Evaluates an IoT framework enhancing operational efficiency in steel production, indicating significant improvements in energy use and maintenance practices.
The steel industry remains a cornerstone of global infrastructure, yet it faces significant challenges regarding energy intensity and operational volatility. Traditional monitoring systems, largely reliant on legacy Programmable Logic Controllers (PLCs) and manual intervention, often fail to address real-time inefficiencies and unplanned downtimes. This research proposes an integrated Internet of Things (IoT) framework designed specifically for Blast Furnace and Direct Reduction of Iron (DRI) operations. By leveraging a multi-layered architecture—comprising industrial sensing, edge computing (ESP32/Raspberry Pi), and cloud-based Artificial Intelligence (AI)—the system enables real-time telemetry of critical parameters including thermal profiles, gas concentrations, and pressure gradients. Experimental evaluations indicate significant improvements in Specific Energy Consumption (SEC) through optimized Waste Heat Recovery (WHR) and AI-driven predictive maintenance. This paper provides a scalable, cost-effective Industry 4.0 solution to modernize small-to-medium scale steel plants, ensuring sustainable and safe industrial practices.
No takes yet. Share an insight, caveat, or question.
Fernandes et al. (2026) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: