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The sorption enhanced steam reforming (SE-SMR) of methane over the surface of 18 wt. % Ni/ Al2O3 catalyst and using CaO as a CO2-sorbent is simulated for an adiabatic packed bed reactor. The developed model accounts for all the aspects of mass and energy transfer, in both gas and solid phase along the axial direction of the reactor. The process was studied under temperature and pressure conditions used in industrial SMR operations. The simulation results were compared with equilibrium calculations and modelling data from literature. A good agreement was obtained in terms of CH4 conversion, hydrogen yield (wt. % of CH4 feed), purity of H2 and CO2 capture under the different operation conditions such as temperature, pressure, steam to carbon ratio (S/C) and gas mass flux. A pressure of 30 bar, 923 K and S/C of 3 can result in CH4 conversion and H2 purity up to 65% and 85% respectively compared to 24% and 49% in the conventional process.

Unemployment remains a major cause for both developed and developing nations, due to which they lose their financial and economic impact as a whole. Unemployment rate prediction achieved researcher attention from a fast few years. The intention of doing our research is to examine the impact of the coronavirus on the unemployment rate. Accurately predicting the unemployment rate is a stimulating job for policymakers, which plays an imperative role in a country's financial and financial development planning. Classical time series models such as ARIMA models and advanced non‐linear time series methods be previously hired for unemployment rate prediction. It is known to us that mostly these data sets are non‐linear as well as non‐stationary. Consequently, a random error can be produced by a distinct time series prediction model. Our research considers hybrid prediction approaches supported by linear and non‐linear models to preserve forecast the unemployment rates much precisely. These hybrid approaches of the unemployment rate can advance their estimates by reproducing the unemployment ratio irregularity. These models' appliance is exposed to six unemployment rate statistics sets from Europe's selected countries, specifically France, Spain, Belgium, Turkey, Italy and Germany. Among these hybrid models, the hybrid ARIMA‐ARNN forecasting model performed well for France, Belgium, Turkey and Germany, whereas hybrid ARIMA‐SVM performed outclass for Spain and Italy. Furthermore, these models are used for the best future prediction. Results show that the unemployment rate will be higher in the coming years, which is the consequence of the coronavirus, and it will take at least 5 years to overcome the impact of COVID‐19 in these countries.