Multivariate Time Series for Data-Driven Endpoint Prediction in the Basic Oxygen Furnace

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.