Process development and techno-economic analysis for mechanochemical recycling of poly(ethylene terephthalate)

Chemical Engineering Journal 2024

Chemical recycling of consumer plastics has garnered great attention recently towards achieving circular economy goals. Particularly in the case of PET waste, mechanochemical depolymerization in ball mill reactors has been identified as a very promising technology due to the high…

Machine learning in process systems engineering: Challenges and opportunities

Computer Aided Chemical Engineering 2023

This “white paper” is a concise perspective of the potential of machine learning in the process systems engineering (PSE) domain, based on a session during FIPSE 5, held in Crete, Greece, June 27–29, 2022. The session included two invited talks…

Optimization with Neural Network Feasibility Surrogates: Formulations and Application to Security-Constrained Optimal Power Flow

Energies 2023

In many areas of constrained optimization, representing all possible constraints that give rise to an accurate feasible region can be difficult and computationally prohibitive for online use. Satisfying feasibility constraints becomes more challenging in high-dimensional, non-convex regimes which are common…

Physics-informed machine learning with optimization-based guarantees: Applications to AC power flow

International Journal of Electrical Power and Energy Systems 2024

This manuscript presents a complete framework for the development and verification of physics-informed neural networks with application to the alternating-current power flow (ACPF) equations. Physics-informed neural networks (PINN)s have received considerable interest within power systems communities for their ability to…

Enabling global interpolation, derivative estimation and model identification from sparse multi-experiment time series data via neural ODEs

Engineering Applications of Artificial Intelligence 2024

Estimation of the rate of change of a system's states from state measurements is a key step in several system analysis and model-building workflows. While numerous interpolating models exist for inferring derivatives of time series when data is disturbed by…

Surrogate-based branch-and-bound algorithms for simulation-based black-box optimization

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,…

Data-Driven Simultaneous Process Optimization and Adsorbent Selection for Vacuum Pressure Swing Adsorption

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…

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

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

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

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…