Francisco Javier López-Flores, Rogelio Ochoa-Barragán, Alma Yunuen Raya-Tapia, César Ramírez-Márquez, José Maria Ponce-Ortega

Machine Learning Tools for Chemical Engineering

Methodologies and Applications

• Broschiertes Buch

Produktbeschreibung

Machine Learning Tools for Chemical Engineering: Methodologies and Applications explores the integration of Machine Learning (ML) techniques within the chemical engineering domain. This book highlights the precision, speed, and flexibility of ML solutions in addressing complex challenges that traditional methods struggle with. It offers both practical tools and a theoretical framework, combining knowledge modeling, representation, and management tailored to the unique needs of chemical engineering. Beyond the introduction of ML, the book delves into philosophies such as knowledge modeling, knowledge representation, search and inference, and knowledge extraction and management. It is an invaluable resource for graduate students, researchers, educators, and industry professionals aiming to optimize and innovate in chemical processes through ML applications.

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Produktdetails

• Verlag: Elsevier Science
• Seitenzahl: 352
• Erscheinungstermin: 1. Mai 2025
• Englisch
• ISBN-13: 9780443290589
• ISBN-10: 044329058X
• Artikelnr.: 71924145

Autorenporträt

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.

Inhaltsangabe

Section I: Introduction to Machine Learning for Chemical Engineering
1. Introduction to Machine Learning
2. Data Science in Chemical Engineering
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