Statistical Robustness Study for Kinetic Models

Abstract

Kinetic models are nonlinear systems that depict the dependence between process variables and components or products where the process variables are usually assumed to be fixed. This is under the assumption that the process variables that govern the outputs are fully controllable. However, process variables are not always fully controllable and are more often hard-to-control during normal operation on a full-scale chemical production plant. This Article outlines the methodology of statistical robustness studies for kinetic models. Such an application is apparently new in engineering design and analysis. We illustrate the use of computer experiments and evaluate different response models and designs for determining optimum conditions, which are robust against the variability in the hard-to-control variables. The methodology is demonstrated with two examples, the main one being the ethoxylation of ethylene glycol in an inter cooled pipe reactor. The practical value of statistical robustness studies is that it quantifies the convoluted effect of model uncertainty and model input deviation.