Applications of Artificial Intelligence in Process Systems Engineering

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

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

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