The chemical processing industry has relied on modeling techniques for process monitoring,
control, diagnosis, optimization, and design, especially since the third industrial revolution …

The transformation of the chemical industry to renewable energy and feedstock supply
requires new paradigms for the design of flexible plants,(bio‐) catalysts, and functional …

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 …

The optimization and machine learning toolkit (OMLT) is an open-source software package
incorporating neural network and gradient-boosted tree surrogate models, which have been …

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 …

We consider the embedding of piecewise-linear deep neural networks (ReLU networks) as
surrogate models in mixed-integer linear programming (MILP) problems. A MILP formulation …

This manuscript reviews recent advances in deterministic global optimization for Mixed-
Integer Nonlinear Programming (MINLP), as well as Constrained Derivative-Free …

Current ammonia production technologies have a significant carbon footprint. In this study,
we present a process synthesis and global optimization framework to discover the efficient …

To reduce greenhouse gas emissions in transportation sector, battery electric vehicle (BEV)
is a better choice towards the ultimate goal of zero-emission. However, the shortened range …

Electric vehicles are one of the effective tools for pollution reduction and sustainable
transportation in emerging markets. In this paper, we investigate the optimal locations and …