04200 a2200433 4500 20260603154647.0 260225b |||||||| |||| 00| 0 eng d 9780443290589 eng 660.0285 T25 LOP by López-Flores, Francisco Javier 23410 Machine learning tools for chemical engineering : methodologies and applications / Francisco Javier López-Flores...[et al.] Elsevier, 2025. Amsterdam : xi, 616p. Product 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. Machine 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. Key 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 Machine learning Applications 23453 Chemical engineering 17183 Artificial intelligence Methods 23413 Predictive models (Computer science) 23454 Chemical engineering 17183 Artificial intelligence 17220 Physics-informed machine learning 23417 Surrogate modeling in chemical processes 23418 Soft sensors 23419 High-throughput screening 23420 Digital twins in chemical engineering 23421 Chemical process control 23455 Chemical processes 23456 Materials informatics or Chemoinformatics 23424 Chemical reactions 23457 Neural networks (Computer science) 23128 Ochoa-Barragán, Rogelio 23426 Raya-Tapia, Alma Yunuen 23427 Ramírez-Márquez, César 23428 Ponce-Ortega, José María 23429 BK 31505 31505 0 0 ddc 0 0 CLIB CLIB 2026-02-25 19438.15 1 660.0285 T25 LOP 22800 2026-09-07 2026-03-11 14:54:00 2026-03-11 2026-02-25 BK