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The effect of process conditions on the co-processing technology of crude oil and coal was investigated. Crude oil/coal matching performance, swelling degree, crude oil/coal slurry viscosity-temperature characteristics and process parameters were obtained via the laboratory scale and pilot scale studies. The optimum reaction temperature of the co-processing was 445~450 oC, the pressure was 19 MPa, the catalyst addition was 3 wt.%, the reaction time was 2 h, and the ratio of hydrogen to crude oil was 1500 (V/V). Furthermore, the co-processing technology including catalyst and corresponding equipment based on the slurry bed hydrogenation were developed. By using this co-processing technology, the feed ratio of crude oil and coal can be 1:1, the coal conversion rate can be over 99%, the light oil (oil and aromatics) yield was over 70%, and the end products were gasoline, diesel, jet fuel, aromatics and LPG. The product quality meets the Euro V standard, whilst aromatics accounted for 48% of the light oil. So it was proved to be feasible to co-refine crude oil and coal at a ratio of 1:1. What's more, the slurry bed hydrogenation plant and its equipment were tested for long-term operation, and it has been proved that this co-processing technology could be deployed as large-scale industrial application. [ABSTRACT FROM AUTHOR]

Machine learning models for exploring structure-property relation for hydroxyapatite nanoparticles (HANPs) are still lacking. A multiscale multisource dataset is presented, including both experimental data (TEM/SEM, XRD/crystallinity, ROS, anti-tumor effects, and zeta potential) and computation results (containing 41,976 data samples with up to 9768 atoms) of nanoparticles with different sizes and morphologies at density functional theory (DFT), semi-empirical DFTB, and force field, respectively. Three geometric descriptors are set for the explainable machine learning methods to predict surface energies and surface stress of HANPs with satisfactory performance. To avoid the pre-determination of features, we also developed a predictive deep learning model within the framework of graph convolution neural network with good generalizability. Energies with DFT accuracy are achievable for large-sized nanoparticles from the learned correlations and scale functions for mapping different theoretical levels and particle sizes. The simulated XRD spectra and crystallinity values are in good agreement with experiments. [ABSTRACT FROM AUTHOR]

The environmental adaptabilities of low-density polyethylene (LDPE) play an important role for high-speed trains' reliability and comfort. The weathering of LDPE depends on environment factors, while the complexity of the weathering processes inhibits the evaluation of environmental weathering risks. To elucidate the correlation between weathering and environmental factors, and to predict the weathering risk of target areas of interest, three-year-long natural weathering tests were conducted at twelve natural exposure stations in China. Properties of weathered LDPE were compared and analysed using factor analysis. The fuzzy recognition method based on analytic hierarchy process (AHP) was established and used to predict the weathering risk based on environmental database. The results indicate that the factor scores can partitioned the atmospheric environments into five weathering risk grades. This article used the accumulated cumulative temperature of the daily maximum temperature for weathering risk evaluation, which is more scientific than the annual average temperature widely used and is useful for revealing the difference in LDPE weathering in Turpan and Korla. A comparative chart of LDPE's weathering risk in China was established by this fuzzy recognition method for the first time, which caters to the continuous extension of high-speed railway to new regions. [ABSTRACT FROM AUTHOR]

The deterioration of polycarbonate (PC) depends on various environmental factors. Meanwhile, the complexity of the related weathering processes inhibits the prediction of service life based on the environmental factors. To elucidate the nonlinear correlation between PC weathering and the environmental factors, three-year-long natural weathering tests were conducted at eight experimental stations in China. The relationship between tensile-property data of PC and environmental and pollutant data is analyzed by extra-trees and multilayer perceptron networks implemented in Python. The results indicated that (1) the degradation of PC tensile properties is mainly affected by the experimental period (76.37%), whilst the effect of the environmental or pollutant factors on the degradation is less pronounced (23.63%); (2) the classification accuracy of the trained model on the training set is 91% (91/100), and on the testing set is 72.13% (44/61); and lastly, (3) it is inferred from the error analysis of the classification results that the performance change of polycarbonate in Qionghai and Wuhan is characterized by an initial reduction followed by a slight improvement. Lastly, we show that the proposed method performs well, especially in the case of areas with incomplete data available. [ABSTRACT FROM AUTHOR]