Machine learning in process systems engineering: Challenges and opportunities
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 …
process systems engineering (PSE) domain, based on a session during FIPSE 5, held in …
[HTML][HTML] Artificial intelligence perspectives: A systematic literature review on modeling, control, and optimization of fluid catalytic cracking
MK Khaldi, M Al-Dhaifallah, O Taha - Alexandria Engineering Journal, 2023 - Elsevier
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 …
technical and economical role in the refining industry. Over the past years, there has been …
Modeling and control of a chemical process network using physics-informed transfer learning
M Xiao, Z Wu - Industrial & Engineering Chemistry Research, 2023 - ACS Publications
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 …
of a nonlinear process network with limited training data. Unlike the conventional transfer …
The application of physics-informed machine learning in multiphysics modeling in chemical engineering
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 …
new approach to tackle multiphysics modeling problems prevalent in the field of chemical …
Physics-constrained cooperative learning-based reference models for smart management of chillers considering extrapolation scenarios
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 …
detection technology, is of great significance to building energy conservation. The core of …
Accelerating heat exchanger design by combining physics-informed deep learning and transfer learning
Recently developed physics-informed deep learning is regarded as a transformative
learning philosophy that has been applied in many scientific domains, but such applications …
learning philosophy that has been applied in many scientific domains, but such applications …
[HTML][HTML] Data-driven modeling methods and techniques for pharmaceutical processes
Y Dong, T Yang, Y Xing, J Du, Q Meng - Processes, 2023 - mdpi.com
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 …
pharmaceutical industry is facing multiple challenges in drug research, development and …
Towards Physics-Informed Machine Learning-Based Predictive Maintenance for Power Converters–A Review
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 …
research and development. With the rapid advancements in deep-learning techniques, new …
[HTML][HTML] Deep learning assisted physics-based modeling of aluminum extraction process
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 …
using pure physics-based models derived from first principles. However, the accuracy of …
[HTML][HTML] Machine learning for industrial sensing and control: A survey and practical perspective
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 …
to utilize data on large-scale nonlinear sensing and control problems. We identify key …