Amine-immobilized HY zeolite for CO_(2) capture from hot flue gas

Solid amine-based adsorbents were widely studied as an alternative to liquid amine for post-combustion CO_(2)capture(PCC).However,most of the amine adsorbents suffer from low thermal stability and poor cyclic regenerability at the temperature of hot flue gases.Here we present an amine loaded proton type Y zeolite(HY)where the amines namely monoethanolamine(MEA)and ethylenediamine(ED)are chemical immobilized via ionic bond to the zeolite framework to overcome the amine degradation problem.The MEA and ED of 5%,10%and 20%(mass)concentration-immobilized zeolites were characterized by X-ray diffraction,Fourier-transform infrared spectroscopy,and N_(2)-196℃ adsorption to confirm the structure integrity,amine functionalization,and surface area,respectively.The determination of the amine loading was given by C,H,N elemental analysis showing that ED has successfully grafted almost twice as many amino groups as MEA within the same solvent concentration.CO_(2)adsorption capacity and thermal stability of these samples were measured using thermogravimetric analyser.The adsorption performance was tested at the adsorption temperature of 30,60 and 90℃,respectively using pure CO_(2)while the desorption was carried out with pure N_(2)purge at the same temperature and then followed by elevated temperature at 150℃.It was found that all the amine@HY have a substantial high selectivity of CO_(2)over N_(2).The sample 20%ED@HY has the highest CO_(2)adsorption capacity of1.76 mmol·g^(-1)at 90℃ higher than the capacity on parent Na Y zeolite(1.45 mmol·g^(-1)only).The amine@HY samples presented superior performance in cyclic thermal stability in the condition of the adsorption temperature of 90℃ and the desorption temperature of 150℃.These findings will foster the design of better adsorbents for CO_(2)capture from flue gas in post-combustion power plants.

Statistical Models for Long-range Forecasting of Southwest Monsoon Rainfall over India Using Step Wise Regression and Neural Network

The long-range forecasts (LRF) based on statistical methods for southwest monsoon rainfall over India (ISMR) has been issued by the India Meteorological Department (IMD) for more than 100 years. Many statistical and dynamical models including the operational models of IMD failed to predict the operational models of IMD failed to predict the deficient monsoon years 2002 and 2004 on the earlier occasions and so had happened for monsoon 2009. In this paper a brief of the recent methods being followed for LRF that is 8-parameter and 10-parameter power regression models used from 2003 to 2006 and new statistical ensemble forecasting system are explained. Then the new three stage procedure is explained. In this the most pertinent predictors are selected from the set of all the potential predictors for April, June and July models. The model equations are developed by using the linear regression and neural network techniques based upon training set of the 43 years of data from 1958 to 2000. The skill of the models is evaluated based upon the validation set of 11 years of data from 2001 to 2011, which has shown the high skill on the validation data set. It can be inferred that these models have the potential to provide a prediction of ISMR, which would significantly improve the operational forecast.