Predictive Modeling of Blast Furnace Gas Utilization Rate Using Different Data Pre-Processing Methods
The gas utilization rate (GUR) is an important indicator parameter for reflecting the energy consumption and smooth operation of a blast furnace (BF). In this study, the original data of a BF are pre-processed by two methods, i.e., box plot and 3σ criterion, and two data sets are obtained. Then, sup...
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| Format: | Article |
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MDPI AG
2022-03-01
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| Series: | Metals |
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| Online Access: | https://www.mdpi.com/2075-4701/12/4/535 |
| _version_ | 1827599903373656064 |
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| author | Dewen Jiang Zhenyang Wang Kejiang Li Jianliang Zhang Le Ju Liangyuan Hao |
| author_facet | Dewen Jiang Zhenyang Wang Kejiang Li Jianliang Zhang Le Ju Liangyuan Hao |
| author_sort | Dewen Jiang |
| collection | DOAJ |
| description | The gas utilization rate (GUR) is an important indicator parameter for reflecting the energy consumption and smooth operation of a blast furnace (BF). In this study, the original data of a BF are pre-processed by two methods, i.e., box plot and 3σ criterion, and two data sets are obtained. Then, support vector regression (SVR) is used to construct a prediction model based on the two data sets, respectively. The state parameters of a BF are selected as input parameters of the model. Gas utilization after one hour (GUR-1h), two hours (GUR-2h), and three hours (GUR-3h) are selected as output parameters, respectively. The simulation result demonstrates that using the 3σ criterion to pre-process the raw data leads to better prediction of the model compared to using the box plot. Moreover, the model has the best predictive effect when the output parameter is selected as GUR-1h. |
| first_indexed | 2024-03-09T04:24:53Z |
| format | Article |
| id | doaj.art-718ea13eece1490eb5e2dcaf7f4a5481 |
| institution | Directory Open Access Journal |
| issn | 2075-4701 |
| language | English |
| last_indexed | 2024-03-09T04:24:53Z |
| publishDate | 2022-03-01 |
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| series | Metals |
| spelling | doaj.art-718ea13eece1490eb5e2dcaf7f4a54812023-12-03T13:42:02ZengMDPI AGMetals2075-47012022-03-0112453510.3390/met12040535Predictive Modeling of Blast Furnace Gas Utilization Rate Using Different Data Pre-Processing MethodsDewen Jiang0Zhenyang Wang1Kejiang Li2Jianliang Zhang3Le Ju4Liangyuan Hao5School of Metallurgical and Ecological Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaSchool of Metallurgical and Ecological Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaSchool of Metallurgical and Ecological Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaSchool of Metallurgical and Ecological Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaSchool of Mathematics, Rose-Hulman Institute of Technology, Terre Haute, IN 47803, USAHe Steel Group Co., Ltd., Shijiazhuang 050024, ChinaThe gas utilization rate (GUR) is an important indicator parameter for reflecting the energy consumption and smooth operation of a blast furnace (BF). In this study, the original data of a BF are pre-processed by two methods, i.e., box plot and 3σ criterion, and two data sets are obtained. Then, support vector regression (SVR) is used to construct a prediction model based on the two data sets, respectively. The state parameters of a BF are selected as input parameters of the model. Gas utilization after one hour (GUR-1h), two hours (GUR-2h), and three hours (GUR-3h) are selected as output parameters, respectively. The simulation result demonstrates that using the 3σ criterion to pre-process the raw data leads to better prediction of the model compared to using the box plot. Moreover, the model has the best predictive effect when the output parameter is selected as GUR-1h.https://www.mdpi.com/2075-4701/12/4/535blast furnacedata pre-processingextreme outliergas utilization ratesupport vector regression |
| spellingShingle | Dewen Jiang Zhenyang Wang Kejiang Li Jianliang Zhang Le Ju Liangyuan Hao Predictive Modeling of Blast Furnace Gas Utilization Rate Using Different Data Pre-Processing Methods Metals blast furnace data pre-processing extreme outlier gas utilization rate support vector regression |
| title | Predictive Modeling of Blast Furnace Gas Utilization Rate Using Different Data Pre-Processing Methods |
| title_full | Predictive Modeling of Blast Furnace Gas Utilization Rate Using Different Data Pre-Processing Methods |
| title_fullStr | Predictive Modeling of Blast Furnace Gas Utilization Rate Using Different Data Pre-Processing Methods |
| title_full_unstemmed | Predictive Modeling of Blast Furnace Gas Utilization Rate Using Different Data Pre-Processing Methods |
| title_short | Predictive Modeling of Blast Furnace Gas Utilization Rate Using Different Data Pre-Processing Methods |
| title_sort | predictive modeling of blast furnace gas utilization rate using different data pre processing methods |
| topic | blast furnace data pre-processing extreme outlier gas utilization rate support vector regression |
| url | https://www.mdpi.com/2075-4701/12/4/535 |
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