Yes / A statistical model combined with CFD (computational fluid dynamic) method was used to explain the detailed phenomena of the process parameters, and a series of experiments were carried out for propylene polymerisation by varying the feed gas composition, reaction initiation temperature, and system pressure, in a fluidised bed catalytic reactor. The propylene polymerisation rate per pass was considered the response to the analysis. Response surface methodology (RSM), with a full factorial central composite experimental design, was applied to develop the model. In this study, analysis of variance (ANOVA) indicated an acceptable value for the coefficient of determination and a suitable estimation of a second-order regression model. For better justification, results were also described through a three-dimensional (3D) response surface and a related two-dimensional (2D) contour plot. These 3D and 2D response analyses provided significant and easy to understand findings on the effect of all the considered process variables on expected findings. To diagnose the model adequacy, the mathematical relationship between the process variables and the extent of polymer conversion was established through the combination of CFD with statistical tools. All the tests showed that the model is an excellent fit with the experimental validation. The maximum extent of polymer conversion per pass was 5.98% at the set time period and with consistent catalyst and co-catalyst feed rates. The optimum conditions for maximum polymerisation was found at reaction temperature (RT) 75 °C, system pressure (SP) 25 bar, and 75% monomer concentration (MC). The hydrogen percentage was kept fixed at all times. The coefficient of correlation for reaction temperature, system pressure, and monomer concentration ratio, was found to be 0.932. Thus, the experimental results and model predicted values were a reliable fit at optimum process conditions. Detailed and adaptable CFD results were capable of giving a clear idea of the bed dynamics at optimum process conditions.

Funding strategy has often been the determinant factor in the level of success for social entrepreneurships. The strategy could be a preference for internally generated revenue, externally generated revenue or a combination of the two. Interestingly, scarcity of resources has always been ‘a clog in the wheel’ of meaningful execution of projects irrespective of the funding model a social entrepreneur chooses. Through a review of existing literatures, this paper weighs the implications of choosing either externally generated revenue or internally generated revenue; specifically contextualizing the study to US. More so, it attempts to find out which of the two models would ensure optimum productivity, given that scarcity of resources would hamper the chances of effectively running the two models simultaneously. To arrive at a ‘plausible’ response, principles of resource valuation become vital in determining the costs and benefits associated with each model of funding. Consequently, the findings show that though both internally and externally generated revenue are sine qua non to an effective and efficient production of social values, internally generated revenue ensures better optimum production than externally generated revenue.