Jingliang Shi, Ying Wu, Lu Liao, Xin-ping Yan, J. Zeng, Rusen Yang
{"title":"炼铁过程中烧透点的软测量","authors":"Jingliang Shi, Ying Wu, Lu Liao, Xin-ping Yan, J. Zeng, Rusen Yang","doi":"10.1109/ICCI-CC.2016.7862070","DOIUrl":null,"url":null,"abstract":"In the iron-making process, the state of burning through point (BTP) is the closure of sintering which is one of the most important parameters in judging the state of sintering. Based on the PSO (Particle Swarm Optimization)-inversion soft-sensing method, the BTP which can not be directly measured in the iron-making process is soft-sensed in this paper. Firstly, the principle of sintering is studied. Four parameters are employed to forecast the BTP, including the suction pressure of main chimney flue, air input, velocity of sintering machine and ignition temperature. And then, a prediction model using PSO is established. At last, the model is applied to production process. It is proved to be effective.","PeriodicalId":135701,"journal":{"name":"2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Soft sensing of the burning through point in iron-making process\",\"authors\":\"Jingliang Shi, Ying Wu, Lu Liao, Xin-ping Yan, J. Zeng, Rusen Yang\",\"doi\":\"10.1109/ICCI-CC.2016.7862070\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the iron-making process, the state of burning through point (BTP) is the closure of sintering which is one of the most important parameters in judging the state of sintering. Based on the PSO (Particle Swarm Optimization)-inversion soft-sensing method, the BTP which can not be directly measured in the iron-making process is soft-sensed in this paper. Firstly, the principle of sintering is studied. Four parameters are employed to forecast the BTP, including the suction pressure of main chimney flue, air input, velocity of sintering machine and ignition temperature. And then, a prediction model using PSO is established. At last, the model is applied to production process. It is proved to be effective.\",\"PeriodicalId\":135701,\"journal\":{\"name\":\"2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)\",\"volume\":\"71 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCI-CC.2016.7862070\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCI-CC.2016.7862070","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Soft sensing of the burning through point in iron-making process
In the iron-making process, the state of burning through point (BTP) is the closure of sintering which is one of the most important parameters in judging the state of sintering. Based on the PSO (Particle Swarm Optimization)-inversion soft-sensing method, the BTP which can not be directly measured in the iron-making process is soft-sensed in this paper. Firstly, the principle of sintering is studied. Four parameters are employed to forecast the BTP, including the suction pressure of main chimney flue, air input, velocity of sintering machine and ignition temperature. And then, a prediction model using PSO is established. At last, the model is applied to production process. It is proved to be effective.