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020			_a9780443290589
041			_aeng
082			_a660.0285 _bT25 LOP
100			_aby López-Flores, Francisco Javier _923410
245			_aMachine learning tools for chemical engineering : _bmethodologies and applications / _cFrancisco Javier López-Flores...[et al.]
260			_bElsevier, _c2025. _aAmsterdam :
300			_axi, 616p.
505			_aProduct content Content includes any type of illustrations. The primary content is text. Content includes a significant number of actionable (clickable) web links to external content, downloadable resources, supplementary material, etc. Content includes a significant number of actionable (clickable) cross-references, hyperlinked notes and annotations, or with other actionable links between largely textual elements (e.g., quiz/test questions, 'choose your own ending', etc.). Content includes photographs, whether in a plate section / insert or not. Content includes figures, diagrams, charts and/or graphs, including other 'mechanical' (i.e. non-photographic) illustrations. Content includes a significant number of web links (printed URLs, QR codes etc.). Content includes mathematical notations, formulae.
520			_aMachine Learning Tools for Chemical Engineering: Methodologies and Applications examines how machine learning (ML) techniques are applied in the field, offering precise, fast, and flexible solutions to address specific challenges. ML techniques and methodologies offer significant advantages (such as accuracy, speed of execution, and flexibility) over traditional modeling and optimization techniques. This book integrates ML techniques to solve problems inherent to chemical engineering, providing practical tools and a theoretical framework combining knowledge modeling, representation, and management, tailored to the chemical engineering field. It provides a precedent for applied Al, but one that goes beyond purely data-centric ML. It is firmly grounded in the philosophies of knowledge modeling, knowledge representation, search and inference, and knowledge extraction and management. Aimed at graduate students, researchers, educators, and industry professionals, this book is an essential resource for those seeking to implement ML in chemical processes, aiming to foster optimization and innovation in the sector.
520			_aKey features Outlines the current and potential future contribution of machine learning, the use of data science, and, ultimately, how to correctly use machine learning tools specifically in chemical engineering • Devoted to the correct application and interpretation of the results in various phases of the development of decision support systems: data collection, model development, training, and testing, as well as application in chemical engineering • Examines chemical engineering-specific challenges and problems, including noise, manufacturing equipment, and domain-specific solutions, such as physical knowledge using relevant case study examples
650		4	_aMachine learning _xApplications _923453
650		4	_aChemical engineering _917183
650		4	_aArtificial intelligence _xMethods _923413
650		4	_aPredictive models (Computer science) _923454
650		4	_aChemical engineering _917183
650		4	_aArtificial intelligence _917220
650		4	_aPhysics-informed machine learning _923417
650		4	_aSurrogate modeling in chemical processes _923418
650		4	_aSoft sensors _923419
650		4	_aHigh-throughput screening _923420
650		4	_aDigital twins in chemical engineering _923421
650		4	_aChemical process control _923455
650		4	_aChemical processes _923456
650		4	_aMaterials informatics or Chemoinformatics _923424
650		4	_aChemical reactions _923457
650		4	_aNeural networks (Computer science) _923128
700			_aOchoa-Barragán, Rogelio _923426
700			_aRaya-Tapia, Alma Yunuen _923427
700			_aRamírez-Márquez, César _923428
700			_aPonce-Ortega, José María _923429
942			_cBK
999			_c31505 _d31505