(264f) Scale Bridging and Uncertainty Propagation in Chemical Process Modeling with Bayesian Nonparametric Regression
AIChE Annual Meeting
2015
2015 AIChE Annual Meeting Proceedings
Computing and Systems Technology Division
Advances in Computational Methods and Numerical Analysis
Tuesday, November 10, 2015 - 10:00am to 10:18am
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.