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.
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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
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Demonstrates the most recent advances in software, methodologies, examples, and applications of machine learning for the Chemical Engineering domain
Outlines the current and potential future contribution of machine learning, the use of data science, and, ultimately, how to correctly use machine learning tools specifically in chemical engineering
• Devoted to the correct application and interpretation of the results in various phases of the development of decision support systems: data collection, model development, training, and testing, as well as application in chemical engineering
• Examines chemical engineering-specific challenges and problems, including noise, manufacturing equipment, and domain-specific solutions, such as physical knowledge using relevant case study examples
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Produktdetaljer
ISBN
9780443290589
Publisert
2025-06-13
Utgiver
Elsevier - Health Sciences Division
Vekt
1690 gr
Høyde
235 mm
Bredde
191 mm
Aldersnivå
UP, P, 05, 06
Språk
Product language
Engelsk
Format
Product format
Heftet
Antall sider
622