Ship fuel sulfur content prediction based on convolutional neural network and ultraviolet camera images.

Abstract

Pollutant emissions in ship exhaust have been continually increasing. SO2 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 SO2 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 SO2 concentration despite the continuous improvement of such estimations. Another drawback is that calculating FSC from SO2 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.