Romagnoli, José A.
AI in chemical engineering : unlocking the power within data / Artificial intelligence in chemical engineering by José A. Romagnoli, Luis Briceno-Meña, and Vidhyadhar Manee. - First edition. - xxi, 285 pages Illustrations (some color) 24 cm -
Includes bibliographical references and index.
Smart manufacturing & machine learning -- Data and data pretreatment.
"Chemical manufacturing is being transformed by Industry 4.0. Today's chemical companies are quickly adapting to the digital world, recognizing the power of connection among products, production equipment, and personnel. As technology evolves and manufactured volumes increase, new computational tools and innovative solutions for daily problems are required. AI in Chemical Engineering: Unlocking the Power Within Data familiarizes readers with the key concepts of machine learning and their implementation in the chemical and process industries for increased efficiency, adaptability, and profitability. It explores the evolution of traditional plant operation into an integrated and smart operational environment and provides readers with the basis for developing and understanding the use of tools to collect and analyze data for insight and application. 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. With concepts and theory introduced in a logical and sequential manner, this practical text is aimed at advanced students of chemical engineering and industrial practitioners and serves as an essential resource to help readers understand current and new developments in this important and evolving field"-- Provided by publisher.
9781032597003 9781032597034
2024023523
Chemical engineering--Data processing
Artificial intelligence--Engineering applications
Chemical processes--Data processing
Artificial intelligence--Industrial applications
TP184 .R66 2025
660.0285/63
AI in chemical engineering : unlocking the power within data / Artificial intelligence in chemical engineering by José A. Romagnoli, Luis Briceno-Meña, and Vidhyadhar Manee. - First edition. - xxi, 285 pages Illustrations (some color) 24 cm -
Includes bibliographical references and index.
Smart manufacturing & machine learning -- Data and data pretreatment.
"Chemical manufacturing is being transformed by Industry 4.0. Today's chemical companies are quickly adapting to the digital world, recognizing the power of connection among products, production equipment, and personnel. As technology evolves and manufactured volumes increase, new computational tools and innovative solutions for daily problems are required. AI in Chemical Engineering: Unlocking the Power Within Data familiarizes readers with the key concepts of machine learning and their implementation in the chemical and process industries for increased efficiency, adaptability, and profitability. It explores the evolution of traditional plant operation into an integrated and smart operational environment and provides readers with the basis for developing and understanding the use of tools to collect and analyze data for insight and application. 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. With concepts and theory introduced in a logical and sequential manner, this practical text is aimed at advanced students of chemical engineering and industrial practitioners and serves as an essential resource to help readers understand current and new developments in this important and evolving field"-- Provided by publisher.
9781032597003 9781032597034
2024023523
Chemical engineering--Data processing
Artificial intelligence--Engineering applications
Chemical processes--Data processing
Artificial intelligence--Industrial applications
TP184 .R66 2025
660.0285/63