Machine Learning Tools for Chemical Engineering : Methodologies and Applications

López-Flores, Francisco Javier/ Ochoa-Barragán, Rogelio/ Raya-Tapia, Alma Yunuen

Elsevier - Health Sciences Division（2025/06発売）

• 製本 Paperback:紙装版/ペーパーバック版／ページ数 622 p.
• 言語 ENG
• 商品コード 9780443290589
• DDC分類 006.31

Full Description

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.

Contents

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