Applications of Artificial Intelligence in Process Systems Engineering
Published on 5. June 2021
540 pages
978-0-12-821743-6 (ISBN)
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.
- 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
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Other editions
Additional editions
Jingzheng Ren | Weifeng Shen | Yi Man
Applications of Artificial Intelligence in Process Systems Engineering
Book
06/2021
Elsevier
€159.70
Shipment within 15-20 days
Content
Part I: Introduction of AI and Big Data Analytics1. Artificial Intelligence in Chemical Engineering: Past, Current, and Prospect.2. Big Data Analytics in Process System Engineering3. Advanced Computational Tools and Platform for Artificial Intelligence
Part II: Property Prediction4. Applications of Artificial Neural Networks for Thermodynamics: Vapor-Liquid Equilibrium Predictions5. Support Vector Machines for The Prediction of Physical-Chemical Properties6. Thermodynamics Prediction: Neural Networks Based Quantitative Structure Property Relationships7. Intelligent Approaches to Forecast the Chemical Property: Case Study in Papermaking Process
Part III: Process Modelling8. Artificial Neural Networks for Modelling of Wastewater Treatment Process9. COD Forecasting Based LSTM Algorithm for Wastewater Treatment Process10. Comparisons of Deep Learning Methods for Process Modelling: A Case Study of Bio-Hydrogen Production11. Deep Learning Based Energy Consumption Forecasting Model for Process Industry12. Chemical Green Product Design Assisted with Machine Learning: Theory and Methods
Part IV: Process Control and Fault Diagnosis13. Artificial Intelligence for the Modelling and Control of Chemical Process Systems14. Artificial Intelligence for Management and Control of The Pollution Minimization15. Neural Network Based Framework for Fault Diagnosis16. Application of Artificial Intelligence in Process Fault Diagnosis
Part V: Process Optimization17. Bi-Level Model Reduction for Multiscale Stochastic Optimization of Cooling Water System18. Artificial Intelligence Algorithm Based Multi-Object Optimization of Flexible Flow Shop Smart Scheduling19. Electricity Scheduling Optimization Model for Flexible Production Process20. Data-driven¿multistage adaptive robust¿optimization¿framework for planning and scheduling under uncertainty
