Cao Kai, Zhang Zhenduo, Li Ying, Zheng Wenbo, Xie Ming
Environmental Information Institute, Navigation College, Dalian Maritime University, Dalian, 116026, China.
Environmental Information Institute, Navigation College, Dalian Maritime University, Dalian, 116026, China.
Environ Pollut. 2021 Jan 12;273:116501. doi: 10.1016/j.envpol.2021.116501.
Pollutant emissions in ship exhaust have been continually increasing. SO is one of the main gaseous pollutants in ship exhaust, resulting from the use of marine heavy fuel oil with high sulfur content. Therefore, it is necessary to detect the fuel sulfur content (FSC) to regulate ship exhaust emissions. Optical remote sensing methods, such as differential optical absorption spectroscopy (DOAS), light detection and ranging (LIDAR), and ultraviolet (UV) camera techniques, are regarded as simple and effective remote monitoring methods. One common technique is to estimate the SO concentration in a ship plume using its local optical characteristics and use this to calculate FSC. One drawback of this technique is that there are always errors in the estimations of the SO concentration despite the continuous improvement of such estimations. Another drawback is that calculating FSC from SO often requires additional measurement methods. Here, a sulfur content prediction model based on a deep convolutional neural network using a UV camera is introduced. First, a ship benchmark test is performed. In the test, a large number of ultraviolet characteristic images of the ship exhaust plume are taken with a UV camera and the corresponding FSC data are collected. Next, a visual geometry group (VGG)-16 convolutional neural network model based on transfer learning is built. The model extracts all the features of the exhaust plume image as input data to the deep neural network and outputs the predicted FSC as a classification label. The results show that the model can predict the FSC value with high accuracy corresponding to the exhaust plume image. This study proves that it is theoretically feasible to apply a convolutional neural network to learn features of ultraviolet ship exhaust plume images for FSC predictions, which can provide guidance for the remote regulation of ship exhaust emissions.
船舶尾气中的污染物排放一直在持续增加。二氧化硫是船舶尾气中的主要气态污染物之一，它是由于使用高硫含量的船用重质燃油而产生的。因此，有必要检测燃油硫含量（FSC）以规范船舶尾气排放。光学遥感方法，如差分光学吸收光谱法（DOAS）、光探测与测距（LIDAR）以及紫外（UV）相机技术，被视为简单有效的远程监测方法。一种常见的技术是利用船舶羽流的局部光学特性来估算其中的二氧化硫浓度，并以此计算燃油硫含量。该技术的一个缺点是，尽管二氧化硫浓度估算在不断改进，但仍总是存在误差。另一个缺点是，从二氧化硫计算燃油硫含量通常需要额外的测量方法。在此，介绍一种基于使用紫外相机的深度卷积神经网络的硫含量预测模型。首先，进行船舶基准测试。在测试中，用紫外相机拍摄大量船舶尾气羽流的紫外特征图像，并收集相应的燃油硫含量数据。接下来，基于迁移学习构建一个视觉几何组（VGG）-16卷积神经网络模型。该模型提取尾气羽流图像的所有特征作为深度神经网络的输入数据，并输出预测的燃油硫含量作为分类标签。结果表明，该模型能够高精度地预测与尾气羽流图像对应的燃油硫含量值。本研究证明，应用卷积神经网络学习紫外船舶尾气羽流图像特征以进行燃油硫含量预测在理论上是可行的，这可为船舶尾气排放的远程监管提供指导。
