Data-driven Process Systems Engineering Lab
Data-driven Process Systems Engineering Lab
Publications
Optimization and Engineering 2022
Black-box surrogate-based optimization has received increasing attention due to the growing interest in solving optimization problems with embedded simulation data. The main challenge in surrogate-based optimization is the lack of consistently convergent behavior, due to the variability introduced by initialization,…
Computer Aided Chemical Engineering 2022
A common step in developing generalizable, dynamic mechanistic models is to fit unmeasured parameters to measured data. Fitting differential equation-based models can be computationally expensive due to the presence of nonlinearity and stiffness. This work proposes a two-stage indirect approach…
Chemical Engineering Research and Design 2022
Technologies for post-combustion carbon capture are essential for the reduction of greenhouse gas emissions to the atmosphere. However, they are still associated with high costs and energy consumption. Intensified processes for carbon capture have the potential to overcome these challenges…
ACS Sustainable Chem. Eng. 2022
Efficient chemical recycling of consumer plastics (i.e., depolymerization down to monomers) is a crucial step needed to achieve a circular material economy. In this work, depolymerization of poly(ethylene terephthalate) (PET) via mechanochemical hydrolysis with sodium hydroxide is studied, with complete…
Computers & Chemical Engineering 2022
Efficiently embedding and/or integrating mechanistic information with data-driven models is essential if it is desired to simultaneously take advantage of both engineering principles and data-science. The opportunity for hybridization occurs in many scenarios, such as the development of a faster…