431633 Scale Bridging and Uncertainty Propagation in Chemical Process Modeling with Bayesian Nonparametric Regression

Tuesday, November 10, 2015: 10:00 AM
Salon D (Salt Lake Marriott Downtown at City Creek)
Evan Ford, Mechanical and Aerospace Engineering, West Virginia University, Morgantown, WV, Fernando V. Lima, Department of Chemical Engineering, West Virginia University, Morgantown, WV and David Mebane, Mechanical and Aerospace Department, West Virginia University, Morgantown, WV

Bayesian nonparametric regression is a powerful method for building reduced models of chemical reactions and distributed systems.   Motivated by Takens' theorem in dynamic systems topology, Gaussian process-based stochastic functions are insinuated into chemical system models, leading to a stochastic dynamic system of drastically reduced order.  Karhunen-Loeve decomposition of the GP function kernels leads to calibration of the models to training data sets using standard approaches.  Low-order, calibrated stochastic models then serve as vehicles for propagation of uncertainty across modeling length scales.  This methodology can be applied to problems in design of bench and pilot-scale experiments for process design, machine learning in chemical process control, or in other settings where reduced modeling is required.  Benchmarking examples derived from amine-based carbon capture and steam reformation of methane will be presented.

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