04350    a2200445   450000500170000000800410001702000180005804100080007608200220008410000470010624501340015326000360028730000140032350508160033752011290115352006640228265000430294665000330298965000450302265000490306765000320311665000350314865000450318365000520322865000240328065000370330465000490334165000360339065000300342665000530345665000300350965000460353970000370358570000350362270000400365770000400369794200070373799900170374495201430376120260603154647.0260225b        |||||||| |||| 00| 0 eng d  a9780443290589  aeng  a660.0285bT25 LOP  aby López-Flores, Francisco Javier923410  aMachine learning tools for chemical engineering : bmethodologies and applications / cFrancisco Javier López-Flores...[et al.]  bElsevier, c2025. aAmsterdam :  axi, 616p.  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.  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.  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 4aMachine learning xApplications923453 4aChemical engineering 917183 4aArtificial intelligence xMethods923413 4aPredictive models (Computer science) 923454 4aChemical engineering917183 4aArtificial intelligence917220 4aPhysics-informed machine learning923417 4aSurrogate modeling in chemical processes923418 4aSoft sensors923419 4aHigh-throughput screening923420 4aDigital twins in chemical engineering923421 4aChemical process control923455 4aChemical processes923456 4aMaterials informatics or Chemoinformatics923424 4aChemical reactions923457 4aNeural networks (Computer science)923128  aOchoa-Barragán, Rogelio923426  aRaya-Tapia, Alma Yunuen923427  aRamírez-Márquez, César923428  aPonce-Ortega, José María923429  cBK  c31505d31505  00102ddc4070aCLIBbCLIBd2026-02-25g19438.15l1o660.0285 T25 LOPp22800q2026-09-07r2026-03-11 14:54:00s2026-03-11w2026-02-25yBK