Machine Learning Tools for Chemical Engineering
Elsevier - Health Sciences Division (Verlag)
978-0-443-29058-9 (ISBN)
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ML techniques and methodologies offer significant advantages (such as accuracy, speed of execution, and flexibility) over traditional modelling and optimization techniques. The 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 modelling, 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.
Francisco Javier López Flores received his Master’s and Ph.D. degrees from the Chemical Engineering Department at the Universidad Michoacana de San Nicolás de Hidalgo in Mexico in 2020 and 2024, respectively. His research interests include process optimization, energy integration, planning strategies, and machine learning. He has published more than ten scientific papers and presented his research at ten international and regional conferences. Rogelio Ochoa-Barragán earned his Ph.D. and Master’s degrees in Chemical Engineering from the Universidad Michoacana de San Nicolás de Hidalgo in Mexico in 2020 and 2024, respectively. His current research focuses on process optimization, energy network management, social justice, and machine learning. His work has been presented at eleven international and regional conferences. He has published more than ten scientific articles and contributed to two books. Alma Yunuen Raya Tapia is currently pursuing a Ph.D. in Chemical Engineering at the Universidad Michoacana de San Nicolás de Hidalgo. She earned her Master of Science in Chemical Engineering in 2021 with honors, following a degree in Chemical Engineering from the Technological Institute of Lázaro Cárdenas in 2019. Her research focuses on materials synthesis, photocatalysis, dye degradation, wastewater treatment, and the strategic planning of the water-energy-food nexus, combined with machine learning techniques. She has published more than ten scientific articles and presented her work at five international and national conferences. César Ramírez-Márquez is a Postdoctoral Fellow at the Chemical Engineering Department of the Universidad Michoacana de San Nicolás de Hidalgo, Mexico. He earned his Ph.D. from the University of Guanajuato, Mexico, in 2020. His current research focuses on the production of materials for the solar energy industry and base chemicals in the chemical industry. He has published more than 55 journal papers, six book chapters, presented his work at over fifteen international and regional conferences, and holds four patents. José María Ponce-Ortega is a Professor in the Chemical Engineering Department at the Universidad Michoacana de San Nicolás de Hidalgo, Mexico. He earned his Ph.D. and Master’s degrees in Chemical Engineering from the Institute of Technology of Celaya, Mexico, in 2009 and 2003, respectively. He completed postdoctoral research at Texas A&M University, USA, and served as a visiting scholar at Carnegie Mellon University, USA. Dr. Ponce-Ortega is a full professor and a member of the National Research System of Mexico. His research focuses on the optimization of chemical processes, sustainable design, energy, mass, water, and property integration, and supply chain optimization. He has published more than 310 papers, five books, and 60 book chapters. He has supervised 30 Ph.D. students and 50 Master’s students and secured funding for 15 research projects totaling approximately $1,000,000. Dr. Ponce-Ortega serves on the editorial boards of Clean Technologies and Environmental Policy and Process Integration and Optimization for Sustainability, and is a subject editor for Sustainable Production and Consumption, as well as associate editor in Frontiers in Chemical Engineering.
Section I: Introduction to Machine Learning for Chemical Engineering
Chapter 1. Introduction to Machine Learning
Chapter 2. Data Science in Chemical Engineering
Chapter 3. Fundamentals of Machine Learning Algorithms
Section II: Tools and Software
4. Machine Learning with Python
5. Machine Learning with R
Section lll: Supervised Learning, Unsupervised Learning and Optimization
6. Linear and polynomial regression
7. Support Vector Machines
8. Decision Trees and Random Forests
9. Deep Learning
10. Clustering and Dimensionality Reduction
11. Machine Learning Model Optimization
12. Machine Learning in Chemical Processes
13. Machine learning in Supply Chain Management
14. Machine Learning in Energy Integration
15. Machine Learning in Time Series Forecasting
16. Machine Learning in Optimal Water Management in the Exploitation of Unconventional Fossil Fuels
17. Challenges and Future Scope
Appendix
| Erscheint lt. Verlag | 1.5.2025 |
|---|---|
| Verlagsort | Philadelphia |
| Sprache | englisch |
| Maße | 152 x 229 mm |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| Naturwissenschaften ► Chemie ► Technische Chemie | |
| Technik | |
| ISBN-10 | 0-443-29058-X / 044329058X |
| ISBN-13 | 978-0-443-29058-9 / 9780443290589 |
| Zustand | Neuware |
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