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) …

This review article explores how emerging generative artificial intelligence (GenAI) models,
such as large language models (LLMs), can enhance solution methodologies within process …

Recent developments in data science and machine learning have inspired a new wave of
research into data-driven modeling for mathematical optimization of process applications …

Most optimization problems in engineering can be formulated as 'expensive'black box
problems whose solutions are limited by the number of function evaluations. Frequently …

This paper investigates a new class of modifier-adaptation schemes to overcome plant-
model mismatch in real-time optimization of uncertain processes. The main contribution lies …

Data-driven models are becoming increasingly popular in engineering, on their own or in
combination with mechanistic models. Commonly, the trained models are subsequently …

It has long been known that non-steady state and periodic catalytic reactor operation in
terms of temperature, pressure, and composition can lead to higher overall productivity …

Abstract Hybrid (ie, grey-box) models are a powerful and flexible paradigm for predictive
science and engineering. Grey-box models use data-driven constructs to incorporate …

Highlights•Precision fermentation can produce a range of bio-molecules.•Fermentation
design depends on process systems engineering tools.•Advanced control approaches can …

Drying technologies have been essential for extending the shelf-life of perishable fruits and
vegetables for over a century. Vacuum freeze-drying (VFD), though invented over a hundred …