Study on Energy Efficiency and Maintenance Optimization of Run-Out Table in Hot Rolling Mills Using Long Short-Term Memory-Autoencoders

The steel industry, as a large-scale equipment-intensive sector, emphasizes the importance of maintaining and managing equipment without failure. In line with the recent Fourth Industrial Revolution, there is a growing shift from preventive to predictive maintenance (PdM) strategies for cost-effective equipment management. This study aims to develop a PdM model for the Run-Out Table (ROT) equipment in hot rolling mills of steel plants, utilizing artificial intelligence (AI) technology, and to propose methods for contributing to energy efficiency through this model. Considering the operational data characteristics of the ROT equipment, an autoencoder (AE), capable of detecting anomalies using only normal data, was selected as the base model. Furthermore, Long Short-Term Memory (LSTM) networks were chosen to address the time-series nature of the data. By integrating the technical advantages of these two algorithms, a predictive maintenance model based on the LSTM-AE algorithm, named the Run-Out Table Predictive Maintenance Model (ROT-PMM), was developed. Additionally, the concept of an anomaly ratio was applied to identify equipment anomalies for each coil production. The performance evaluation of the ROT-PMM demonstrated an F1-score of 91%. This study differentiates itself by developing an optimized model that considers the specific environment and large-scale equipment operation of steel plants, and by enhancing its applicability through performance verification using actual failure data. Furthermore, it emphasizes the importance of PdM strategies in contributing to energy efficiency. It is expected that this research will contribute to increased energy efficiency and productivity in industrial settings, including the steel industry.