Recently, corrosion perforation has been frequently seen in surface pipelines in the oil and gas industry, resulting in operational and environmental challenges. Due to the complex characteristics and mechanisms of such corrosion, a new and pragmatic method has been designed to identify and evaluate the corrosion phenomenon via a hanging ring installed in a surface pipeline. In addition to respectively analyzing the ions of water samples with chemical titration, ion chromatography, and mass spectrometry, the micro-surface morphology of the corroded hanging rings was observed and evaluated by using a scanning electron microscope (SEM) equipped with energy dispersive spectroscopy (EDS), and the surface composition of the corroded hanging rings was analyzed by using X-ray diffraction (XRD). The water ions of each selected position were found to mainly contain Ca2+, Ba2+, SO42−, and HCO3−, while the barium scale and calcium carbonate scale were formed in situ. In addition to the common corrosion induced by CO2, corrosion induced by both CO2 and H2S leads to extremely serious corrosion and scaling in surface pipelines. In addition, the injection dose of corrosion inhibitor was also evaluated.
The three-dimensional morphology has sufficient interface contact and can be in favor of the electronic transport process. In this work, the demand for highperformance electrodes such as energy storage devices has been designed. Polypyrrole and tungsten oxide composite materials (PPy-WO 3) have been synthesized by cyclic voltammetry (CV) technology at −0.6 to 0.9 V versus saturated calomel electrode (SCE) for 20 cycles. The PPy-WO 3 20 mV/s , PPy-WO 3 60 mV/s , and PPy-WO 3 120 mV/s electrodes have been prepared by CV technology at sweep rates of 20, 60, and 120 mV/s. The influences of scan rate on morphologies and charge storage properties of the composites are discussed. Among them, a three-dimensional flake structure for PPy-WO 3 20 mV/s with a size of up to several micrometers was synthesized. PPy-WO 3 20 mV/s composites
Limited by the computational efficiency and accuracy, generating complex 3D scenes remains a challenging problem for existing generation networks. In this work, we propose DepthGAN, a novel method of generating depth maps with only semantic layouts as input. First, we introduce a well-designed cascade of transformer blocks as our generator to capture the structural correlations in depth maps, which makes a balance between global feature aggregation and local attention. Meanwhile, we propose a cross-attention fusion module to guide edge preservation efficiently in depth generation, which exploits additional appearance supervision information. Finally, we conduct extensive experiments on the perspective views of the Structured3d panorama dataset and demonstrate that our DepthGAN achieves superior performance both on quantitative results and visual effects in the depth generation task. Furthermore, 3D indoor scenes can be reconstructed by our generated depth maps with reasonable structure and spatial coherency.
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