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This study addresses the challenges of time‐delay and low accuracy in online gas‐phase composition monitoring during olefin copolymerization processes. Three flowmeters based on different mechanisms are installed in series to measure the real‐time exhaust gas flow rate from the reactor. For the same gas flow, the three flowmeters display different readings, which vary with the properties and composition of the gas mixture. Consequently, the composition of the mixed gas can be determined by analyzing the reading of the three flowmeters. Fitting equations and three machine learning models, namely decision trees, random forests, and extreme gradient boosting, are employed to calculate the gas composition. The results from cold‐model experimental data demonstrate that the XGBoost model outperforms others in terms of accuracy and generalization capabilities. For the concentration of ethylene, propylene, and hydrogen, the determination coefficients (R2) were 0.9852, 0.9882, and 0.9518, respectively, with corresponding normalized root mean square error (NRMSE) values of 0.0352, 0.0312, and 0.0706. The effectiveness of the online monitoring device is further validated through gas phase copolymerization experiments involving ethylene and propylene. The yield and composition of the ethylene and propylene copolymers are successfully predicted using the online measurement data. [ABSTRACT FROM AUTHOR]

In recent years, vehicle detectors built on deep convolutional neural network (DCNN) have been widely used in autonomous driving. Under the complex conditions of road traffic, the detector is expected to run in high speed and high accuracy. However, due to the limited computing power and storage space on the autonomous vehicle, the deployment of advanced DCNN detectors is often restricted. The design of lightweight and powerful detectors is in a great desire. Recently, group convolution, as a novel convolution algorithm, has been proposed to reduce the floating-point operations and make the detection network lighter and faster. However, in practice, it is found that the increase of group number does not always boost the detection speed, but sometimes leads to the performance degradation. In addition, the existing guidelines for network design do not indicate how to choose the group number in the group convolution in order to maximize the overall detection speed. To this end, in this paper, we propose three new guidelines to determine the valid range of the group number, and design a lightweight detection network—DenseLightNet—based on these new design criteria. The proposed detector runs at a speed of three times faster than the current real-time detector YoloV3, while holding a much smaller model size. [ABSTRACT FROM AUTHOR]

Abstract: Large-scale calcium hexaboride (CaB6) nanostructures have been successfully fabricated with self-catalyst method using calcium (Ca) powders and boron trichloride (BCl3) gas mixed with hydrogen and argon. X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and selected-area electron diffraction (SAED) were used to characterize the compositions, morphologies, and structures of the samples. Our results show that the nanowires are highly single crystals elongated preferentially in the [110] direction. The growth mechanism based on the self-catalyst process is simply discussed. [Copyright &y& Elsevier]

Abstract: Europium hexaboride (EuB6) nanowires and nanotubes have been successfully fabricated with self-catalyst method using europium (Eu) powders and boron trichloride (BCl3) gas mixed with hydrogen and argon. X-ray diffraction (XRD), scanning electron microscopy (SEM), and transmission electron microscopy (TEM) were used to characterize the compositions, morphologies, and structures of the samples. Our results show that EuB6 nanowires are 60–300nm thick and 1–10μm long. TEM reveals that the nanowires are highly crystalline with growth direction of [100]. In our experiment, EuB6 nanotubes have also been observed. High-resolution transmission electron microscopy (HRTEM) and selected area electron diffraction (SAED) patterns identify that the EuB6 nanotubes are polycrystalline in nature. A growth mechanism based on the self-catalyst process is proposed for the formation of nanowires. [Copyright &y& Elsevier]