基于优化高斯气泡模型和粒子群优化算法的船舶排放源强度的反向计算
Hao Wu1, Xueyao Li2, Chao Wang3
1School of Network &Communication Engineering, Jinling Institute of Technology, China; School of Transportation, Southeast University, China.
Marine pollution bulletin
|October 15, 2024
概括
这项研究开发了一个反向模型来监测船舶排放,发现粒子群优化 (PSO) 算法对估计海洋废气源强度和识别船舶中高硫含量的燃料非常准确.
科学领域:
- 环境科学 环境科学
- 大气化学 大气化学
- 海洋工程 海洋工程
背景情况:
- 海运推动经济增长,但也造成了严重的空气污染.
- 对船舶排放的精确监测对于环境管理至关重要.
- 传统模型需要对特定船舶排放特征进行增强.
研究的目的:
- 为船舶排放源强度开发一个反向计算框架.
- 改进海洋废气排放监测和遥感技术.
- 评估燃料硫含量在海上运输中的合规性.
主要方法:
- 增强了传统的高斯扩散模型,并增加了船舶特定的因素.
- 使用模式搜索和粒子群优化 (PSO) 开发了一个反向模型.
- 通过模拟实验和来自86艘船的真实世界数据验证了模型.
主要成果:
- 该PSO算法在估计源强度方面表现出卓越的准确性和效率.
- 对于PSO算法来说,0.1秒的代步骤大小被认为是最佳的.
- 实际应用发现86艘船舶中有76艘超过了0.1%的燃料硫含量值 (88.37%的准确率).
结论:
- 开发的反向模型有效地估计了海洋废气源的强度.
- PSO算法是排放监测的高度准确和高效的工具.
- 调查结果支持加强海运排放的环境管理和监管合规性.
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