Interpretation of non-linear empirical data-based process models using global sensitivity analysis

Flexible non-linear regression techniques have been widely used for data-based modeling of chemical processes, and they form the basis of process design under the framework of response surface methodology (RSM). These non-linear models typically achieve more accurate approximation to the factor-response relationship than traditional polynomial regressions. However, non-linear models usually lack a clear interpretation as to how the factors contribute to the prediction of process response. This paper applies the technique of sensitivity analysis (SA) to facilitate the interpretation of non-linear process models. By recognizing that derivative-based local SA is only valid within the neighborhood of certain "nominal" values, global SA is adopted to study the entire range of the factors. Global SA is based on the decomposition of the model and the variance of response into contributing terms of main effects and interactions. Therefore, the effect of individual factors and their interactions can be both visualized by graphs and quantified by sensitivity indices. The proposed methodology is demonstrated on two catalysis processes where non-linear data-based models have been developed to aid process design. The results indicate that global SA is a powerful tool to reveal the impact of process factors on the response variables.