Investigation Into Dynamic Monitoring and Adjustment of Manufacturing Carbon Emissions Integrating IIoT and 5G

Abstract

The manufacturing industry confronts challenges including inefficient data handling, inadequate real-time monitoring, and vague adjustment mechanisms regarding carbon emission management. Integrating the Industrial Internet of Things (IIoT) and Fifth-generation mobile communication technology (5G) technologies is urgently needed. By constructing a carbon emission sensing network with IIoT-5G converged architecture, a low-latency time-sensitive network (TSN) communication protocol for heterogeneous industrial devices is designed to meet the needs of efficient communication among multiple devices. Combining digital twins and edge computing technology, this study developed a carbon footprint dynamic visualization engine to display carbon emission data in real-time and support carbon emission propagation path modeling based on graph neural network (GNN) to predict and analyze the dynamic changes of carbon emissions accurately. Applying IIoT and 5G technologies has improved monitoring accuracy in a pilot manufacturing site and shown significant energy conservation and emission reduction results. After implementing this technology, the carbon emission intensity decreased from 75.4 to 59.8, and the energy utilization efficiency increased from 32.7% to 90.1%. The waste gas treatment efficiency increased from 16.4% to 34.2%.