{"title":"利用深度学习神经网络检测搅拌罐中的泡沫","authors":"","doi":"10.1016/j.cherd.2024.08.005","DOIUrl":null,"url":null,"abstract":"<div><p>A deep learning method based on the Convolutional Neural Network (CNN) U-Net architecture has been explored as an image analysis tool for the detection and automated quantification of foam generated in a stirred tank. Multiple datasets of different sizes and with different types of foam were obtained experimentally and processed with data augmentation techniques in order to train the deep learning networks. The impact of adding attention and residual gates to the U-Net model to improve its performance, as well as the size of the dataset used to train the model have been assessed. The main factor influencing the accuracy of the models to detect the foam in the stirred tank is the size and quality of the dataset use to train the U-Net models; it must be sufficiently large and varied in terms of foam type/structure. The addition of attention gates and residual blocks to the U-Net model slightly improves the accuracy of foam detection, particularly for images that contain additional objects or obstructions, however the training time is 30 % longer than the standard U-Net model. In all cases, this image analysis method based on the U-Net model largely out performs conventional image analysis, which was not able to automatically differentiate between foam, additional objects in the tank and bubbles in the liquid. Two methods for the measurement of foam quantity (maximum foam height and foam volume) have also been developed based on the foam regions detected by the models. This is important for the application of the tool, which is to be used to understand the impact of operating conditions on foam formation in lab-/pilot-scale stirred tanks and then develop scale-up guidelines for foam control in industrial tanks.</p></div>","PeriodicalId":10019,"journal":{"name":"Chemical Engineering Research & Design","volume":null,"pages":null},"PeriodicalIF":3.7000,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Foam detection in a stirred tank using deep learning neural networks\",\"authors\":\"\",\"doi\":\"10.1016/j.cherd.2024.08.005\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>A deep learning method based on the Convolutional Neural Network (CNN) U-Net architecture has been explored as an image analysis tool for the detection and automated quantification of foam generated in a stirred tank. Multiple datasets of different sizes and with different types of foam were obtained experimentally and processed with data augmentation techniques in order to train the deep learning networks. The impact of adding attention and residual gates to the U-Net model to improve its performance, as well as the size of the dataset used to train the model have been assessed. The main factor influencing the accuracy of the models to detect the foam in the stirred tank is the size and quality of the dataset use to train the U-Net models; it must be sufficiently large and varied in terms of foam type/structure. The addition of attention gates and residual blocks to the U-Net model slightly improves the accuracy of foam detection, particularly for images that contain additional objects or obstructions, however the training time is 30 % longer than the standard U-Net model. In all cases, this image analysis method based on the U-Net model largely out performs conventional image analysis, which was not able to automatically differentiate between foam, additional objects in the tank and bubbles in the liquid. Two methods for the measurement of foam quantity (maximum foam height and foam volume) have also been developed based on the foam regions detected by the models. This is important for the application of the tool, which is to be used to understand the impact of operating conditions on foam formation in lab-/pilot-scale stirred tanks and then develop scale-up guidelines for foam control in industrial tanks.</p></div>\",\"PeriodicalId\":10019,\"journal\":{\"name\":\"Chemical Engineering Research & Design\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2024-08-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Chemical Engineering Research & Design\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0263876224004714\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, CHEMICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Chemical Engineering Research & Design","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0263876224004714","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}
Foam detection in a stirred tank using deep learning neural networks
A deep learning method based on the Convolutional Neural Network (CNN) U-Net architecture has been explored as an image analysis tool for the detection and automated quantification of foam generated in a stirred tank. Multiple datasets of different sizes and with different types of foam were obtained experimentally and processed with data augmentation techniques in order to train the deep learning networks. The impact of adding attention and residual gates to the U-Net model to improve its performance, as well as the size of the dataset used to train the model have been assessed. The main factor influencing the accuracy of the models to detect the foam in the stirred tank is the size and quality of the dataset use to train the U-Net models; it must be sufficiently large and varied in terms of foam type/structure. The addition of attention gates and residual blocks to the U-Net model slightly improves the accuracy of foam detection, particularly for images that contain additional objects or obstructions, however the training time is 30 % longer than the standard U-Net model. In all cases, this image analysis method based on the U-Net model largely out performs conventional image analysis, which was not able to automatically differentiate between foam, additional objects in the tank and bubbles in the liquid. Two methods for the measurement of foam quantity (maximum foam height and foam volume) have also been developed based on the foam regions detected by the models. This is important for the application of the tool, which is to be used to understand the impact of operating conditions on foam formation in lab-/pilot-scale stirred tanks and then develop scale-up guidelines for foam control in industrial tanks.
期刊介绍:
ChERD aims to be the principal international journal for publication of high quality, original papers in chemical engineering.
Papers showing how research results can be used in chemical engineering design, and accounts of experimental or theoretical research work bringing new perspectives to established principles, highlighting unsolved problems or indicating directions for future research, are particularly welcome. Contributions that deal with new developments in plant or processes and that can be given quantitative expression are encouraged. The journal is especially interested in papers that extend the boundaries of traditional chemical engineering.