TY - GEN
T1 - Multivariate Time Series for Data-Driven Endpoint Prediction in the Basic Oxygen Furnace
AU - Sala, Davi Alberto
AU - Jalalvand, Azarakhsh
AU - Van Yperen-De Deyne, Andy
AU - Mannens, Erik
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - Industrial processes are heavily instrumented by employing a large number of sensors, generating huge amounts of data. One goal of the Industry 4.0 era is to apply data-driven approaches to optimize such processes. At the basic oxygen furnace (BOF), molten iron is transformed into steel by lowering its carbon content and achieving a certain chemical endpoint. In this work, we propose a data-driven approach to predict the endpoint temperature and chemical concentration of phosphorus, manganese, sulfur and carbon at the basic oxygen furnace. The prediction is based on two distinct datasets. First, a collection of static features is used which represent a more classic data-driven solution. The second approach includes time-series data that provide a better estimate of the final endpoint and enable further tuning of the process parameters, if necessary. For both approaches, model-based feature selection is used to filter the most relevant information. Results obtained by both models are compared in order to estimate the added value of including the time series data analysis on the performance of the BOF process. Results show that a simple feature extraction approach can enhance the prediction for phosphorus, manganese and temperature.
AB - Industrial processes are heavily instrumented by employing a large number of sensors, generating huge amounts of data. One goal of the Industry 4.0 era is to apply data-driven approaches to optimize such processes. At the basic oxygen furnace (BOF), molten iron is transformed into steel by lowering its carbon content and achieving a certain chemical endpoint. In this work, we propose a data-driven approach to predict the endpoint temperature and chemical concentration of phosphorus, manganese, sulfur and carbon at the basic oxygen furnace. The prediction is based on two distinct datasets. First, a collection of static features is used which represent a more classic data-driven solution. The second approach includes time-series data that provide a better estimate of the final endpoint and enable further tuning of the process parameters, if necessary. For both approaches, model-based feature selection is used to filter the most relevant information. Results obtained by both models are compared in order to estimate the added value of including the time series data analysis on the performance of the BOF process. Results show that a simple feature extraction approach can enhance the prediction for phosphorus, manganese and temperature.
KW - Basic oxygen furnace
KW - Prediction
KW - Sensors
KW - Steel industry
KW - Time-series data analysis
UR - http://www.scopus.com/inward/record.url?scp=85062219324&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85062219324&partnerID=8YFLogxK
U2 - 10.1109/ICMLA.2018.00231
DO - 10.1109/ICMLA.2018.00231
M3 - Conference contribution
AN - SCOPUS:85062219324
T3 - Proceedings - 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018
SP - 1419
EP - 1426
BT - Proceedings - 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018
A2 - Wani, M. Arif
A2 - Kantardzic, Mehmed
A2 - Sayed-Mouchaweh, Moamar
A2 - Gama, Joao
A2 - Lughofer, Edwin
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018
Y2 - 17 December 2018 through 20 December 2018
ER -