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A Quantitative Structure -Property Relationship (QSPR) model was developed to predict the flash points of organic compounds. The widely used group contribution method was employed, and a new collection of 57 functional groups were selected as the molecular descriptors. The new chemometrics method of Support Vector Machine (SVM) was employed for fitting the possible quantitative relationship that existed between these functional groups and flash points. A total of 1282 organic compounds of various chemical families were used and randomly divided into a training set (1026) and an external prediction set (256).The optimum parameters of the SVM were obtained by employing the leave-one-out cross-validation method. Simulated with the final optimum SVM, the results show that most of the predicted flash point values are in good agreement with the experimental data, with the average absolute error being 6.894 K, and the root mean square error being 11.367 for the whole dataset, which are lower than those obtained by previous works. Moreover, by employing the convenient group contribution method as well as the large modeling dataset, the presented model is also expected to be simple to apply and with a wide applicability range.

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Because of the capacity of capturing both the spatial and angular information of the light rays simultaneously, light field images (LFIs) contain richer scene information compared with conventional images, but at the cost of huge volume. This paper proposes a novel LFI sparse compression framework driven by convolutional neural network (CNN). The epipolar plane image (EPI) super-resolution is for compensating the information loss caused by sparse sampling and the decoder-side sub-aperture images (SAIs) quality enhancement is for compensating the information loss caused by lossy compression. Specifically, we choose those SAIs both in odd rows and odd columns as our key SAIs and compress them using standard video encoder. For those non-key SAIs, we predict them using decompressed key SAIs by taking advantage of the special structure of EPI. The low-resolution EPIs generated from the sparse SAIs are super-resolved by a CNN and the outputs, high-resolution EPIs, are used to rebuild the dense SAIs. Moreover, in order to improve the quality of the predicted SAI, we add decoder-side quality enhancement before prediction. We propose a multi-scale dense residual network (MSDRN) to implement both EPI super-resolution and quality enhancement. Transfer learning strategies are used to improve the training performance of quality enhancement. The experimental results show the superior performance of the proposed framework over existing methods in terms of rate-distortion performance.INDEX TERMS Light field image, sparse compression, EPI super-resolution, quality enhancement, convolutional neural network.

The emission of the traditional energy chemical industry accounts for 20% of the total man-made VOC emission in China, of which coal chemical and petrochemical plants are one of the most important VOC emission sources. VOC emission sources mainly include the leakage of oil refinery units and equipment, pipes and valves, respiration and leakage of various types of storage tanks, effusion of oils during loading and unloading, effusion of sewage treatment systems, all kinds of process tail gas, etc. In this paper, the current management status of VOC emission in China's coal chemical industry and petrochemical industry are analyzed, which divides VOC management into intentional and fugitive emission. The Leak Detection and Repair (LDAR) management method and technology for equipment, pipes and valves implemented in the United States are studied to propose self-inspection management methods and measures for VOC emissions in the energy chemical industry, providing strategies and recommendations for energy conservation, emission reduction and cleaner production in the traditional energy chemical industry.

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