Abstract
There is significant interest in accurately modeling the operational variables of the steel-making process in LD converters. Despite this, the task is challenging due to the complex interactions between process variables, which are not entirely comprehended. Often, decisions in the industry are grounded in experience. This study aims to introduce a robust model that can effectively guide engineers and technicians by forecasting the future behavior of steelmaking variables in the BOF furnace. We employed multivariate time series analysis to reach this goal, utilizing tools like Vector Autoregression models, ElasticNet, K-Nearest-Neighbors, Multiple Linear Regression, and Long Short-Term Memory Neural networks. These models were tested on data from three distinct steel production campaigns. A successful model was identified, predicting 35 out of the 42 chosen variables, demonstrating the potential to correlate a majority of the selected parameters.
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