Applications of Artificial Intelligence in Process Systems Engineering

Herausgegeben:Ren, Jingzheng; Shen, Weifeng; Man, Yi; DOng, Lichun

• Broschiertes Buch

Produktbeschreibung

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.

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Produktdetails

• Verlag: Elsevier / Elsevier Science & Technology
• Artikelnr. des Verlages: C2019-0-02806-3
• Seitenzahl: 540
• Erscheinungstermin: 17. Juni 2021
• Englisch
• Abmessung: 152mm x 227mm x 33mm
• Gewicht: 860g
• ISBN-13: 9780128210925
• ISBN-10: 0128210923
• Artikelnr.: 59961349

Autorenporträt

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

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.

Inhaltsangabe

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