(116c) Guided Experimental Design for Modeling Reaction Rate Expressions
AIChE Spring Meeting and Global Congress on Process Safety
Wednesday, March 15, 2023 - 9:00am to 9:30am
In this work, the experimental objective is obtaining accurate and reliable models for reaction rate expressions. BO convergence rates will be evaluated for different cases. The effect of increasing the dimension of the input variables, level of noise, and number of hyperparameters of the kernel functions on the BO convergence rates will be studied. Furthermore, BO becomes computationally and statistically inefficient for high dimensional systems. Developing BO methods that work well in higher dimensions is thus of great practical and theoretical interest. A large body of literature has been devoted to mitigate the challenges of high dimensional BO problems. Several of which exploit the intrinsic lower dimensionality of the objective function, and perform BO in the lower dimensional space, followed by an inversion back to the high dimensional space. In this work, the problem of higher dimensions will be addressed for the reaction modeling application where a limited number of initial samples creates a unique dimensionality reduction problem.