Investigation Into Dynamic Monitoring and Adjustment of Manufacturing Carbon Emissions Integrating IIoT and 5G
Year: 2025
Type: article
Abstract: 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%.
Source: Internet Technology Letters
DOI: 10.1002/itl2.70081
Authors: Shu Ying, Han Hang, Peng Shijie
Institutions: Nanjing Forestry University, Nanjing University
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
Awards: G705607028, G8852008201
OpenAlex ID: W4412083705