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.

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.

Coastal governments have been preventing and controlling pollution in the marine environment by enhancing the construction of hardware and software facilities. The dispatch of offshore oil spill cleaning materials must be upgraded and optimized to cope with repeated offshore oil leak incidents while simultaneously improving cleaning efficiency and the ability to resist oil spill hazards. Accordingly, we set up a multiobjective optimization model with time window constraints to solve the scheduling optimization problem of offshore oil spill accidents with multiple locations and oil types. This model integrates the minimal sum of fixed costs, fuel consumption costs, maximum load violation costs, and time window penalty costs to solve the scheduling optimization problem of an offshore oil spill accident. An improved genetic algorithm is designed to solve the proposed mathematical model effectively and to make a scientific decontaminated decision-scheduling scheme. The practicality of the model and algorithm is validated by using a specific instance, demonstrating that the suggested method can effectively solve the schedule optimization problem for cleaning materials.