Measuring the effect on chemical processes due to uncertain input states: Uncertainty-cum-sensitivity analysis using a gPC approach

In this study, uncertainty quantification(UQ) of a surrogate model of the complex chemical process was carried out using generalized polynomial chaos (gPC)-based approach. Results show that the proposed approach can save about 90% computational time compare to conventional Monte Carlo (MC) and quasi-MC (QMC) based simulation approaches. Sensitivity analysis (SA) of parameters was also performed simultaneously, which further enhances the time efficiency. Uncertainties quantification (UQ) besides the parametric sensitivity analysis (SA) is an important part of the design of the process. As in the case of optimization, the constrained Violation always occur hence, a little deviation of the optimized variables Will make the system worse/unreliable.