(11c) Meta-Modeling-Based Sensitivity Analysis of Hybrid Models
AIChE Annual Meeting
Sunday, November 13, 2022 - 4:04pm to 4:21pm
Sensitivity analysis (SA) has been historically used to understand models, their important parameters, and for validation purposes. Sensitivity (or what-if) analysis refers to the study of changes in the output of a model due to large or extreme changes in the input . Global sensitivity analysis methods have been proposed to study the variation in inputs over the input domain and associate it with the output uncertainties . Functional ANOVA (FANOVA) is an SA tool that models the multivariate functions as a sum of their individual input effects and interactions and has been commonly used for first-principles models . On the other hand, data-driven models are mainly optimized and analyzed through Bayesian optimization; a Gaussian process-based global optimization. Bayesian Optimization is well-suited for handling computationally expensive objective functions as it uses the acquisition function for optimization . Given there is no proposed common ground for sensitivity analysis of first-principles and data-driven models, we extend sensitivity analysis studies to hybrid models. In our proposed work, we plan to decompose the hybrid model into Koopman eigenfunctions as they capture nonlinear dynamics in the form of linear models . The linear model will be optimized using a Bayesian Optimization-based Dynamic Mode Decomposition method. The sensitivity of parameters will be finally analyzed using FANOVA through Sobolâs indices estimation. This work will provide insights into the impact of chosen black box model on the overall hybrid model. The advantage of this approach is demonstrated through application to isothermal CSTR, bioreactor, and hydraulic fracturing systems.
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