(661f) Towards Domain-Informed Machine Learned Models from High Throughput Experimental Data
AIChE Annual Meeting
2023
2023 AIChE Annual Meeting
Catalysis and Reaction Engineering Division
Data Science and Machine Learning Approaches to Catalysis I: Data-enhanced Multiscale Simulations
Monday, November 6, 2023 - 10:06am to 10:24am
In this talk, we will present how such data-driven kinetic models can be developed from HTE data with an illustrative example of oxidative coupling of methane. In particular, we will discuss three approaches. First, we will show that simple neural network models can be trained but keeping in mind plausible reactions of the system. Second, we will show how reactor design equations can be approximated by training residual deep neural networks. Third, we will demonstrate how treating the packed bed reactor system of the experimental set up using neural ordinary differential equations allows for building rate models that are both chemistry cognizant and thermodynamically consistent. This work, thereby, extends the general idea of physics-informed machine learning to building reaction models.