High-Performance Computing and Artificial Intelligence in Process Engineering

by Mingheng Li (Editor), Yi Heng (Editor)

Overview

High-performance computing (HPC) and artificial intelligence (AI) in process engineering involve complex system modelling, data analysis, optimization design, and real-time monitoring. Key methods include data integration, model construction, optimization algorithms, machine learning, deep learning, parallel computing, and real-time analytics. These techniques significantly enhance production efficiency, reduce costs, and improve system stability. They also promote industrial intelligence, creating new opportunities and challenges in process engineering. This integration supports the advancement of Industry 4.0 and smart manufacturing.

Key Features:

• Provides a systematic review of state-of-the-art artificial intelligence technologies and high-performance computing, and their applications in process engineering
• Introduces the development of traditional process simulators in process engineering and new numerical solvers based on data-driven and physics-informed neural networks approaches
• Provides perspectives of high-performance computing and artificial intelligence from industrial leaders in software and hardware
• Aimed at researchers and industrial practitioners in process engineering, manufacturing, data science, artificial intelligence and high-performance computing

Product Details

ISBN-13:	9780750361743
Publisher:	Institute of Physics Publishing
Publication date:	04/04/2025
Series:	IOP ebooks
Sold by:	Barnes & Noble
Format:	eBook
Pages:	300
File size:	24 MB
Note:	This product may take a few minutes to download.

About the Author

Mingheng Li is a professor of chemical engineering specializing in process systems engineering, with a focus on materials, energy, and environmental applications. He has pioneered innovations in the processing of low-emissivity and self-cleaning coatings and advanced non-conventional dynamic and cyclic reverse osmosis techniques. He has served as an editor for the American Institute of Physics Publishing.

Yi Heng obtained his Ph.D. degree from RWTH Aachen University, Germany. He is a professor of applied mathematics. His work focuses on inverse problems, high performance computing, artificial intelligence, and their applications to various areas of science and engineering. He has served as an executive member of the editorial board for Science Bulletin.

Table of Contents

Chapter 1: Artificial intelligence and the future of process engineering

Chapter 2: Machine learning in optimal control and process modeling

Chapter 3: Graph-based control invariant set approximation and its applications

Chapter 4: Machine learning-based multiscale modeling and control of quantum dot manufacturing and their applications

Chapter 5: The rise of time-travelers: are transformer-based models the key to unlocking a new paradigm in surrogate modeling for dynamic systems?

Chapter 6: Optimization-based algorithms for solving inverse problems of parabilic PDEs

Chapter 7: Deep learning-based approach for solving forward and inverse partial differential equation problems

Chapter 8: An active subspace based swarm intelligence method with its application in an optimal design problem

Chapter 9: Supercomputing and machine-learning-aided optimal design of high permeability seawater reverse osmosis membrane systems

Chapter 10: Supercomputing-based inverse identification of high-resolution atmospheric pollutant source intensity deistributions

Chapter 11: Enhancing boiling heat transfer via model-based experimental analysis