04730cam a2200493 i 4500 23704533 20260310135607.0 t| 240523s2025 flua b 001 0 eng 2024023523 9781032597003 hardback 9781032597034 paperback 9781003455905 ebook 23704533 (DLC)23704533 DLC eng rda DLC DLC DLC-MRC DLC pcc TP184 .R66 2025 660.0285/63 23/eng/20241009 Romagnoli, José A. (José Alberto) author aut http://id.loc.gov/vocabulary/relators/aut http://id.loc.gov/authorities/names/no99088029 http://id.loc.gov/rwo/agents/no99088029 AI in chemical engineering : unlocking the power within data / by José A. Romagnoli, Luis Briceno-Meña, and Vidhyadhar Manee. Artificial intelligence in chemical engineering First edition. Boca Raton : CRC Press, 2025. xxi, 285 pages Illustrations (some color) 24 cm text txt rdacontent http://id.loc.gov/vocabulary/contentTypes/txt unmediated n rdamedia http://id.loc.gov/vocabulary/mediaTypes/n volume nc rdacarrier http://id.loc.gov/vocabulary/carriers/nc http://id.loc.gov/vocabulary/millus/ill 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. Chemical engineering Data processing http://id.loc.gov/authorities/subjects/sh2009118792 Artificial intelligence Engineering applications http://id.loc.gov/authorities/subjects/sh2009007735 Chemical processes Data processing Artificial intelligence Industrial applications Briceno-Mena, Luis author http://id.loc.gov/vocabulary/relators/aut http://id.loc.gov/rwo/agents/n2024028773 http://id.loc.gov/resources/instances/23704533 http://id.loc.gov/ontologies/bibframe/instanceOf http://id.loc.gov/resources/works/23704533 DLC bibframe2marc v2.10-dev 20250509 DLC https://github.com/lcnetdev/bibframe2marc/releases/tag/v2.10-dev 7 cbc orignew 1 ecip 20 y-gencatlg lcc BOOK 12941 12941 0 0 lcc 0 0 MAIN MAIN 2026-03-10 0 TP184 .R66 2025 51952000222835 2026-03-10 2026-03-10 BOOK 0 0 lcc 0 0 MAIN FEMALE 2026-03-10 0 TP184 .R66 2025 51952000222828 2026-03-10 2026-03-10 BOOK