This “white paper” is a concise perspective of the potential of machine learning in the
process systems engineering (PSE) domain, based on a session during FIPSE 5, held in …

Abstract The Fluid Catalytic Cracking unit (FCC) is a key process that plays an important
technical and economical role in the refining industry. Over the past years, there has been …

This work develops a physics-informed transfer learning framework for modeling and control
of a nonlinear process network with limited training data. Unlike the conventional transfer …

Physics-Informed Machine Learning (PIML) is an emerging computing paradigm that offers a
new approach to tackle multiphysics modeling problems prevalent in the field of chemical …

Smart management of building energy devices, including their optimal control and fault
detection technology, is of great significance to building energy conservation. The core of …

Recently developed physics-informed deep learning is regarded as a transformative
learning philosophy that has been applied in many scientific domains, but such applications …

As one of the most influential industries in public health and the global economy, the
pharmaceutical industry is facing multiple challenges in drug research, development and …

Predictive maintenance for power electronic converters has emerged as a critical area of
research and development. With the rapid advancements in deep-learning techniques, new …

Modeling complex physical processes such as the extraction of aluminum is mainly done
using pure physics-based models derived from first principles. However, the accuracy of …

With the rise of deep learning, there has been renewed interest within the process industries
to utilize data on large-scale nonlinear sensing and control problems. We identify key …