Machine Learning-Based Prediction of a BOS Reactor Performance from Operating Parameters
Abstract
1. Introduction
2. The Multiphysics of the Basic Oxygen Steelmaking Process
3. Dataset
4. Method
5. Results and Discussion
5.1. Machine Learning Predictions of the Decarburization Rate dc/dt
5.2. Prediction of dc/dt With All the Features Included in the Dataset
5.3. Prediction of the dc/dt After Excluding Parameters
5.4. Prediction of the dc/dt for an Industrial Dataset
6. Conclusions
- A strong positive correlation between the rate of decarburization (dc/dt) and total oxygen flow.
- A negative correlation with lance height.
- Less obviously, the decarburization also showed a positive correlation with the temperature of the waste gas, CO2 content in the waste gas and O2 in the waste gas.
- The pilot plant dataset was used for training and test data to develop a neural network-based regression to predict the decarburization rate. The developed algorithm was used successfully to predict the decarburization rate in a BOS furnace in an actual manufacturing plant based on the two operating parameters of total oxygen flow and lance height only.
- The performance was satisfactory, with a coefficient of determination of 0.98, confirming that the trained model can adequately predict the variation in the dc/dt within BOS reactors.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Parameter | Feature |
---|---|
Charge Time (min) | Blowing time (in minutes from O2 ignition) |
dc/dt (kg C/min) | Instant decarburization rate |
Oxygen Yield (%) | Instant oxygen yield |
Total Oxygen flow rate (Nm3/min) | Instant oxygen flow rate |
Total C removal (kg) | Calculated total C removed from the start of O2 |
Total N2 flow rate (Nm3/min) | Instant N2 flow rate |
Total O2 (Nm3/min) | Total blown oxygen volume |
Total N2 (Nm3/min) | Total bottom blown nitrogen volume |
Total propane (Nm3/min) | Total blown propane volume |
Lance height (mm) | Instant lance height from the point of calculated metal bath level |
dC/dt (kg C/s) | Calculated from off-gas composition |
dO/dt (kg O/s) | Calculated from off-gas, oxygen for decarburization |
dOs/dt (kg O/s) | Calculated from off-gas composition, oxygen into slag |
Temp. waste gas (°C) | Off-gas temperature (measured after water cooling) |
CO waste gas (%) | Measured waste gas composition |
CO2 waste gas (%) | Measured waste gas composition |
O2 waste gas (%) | Measured waste gas composition |
6t pilot BOF. | C | Si | Mn | P | S | T (֯C) | |
---|---|---|---|---|---|---|---|
Hot metal (%) | 3.78~4.25 | 0.41~0.88 | 0.39~0.48 | 0.067~0.095 | 0.037~0.081 | 1272~1316 | |
Steel (%) | 0.01~0.41 | 0~0.15 | 0.05~0.27 | 0.008~0.042 | 0.018~0.035 | 1669~1772 | |
CaO | SiO2 | FeO | MnO | Al2O3 | MgO | P2O5 | |
Slag (%) | 26.2~52.3 | 6.7~19.2 | 8.3~30.5 | 2.6~4.1 | 0.7~1.6 | 4.6~17.7 | 0.73~1.62 |
330t BOF | C | Si | Mn | P | S | T (℃) | |
Hot metal (%) | 3.60~5.18 | 0.30~1.20 | 0.11~0.52 | 0.058~0.125 | 0.025~0.072 | 1341~1412 | |
Steel (%) | 0.021~0.55 | 0~0.10 | 0.03~0.21 | 0.001~0.057 | 0.011~0.032 | 1579~1738 | |
CaO | SiO2 | FeO | MnO | Al2O3 | MgO | P2O5 | |
Slag (%) | 36.07~55.35 | 9.13~19.58 | 12.15~31.34 | 2.04~5.62 | 0.64~4.97 | 3.61~10.73 | 0.94~2.38 |
6t pilot BOF | 330t BOF | ||||||
hot metal | 4410~5170 kg | 269.6~302.5 t | |||||
scrap | 500~750 kg | 46.0~85.5 t | |||||
lime | 250~350 kg | 5~25 t | |||||
dolomet | 0~30 kg | 0~11.5 t | |||||
iron ore | 0 kg | 0~6.5 t | |||||
total oxygen | 263~307 Nm3 | 12265~21641 Nm3 | |||||
Lance height | 110~180 mm | 2.0~2.6 m |
Evaluated Metrics | Pilot Dataset with All Features (1) | Pilot Dataset with All Features (2) | Pilot Dataset without dc/dt (2) | Pilot Dataset with Total O2 Flow and Lance Height Only (2) | Industrial Dataset with Total O2 Flow and Lance Height Only (2) |
---|---|---|---|---|---|
Mean Absolute Error | 0.12 | 0.029 | 0.030 | 0.034 | 0.25 |
Root Mean Square Error | 0.51 | 0.043 | 0.055 | 0.060 | 0.62 |
Relative Absolute Error | 0.42 | 0.005 | 0.006 | 0.008 | 0.04 |
Relative Square Error | 0.48 | 0.000046 | 0.00006 | 0.0001 | 0.009 |
Coefficient of Determination | 0.45 | 0.99 | 0.99 | 0.97 | 0.98 |
Mean | Median | Min | Max | Standard Deviation | |
---|---|---|---|---|---|
Actual Statistics | 4.43 | 0.081 | 0 | 19.105 | 6.42 |
Predicted Statistics | 4.45 | 0.092 | 0 | 19.09 | 6.43 |
dC/dt | dO/dt | Oxygen Yield | CO2 Waste Gas | CO Waste Gas | Total O2 Flow | dOs/dt | Temp. of Waste Gas | Total O2 | O2 Waste Gas |
---|---|---|---|---|---|---|---|---|---|
9.06 | 0.16 | 0.07 | 0.04 | 0.004 | 0.002 | 0.0006 | 0.0002 | 0.00014 | 0.00013 |
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Rahnama, A.; Li, Z.; Sridhar, S. Machine Learning-Based Prediction of a BOS Reactor Performance from Operating Parameters. Processes 2020, 8, 371. https://doi.org/10.3390/pr8030371
Rahnama A, Li Z, Sridhar S. Machine Learning-Based Prediction of a BOS Reactor Performance from Operating Parameters. Processes. 2020; 8(3):371. https://doi.org/10.3390/pr8030371
Chicago/Turabian StyleRahnama, Alireza, Zushu Li, and Seetharaman Sridhar. 2020. "Machine Learning-Based Prediction of a BOS Reactor Performance from Operating Parameters" Processes 8, no. 3: 371. https://doi.org/10.3390/pr8030371
APA StyleRahnama, A., Li, Z., & Sridhar, S. (2020). Machine Learning-Based Prediction of a BOS Reactor Performance from Operating Parameters. Processes, 8(3), 371. https://doi.org/10.3390/pr8030371