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050	0	0	_aTP184 .R66 2025
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100	1		_aRomagnoli, José A. _q(José Alberto) _eauthor _4aut _4http://id.loc.gov/vocabulary/relators/aut _0http://id.loc.gov/authorities/names/no99088029 _1http://id.loc.gov/rwo/agents/no99088029
245	1	0	_aAI in chemical engineering : _bunlocking the power within data / _cby José A. Romagnoli, Luis Briceno-Meña, and Vidhyadhar Manee.
246	3		_aArtificial intelligence in chemical engineering
250			_aFirst edition.
264		1	_aBoca Raton : _bCRC Press, _c2025.
300			_axxi, 285 pages _bIllustrations (some color) _c24 cm
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504			_aIncludes bibliographical references and index.
505	2		_aSmart manufacturing & machine learning -- Data and data pretreatment.
520			_a"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.
650		0	_aChemical engineering _xData processing _0http://id.loc.gov/authorities/subjects/sh2009118792
650		0	_aArtificial intelligence _xEngineering applications _0http://id.loc.gov/authorities/subjects/sh2009007735
650		0	_aChemical processes _xData processing
650		0	_aArtificial intelligence _xIndustrial applications
700	1		_aBriceno-Mena, Luis _eauthor _4http://id.loc.gov/vocabulary/relators/aut _1http://id.loc.gov/rwo/agents/n2024028773
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