Investigation of H2S and CO2 Removal from Gas Streams Using Hollow Fiber Membrane Gas–liquid Contactors

Chemical absorption of H2S and CO2 from CH4 was carried out in a polypropylene porous asymmetric hollow fiber membrane contactor (HFMC). A 0.5 mol L–1 aqueous solution of methyldiethanolamine (MDEA) was used as chemical absorbent solution. Effects of gas flow rate, liquid flow rate, H2S concentration and CO2 concentration on the H2S outlet concentrations and CO2 removal percentage were investigated. The results showed that the removal of H2S with aqueous solution of MDEA was very high and indicated almost total removal of H2S. Experimental results also indicated that the membrane contactor was very efficient in the removal of trace H2S at high gas/liquid flow ratio. The removal of H2S was almost complete with a recovery of more than 96 %. Using feed gas mixtures containing 5000 ppm H2S with CO2 concentrations in the range of 4–12 vol.%, the outlet H2S concentration of less than 1.0 ppm was attained with less than 4.0 vol.% of CO2 permeated and absorbed. This work is licensed under a Creative Commons Attribution 4.0 International License

Analysis of Blood Transfusion Data Using Bivariate Zero-Inflated Poisson Model: A Bayesian Approach

Recognizing the factors affecting the number of blood donation and blood deferral has a major impact on blood transfusion. There is a positive correlation between the variables "number of blood donation" and "number of blood deferral": as the number of return for donation increases, so does the number of blood deferral. On the other hand, due to the fact that many donors never return to donate, there is an extra zero frequency for both of the above-mentioned variables. In this study, in order to apply the correlation and to explain the frequency of the excessive zero, the bivariate zero-inflated Poisson regression model was used for joint modeling of the number of blood donation and number of blood deferral. The data was analyzed using the Bayesian approach applying noninformative priors at the presence and absence of covariates. Estimating the parameters of the model, that is, correlation, zero-inflation parameter, and regression coefficients, was done through MCMC simulation. Eventually double-Poisson model, bivariate Poisson model, and bivariate zero-inflated Poisson model were fitted on the data and were compared using the deviance information criteria (DIC). The results showed that the bivariate zero-inflated Poisson regression model fitted the data better than the other models. © 2016 Tayeb Mohammadi et al