Applications of Artificial Intelligence in Process Systems Engineering
- 1st Edition - June 5, 2021
- Imprint: Elsevier
- Editors: Jingzheng Ren, Weifeng Shen, Yi Man, Lichun Dong
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 2 1 0 9 2 - 5
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 2 1 7 4 3 - 6
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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Request a sales quoteApplications 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.
- 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
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
- Cover image
- Title page
- Table of Contents
- Copyright
- Contributors
- Chapter 1: Artificial intelligence in process systems engineering
- Abstract
- Acknowledgment
- 1: What is process system engineering
- 2: What is artificial intelligence?
- 3: The AI-based application in PSE
- 4: Summary
- Chapter 2: Deep learning in QSPR modeling for the prediction of critical properties
- Abstract
- Acknowledgments
- 1: Introduction
- 2: Methodology
- 3: Results and discussion
- 4: Conclusions
- Chapter 3: Predictive deep learning models for environmental properties
- Abstract
- Acknowledgments
- 1: Introduction
- 2: Methodology
- 3: Results and discussion
- 4: Conclusions
- Chapter 4: Automated extraction of molecular features in machine learning-based environmental property prediction
- Abstract
- Acknowledgments
- 1: Introduction
- 2: Methodology
- 3: Results and discussion
- 4: Conclusions
- Chapter 5: Intelligent approaches to forecast the chemical property: Case study in papermaking process
- Abstract
- Acknowledgments
- 1: Introduction
- 2: Literature review
- 3: Intelligent prediction method
- 4: Beating degree prediction model of tissue paper
- 5: Conclusion
- Chapter 6: Machine learning-based energy consumption forecasting model for process industry—Hybrid PSO-LSSVM algorithm electricity consumption forecasting model for papermaking process
- Abstract
- 1: Introduction
- 2: Methodology
- 3: Results and discussion
- 4: Conclusions
- Appendix A
- Appendix B
- Appendix C
- Appendix D
- Chapter 7: Artificial intelligence algorithm application in wastewater treatment plants: Case study for COD load prediction
- Abstract
- 1: Introduction
- 2: Literature review
- 3: Artificial intelligence algorithm model
- 4: Case study: COD prediction model based on the GBDT algorithm
- 5: Discussion of results
- 6: Conclusion
- Chapter 8: Application of machine learning algorithms to predict the performance of coal gasification process
- Abstract
- 1: Introduction
- 2: Materials and methods
- 3: Results and discussion
- 4: Conclusions and future perspectives
- Chapter 9: Artificial neural network and its applications: Unraveling the efficiency for hydrogen production
- Abstract
- Acknowledgments
- 1: Introduction
- 2: Artificial neural network
- 3: Principle of ANN
- 4: Methodology of ANN model
- 5: Applications of ANN model
- 6: ANN and hydrogen production
- 7: Conclusion
- Chapter 10: Fault diagnosis in industrial processes based on predictive and descriptive machine learning methods
- Abstract
- Acknowledgment
- 1: Introduction
- 2: FDD in large-scale processes
- 3: Data-driven FDD methods in industrial systems: A review
- 4: Fault diagnosis in pulp and paper mills: Case studies
- 5: Concluding remarks and lesson learned
- Chapter 11: Application of artificial intelligence in modeling, control, and fault diagnosis
- Abstract
- 1: Artificial neural network
- 2: Fuzzy logic
- 3: Support vector machine
- Chapter 12: Integrated machine learning framework for computer-aided chemical product design
- Abstract
- Acknowledgments
- 1: Introduction
- 2: An integrated ML framework for computer-aided molecular design
- 3: Establishment of ML model for computer-aided molecular design
- 4: Case studies
- 5: Conclusions
- Chapter 13: Machine learning methods in drug delivery
- Abstract
- 1: Introduction
- 2: Types of machine learning methods used in drug delivery
- 3: Applications of machine learning methods in drug delivery
- 4: Conclusion
- Chapter 14: On the robust and stable flowshop scheduling under stochastic and dynamic disruptions
- Abstract
- Acknowledgments
- 1: Introduction
- 2: Literature review
- 3: Problem formulation
- 4: Hybridized evolutionary multiobjective optimization (EMO) methods
- 5: Computational study
- 6: Conclusions and future research directions
- Chapter 15: Bi-level model reductions for multiscale stochastic optimization of cooling water system
- Abstract
- 1: Introduction
- 2: Methodology
- 3: Optimal design of experiments
- 4: Multisample CFD simulation
- 5: Reduced models construction
- 6: Illustrative example
- 7: Conclusion
- Chapter 16: Artificial intelligence algorithm-based multi-objective optimization model of flexible flow shop smart scheduling
- Abstract
- 1: Introduction
- 2: Literature review
- 3: Flexible flow shop production scheduling model
- 4: Case study
- 5: Conclusions
- Chapter 17: Machine learning-based intermittent equipment scheduling model for flexible production process
- Abstract
- 1: Introduction
- 2: Problem description and solution
- 3: Case study
- 4: Conclusion
- Chapter 18: Artificial intelligence algorithms for proactive dynamic vehicle routing problem
- Abstract
- 1: Introduction
- 2: Main approaches for PDVRP
- 3: Problem description
- 4: TDVRPSTW formulation
- 5: The algorithm
- 6: Numerical experiment
- 7: Conclusions and future research directions
- Edition: 1
- Published: June 5, 2021
- Imprint: Elsevier
- No. of pages: 540
- Language: English
- Paperback ISBN: 9780128210925
- eBook ISBN: 9780128217436
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, ChinaWS
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, ChinaYM
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, ChinaLD
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, ChinaRead Applications of Artificial Intelligence in Process Systems Engineering on ScienceDirect