IOP Conference Series,2017年
Fuqiang Jin, Xiaodong Zhang, Dongliang Hua, Haipeng Xu, Yan Li, Hui Mu
LicenseType:CC BY |
The transfer process of the in-situ coupling process of fermentation, extraction and distillation for biobutanol production was discussed from a theoretical point of view. The existence of temperature gradient in the extraction section was proved. The force of solute in the extracted liquid was discussed. And the mass transfer mechanism and impetus of the FEDIC process was analyzed. The theoretical analysis could provide a foundation for the following research.
IOP Conference Series,2017年
Huan Li, Yan Li, Cheng Li, Guosong Wang, Shanshan Xu, Jun Song, Song Zhang
LicenseType:CC BY |
Marine oil spill has long-term harmful impact on both marine ecosystem and economics. Recently as the increase in China's rapid economic growth, the demand for energy is increasing, leading to the high risk of marine oil spill pollution. So it is essential that we improve emergency response capacity in marine oil spill pollution and develop oil spill prediction and early warning in China. In this study, based on Lagrange tracking approach, we have developed an oil spill model. Combining with high-resolution meteorological and hydrodynamic model, the oil spill model was applied to predict the drift and diffusion processes of Dalian oil spill. The predicted results are well agreed with the analyzed synthetic aperture radar (SAR) image, and provided effective oil spill behaviour prediction to Shandong Maritime Safety Administration.
IOP Conference Series,2017年
Yan Li, Guosong Wang, Xue Han, Huan Li, Wenjing Fan, Kexiu Liu, Hui Wang
LicenseType:CC BY |
Using the PMTred algorithm (penalized maximum T test) and the detailed metadata archive, the monthly mean sea surface temperature (SST) datasets from 1960 to 2011 have been detected and adjusted. Results show that the SST time series has serious problems of inhomogeneity. The changes in observation instruments and observation system are the main causes of the discontinuity. For the monthly SST time series, the negative adjustments have the high proportion, which may be greatly due to the SST decreasing after automation. It is found that the annual mean SST trends have changed obviously before and after adjusted. The increasing trend of annual mean SST after adjustment is higher than before, by up to 0.252 °C/10 yr.