Uncertainty quantification and global sensitivity analysis of complex chemical process using a generalized polynomial chaos approach

Uncertainties are ubiquitous and unavoidable in process design and modeling. Because they can significantly affect the safety, reliability and economic decisions, it is important to quantify these uncertainties
and reflect their propagation effect to process design. This paper proposes the application of generalized
polynomial chaos (gPC)-based approach for uncertainty quantification and sensitivity analysis of complex
chemical processes. The gPC approach approximates the dependence of a process state or output on the
process inputs and parameters through expansion on an orthogonal polynomial basis. All statistical information of the interested quantity (output) can be obtained from the surrogate gPC model. The proposed
methodology was compared with the traditional Monte-Carlo and Quasi Monte-Carlo sampling-based
approaches to illustrate its advantages in terms of the computational efficiency. The result showed that
the gPC method reduces computational effort for uncertainty quantification of complex chemical processes with an acceptable accuracy. Furthermore, Sobol’s sensitivity indices to identify influential random
inputs can be obtained directly from the surrogated gPC model, which in turn further reduces the required
simulations remarkably. The framework developed in this study can be usefully applied to the robust
design of complex processes under uncertainties.