基于深度学习的气溶颗粒分类,用于检测船舶排放
Guanzhong Wang1, Heinrich Ruser1, Julian Schade2
1Institute for Applied Physics and Measurement Technology, University of the Bundeswehr Munich, 85577 Neubiberg, Germany.
The Science of the total environment
|July 11, 2025
概括
一个新的系统使用单粒子质谱和深度学习实时检测有害船舶排放. 这项技术可以准确识别使用重燃料油的船舶,改善排放控制区域的空气质量监测.
科学领域:
- 环境科学 环境科学
- 分析化学 分析化学
- 人工智能的人工智能
背景情况:
- 国际海事组织 (IMO) 设立了硫排放控制区 (SECA),以减轻航运对空气污染的影响.
- 在SECA中的船舶必须使用低硫燃料或废气清洁系统 (清洗器).
- 传统的监控方法缺乏实时数据,检测范围和来源归因能力.
研究的目的:
- 开发一个先进的监测系统,用于实时识别船舶排放.
- 克服传统方法在检测和归因空气污染源方面的局限性.
- 在SECA中专门识别燃烧重燃料油 (HFO) 的船舶.
主要方法:
- 单粒子质谱法 (SPMS) 与深度学习 (卷积神经网络 - CNN) 的整合.
- SPMS用于对单个气溶颗粒的详细化学成分分析.
- CNN用于气溶颗粒的自动分类 (在13个类别中准确率为92%).
- 美国有线电视新闻网 (CNN) 预测与风力数据和自动识别系统 (AIS) 船舶轨迹的相关性.
主要成果:
- 该系统准确地实时分类来自多个来源的气溶颗粒.
- 成功识别出富含,和铁的独特颗粒,表明HFO燃烧.
- 在一周的监测期间,在1.3公里范围内检测到21艘使用HFO的船只.
结论:
- 拟议的SPMS-深度学习系统为实时船舶排放监测提供了一个强大的解决方案.
- 能够快速可靠地检测重型燃料油燃烧情况,这对于执行排放法规至关重要.
- 提高了海上环境污染源归因和空气质量管理的能力.
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