Artificial Intelligence in Chemical Engineering explores the integration of artificial intelligence (AI) into various facets of chemical engineering. The book introduces historical information, highlights current state and trends in AI applications, and discusses challenges and opportunities within the field. Foundational principles of AI and machine learning are thoroughly covered, giving readers a solid understanding of basic AI principles, machine learning algorithms, and the crucial processes of model training and validation. The book then delves into the critical phase of data acquisition and preprocessing for AI models, addressing strategies for data collection, ensuring data quality, and techniques for feature engineering and selection.
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.
1. Chapter 1. Introduction to artificial intelligence in chemical engineering - p. 3 2. Chapter 2. Artificial intelligence in chemical engineering data acquisition and integration - p. 57 3. Chapter 3. Predictive modeling and process optimization in chemical engineering - p. 103 4. Chapter 4. Artificial intelligence in chemical engineering and process technologies - p. 135 5. Chapter 5. Artificial intelligence in chemical reaction engineering - p. 161 6. Chapter 6. Artificial intelligence in chemical kinetics and reactor design - p. 183 7. Chapter 7. Artificial intelligence in heat and mass transfer in chemical engineering processes - p. 215 8. Chapter 8. Artificial intelligence in fluid dynamics and multiphase flow systems - p. 257 9. Section II: Monitoring, Control, and Safety - p. S285 10. Chapter 9. Artificial intelligence in chemical engineering process monitoring and predictive maintenance - p. 287 11. Chapter 10. Real-time decision support systems in chemical and process engineering - p. 313 12. Chapter 11. Artificial intelligence in quality control and product development - p. 349 13. Chapter 12. Artificial intelligence in process safety and risk management - p. 383 14. Section III: Sustainable and smart chemical engineering - p. S415 15. Chapter 13. Artificial intelligence in sustainable manufacturing and chemical processes - p. 417 16. Chapter 14. Artificial intelligence in sustainable and smart agrochemicals - p. 443 17. Chapter 15. Artificial intelligence in energy optimization and renewable energy system integration - p. 471 18. Chapter 16. AI-enhanced supply chain management in chemical engineering - p. 499 19. Section IV: Education, governance and ethics - p. S519 20. Chapter 17. Artificial intelligence in chemical engineering and applied sciences education - p. 521 21. Chapter 18. Artificial intelligence for regulatory compliance in chemical engineering industries - p. 555 22. Chapter 19. Human-AI collaboration in chemical engineering - p. 593 23. Chapter 20. Regulatory compliance and ethical considerations in integration artificial intelligence - p. 627 24. Index - p. 651
Artificial Intelligence in Chemical Engineering explores the integration of artificial intelligence (AI) into various facets of chemical engineering. The book introduces historical information, highlights current state and trends in AI applications, and discusses challenges and opportunities within the field. Foundational principles of AI and machine learning are thoroughly covered, giving readers a solid understanding of basic AI principles, machine learning algorithms, and the crucial processes of model training and validation. The book then delves into the critical phase of data acquisition and preprocessing for AI models, addressing strategies for data collection, ensuring data quality, and techniques for feature engineering and selection.
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.
1. Chapter 1. Introduction to artificial intelligence in chemical engineering - p. 3 2. Chapter 2. Artificial intelligence in chemical engineering data acquisition and integration - p. 57 3. Chapter 3. Predictive modeling and process optimization in chemical engineering - p. 103 4. Chapter 4. Artificial intelligence in chemical engineering and process technologies - p. 135 5. Chapter 5. Artificial intelligence in chemical reaction engineering - p. 161 6. Chapter 6. Artificial intelligence in chemical kinetics and reactor design - p. 183 7. Chapter 7. Artificial intelligence in heat and mass transfer in chemical engineering processes - p. 215 8. Chapter 8. Artificial intelligence in fluid dynamics and multiphase flow systems - p. 257 9. Section II: Monitoring, Control, and Safety - p. S285 10. Chapter 9. Artificial intelligence in chemical engineering process monitoring and predictive maintenance - p. 287 11. Chapter 10. Real-time decision support systems in chemical and process engineering - p. 313 12. Chapter 11. Artificial intelligence in quality control and product development - p. 349 13. Chapter 12. Artificial intelligence in process safety and risk management - p. 383 14. Section III: Sustainable and smart chemical engineering - p. S415 15. Chapter 13. Artificial intelligence in sustainable manufacturing and chemical processes - p. 417 16. Chapter 14. Artificial intelligence in sustainable and smart agrochemicals - p. 443 17. Chapter 15. Artificial intelligence in energy optimization and renewable energy system integration - p. 471 18. Chapter 16. AI-enhanced supply chain management in chemical engineering - p. 499 19. Section IV: Education, governance and ethics - p. S519 20. Chapter 17. Artificial intelligence in chemical engineering and applied sciences education - p. 521 21. Chapter 18. Artificial intelligence for regulatory compliance in chemical engineering industries - p. 555 22. Chapter 19. Human-AI collaboration in chemical engineering - p. 593 23. Chapter 20. Regulatory compliance and ethical considerations in integration artificial intelligence - p. 627 24. Index - p. 651
