AI in Chemical Engineering: Unlocking the Power Within Data
About this ebook
- 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.
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About the author
Jose A. Romagnoli is the Gordon & Mary Cain Endowed Chair Professor of Process Systems Engineering, Department of Chemical Engineering, Louisiana State University. He received his Ph.D. from University of Minnesota.
Luis A. Briceno-Mena works at Dow on their Machine Learning Optimization and Statistics team. He received his Ph.D. in Chemical Engineering from Louisiana State University.
Vidhyadhar Manee is a Senior Scientist in Process Research at Boehringer Ingelheim Pharmaceuticals Inc. He received his Ph.D. in Chemical Engineering from Louisiana State University.
