Sensitivity Analysis for Chemical Models

Chemists routinely create models of reaction systems to understand reaction mechanisms, kinetic properties, process yields under various operating conditions, or the impact of chemicals on man and the environment. As opposed to concise physical laws, these models are attempts to mimic the system by hypothesizing, extracting, and encoding system features (e.g. a potentially relevant reaction pathway), within a process that can hardly be formalized scientifically. A model will hopefully help to corroborate or falsify a given description of reality, e.g. by validating a reaction scheme for a photochemical process in the atmosphere, and possibly to influence reality, e.g. by allowing the identification of optimal operating conditions for an industrial process or suggesting mitigating strategies for an undesired environmental impact. These models are built in the presence of uncertainties of various levels, in the pathway, in the order of the kinetics associated to the pathway, in the numerical value of the kinetic and thermodynamic constants for that pathway, and so on. Propagating via the model all these uncertainties onto the model output of interest, e.g. the yield of a process, is the job of uncertainty analysis. Determining the strength of the relation between a given uncertain input and the output is the job of sensitivity analysis. In the paper we select and review those practice for sensitivity analysis which we consider as the most promising modern approaches to sensitivity analysis, with emphasis on variance based methods, comparing, with the help of worked examples, the performances of these with different kinds of local, regression, meta-modeling or screening-based measures. The literature offers more methods for sensitivity analysis than are reported in the present review. Some of these methods are domain specific, such as for instance methods for use in reliability engineering,97 other require a direct intervention of the analyst on the model. We have privileged general methods that treat the model as a black box and that are not too laborious to implement. A few computational recipes are also offered for the reader.JRC.G.3-Econometrics and applied statistic