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Carbon footprint tracing and pattern recognition framework based on visual analytics
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AI
DOI：10.1016/j.spc.2024.07.019.png)
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Abstract
En 中文
With growing concerns about global warming, industrial carbon footprints have garnered increased attention due to the energy-intensive and uninterrupted operation of industrial equipment. Fine-grained modeling and visual analytics of industrial carbon footprints can reveal the mechanisms behind the formation and evolution of carbon chains. However, the mechanisms underlying industrial carbon emissions remain unclear, leading to a lack of accuracy and specificity in current carbon quantification models. To address these gaps, we developed a comprehensive quantitative model that considers specific pathways involved in industrial processes, providing more accurate estimations of carbon emissions. We also designed an innovative visual analytical framework that uncovers implicit patterns and spatiotemporal distributions of industrial carbon footprints. By comparing our approach with state-of-the-art studies, we validated the superiority of our method in terms of its intuitiveness and interactivity. Empirical studies revealed potential emission patterns and spatiotemporal dynamics that traditional studies could not identify. We identified four consistent patterns in industrial carbon emissions: normal, high-emission, low-emission, and dedicated patterns. Our findings also led to optimization suggestions for different emission patterns, highlighting the system's capability in extracting valuable insights from workshop carbon emission data. Our research showcases a unified visual analytical approach that supports exploratory analysis, and we believe it will uncover implicit knowledge within industrial carbon data, providing valuable insights for optimization.
Keywords:
Energy consumption
Industrial carbon footprint
Visual analytics
Industry 4.0
Sustainable development
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