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A Dataset of CFD Simulated Industrial Furnace Images for Conditional Automatic Generation with GANs...

by Ricardo Calix, Orlando Ugarte, Hong Wang, Tyamo Okosun
Publication Type
Conference Paper
Book Title
TMS 2024 153rd Annual Meeting & Exhibition Supplemental Proceedings
Publication Date
Page Numbers
775 to 783
Publisher Location
Cham, Switzerland
Conference Name
TMS 2024 Annual Meeting & Exhibition
Conference Location
Orlando, Florida, United States of America
Conference Sponsor
Conference Date

The steel industry is constantly looking for ways to automate processes and improve efficiency. A standard practice in industry is to simulate how complex systems will operate before they are actually used. Some complex systems, including steel industry processes such as blast furnaces, require complex physics-based simulations utilizing computational fluid dynamics (CFD). These CFD physics-based simulations are very accurate but can take significant time and computational resources to process, resulting in challenges for the implementation of the models in real-world operational environments. In recent years, deep learning (DL) has been considered as a substitute for these CFD models. DL models can be trained on validated CFD simulation data and then used for industrial process inference. Previous DL-based solutions have made great contributions for industrial automation but are currently missing the additional visualization component that CFD simulations also provide. In this paper, we propose a dataset for simple DL generative approaches that can help to address this issue. The dataset and methodology under development to approach this prediction are discussed in this work.