Accurate prediction
of coke yield is the basis for simulation and
operation of the in situ combustion process. In view of the low asphaltene
content of heavy oil in China, this study established coke yield prediction
models for the pyrolysis and oxidation processes of low-asphaltene
heavy oil based on the coking characteristics of the main fractions,
including saturates (S), aromatics (A), and resins (R). The models
were verified by the experimental coke yields of heavy oils using
a fixed-bed reaction system. The prediction model for the pyrolysis
process included evaporation rate equations and kinetic equations
for coking reactions. Besides the same parts with pyrolysis, the model
for the oxidation process included the kinetic equation of coke oxidation.
The evaporation rate equations were determined by fitting the mass
loss rate in the evaporation range of thermogravimetric analysis (TGA)
pyrolysis results with a maximum error of 6%. The kinetic parameters
for the conversion of each fraction to the coke precursor were determined
by the isoconversional method based on TGA results of the separated
SAR fractions. Considering the interactions among fractions during
oxidation, this study included mixtures of SAR fractions as the samples
and obtained the mass loss results of a single SAR by subtraction.
The activation energies of SAR pyrolysis range from 99 to 180 kJ/mol
and those of SAR oxidation range from 92 to 130 kJ/mol. The kinetic
parameters of coke from the coke precursor were obtained by temperature-programmed
coking data of a prepared intermediate, with an activation energy
of 177 kJ/mol in an inert atmosphere and 65 kJ/mol in an oxidizing
atmosphere. The kinetic parameters of coke oxidation were derived
by the isothermal kinetic method in TGA, with an activation energy
of 136 kJ/mol. The experimental coke yields of Fengcheng and Hongqian
heavy oils under various pyrolysis and oxidation conditions were predicted
by this model with an error around 10%, indicating that this model
could be successfully applied to the prediction of the coking process
of low-asphaltene heavy oil under complex conditions.
Abstract. With the speeding up of urbanization process, ecological problems, such as unsustainable land use and environmental pollution,have emerged one after another in cites. Nowadays, green development and ecological priority are the important concepts and trends of the current new urban planning in China. In this study, Pingtan County, a coastal city in Fujian Province, China, was taken as the research area. Based on two Landsat 8 remote sensing images (2016, 2017), and two Sentinel-2A remote sensing images (2016, 2017), we first adopt the modified normalized water body index (MNDWI) to mask the water body. Four indicators, including greenness, humidity, dryness and heat were extracted to synthesize the remote sensing ecological index (RSEI), which were obtained by principal component analysis method. Based on the RSEI values acquired from Landsat 8 and Sentinel-2A images, the ecological environment change trend in Pingtan County was evaluated .The experimental results show that: 1) The RSEI indicators based on Landsat 8 and sentinel data all show a downward trend, but due to due to the influence of image spatial resolution and PCA weighting coefficient, the RSEI index has different degrees of decline. 2) The main reason for the decline in RSEI is the increase in NDSI indicators. Compared with July 2016, the bare ground increased in April 2017. Although the NDVI has increased, the overall trend is still declining. Therefore, it is necessary to ecologically return farmland and improve vegetation coverage in the future development process. 3) In recent years, the ecological quality of new construction land near drinking water sources has declined, so it is necessary to strengthen monitoring of changes in the region.
Scale effect is a crucial scientific problem in quantitative remote sensing (RS), and scholars attempt to solve it with scale conversion models, which can characterize the numerical relationship of RS land surface parameters at different resolutions (scales). As a significant land surface parameter, scale conversion of normalized difference vegetation index (NDVI) has been studied for a long time. Therefore, taking NDVI as an example, the development of scaling research is described and analyzed in the paper, and based on fractal theory, the development trends are discussed for land surface parameters in quantitative remote sensing. These are our conclusions: it will be the new trend to establish downscaling models based on fractal theory for land surface parameters in quantitative remote sensing; additionally, it still is the hotspot to establish spatiotemporal scale conversion models for land surface parameters in quantitative remote sensing in the future, and addressed on that, the multi-fractal scaling methodology is proposed, and its availability is analyzed in the paper, which presents significant potential.
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