Subsequent chapters cover a wide spectrum of AI applications in chemical engineering. From supervised and unsupervised learning for process modeling to the advanced realm of deep learning applications, this book explores neural networks, convolutional and recurrent architectures, and their real-world applications in process optimization and analysis.
Table of Contents
1. Introduction to Artificial Intelligence in Chemical Engineering
2. Fundamentals of Artificial Intelligence and Machine Learning for Chemical Engineers
3. Data Acquisition and Integration in AI Applications
4. Predictive Modeling and Process Optimization
5. Control Systems and Decision Support with AI
6. Real-time Decision Support Systems in Process Industries
7. AI Applications in Chemical Reaction Engineering
8. AI in Process Safety and Risk Management
9. AI in Sustainable and Green Processes
10. Sustainable Manufacturing and Green Chemistry with AI
11. AI for Energy Efficiency and Renewable Integration
12. Smart Manufacturing and Industry 4.0 Integration
13. AI in Quality Control and Product Development
14. Advanced Process Monitoring and Predictive Maintenance with AI
15. Human-AI Collaboration in Chemical Engineering
16. AI in Chemical Education and Training
17. AI for Regulatory Compliance in Chemical Industries
18. Case Studies and Practical Implementations
19. Ethical Considerations and Challenges in AI Integration
20. Future Trends and Innovations in AI and Chemical Engineering
21. Conclusion and Outlook