Process intensification offers the potential to drastically reduce the energy consumption and
cost of producing chemicals from both bulk and distributed feedstocks. This review article …

Carbon capture, utilization, and storage (CCUS) is a promising pathway to decarbonize
fossil-based power and industrial sectors and is a bridging technology for a sustainable …

Multi-energy systems (MESs) make it possible to satisfy consumer's energy demand using
multiple coupled energy infrastructures, thus increasing the reliability of the energy supply …

The energy and water sectors are intrinsically linked to meet several consumer needs. A
holistic analysis is required to quantify the technoeconomic and environmental benefits and …

We present a framework for the application of design and control optimization via multi‐
parametric programming through four case studies. We develop design dependent multi …

This paper proposes a novel risk-based robust mixed-integer linear programming to design
a decentralized closed-loop supply chain. The model is formulated as an uncertain bi-level …

Deep learning models are a class of approximate models that are proven to have strong
predictive capabilities for representing complex phenomena. The introduction of deep …

We present differentiable predictive control (DPC) as a deep learning-based alternative to
the explicit model predictive control (MPC) for unknown nonlinear systems. In the DPC …

The inevitable presence of uncertain parameters in critical applications of process
optimization can lead to undesirable or infeasible solutions. For this reason, optimization …

We introduce the concept of decision‐focused surrogate modeling for solving
computationally challenging nonlinear optimization problems in real‐time settings. The …