No abstract

In this paper, to analyze the oxygen corrosion of the casing string under the nitrogen blanket condition of the salt cavern underground gas storage and to predict the casing's service life, an indoor corrosion weight-loss experiment was used to study the effects of oxygen concentration, temperature, and pressure on the gas and liquid phase of N80 steel pipe corrosion laws. The corrosion products were examined using scanning electron microscopy, X-ray fluorescence spectroscopy, and X-ray diffraction analyses. Finally, the corrosion life of the casing was predicted based on the uniform corrosion rate and casing strength check. The results indicated the following: (1) under the experimental conditions, the gas-liquid two-phase corrosion rate gradually decreased with the increase in nitrogen concentration, while when the concentration was 78%-95%, the liquidphase corrosion was serious, and in the liquid nitrogen environment (nitrogen concentration is 99.9%), the liquid-phase corrosion rate was lower than the oilfield corrosion control index (0.076 mm/a); (2) the temperature and pressure had no significant effect on the casing corrosion; (3) with the increase in nitrogen concentration, the service life of the inner and outer pipes under different working conditions gradually increased, with the minimum service life equaling 28 years when the concentration was 99.99%; (4) nitrogen concentrations of 99.99%, 95%, 90%, and 78% can meet the column strength of the 3-year cavitybuilding cycle, while under the 5-year cavity-building cycle, it is recommended that the lower limit of the purity is 90%. Overall, the research results will provide a strong information basis for the construction of salt cavern gas storage cavities.

This paper mainly studied the removal of Hg by biomass activated carbon in the simulated flue gas. Biomass activated carbon of different materials were made through the control of activator concentration, activation temperature in the self-developed biomass preparation system. In the fixed bed system, the flue gas Hg removal efficiency of activated carbon samples were evaluated. The experimental results showed that the adsorption performance of biomass activated carbon was not proportional with the change of the activator concentration and activation temperature, the overall trend was firstly increased and then decreased.

Thousands of electric vehicles (EV), which are large in number and flexible in their use of electricity, will be connected to the power system in the near future, which will bring more uncertainty to the power system. Therefore, it is necessary to study the general characteristics of EV charging behaviours. In the charging process, big data regarding charging behaviour of EVs are generated. This paper proposes a big data mining technique based on Random Forest and Principle Component Analysis for EV charging behaviour to identify and analyse clusters with different charging characteristics from the big data. This paper uses Dundee’s January 2018 EV charging data to conduct experiments, and obtains the charging behaviour clusters of the workdays, weekends, and holidays of January. The superiority of the random forest algorithm in the EV clustering problem is reflected when compared to the Euclidean distance method. The clusters obtained by the random forest algorithm have clearer characteristics, including the user’s charging method and travel behaviour. The results show that the charging behaviour of EVs has certain regularity, and the charging load has obvious peak-to-valley difference that is necessary to be regulated.