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Elsevier

Applications of Artificial Intelligence in Process Systems Engineering

  • 1st Edition - June 5, 2021
  • Latest edition
  • Editors: Jingzheng Ren, Weifeng Shen, Yi Man, Lichun Dong
  • Language: English

Applications of Artificial Intelligence in Process Systems Engineering offers a broad perspective on the issues related to artificial intelligence technologies and their applicati… Read more

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Description

Applications of Artificial Intelligence in Process Systems Engineering offers a broad perspective on the issues related to artificial intelligence technologies and their applications in chemical and process engineering. The book comprehensively introduces the methodology and applications of AI technologies in process systems engineering, making it an indispensable reference for researchers and students. As chemical processes and systems are usually non-linear and complex, thus making it challenging to apply AI methods and technologies, this book is an ideal resource on emerging areas such as cloud computing, big data, the industrial Internet of Things and deep learning.

With process systems engineering's potential to become one of the driving forces for the development of AI technologies, this book covers all the right bases.

Key features

  • Explains the concept of machine learning, deep learning and state-of-the-art intelligent algorithms
  • Discusses AI-based applications in process modeling and simulation, process integration and optimization, process control, and fault detection and diagnosis
  • Gives direction to future development trends of AI technologies in chemical and process engineering

Readership

Researchers at universities and institutes (professors, postdoctoral fellows, PhD and master students) in chemical and process engineering, focusing on process modelling and simulation, process analysis and synthesis, process control, process integration and optimization. Chemical engineering experts (professors, researchers, engineers and technicians) working in the field of process systems engineering, intelligent manufacturing, intelligent process control, big data based chemical engineering, industrial 4.0 research. Chemical and energy consultants working on promoting the sustainable development for chemical and energy production processes. Undergraduate/graduate students: as textbook for (under)graduate students majored in Chemical Engineering, specialization Process System Engineering

Table of contents

Part I: Introduction of AI and Big Data Analytics

1. Artificial Intelligence in Chemical Engineering: Past, Current, and Prospect.

2. Big Data Analytics in Process System Engineering

3. Advanced Computational Tools and Platform for Artificial Intelligence

Part II: Property Prediction

4. Applications of Artificial Neural Networks for Thermodynamics: Vapor-Liquid Equilibrium Predictions

5. Support Vector Machines for The Prediction of Physical-Chemical Properties

6. Thermodynamics Prediction: Neural Networks Based Quantitative Structure Property Relationships

7. Intelligent Approaches to Forecast the Chemical Property: Case Study in Papermaking Process

Part III: Process Modelling

8. Artificial Neural Networks for Modelling of Wastewater Treatment Process

9. COD Forecasting Based LSTM Algorithm for Wastewater Treatment Process

10. Comparisons of Deep Learning Methods for Process Modelling: A Case Study of Bio-Hydrogen Production

11. Deep Learning Based Energy Consumption Forecasting Model for Process Industry

12. Chemical Green Product Design Assisted with Machine Learning: Theory and Methods

Part IV: Process Control and Fault Diagnosis

13. Artificial Intelligence for the Modelling and Control of Chemical Process Systems

14. Artificial Intelligence for Management and Control of The Pollution Minimization

15. Neural Network Based Framework for Fault Diagnosis

16. Application of Artificial Intelligence in Process Fault Diagnosis

Part V: Process Optimization

17. Bi-Level Model Reduction for Multiscale Stochastic Optimization of Cooling Water System

18. Artificial Intelligence Algorithm Based Multi-Object Optimization of Flexible Flow Shop Smart Scheduling

19. Electricity Scheduling Optimization Model for Flexible Production Process

20. Data‐driven multistage adaptive robust optimization framework for planning and scheduling under uncertainty

Product details

  • Edition: 1
  • Latest edition
  • Published: June 5, 2021
  • Language: English

About the editors

JR

Jingzheng Ren

Jingzheng Ren is Assistant Professor of Modelling for Energy, Environment and Sustainability at the Department of Industrial and Systems Engineering of Hong Kong Polytechnic University (PolyU). He has also been nominated as adjunct/honorary associate professor of University of Southern Denmark (Denmark) and associated senior research fellow of the Institute for Security & Development Policy (Stockholm, Sweden). Prof. Ren serves as board member of several scientific journals and published more than 150 papers, authored 1 book, edited more than 10 books and published more than 40 book chapters. His research focuses on process system engineering for better sustainability and mathematical models for solving energy and environmental problems and promoting sustainability transition
Affiliations and expertise
Assistant Professor, Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China

WS

Weifeng Shen

Dr. Weifeng Shen is currently a Professor in chemical engineering at Chongqing University. He obtained his PhD in 2012 at University of Toulouse, France. He worked as a Research Associate at Clarkson University, USA, from 2012 to 2015. He was selected as the "Young Top-Notch Talents ", the "High Level Talents" in Chongqing Province and the " Hundred Young Talents" of Chongqing University. His current research interests reside on artificial intelligence based green chemical products and sustainable processes developments. He has published more than 30 papers in Peer-Reviewed Journals including AIChE Journal, Energy, Industrial & Engineering Chemistry Research.
Affiliations and expertise
School of Chemistry and Chemical Engineering, Chongqing University, Chongqing, China

YM

Yi Man

Dr. Yi Man is currently an Assistant Professor of Process Systems Engineering of South China University of Technology. He has engaged in the teaching and research work in the scientific field of artificial intelligence based process system engineering for a long time. He is active member in China Technical Association of Paper Industry and System Engineering Society of China. He has published more than 30 papers on AI based chemical engineering with focus on methodological aspects of the algorithms development and application of case studies.
Affiliations and expertise
State Key Laboratory of Pulp and Paper Engineering, South China University of Technology, Guangzhou, China

LD

Lichun Dong

Dr. Lichun Dong is a Professor of Chemical Engineering and Deputy Dean at School of Chemistry and Chemical Engineering, Chongqing University. Prof. Dong received his BS degree in chemical engineering at Hebei University of Technology in 1993; and his MS degree in industrial catalysis at Tianjin University in 1996. After spending three years in industry as a process engineer, He went to University of Alabama, USA, in 1999 and received his Ph.D degree in chemical engineering in 2004. Afterwards, he was a postdoctoral scientist in Lehigh University before going back Chongqing University as an Associate Professor in 2007. Prof. Dong’s research focuses on: 1). Process System Engineering for better Sustainability; 2).Nanocatalyst and Nanomaterials; 3). Chemical Process Simulation and Optimization. He has authored or co-authored more than 100 papers in the leading refereed journals including AIChE J, Industrial & Engineering Chemistry, Energy, Renewable & Sustainable Energy Reviews, Chemical Engineering Science, Metabolic Engineering, and granted 18 invention patents.
Affiliations and expertise
School of Chemistry and Chemical Engineering, Chongqing University, Chongqing, China

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