ABSTRACT
Fire insurance is a crucial component of property insurance, and its rating depends on the forecast of insurance loss claim data. Fire insurance loss claim data have complicated characteristics such as skewness and heavy tail. The traditional linear mixed model is commonly difficult to accurately describe the distribution of loss. Therefore, it is crucial to establish a scientific and reasonable distribution model of fire insurance loss claim data. In this study, the random effects and random errors in the linear mixed model are firstly assumed to obey the skew-normal distribution. Then, a skew-normal linear mixed model is established using the Bayesian MCMC method based on a set of U.S. property insurance loss claims data. Comparative analysis is conducted with the linear mixed model of logarithmic transformation. Afterward, a Bayesian skew-normal linear mixed model for Chinese fire insurance loss claims data is designed. The posterior distribution of claim data parameters and related parameter estimation are employed with the R language JAGS package to obtain the predicted and simulated loss claim values. Finally, the optimization model in this study is used to determine the insurance rate. The results demonstrate that the model established by the Bayesian MCMC method can overcome data skewness, and the fitting and correlation with the sample data are better than the log-normal linear mixed model. Hence, it can be concluded that the distribution model proposed in this paper is reasonable for describing insurance claims. This study innovates a new approach for calculating the insurance premium rate and expands the application of the Bayesian method in the fire insurance field.
ABSTRACT
In the present study, to insert the Cu nanoparticles (NPs) into mesoporous carbon aerogels and first applied it to remove H2S efficiently. This desulfurizer was made based on the dimensional policy by inserting the Cu NPs on mesoporous carbon aerogels to overcome the sintering problem and improve the activity, which has potential performance at high-temperature catalysis. The obtained desulfurizer was employed for H2S removal at middle temperature conditions (optimal H2S adsorption at 550 °C). We explored the optimum doping amount of CuOx, optimum temperature conditions, and the influence of textural parameters of carbon aerogels. The desulfurizers were characterized by means of field-emission scanning electron microscopy (FESEM), N2-adsorption, X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), H2 temperature-programmed reduction (H2-TPR), and Raman spectra techniques. The results confirmed that the presence of H2 was unfavorable for sulfidation and obviously shortened the breakthrough time. However, the existence of CO had little impact on the desulfurization and sulfur capacity. In a nutshell, this work could provide a new synthetic route to prepare Cu NPs deep into the lattice of carbon aerogels structure of desulfurizers and understand the desulfurization mechanism.
