000 04345cam a22005418i 4500
001 9781003455905
003 FlBoTFG
005 20251030122308.0
006 m o d
007 cr |||||||||||
008 240523s2025 flu ob 001 0 eng
040 _aOCoLC-P
_beng
_erda
_cOCoLC-P
020 _a9781003455905
_q(ebook)
020 _a1003455905
020 _a9781040223475
_q(electronic bk. : PDF)
020 _a1040223478
_q(electronic bk. : PDF)
020 _a9781040223499
_q(electronic bk. : EPUB)
020 _a1040223494
_q(electronic bk. : EPUB)
020 _z9781032597003
_q(hardback)
020 _z9781032597034
_q(paperback)
024 7 _a10.1201/9781003455905
_2doi
035 _a(OCoLC)1460025700
035 _a(OCoLC-P)1460025700
050 0 0 _aTP184
072 7 _aCOM
_x094000
_2bisacsh
072 7 _aTDCB
_2bicssc
100 1 _aRomagnoli, José A.
_q(José Alberto),
_eauthor.
_1https://id.oclc.org/worldcat/entity/E39PCjtMM9JrDjJq36Y7QHmPQq
_996285
245 1 0 _aAI in chemical engineering :
_bunlocking the power within data /
_cby José A. Romagnoli and Luis Briceno-Mena.
250 _aFirst edition.
264 1 _aBoca Raton :
_bCRC Press,
_c2025.
300 _a1 online resource
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
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"--
_cProvided by publisher.
588 _aOCLC-licensed vendor bibliographic record.
650 0 _aChemical engineering
_xData processing.
_964662
650 0 _aArtificial intelligence
_xEngineering applications.
_996286
650 0 _aChemical processes
_xData processing.
_996287
650 0 _aArtificial intelligence
_xIndustrial applications.
_965268
700 1 _aBriceno-Mena, Luis,
_eauthor.
_996288
856 4 0 _3Taylor & Francis
_uhttps://www.taylorfrancis.com/books/9781003455905
856 4 2 _3OCLC metadata license agreement
_uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
942 _cE-Books
999 _c68323
_d68323