Abstract Machine learning models are promising as surrogates in optimization when
replacing difficult to solve equations or black-box type models. This work demonstrates the …

Following the disruption of Russian natural gas flows to Europe, we investigate the impact of
collaborative and selfish behavior of European countries to tackle energy scarcity and …

Nonlinear modeling and optimization is a valuable tool for aiding decisions by engineering
practitioners, but programming an optimization problem based on a complex electrical …

Optimization of chemical processes is challenging due to nonlinearities arising from
chemical principles and discrete design decisions. The optimal synthesis and design of …

Carbon capture and utilization (CCU) can be a pertinent solution to avoid millions of tons of
carbon emission. The challenge is to identify, among numerous available options of carbon …

This study introduces the logic‐based discrete‐Benders decomposition (LD‐BD) for
Generalized Disjunctive Programming (GDP) superstructure problems with ordered Boolean …

The synthesis and optimization of multilevel refrigeration systems is challenging because
it'sa highly non-linear, multi-variable, and multi-modal problem. This work presents a novel …

We present Pyosyn, an open-source framework for systematic superstructure-based process
synthesis, including a new representation, superstructure generation approaches, modeling …

This work proposes a general discrete-time simultaneous scheduling and dynamic
optimization (SSDO) formulation based on the state-task network (STN) representation. This …

In chemical process designs, optimizing stage numbers in the multi-stage separation
process is a common and challenging mixed integer nonlinear programming (MINLP) …