Improved SVM Model for Predicting Pellet Metallurgical Properties Based on Textural Characteristics
From the point of view that pellet microstructure determines its metallurgical properties, an improved support vector machine (SVM) model for pellet metallurgical properties forecast is studied based on the mineral phase characteristics, in order to improve the evaluation efficiency of pellet metall...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-10-01
|
| Series: | Metals |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4701/12/10/1662 |
| _version_ | 1827648897211695104 |
|---|---|
| author | Yang Han Lijing Wang Wei Wang Tao Xue Yuzhu Zhang |
| author_facet | Yang Han Lijing Wang Wei Wang Tao Xue Yuzhu Zhang |
| author_sort | Yang Han |
| collection | DOAJ |
| description | From the point of view that pellet microstructure determines its metallurgical properties, an improved support vector machine (SVM) model for pellet metallurgical properties forecast is studied based on the mineral phase characteristics, in order to improve the evaluation efficiency of pellet metallurgical properties. The forecast model is composed of a SVM with self-adaptive selection of kernel parameters and a SVM with self-adaptive compounding of kernel types. This not only guarantees the super interpolation ability of the forecast model, but also takes into account its good generalization performance. Based on 200 sets of original sample information, the quantitative relationship between the main characteristics of mineral phase and the grade labels of pellet metallurgical properties (reduction expansion index RSI, reduction index RI, low temperature reduction and pulverization index RDI) was determined by the improved SVM model. With the simulation results of RSI, RI, and RDI with the accuracy of 100%, 98%, and 100% respectively, the precise forecast of pellet metallurgical properties based on mineral phase is realized. |
| first_indexed | 2024-03-09T19:48:24Z |
| format | Article |
| id | doaj.art-831ef3cdc0e942d6bf15750c6a90eada |
| institution | Directory Open Access Journal |
| issn | 2075-4701 |
| language | English |
| last_indexed | 2024-03-09T19:48:24Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metals |
| spelling | doaj.art-831ef3cdc0e942d6bf15750c6a90eada2023-11-24T01:18:42ZengMDPI AGMetals2075-47012022-10-011210166210.3390/met12101662Improved SVM Model for Predicting Pellet Metallurgical Properties Based on Textural CharacteristicsYang Han0Lijing Wang1Wei Wang2Tao Xue3Yuzhu Zhang4Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes, North China University of Science and Technology, Tangshan 063210, ChinaHebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes, North China University of Science and Technology, Tangshan 063210, ChinaHebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes, North China University of Science and Technology, Tangshan 063210, ChinaHebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes, North China University of Science and Technology, Tangshan 063210, ChinaCollege of Metallurgy and Energy, North China University of Science and Technology, Tangshan 063210, ChinaFrom the point of view that pellet microstructure determines its metallurgical properties, an improved support vector machine (SVM) model for pellet metallurgical properties forecast is studied based on the mineral phase characteristics, in order to improve the evaluation efficiency of pellet metallurgical properties. The forecast model is composed of a SVM with self-adaptive selection of kernel parameters and a SVM with self-adaptive compounding of kernel types. This not only guarantees the super interpolation ability of the forecast model, but also takes into account its good generalization performance. Based on 200 sets of original sample information, the quantitative relationship between the main characteristics of mineral phase and the grade labels of pellet metallurgical properties (reduction expansion index RSI, reduction index RI, low temperature reduction and pulverization index RDI) was determined by the improved SVM model. With the simulation results of RSI, RI, and RDI with the accuracy of 100%, 98%, and 100% respectively, the precise forecast of pellet metallurgical properties based on mineral phase is realized.https://www.mdpi.com/2075-4701/12/10/1662SVM kernel functionself adaptationpellet mineral phasemetallurgical propertiesforecast Model |
| spellingShingle | Yang Han Lijing Wang Wei Wang Tao Xue Yuzhu Zhang Improved SVM Model for Predicting Pellet Metallurgical Properties Based on Textural Characteristics Metals SVM kernel function self adaptation pellet mineral phase metallurgical properties forecast Model |
| title | Improved SVM Model for Predicting Pellet Metallurgical Properties Based on Textural Characteristics |
| title_full | Improved SVM Model for Predicting Pellet Metallurgical Properties Based on Textural Characteristics |
| title_fullStr | Improved SVM Model for Predicting Pellet Metallurgical Properties Based on Textural Characteristics |
| title_full_unstemmed | Improved SVM Model for Predicting Pellet Metallurgical Properties Based on Textural Characteristics |
| title_short | Improved SVM Model for Predicting Pellet Metallurgical Properties Based on Textural Characteristics |
| title_sort | improved svm model for predicting pellet metallurgical properties based on textural characteristics |
| topic | SVM kernel function self adaptation pellet mineral phase metallurgical properties forecast Model |
| url | https://www.mdpi.com/2075-4701/12/10/1662 |
| work_keys_str_mv | AT yanghan improvedsvmmodelforpredictingpelletmetallurgicalpropertiesbasedontexturalcharacteristics AT lijingwang improvedsvmmodelforpredictingpelletmetallurgicalpropertiesbasedontexturalcharacteristics AT weiwang improvedsvmmodelforpredictingpelletmetallurgicalpropertiesbasedontexturalcharacteristics AT taoxue improvedsvmmodelforpredictingpelletmetallurgicalpropertiesbasedontexturalcharacteristics AT yuzhuzhang improvedsvmmodelforpredictingpelletmetallurgicalpropertiesbasedontexturalcharacteristics |