An overview of the recent developments of time-series neural network modeling is
presented along with its use in model predictive control (MPC). A tutorial on the construction …

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 …

Abstract Process Systems Engineering (PSE) is the scientific discipline of integrating scales
and components describing the behavior of a physicochemical system, via mathematical …

The idea of using a simpler surrogate to represent a complex phenomenon has gained
increasing popularity over past three decades. Due to their ability to exploit the black-box …

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 …

Demand forecasting has been a major concern of operational strategy to manage the
inventory and optimize the customer satisfaction level. The researchers have proposed …

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 …

The term hybrid modeling refers to the combination of parametric models (typically derived
from knowledge about the system) and nonparametric models (typically deduced from data) …

The ability to accurately model and simulate chemical processes has been paramount to the
growing success and efficiency in process design and operation. These improvements …