336,China;Key Laboratory of Thermo-Fluid Science and Engineering of MOE,Xi’an Jiaotong University,Xi’an 710049,China)

机构地区：[1]浙江省健康智慧厨房系统集成重点实验室,浙江宁波315336 [2]宁波方太厨具有限公司,浙江宁波315336 [3]西安交通大学热流科学与工程教育部重点实验室,西安710049

出　　处：《西安交通大学学报》2024年第7期73-83,共11页Journal of Xi'an Jiaotong University

基　　金：国家自然科学基金青年基金资助项目(52206113)。

摘　　要：大容积电烤箱内存在严重加热不均匀问题,限制其在商业和家用领域的广泛应用,传统比例-积分-微分(PID)控制算法存在弛豫时间长、温控精度差等问题,导致被加热目标无法维持在最佳烹饪热环境。通过自编程构建了一种反向传播神经网络(BPNN)控制策略,以改善大容积电烤箱的加热速率、温控精度及热均匀性为目标,通过局部速度、温度分布与美拉德反应可视化实验测试,探究了风扇转速、对流与辐射加热功率和排气流量等因素的影响。实验结果表明:在提升算法鲁棒性后,BPNN算法对烤箱内温度预测误差显著降低;相比PID控制方法,采用BPNN算法的被加热目标过热度最多降至6℃,温控精度显著提高;被加热目标表面温度的相对极差从54%降至36%,速度相对极差从71.4%下降至39%,均匀性显著增强;电烤箱的加热弛豫时间从230 s降至100 s。BPNN算法能够实现大容积电烤箱更精确、更快速、更均匀的温度控制。Serious problem on the heating uniformity exist in the large-volume electric oven,which limits its extensive application in commercial and household fields.The commonly applied Proportion Integration Differentiation(PID)algorithm has concerns of long relaxation time and poor temperature control accuracy.This study involves a self-coding backpropagation neural network(BPNN)control strategy,aiming to improve the heating efficiency,temperature control accuracy and uniformity.The local velocity and temperature measurement as well as the egg-tart visualization methods are utilized to assess the control sensitivity of fan speed,power of airflow heating rods,power of radiation heating rods,and exhaust flowrate.Experimental result shows that,following effective data training and robustness enhancement,the BPNN control strategy can significantly reduce the prediction errors.In comparison to the PID strategy,the overheating is reduced by up to 6℃.Meanwhile,the maximum temperature difference decreases from 54%to 36%.Accordingly,the velocity difference drops from 71.4%to 39%,and the relaxation time shorts from 230 seconds to 100 seconds.It is indicated that the BPNN strategy can provide much quicker,more precise and uniform temperature control in the large-volume electric oven.

关 键 词：电烤箱 反向传播神经网络 对流与辐射 热均匀性 弛豫时间 

分 类 号：TK124[动力工程及工程热物理—工程热物理]