Chemical engineers who develop fundamental models often have difficulties estimating all
model parameters due to problems with parameter identifiability and estimability. These two …

Building a representative model of a complex dynamical system from empirical evidence
remains a highly challenging problem. Classically, these models are described by systems …

Chemical engineering applications have been a source of challenging optimization
problems for over 50 years. For many chemical process systems, detailed steady state and …

Integration of real-time optimization and control with higher level decision-making
(scheduling and planning) is an essential goal for profitable operation in a highly …

To facilitate the real-time implementation of nonlinear model predictive control (NMPC), this
paper proposes a deep learning-based NMPC scheme, in which the NMPC law is …

With growing needs to develop and improve climate-friendly processes, optimization
strategies are essential at all levels of decision-making in chemical and energy processes …

We describe pyomo. dae, an open source Python-based modeling framework that enables
high-level abstract specification of optimization problems with differential and algebraic …

Distributed optimization, based on a decomposition of the entire optimization problem, has
been applied to many complex decision making problems in process systems engineering …

We present a detailed optimal control model that captures spatiotemporal interactions
between gas and electric transmission networks. We use the model to study flexibility and …

We present a stochastic optimal control model to optimize gas network inventories in the
face of system uncertainties. The model captures detailed network dynamics and …