Machine Learning and IoT Applications in Optimizing MIDREX Shaft Furnace Operations

Abstract

The combination of Machine Learning (ML) and Internet of Things (IoT) technologies is transforming industrial processes by providing real-time optimization and delivering predictive insights. This paper delves into the application of ML and IoT in optimizing MIDREX shaft furnace operations, a pivotal process in direct reduction ironmaking. By leveraging IoT-enabled sensors, real-time data on temperature, pressure, and gas flow is collected and processed. Machine learning models are then employed for predictive maintenance, energy optimization, and dynamic process control. The proposed framework not only enhances operational efficiency but also reduces downtime and environmental impact by minimizing energy consumption and carbon emissions. Experimental validations demonstrate significant improvements in process stability and energy utilization. Furthermore, a comparative analysis with conventional methods underscores the economic and operational advantages of integrating ML and IoT. This study provides actionable insights and a robust framework for advancing smart industrial systems, paving the way for sustainable ironmaking practices.