Zhu Guanyu, Raghavan G S V, Xu Wanxiu, Pei Yongsheng, Li Zhenfeng
Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment and Technology, School of Mechanical Engineering, Jiangnan University, Wuxi 214122, China.
Department of Bioresource Engineering, McGill University, 21111 Lakeshore Road, Sainte-Anne-de-Bellevue, QC H9X 3V9, Canada.
Foods. 2023 Mar 23;12(7):1372. doi: 10.3390/foods12071372.
Online microwave drying process monitoring has been challenging due to the incompatibility of metal components with microwaves. This paper developed a microwave drying system based on online machine vision, which realized real-time extraction and measurement of images, weight, and temperature. An image-processing algorithm was developed to capture material shrinkage characteristics in real time. Constant-temperature microwave drying experiments were conducted, and the artificial neural network (ANN) and extreme learning machine (ELM) were utilized to model and predict the moisture content of materials during the drying process based on the degree of material shrinkage. The results demonstrated that the system and algorithm operated effectively, and ELM provided superior predictive performance and learning efficiency compared to ANN.
由于金属部件与微波不兼容，在线微波干燥过程监测一直具有挑战性。本文开发了一种基于在线机器视觉的微波干燥系统，该系统实现了对图像、重量和温度的实时提取与测量。开发了一种图像处理算法以实时捕捉物料收缩特性。进行了恒温微波干燥实验，并利用人工神经网络（ANN）和极限学习机（ELM）基于物料收缩程度对干燥过程中物料的水分含量进行建模和预测。结果表明，该系统和算法运行有效，与ANN相比，ELM具有更好的预测性能和学习效率。
