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为探索生物活性未知的双对苯醌(2,7-dihydroxy-3,6,9-trimethyl-9H-xanthene-1,4,5,8-tetraone,DTXT)的抗氧化活性,并提高其发酵产量,考察DTXT的还原力以及对超氧阴离子自由基、羟自由基、1,1-二苯基-2-三硝基苯肼(1,1-diphenyl-2-picrylhydrazyl,DPPH)自由基的清除效果,在单因素试验基础上,采用响应面法优化了DTXT产生菌瓶生顶孢霉(Acremonium cavaraeanum)CA022菌株的固体发酵培养基。结果表明:在200 μg/mL质量浓度下,DTXT的还原力与芦丁差异不显著,高于VE和2,6-二叔丁基-4甲基苯酚,对超氧阴离子自由基清除率达到67.00%,对羟自由基清除率达到78.83%,对DPPH自由基清除率达到76.53%。通过响应面试验,得到最佳培养基配方为葡萄糖0.773%、硝酸钠0.185%、H3BO3 0.032%、VB1 100 μg/100 g,在此条件下实际获得的DTXT产量为4 150.8 mg/kg,是优化前产量的(2 864.83 mg/kg)1.45 倍。 相似文献
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《International Journal of Hydrogen Energy》2022,47(12):8130-8144
Compared to liquid/gas hydrogen tank, the pipeline is an economical way for hydrogen transportation. With the quick development of utility tunnel in China, hydrogen pipeline enters the gas compartment can be expected soon. However, all the safety requirements of the gas compartment in the current standards are designed for natural gas, and the applicability for hydrogen is unknown. Therefore, a series of studies were started to investigate the safety of hydrogen in utility tunnel. In this work, a real utility tunnel locates at Shanghai was selected as the physical object. A 3D numerical model was built and successfully validated by a scaled tunnel test. The model has the maximum deviation of +9.5%. After that, a comparatively study of the dispersion behavior of CH4 and H2 was conducted. The assumed scenario was a 20 mm small-hole leaks with gauge pressure of 1.0 MPa in the middle of the tunnel. Numerical results shown that, H2 has a larger dispersion velocity and higher concentration, and is more dangerous compared to CH4. The current emergency ventilation strategy of air change rate of 12 times/h is not effective enough to dilute the H2 flammable cloud. The alarm time of the testing points shown strong linear law. There was a sharp variation in the range of 20%–100% LFL (Lower Flammable Limit), so the alarm strategy in the tunnel standards is too ideal for both CH4 and H2. The numerical results in the present study could provide a guidance for the design and safety management of the hydrogen tunnel. 相似文献
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《International Journal of Hydrogen Energy》2022,47(57):24242-24253
As hydrogen refueling stations become increasingly common, it is clear that a high level of economic efficiency and safety is crucial to promoting their use. One way to reduce costs is to use a simple orifice instead of an excess flow valve, which Japanese safety regulations have identified as a safety device. However, there is concern about its effect on refueling time and on risk due to hydrogen leakage. To clarify the effect, we did a study of model-based refueling time evaluation and quantitative risk assessment for a typical refueling station. This study showed that an orifice is an effective alternative safety device. The increase in refueling time was less than 10%, based on simulations using a dynamic physical model of the station. Neither was there a significant difference in the risk between a configuration with excess flow valves and one with an orifice. 相似文献
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《岩石力学与岩土工程学报(英文版)》2022,14(5):1421-1432
Prediction of mode I fracture toughness (KIC) of rock is of significant importance in rock engineering analyses. In this study, linear multiple regression (LMR) and gene expression programming (GEP) methods were used to provide a reliable relationship to determine mode I fracture toughness of rock. The presented model was developed based on 60 datasets taken from the previous literature. To predict fracture parameters, three mechanical parameters of rock mass including uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), and elastic modulus (E) have been selected as the input parameters. A cluster of data was collected and divided into two random groups of training and testing datasets. Then, different statistical linear and artificial intelligence based nonlinear analyses were conducted on the training data to provide a reliable prediction model of KIC. These two predictive methods were then evaluated based on the testing data. To evaluate the efficiency of the proposed models for predicting the mode I fracture toughness of rock, various statistical indices including coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE) were utilized herein. In the case of testing datasets, the values of R2, RMSE, and MAE for the GEP model were 0.87, 0.188, and 0.156, respectively, while they were 0.74, 0.473, and 0.223, respectively, for the LMR model. The results indicated that the selected GEP model delivered superior performance with a higher R2 value and lower errors. 相似文献
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