Search

This work developed a new method for measuring the volume expansion of a CO 2 + n -dodecane mixture, using a fused silica capillary cell (FSCC), combined with a heating-cooling stage and a confocal Raman spectrometer. The cell was constructed on the micron scale to reduce the temperature gradient and reagent consumption. The results confirmed the feasibility of this method that using Raman spectra to verify phase equilibrium, a digital camera to record images of the CO 2 + n -dodecane mixture and a micrometer to measure the volume, a section of water (water seal) between n -dodecane and gas phase to prevent n -dodecane from evaporating. It was shown that the volume expansion factor of the mixture increased with pressure going from 1.0 to 10.0 MPa, but decreased as the temperature was raised from 30 to 80 °C. In addition, a positive quadratic relationship ( R 2 > 0.99) was obtained between Raman peak intensity ratio and volume expansion factor, such that more volume expansion factors could be obtained according to the equations with measured Raman peak intensity ratio data. This experiment widens the ranges of temperature and pressure in the measurements of the volume expansion, and provides more volume expansion data for CO 2 -enhanced oil recovery. [ABSTRACT FROM AUTHOR]

Abstract: Decomposition of 1,1,1-trichloroethane (TCA) in hot compressed water, with or without an oxidizer, was studied using an optically transparent anti-corrosive fused silica capillary reactor (FSCR), and Raman spectroscopy. The phase behavior of TCA in water during hydrolysis/oxidation was observed continuously under a microscope and recorded with a digital-camera/recording system, A new phase behavior of TCA was found in the presence of excess H2O2; TCA was first gasified during heating, followed by liquefaction under increasing internal pressure as a result of the formation of O2 from the decomposition of H2O2, and subsequently gasified again during further heating up to 400°C. The gaseous products of TCA oxidation in hot compressed water were monitored quantitatively and qualitatively using Raman spectroscopy. The effects of the operating parameters, namely the stoichiometric amount of oxidizer, temperature, and reaction time, were investigated. In our experiments, it was found that 100% CO2 yield was achieved with a 175% stoichiometric amount of H2O2 at 420°C after 360s. Temperature plays a key role in the decomposition of TCA and the formation of CO2 in the supercritical water oxidation (SCWO) process. Based on our results, a reaction mechanism for TCA decomposition in hot compressed water was proposed. The global reaction kinetics showed that the formation of CO2 in the SCWO of TCA was a first-order reaction. [Copyright &y& Elsevier]

At present, realizing high-quality automatic welding through online monitoring is a research focus in engineering applications. In this paper, a CNN–LSTM algorithm is proposed, which combines the advantages of convolutional neural networks (CNNs) and long short-term memory networks (LSTMs). The CNN–LSTM algorithm establishes a shallow CNN to extract the primary features of the molten pool image. Then the feature tensor extracted by the CNN is transformed into the feature matrix. Finally, the rows of the feature matrix are fed into the LSTM network for feature fusion. This process realizes the implicit mapping from molten pool images to welding defects. The test results on the self-made molten pool image dataset show that CNN contributes to the overall feasibility of the CNN–LSTM algorithm and LSTM network is the most superior in the feature hybrid stage. The algorithm converges at 300 epochs and the accuracy of defects detection in CO2 welding molten pool is 94%. The processing time of a single image is 0.067 ms, which fully meets the real-time monitoring requirement based on molten pool image. The experimental results on the MNIST and FashionMNIST datasets show that the algorithm is universal and can be used for similar image recognition and classification tasks. [ABSTRACT FROM AUTHOR]