Formation-consumption neural networks for efficient chemical kinetics modeling

Reaction kinetics lie at the center of reactive systems engineering, yet discovering them from data and incorporating them into predictive simulations remain time-consuming tasks that slow down model-based process design and control. To address this challenge, we introduce the Exponential Scaling Formation Consumption Neural Network ( + – ExpNet ), a physics-inspired neural network architecture that can learn kinetics directly from integral reactor data and easily integrates with computational fluid dynamics to accelerate multiscale reactive simulations. The + – ExpNet models latent formation and consumption rates which are subtracted to compute source terms, and enforces atom conservation as a hard constraint by exploiting the principles of elementary reaction kinetics. Using two catalytic and one non-catalytic test case we demonstrate that this relatively simple approach represents source terms with surprising accuracy, in most cases en par with previous approaches that require significantly more specific knowledge of the reaction system. Further, we employ the + – ExpNet to extract kinetics for the direct DME synthesis using integral reactor data from literature. In doing so, we simultaneously derive kinetic models for both catalysts in this dual-catalyst system, without having to specify the global reactions occurring on the two catalysts or any thermodynamic constraints. This demonstrates that the + – ExpNet provides a general and interpretable framework for automated kinetic modeling and efficient multiscale reactive simulations. Its simplicity makes it easy to integrate into big data frameworks and high-throughput experimentation, offering a path to make accurate kinetic models more widely accessible across catalysis, combustion, and other data-rich chemical disciplines.