Volatile organic compounds (VOCs) emitted from industrial processes have high stability, low activity, and toxicity which cause continuous harm to human health and the atmospheric environment. Catalytic combustion has the advantages of low energy consumption and low cost and is expected to be one of the most effective methods to remove VOCs. At present, the selection of low cost, high activity, and durability catalysts are still a difficult problem. Industrial emissions of VOCs contain a certain amount of aromatic hydrocarbons; these substances are highly toxic substances, and, once inhaled by the human body, will cause serious harm to health. In this paper, the principle, advantages, and disadvantages of VOCs processing technology are analyzed in detail, and the catalytic combustion of aromatic hydrocarbons in VOCs is reviewed, including catalyst, reaction conditions, catalyst selection, inactivation reasons, and structure use. In addition, the deactivation effects of chlorine and sulfur on catalysts during the catalytic combustion of VOCs are discussed in detail. Finally, on the basis of literature research, the prospect of catalytic combustion of VOCs is presented, which provides influential information for further research on VOCs processing technology.
Oil spill accidents have gradually increased due to the continuous development of marine transportation and petroleum processing industries. Monitoring and managing marine oil spills present important economic, social, and practical implications in preventing offshore oil pollution and maintaining ecological balance. Unmanned aerial vehicle (UAV) has become a suitable carrier for low-altitude oil spill detection because of their fast deployment and low cost. Thermal infrared remote sensing images are used as the research object in this study. A method around histogram of gradient (HOG) features combined with a support vector machine (SVM) is proposed for identifying oil spills at sea to improve the accuracy of offshore low-altitude oil spill recognition and realize all-weather monitoring of offshore oil spills in offshore waters. Steps for extracting HOG features and basic principles of the SVM classification are first investigated. Image preprocessing is then performed on collected thermal infrared image data to produce samples. HOG features of samples are extracted, and the radial basis function is selected as the kernel function for training the SVM classifier. HOG features of the infrared image to be tested are calculated and then sent to the classifier for identifying the oil spills. In addition, the proposed method is compared with the back propagation(BP) neural network method and local binary pattern (LBP) combined with the SVM classification method for analysis. The results show that the oil film recognition method based on the HOG feature and SVM has a recognition accuracy of 91.3% in the environment of small infrared oil film samples, which is significantly better than the BP and LBP-SVM recognition methods, and obtains a shorter training time. The method proposed in this study has obvious advantages in terms of small sample size and processing efficiency, can meet the requirements of all-weather inspection of oil film pollutants by UAV in offshore port areas, and has great application potential in the field of maritime supervision informatization in the future.
Accurate reconnaissance of Marine oil spill is very important for emergency management of Marine oil spill accidents. Unmanned aerial vehicles (UAV) is a suitable carrier for offshore oil spill reconnaissance because of its fast deployment speed and low cost. Aiming at the identification accuracy of small oil spill accident in offshore port area and the problem of day and night reconnaissance, this study takes thermal infrared remote sensing images of oil leakage captured by UAV as the research object and proposes an oil spill detection method based on a Gray Level Co-occurrence Matrix (GLCM) and Support Vector Machine (SVM) method. Firstly, the extraction steps of image GLCM feature and the basic principle of SVM classification are studied. Then, the thermal infrared image data collected by UAV is preprocessed, including image filtering, clipping and rotation, and the sample database is generated. Subsequently, GLCM features of the samples were extracted, and the energy and correlation in GLCM were selected as classification features and sent to the SVM classifier to complete the oil spill detection of real-time thermal infrared images. The experimental results show that, compared with Classification and Regression Tree algorithm (CART) and Random Forests of Decision Trees (RF) algorithm, the detection accuracy of the method proposed in this paper reaches 95%, which were 10 and 2 percentage points higher than them respectively. The proposed method in this paper has fast recognition speed and high accuracy, and can provide all-weather recognition of oil spills for the detection of small oil spills in the offshore port area.
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