{"title":"基于绿色颗粒尺寸表征的自动图像处理","authors":"V. Subramanyam, Prabal Patra, M. Singh","doi":"10.5875/AUSMT.V7I3.1133","DOIUrl":null,"url":null,"abstract":"The size of pellets has a significant effect on the performance of blast furnaces. Based on experience, the universally accepted size of pellets for efficient blast furnace operations is between 9 millimeters to 16 millimeters. But presence of smaller size pellets or fines lowers the blast furnace stack permeability, increases dust losses and may lower the maximum permissible blast temperature for smooth operation of the furnace. Pellets too big are also undesirable, particularly if its reducibility is low and is poor in strength, thus undergoing physical degradation during furnace operation.This paper relates to a non-contact method of measuring the size of green pellets being fed into the Blast furnace. A method is developed for the automatic size characterization of green pellets in the conveyor belts before the pellets are being indurated in a travelling grate furnace to confer the required physical and metallurgical properties. The method employs imaging and illumination devices installed in the conveyor belt in the pellet plant of Tata Steel and then, processing those images using imaging algorithms to obtain the size distribution. Apart from the size distribution of the green pellets, the system also identifies the pelletizing disc from which the green balls originated so as to effectively control the pelletizing process.","PeriodicalId":38109,"journal":{"name":"International Journal of Automation and Smart Technology","volume":"7 1","pages":"85-91"},"PeriodicalIF":0.0000,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Automatic Image Processing based Size Characterization of Green Pellets\",\"authors\":\"V. Subramanyam, Prabal Patra, M. Singh\",\"doi\":\"10.5875/AUSMT.V7I3.1133\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The size of pellets has a significant effect on the performance of blast furnaces. Based on experience, the universally accepted size of pellets for efficient blast furnace operations is between 9 millimeters to 16 millimeters. But presence of smaller size pellets or fines lowers the blast furnace stack permeability, increases dust losses and may lower the maximum permissible blast temperature for smooth operation of the furnace. Pellets too big are also undesirable, particularly if its reducibility is low and is poor in strength, thus undergoing physical degradation during furnace operation.This paper relates to a non-contact method of measuring the size of green pellets being fed into the Blast furnace. A method is developed for the automatic size characterization of green pellets in the conveyor belts before the pellets are being indurated in a travelling grate furnace to confer the required physical and metallurgical properties. The method employs imaging and illumination devices installed in the conveyor belt in the pellet plant of Tata Steel and then, processing those images using imaging algorithms to obtain the size distribution. Apart from the size distribution of the green pellets, the system also identifies the pelletizing disc from which the green balls originated so as to effectively control the pelletizing process.\",\"PeriodicalId\":38109,\"journal\":{\"name\":\"International Journal of Automation and Smart Technology\",\"volume\":\"7 1\",\"pages\":\"85-91\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Automation and Smart Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5875/AUSMT.V7I3.1133\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Computer Science\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Automation and Smart Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5875/AUSMT.V7I3.1133","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Computer Science","Score":null,"Total":0}
Automatic Image Processing based Size Characterization of Green Pellets
The size of pellets has a significant effect on the performance of blast furnaces. Based on experience, the universally accepted size of pellets for efficient blast furnace operations is between 9 millimeters to 16 millimeters. But presence of smaller size pellets or fines lowers the blast furnace stack permeability, increases dust losses and may lower the maximum permissible blast temperature for smooth operation of the furnace. Pellets too big are also undesirable, particularly if its reducibility is low and is poor in strength, thus undergoing physical degradation during furnace operation.This paper relates to a non-contact method of measuring the size of green pellets being fed into the Blast furnace. A method is developed for the automatic size characterization of green pellets in the conveyor belts before the pellets are being indurated in a travelling grate furnace to confer the required physical and metallurgical properties. The method employs imaging and illumination devices installed in the conveyor belt in the pellet plant of Tata Steel and then, processing those images using imaging algorithms to obtain the size distribution. Apart from the size distribution of the green pellets, the system also identifies the pelletizing disc from which the green balls originated so as to effectively control the pelletizing process.
期刊介绍:
International Journal of Automation and Smart Technology (AUSMT) is a peer-reviewed, open-access journal devoted to publishing research papers in the fields of automation and smart technology. Currently, the journal is abstracted in Scopus, INSPEC and DOAJ (Directory of Open Access Journals). The research areas of the journal include but are not limited to the fields of mechatronics, automation, ambient Intelligence, sensor networks, human-computer interfaces, and robotics. These technologies should be developed with the major purpose to increase the quality of life as well as to work towards environmental, economic and social sustainability for future generations. AUSMT endeavors to provide a worldwide forum for the dynamic exchange of ideas and findings from research of different disciplines from around the world. Also, AUSMT actively seeks to encourage interaction and cooperation between academia and industry along the fields of automation and smart technology. For the aforementioned purposes, AUSMT maps out 5 areas of interests. Each of them represents a pillar for better future life: - Intelligent Automation Technology. - Ambient Intelligence, Context Awareness, and Sensor Networks. - Human-Computer Interface. - Optomechatronic Modules and Systems. - Robotics, Intelligent Devices and Systems.