Inverse calculation of vessel emission source intensity based on optimized Gaussian puff model and particle swarm
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
Summary
This study developed an inverse model to monitor vessel emissions, finding the Particle Swarm Optimization (PSO) algorithm highly accurate for estimating marine exhaust source strengths and identifying high sulfur fuel content in ships.
Area of Science:
- Environmental Science
- Atmospheric Chemistry
- Marine Engineering
Background:
- Maritime transport drives economic growth but causes significant air pollution.
- Accurate monitoring of vessel emissions is crucial for environmental management.
- Traditional models require enhancement for specific ship emission characteristics.
Purpose of the Study:
- To develop an inverse calculation framework for vessel emission source intensity.
- To improve marine exhaust emission monitoring and remote sensing technologies.
- To assess fuel sulfur content compliance in maritime transport.
Main Methods:
- Enhanced traditional Gaussian diffusion model with ship-specific factors.
- Developed an inverse model using pattern search and particle swarm optimization (PSO).
- Validated the model with simulation experiments and real-world data from 86 ships.
Main Results:
- The PSO algorithm demonstrated superior accuracy and efficiency for estimating source strengths.
- An iteration step size of 0.1 s was found optimal for the PSO algorithm.
- Practical application identified 76 out of 86 ships exceeding the 0.1% fuel sulfur content threshold (88.37% accuracy).
Conclusions:
- The developed inverse model effectively estimates marine exhaust source strengths.
- PSO algorithm is a highly accurate and efficient tool for emission monitoring.
- Findings support enhanced environmental management of maritime transport emissions and regulatory compliance.
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