| ISBN13: | 9781032597003 |
| ISBN10: | 1032597003 |
| Binding: | Hardback |
| No. of pages: | 308 pages |
| Size: | 234x156 mm |
| Weight: | 730 g |
| Language: | English |
| Illustrations: | 15 Illustrations, black & white; 182 Illustrations, color; 53 Halftones, color; 15 Line drawings, black & white; 129 Line drawings, color; 14 Tables, black & white |
| 700 |
AI in Chemical Engineering
GBP 110.00
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This book explains machine learning and its implementation in the chemical and process industries. It explores the evolution of traditional plant operation into an integrated and smart operational environment and provides readers with the basis for understanding the use of tools to collect and analyze data for insight and application.
Industry 4.0 is revolutionizing chemical manufacturing. Today's chemical companies are swiftly embracing the digital era, recognizing the significant benefits of interconnected products, production equipment, and personnel. As technology advances and production volumes grow, there is an increasing need for new computational tools and innovative solutions to address everyday challenges. AI in Chemical Engineering: Unlocking the Power Within Data introduces readers to the essential concepts of machine learning and their application in the chemical and process industries, aiming to enhance efficiency, adaptability, and profitability. This work delves into the transformation of traditional plant operations into integrated and intelligent systems, providing readers with a foundation for developing and understanding the tools necessary for data collection and analysis, thereby gaining valuable insights and practical applications.
- Introduces the principles and applications of unsupervised learning and discusses the role of machine learning in extracting information from plant data and transforming it into knowledge
- Conveys the concepts, principles, and applications of supervised learning, setting the stage for developing advanced monitoring systems, complex predictive models, and advanced computer vision applications
- Explores implementation of reinforced learning ideas for chemical process control and optimization, investigating various model structures and discussing their practical implementation in both simulation and experimental units
- Incorporates sample code examples in Python to illustrate key concepts
- Includes real-life case studies in the context of chemical engineering and covers a wide variety of chemical engineering applications from oil and gas to bioengineering and electrochemistry
- Clearly defines types of problems in chemical engineering subject to AI solutions and relates them to subfields of AI
This practical text, designed for advanced chemical engineering students and industry practitioners, introduces concepts and theories in a logical and sequential manner. It serves as an essential resource, helping readers understand both current and emerging developments in this important and evolving field.
1. Smart Manufacturing and Machine Learning. 2. Data and Data Pretreatment. 3. Dimensionality Reduction (DR). 4. Clustering. 5. Unsupervised Learning Case Study. 6. Concepts and Definitions. 7. Predictive Models. 8. Supervised Learning Case Studies. 9. Deep Learning. 10. Deep Learning Case Studies. 11. Reinforcement Learning. 12. Reinforcement Learning Case Studies. 13. Generative AI. Appendix A. FASTMAN-JMP Tool Architecture. Appendix B. Tennessee Eastman Process (TEP). Appendix C. High-Temperature PEM Fuel Cell Modelling. Appendix D. Distance Metrics for Clustering.
