SYSTEM ANALYSIS AND SENSITIVITY USING DATA COLLABORATION

M. Frenklach*, T. Russi, R. Feeley, A. Packard
Department of Mechanical Engineering, University of California, Berkeley, CA 94720-1740, USA
myf@me.berkeley.edu

Predictive ability of chemical kinetics models, as of models in general, is one of the most sought-after characteristics that underlie scientific activity in reaction chemistry.  Reliable model predictions are needed for both establishment of poorly-understood reaction mechanisms and quantitative application of established mechanisms.  The current level of predictiveness in most cases is far from satisfactory, and one is interested in identifying possible actions that could measurably improve it:  What causes/skews the model predictiveness?  Are there new experiments to be performed, old repeated, and/or theoretical studies to be carried out?  What impact could a planned experimental work have?  What would it take to bring a given chemical kinetics model to a desired level of accuracy?  et cetera.

The mathematical quest for the model predictiveness typically takes the form of propagation of errors [1-4], sensitivity analysis [5-8], and straight-forward optimization [9-14].  All such venues follow the two-state approach: estimation of model parameters and their uncertainties (generally, from experimental data) followed by the analysis of the influence of the estimated parameter values and uncertainties on model prediction.  So doing presumes model parameters to be “unique”, predetermined values and often with individual, uncorrelated uncertainties.

An alternative approach, which we termed Data Collaboration, allows one to transfer uncertainties of the experimental data into model prediction directly.  It is a framework designed to make inferences from experimental observations in the context of an underlying model.  In the previous studies, we addressed collaborative features of this approach [15], mutual consistency of a set of experiments [15,16], and discrimination among competing reaction models [17].  In the present study, we return to our initial objective [18]: model prediction.  We show that Data Collaboration allows one to assess the propagation of uncertainty more deeply— determining which experiment/parameter contributes the most to the current uncertainty in model prediction, ranking such effects, and considering new or even hypothetical experiments—thereby providing guidance in selecting additional experiments to perform.

Acknowledgement: The work was supported by the NSF Chemistry Division initiative on Collaborative Research: Cyberinfrastructure and Research Facilities, Grant No. CHE-0535542.

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