Applied Machine Learning in Chemical Process Engineering : A Practical Approach

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Applied Machine Learning in Chemical Process Engineering : A Practical Approach

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  • 製本 Paperback:紙装版/ペーパーバック版/ページ数 350 p.
  • 言語 ENG
  • 商品コード 9780443339431

Full Description

As machine learning capabilities and functionality increases, more industry experts and researchers are integrating applied machine learning into their research. Applied Machine Learning in Chemical Process Engineering: A Practical Approach serves as a comprehensive guide to equip the reader with the fundamental theory, practical guidance, methodologies, experimental design and troubleshooting knowledge needed to integrate machine learning into their processes. This book offers a comprehensive overview of all aspects of machine learning, from inception to integration that will allow readers from any scientific discipline to begin to examine the capabilities of machine learning. This book will then build upon this overview to offer worked examples and case studies, alongside practical methods-based guidance to walk the reader through integrating machine learning end-to-end. Finally, this book will offer critical discussion of concepts that are interwoven into the ever-evolving principles of machine learning such as ethics, safety and culpability that are crucial when working with machine learning. Applied Machine Learning in Chemical Process Engineering: A Practical Approach will be an invaluable resource for researchers, professionals in industry and academia, and students at graduate level and above who work in chemical engineering and are looking to automate, optimize or intensify their chemical processes. This book will also help professionals in other disciplines and industries looking into integrate machine learning into their work, such as though looking to scale up their processes to an industrial scale or conduct novel research.

Contents

1. Introduction to Machine Learning for Chemical Engineers
2. Data Handling and Preprocessing in Chemical Datasets
3. Predictive Modeling for Chemical Processes
4. Unsupervised Learning and Pattern Recognition in Chemical Data
5. Process Optimization and Control using Machine Learning
6. Molecular Simulations and Deep Learning
7. Reinforcement Learning in Process Design
8. Challenges and Ethical Considerations in Implementing ML
9. Case Studies: Breakthroughs at the Intersection of ML and Chemical Engineering
10. Physics-Informed Neural Networks in Chemical Engineering
11. Explainable AI and Sustainable Computing in Machine Learning
12. Future of AI in Chemical and Process Engineering Scope: Future trends and technologies in ML for chemical engineering

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