Feature Engineering to Embed Process Knowledge: Analyzing the Energy Efficiency of Electric Arc Furnace Steelmaking
The importance of electric arc furnace (EAF) steelmaking is expected to increase worldwide as parts of the industry transition to lower carbon dioxide emissions. This work analyzed one year’s operational data from an EAF plant that uses a large proportion of direct-reduced iron (DRI) in the furnace...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Metals |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4701/15/1/13 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832588017203675136 |
|---|---|
| author | Quantum Zhuo Mansour N. Al-Harbi Petrus C. Pistorius |
| author_facet | Quantum Zhuo Mansour N. Al-Harbi Petrus C. Pistorius |
| author_sort | Quantum Zhuo |
| collection | DOAJ |
| description | The importance of electric arc furnace (EAF) steelmaking is expected to increase worldwide as parts of the industry transition to lower carbon dioxide emissions. This work analyzed one year’s operational data from an EAF plant that uses a large proportion of direct-reduced iron (DRI) in the furnace feed. The data were used to test different approaches to quantifying the effects of process conditions on specific electricity consumption (kWh per ton of crude steel). In previous work, inputs such as the proportion of DRI, fluxes, natural gas, and oxygen were linearly correlated with the specific electricity consumption. The current work has confirmed that conventional multiple linear regression (MLR) reproduces electricity consumption trends in EAF steelmaking, but many model coefficients deviated significantly from expected values and appeared unphysical. The implementation of engineered features—the slag volume and total carbon input—in an MLR model resulted in coefficients that were closer to expectations, but did not improve prediction accuracy. Further improvement was obtained by applying the engineered features to a non-linear machine-learned model (based on XGBoost), yielding both physically reasonable trends and smaller prediction errors. Trends from Shapley dependence analysis (applied to the XGBoost model) are quantitatively consistent with theoretical trends. These include the energy needed to melt slag, and the endothermic effect of carbon additions. The fitted models demonstrate the potential to diagnose poor slag foaming by showing an increase in electricity consumption with increased oxygen use. This example demonstrates that practically important steelmaking process insights inferred via a linear regression approach can be improved by applying Shapley analysis to a machine-learned model based on engineered features. |
| format | Article |
| id | doaj-art-c1f2e101057a485bb5172d74a9b11b06 |
| institution | Kabale University |
| issn | 2075-4701 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metals |
| spelling | doaj-art-c1f2e101057a485bb5172d74a9b11b062025-01-24T13:41:24ZengMDPI AGMetals2075-47012024-12-011511310.3390/met15010013Feature Engineering to Embed Process Knowledge: Analyzing the Energy Efficiency of Electric Arc Furnace SteelmakingQuantum Zhuo0Mansour N. Al-Harbi1Petrus C. Pistorius2Department of Materials Science and Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USAHadeed, Tareeq 162 with 241, P.O. Box 11669, Jubail 31961, Saudi ArabiaDepartment of Materials Science and Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USAThe importance of electric arc furnace (EAF) steelmaking is expected to increase worldwide as parts of the industry transition to lower carbon dioxide emissions. This work analyzed one year’s operational data from an EAF plant that uses a large proportion of direct-reduced iron (DRI) in the furnace feed. The data were used to test different approaches to quantifying the effects of process conditions on specific electricity consumption (kWh per ton of crude steel). In previous work, inputs such as the proportion of DRI, fluxes, natural gas, and oxygen were linearly correlated with the specific electricity consumption. The current work has confirmed that conventional multiple linear regression (MLR) reproduces electricity consumption trends in EAF steelmaking, but many model coefficients deviated significantly from expected values and appeared unphysical. The implementation of engineered features—the slag volume and total carbon input—in an MLR model resulted in coefficients that were closer to expectations, but did not improve prediction accuracy. Further improvement was obtained by applying the engineered features to a non-linear machine-learned model (based on XGBoost), yielding both physically reasonable trends and smaller prediction errors. Trends from Shapley dependence analysis (applied to the XGBoost model) are quantitatively consistent with theoretical trends. These include the energy needed to melt slag, and the endothermic effect of carbon additions. The fitted models demonstrate the potential to diagnose poor slag foaming by showing an increase in electricity consumption with increased oxygen use. This example demonstrates that practically important steelmaking process insights inferred via a linear regression approach can be improved by applying Shapley analysis to a machine-learned model based on engineered features.https://www.mdpi.com/2075-4701/15/1/13electric arc furnacemultiple linear regressionXGBoostShapleyfeature engineering |
| spellingShingle | Quantum Zhuo Mansour N. Al-Harbi Petrus C. Pistorius Feature Engineering to Embed Process Knowledge: Analyzing the Energy Efficiency of Electric Arc Furnace Steelmaking Metals electric arc furnace multiple linear regression XGBoost Shapley feature engineering |
| title | Feature Engineering to Embed Process Knowledge: Analyzing the Energy Efficiency of Electric Arc Furnace Steelmaking |
| title_full | Feature Engineering to Embed Process Knowledge: Analyzing the Energy Efficiency of Electric Arc Furnace Steelmaking |
| title_fullStr | Feature Engineering to Embed Process Knowledge: Analyzing the Energy Efficiency of Electric Arc Furnace Steelmaking |
| title_full_unstemmed | Feature Engineering to Embed Process Knowledge: Analyzing the Energy Efficiency of Electric Arc Furnace Steelmaking |
| title_short | Feature Engineering to Embed Process Knowledge: Analyzing the Energy Efficiency of Electric Arc Furnace Steelmaking |
| title_sort | feature engineering to embed process knowledge analyzing the energy efficiency of electric arc furnace steelmaking |
| topic | electric arc furnace multiple linear regression XGBoost Shapley feature engineering |
| url | https://www.mdpi.com/2075-4701/15/1/13 |
| work_keys_str_mv | AT quantumzhuo featureengineeringtoembedprocessknowledgeanalyzingtheenergyefficiencyofelectricarcfurnacesteelmaking AT mansournalharbi featureengineeringtoembedprocessknowledgeanalyzingtheenergyefficiencyofelectricarcfurnacesteelmaking AT petruscpistorius featureengineeringtoembedprocessknowledgeanalyzingtheenergyefficiencyofelectricarcfurnacesteelmaking |