Optimization of process families for improved deployment of industrial decarbonization processes using machine learning surrogates

Stinchfield, G., Ammari, B., Morgan, J. C., Siirola, J. D., Zamarripa, M. A., & Laird, C. D. (2023). Optimization of process families for improved deployment of industrial decarbonization processes using machine learning surrogates. In Kokossis, A., Georgiadis, M., & Pistikopoulos, S. (Eds.), 33rd European Symposium on Computer Aided Process Engineering. Elsevier.

You might also like
Diagnostic tools for nonlinear algebraic models of dynamic chemical processes in Pyomo.Dae
A multi-scale modeling paradigm for Integrated Energy System operation and design
Toward future energy generation systems: Multi-scale optimization with market interactions
NMPC for setpoint tracking operation of a solid oxide electrolysis cell system
Multi-scale modeling using IDAES
Design centering through derivative-free optimization
On representative day selection for capacity expansion planning of power systems under extreme events
Rate-based dynamic modeling and optimization of an amine-based carbon capture unit for flexible operation