Analysis of Pyrolysis Kinetic Model for Processing of Thermogravimetric Analysis Data

Pyrolysis has profound implications for coal as a raw material to make phase change material (PCM). It is necessary to derive a pyrolysis kinetic model for predicting the yield of volatiles and reaction performance during pyrolysis of coal, which is of significant importance for its thermal processing. The devolatilization of coal is characterized by thermogravimetric analysis (TGA) at different heating rates, and many kinetic models can be achieved by analyzing the TGA data. This work was aimed to find an appropriate model to describe the pyrolysis of coal and took Zhundong coal as an example. Four types of isoconversion kinetic methods, that is, Friedman, Flynn-Wall-Ozawa (FWO), Kissinger-Akahira-Sunose (KAS), Miura-Maki method, and different distributed activation energy models (DAEM) were employed here to fit TGA data for pyrolysis of Zhundong coal. The pre-exponential factors and activation energies obtained from different kinetic models were analyzed. An m-nth-DAEM was developed by considering that m classes of reactions took place with the same pre-exponential factor k0 but different distribution activation energy following logistic distribution or Gaussian distribution. The results showed that the FWO model was better for description of pyrolysis process of Zhundong coal, and the 2-nth-DAEM assuming Gaussian distribution of activation energy gave the best fit for the TGA data of Zhundong coal. The research provides a valuable reference to the development of thermal utilization technology of Zhundong coal

GARCH Model With Fat-Tailed Distributions and Bitcoin Exchange Rate Returns

In the era of diminishing power from US dollar and increasing competition among world currencies, Bitcoin, as a completely new concept as a medium of exchange, has received increasing attentions over the world. Nowadays, Bitcoin also becomes an investment vehicle, which carries attractive opportunities but also significant risks for the investment community. In this paper, we have compared the empirical performance of a newly-developed heavy-tailed distribution, the normal reciprocal inverse Gaussian (NRIG), with the most popular heavy-tailed distribution, the Student’s t distribution, under the GARCH framework in fitting the daily Bitcoin exchange rate returns. Our results indicate the heavy-tailed distribution has better performance in capture the daily Bitcoin exchange rate returns dynamics than the standard normal distribution. Our results also show the older fashioned Student’s t distribution still performs better than the new heavy-tailed distribution