Machine Learning Tools for Chemical Engineering: Methodologies and Applications examines how machine learning (ML) techniques are applied in the field, offering precise, fast, and flexible solutions to address specific challenges.
ML techniques and methodologies offer significant advantages (such as accuracy, speed of execution, and flexibility) over traditional modeling and optimization techniques. This book integrates ML techniques to solve problems inherent to chemical engineering, providing practical tools and a theoretical framework combining knowledge modeling, representation, and management, tailored to the chemical engineering field. It provides a precedent for applied Al, but one that goes beyond purely data-centric ML. It is firmly grounded in the philosophies of knowledge modeling, knowledge representation, search and inference, and knowledge extraction and management.
Aimed at graduate students, researchers, educators, and industry professionals, this book is an essential resource for those seeking to implement ML in chemical processes, aiming to foster optimization and innovation in the sector.
1. Section I. Introduction to machine learning for chemical engineering - p. 1 2. Chapter 1. Introduction to machine learning - p. 3 3. Chapter 2. Data science in chemical engineering - p. 9 4. Chapter 3. Fundamentals of machine learning algorithms - p. 21 5. Section II. Tools and software - p. 29 6. Chapter 4. Machine learning with Python - p. 31 7. Chapter 5. Machine learning with R - p. 57 8. Section III. Supervised learning, unsupervised learning and optimization - p. 105 9. Chapter 6. Linear and polynomial regression - p. 107 10. Chapter 7. Support vector machines - p. 147 11. Chapter 8. Decision trees and random forests - p. 195 12. Chapter 9. Deep learning - p. 241 13. Chapter 10. Clustering and dimensionality reduction - p. 293 14. 10.1 Introduction - p. 293 15. 10.2 Data preprocessing (feature scaling) - p. 294 16. 10.3 Methods - p. 302 17. 10.4 Implementation and expression of results - p. 315 18. 10.5 Conclusion - p. 356 19. Supplementary material - p. 356 20. Chapter 11. Machine learning model optimization - p. 359 21. Section IV. Applications in chemical engineering - p. 391 22. Chapter 12. Machine learning in chemical processes - p. 393 23. Chapter 13. Machine learning in supply chain management - p. 457 24. Chapter 14. Machine learning in energy integration - p. 511 25. Chapter 15. Machine learning in time series forecasting - p. 543 26. Chapter 16. Machine learning in optimal water management in the exploitation of unconventional fossil fuels - p. 567 27. 16.1 Introduction - p. 567 28. 16.2 Problem statement - p. 568 29. 16.3 Methodology - p. 569 30. 16.4 Case study - p. 577 31. 16.5 Results and discussion - p. 578 32. 16.6 Conclusions - p. 585 33. 16.7 Nomenclature - p. 586 34. Chapter 17. Challenges and future scope - p. 591 35. Appendix - p. 599 36. Index - p. 613
Machine Learning Tools for Chemical Engineering: Methodologies and Applications examines how machine learning (ML) techniques are applied in the field, offering precise, fast, and flexible solutions to address specific challenges.
ML techniques and methodologies offer significant advantages (such as accuracy, speed of execution, and flexibility) over traditional modeling and optimization techniques. This book integrates ML techniques to solve problems inherent to chemical engineering, providing practical tools and a theoretical framework combining knowledge modeling, representation, and management, tailored to the chemical engineering field. It provides a precedent for applied Al, but one that goes beyond purely data-centric ML. It is firmly grounded in the philosophies of knowledge modeling, knowledge representation, search and inference, and knowledge extraction and management.
Aimed at graduate students, researchers, educators, and industry professionals, this book is an essential resource for those seeking to implement ML in chemical processes, aiming to foster optimization and innovation in the sector.
1. Section I. Introduction to machine learning for chemical engineering - p. 1 2. Chapter 1. Introduction to machine learning - p. 3 3. Chapter 2. Data science in chemical engineering - p. 9 4. Chapter 3. Fundamentals of machine learning algorithms - p. 21 5. Section II. Tools and software - p. 29 6. Chapter 4. Machine learning with Python - p. 31 7. Chapter 5. Machine learning with R - p. 57 8. Section III. Supervised learning, unsupervised learning and optimization - p. 105 9. Chapter 6. Linear and polynomial regression - p. 107 10. Chapter 7. Support vector machines - p. 147 11. Chapter 8. Decision trees and random forests - p. 195 12. Chapter 9. Deep learning - p. 241 13. Chapter 10. Clustering and dimensionality reduction - p. 293 14. 10.1 Introduction - p. 293 15. 10.2 Data preprocessing (feature scaling) - p. 294 16. 10.3 Methods - p. 302 17. 10.4 Implementation and expression of results - p. 315 18. 10.5 Conclusion - p. 356 19. Supplementary material - p. 356 20. Chapter 11. Machine learning model optimization - p. 359 21. Section IV. Applications in chemical engineering - p. 391 22. Chapter 12. Machine learning in chemical processes - p. 393 23. Chapter 13. Machine learning in supply chain management - p. 457 24. Chapter 14. Machine learning in energy integration - p. 511 25. Chapter 15. Machine learning in time series forecasting - p. 543 26. Chapter 16. Machine learning in optimal water management in the exploitation of unconventional fossil fuels - p. 567 27. 16.1 Introduction - p. 567 28. 16.2 Problem statement - p. 568 29. 16.3 Methodology - p. 569 30. 16.4 Case study - p. 577 31. 16.5 Results and discussion - p. 578 32. 16.6 Conclusions - p. 585 33. 16.7 Nomenclature - p. 586 34. Chapter 17. Challenges and future scope - p. 591 35. Appendix - p. 599 36. Index - p. 613
