Machine Learning-Based Regression Models for Ironmaking Blast Furnace Automation

Author:

Calix Ricardo A.1,Ugarte Orlando2,Okosun Tyamo2,Wang Hong3

Affiliation:

1. Department of Computer Information Technology, Purdue University Northwest, Hammond, IN 46323, USA

2. Center for Innovation through Visualization and Simulation (CIVS) and Steel Manufacturing Simulation and Visualization Consortium (SMSVC), Purdue University Northwest, Hammond, IN 46323, USA

3. Buildings and Transportation Science Division, Oak Ridge National Laboratory, Knoxville, TN 37932, USA

Abstract

Computational fluid dynamics (CFD)-based simulation has been the traditional way to model complex industrial systems and processes. One very large and complex industrial system that has benefited from CFD-based simulations is the steel blast furnace system. The problem with the CFD-based simulation approach is that it tends to be very slow for generating data. The CFD-only approach may not be fast enough for use in real-time decisionmaking. To address this issue, in this work, the authors propose the use of machine learning techniques to train and test models based on data generated via CFD simulation. Regression models based on neural networks are compared with tree-boosting models. In particular, several areas (tuyere, raceway, and shaft) of the blast furnace are modeled using these approaches. The results of the model training and testing are presented and discussed. The obtained R2 metrics are, in general, very high. The results appear promising and may help to improve the efficiency of operator and process engineer decisionmaking when running a blast furnace.

Funder

DOE

Publisher

MDPI AG

Subject

General Medicine

Reference33 articles.

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