04345cam a22005418i 4500 9781003455905 FlBoTFG 20251030122308.0 m o d cr ||||||||||| 240523s2025 flu ob 001 0 eng OCoLC-P eng rda OCoLC-P 9781003455905 (ebook) 1003455905 9781040223475 (electronic bk. : PDF) 1040223478 (electronic bk. : PDF) 9781040223499 (electronic bk. : EPUB) 1040223494 (electronic bk. : EPUB) 9781032597003 (hardback) 9781032597034 (paperback) 10.1201/9781003455905 doi (OCoLC)1460025700 (OCoLC-P)1460025700 TP184 COM 094000 bisacsh TDCB bicssc Romagnoli, José A. (José Alberto), author. https://id.oclc.org/worldcat/entity/E39PCjtMM9JrDjJq36Y7QHmPQq 96285 AI in chemical engineering : unlocking the power within data / by José A. Romagnoli and Luis Briceno-Mena. First edition. Boca Raton : CRC Press, 2025. 1 online resource text txt rdacontent computer c rdamedia online resource cr rdacarrier 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. OCLC-licensed vendor bibliographic record. Chemical engineering Data processing. 64662 Artificial intelligence Engineering applications. 96286 Chemical processes Data processing. 96287 Artificial intelligence Industrial applications. 65268 Briceno-Mena, Luis, author. 96288 Taylor & Francis https://www.taylorfrancis.com/books/9781003455905 OCLC metadata license agreement http://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf E-Books 68323 68323