| 1 | Comparing the deactivation behaviour of Co/CNT and Co/γ-Al_2O_3 nano catalysts in Fischer-Tropsch synthesis显示文摘An extensive study of Fischer-Tropsch (FT) synthesis on cobalt nano particles supported on γ-alumina and carbon nanotubes (CNTs) catalysts is reported.20 wt% of cobalt is loaded on the supports by impregnation method.The deactivation of the two catalysts was studied at 220 C,2 MPa and 2.7 L/h feed flow rate using a fixed bed micro-reactor.The calcined fresh and used catalysts were characterized extensively and different sources of catalyst deactivation were identified.Formation of cobalt-support mixed oxides in the form of xCoO yAl2O3 and cobalt aluminates formation were the main sources of the Co/γ-Al2O3 catalyst deactivation.However sintering and cluster growth of cobalt nano particles are the main sources of the Co/CNTs catalyst deactivation.In the case of the Co/γ-Al2O3 catalyst,after 720 h on stream of continuous FT synthesis the average cobalt nano particles diameter increased from 15.9 to 18.4 nm,whereas,under the same reaction conditions the average cobalt nano particles diameter of the Co/CNTs increased from 11.2 to 17.8 nm.Although,the initial FT activity of the Co/CNTs was 26% higher than that of the Co/γ-Al2O3,the FT activity over the Co/CNTs after 720 h on stream decreased by 49% and that over the Co/γ-Al2O3 by 32%.For the Co/γ-Al2O3 catalyst 6.7% of total activity loss and for the Co/CNTs catalyst 11.6% of total activity loss cannot be recovered after regeneration of the catalyst at the same conditions of the first regeneration step.It is concluded that using CNTs as cobalt catalyst support is beneficial in carbon utilization as compared to γ-Al2O3 support,but the Co/CNTs catalyst is more susceptible for deactivation. | Ahmad Tavasoli Saba Karimi Somayeh Taghavi Zahra Zolfaghari Hamideh Amirfirouzkouhi | 2012 | Journal of Natural Gas Chemistry2012，21，5： | 4 |
| 2 | Mathematical model to predict COVID-19 mortality rate显示文摘Objective:Covid-19 is a highly contagious viral infection that has recently become a pandemic.Since the beginning of the pandemic,the disease has affected millions of people and taken many people's lives.The purpose of this paper is to predict and compare the number of cases and mortality rate due to Covid-19 every quarter in 2020 and 2021 in three countries:Iran,the United States,and South Korea.Materials and methods:The data of this study include the mortality rate of different countries of the world due to Covid-19,which has been approved by the World Health Organization(WHO).In this paper,to develop the mathematical model for mortality rate prediction,the data of the countries of Iran,the United States,and South Korea during the last two years from March 1,2020,to March 1,2022,have been used.In addition,the mortality trend was modeled using the MATLAB software toolbox version 2022b.During modeling,six methods including Fourier,Interpolant,Gaussian,Polynomial,Sum of Sine,and Smoothing Spline were implemented.Root Mean square error(RMSE)and final prediction error were used to evaluate the performance of these proposed methods.Results:As a result of the analysis,it was shown that the Smoothing Spline model with the lowest error rate was capable of accurately evaluating and predicting Covid-19 incidence and mortality rate.Using RMSE,a prediction of the Covid-19 mortality rate for three countries is 3.76498×10^(-5).The values of R-Square and Adj R-sq were 1 in all the experiments,which indicates the full compliance of the prediction model.Conclusion:Using the proposed method,the incidence rate and mortality rate can be properly assessed and compared with each other in three countries.This provides a better view of the progression of the coronavirus outbreak in spring,summer,autumn,and winter.By using the proposed method,governments will be able to prevent disease and alert people to follow health guidelines more closely,thereby reducing infection numbers and mortality rates. | Melika Yajada Mohammad Karimi Moridani Saba Rasouli | 2022 | Infectious Disease Modelling2022，7，4： | 0 |
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