Data-driven Process Systems Engineering Lab

Data-driven Process Systems Engineering Lab

Publications

Perspectives on the Integration between First-Principles and Data-Driven Modeling

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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…

Two-Stage Approach to Parameter Estimation of Differential Equations Using Neural ODEs

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Industrial & Engineering Chemistry Research*
2021

Modeling physiochemical relationships using dynamic data is a common task in fields throughout science and engineering. A common step in developing generalizable, mechanistic models is to fit unmeasured parameters to measured data. However, fitting differential equation-based models can be computation-intensive…

AC-Optimal Power Flow Solutions with Security Constraints from Deep Neural Network Models

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Computer Aided Chemical Engineering*
2021

In power grid operation, optimal power flow (OPF) problems are solved several times per day to find economically optimal generator setpoints that balance given load demands. Ideally, we seek an optimal solution that is also “N-1 secure”, meaning the system can…

Data-driven Spatial Branch-and-bound Algorithm for Box-constrained Simulation-based Optimization

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Journal of Global Optimization*
2021

The ability to use complex computer simulations in quantitative analysis and decision-making is highly desired in science and engineering, at the same rate as computation capabilities and first-principle knowledge advance. Due to the complexity of simulation models, direct embedding of…

Surrogate-Based Optimization for Mixed-Integer Nonlinear Problems

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Computers & Chemical Engineering*
2020

Simulation-based optimization using surrogate models enables decision-making through the exchange of data from high-fidelity models and development of approximations. Many chemical engineering optimization problems, such as process design and synthesis, rely on simulations and contain both discrete and continuous decision…