Machine learning tools for chemical engineering : (Record no. 31505)

MARC details
000 -LEADER
fixed length control field 04200 a2200433 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20260603154647.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 260225b |||||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9780443290589
041 ## - LANGUAGE CODE
Language code of text/sound track or separate title eng
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 660.0285
Item number T25 LOP
100 ## - MAIN ENTRY--AUTHOR NAME
Personal name by López-Flores, Francisco Javier
245 ## - TITLE STATEMENT
Title Machine learning tools for chemical engineering :
Remainder of title methodologies and applications /
Statement of responsibility, etc Francisco Javier López-Flores...[et al.]
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Name of publisher Elsevier,
Year of publication 2025.
Place of publication Amsterdam :
300 ## - PHYSICAL DESCRIPTION
Number of Pages xi, 616p.
505 ## - FORMATTED CONTENTS NOTE
Formatted contents note Product content<br/>Content includes any type of illustrations.<br/>The primary content is text.<br/>Content includes a significant number of actionable (clickable) web links to external content, downloadable resources, supplementary material, etc.<br/>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.).<br/>Content includes photographs, whether in a plate section / insert or not.<br/>Content includes figures, diagrams, charts and/or graphs, including other 'mechanical' (i.e. non-photographic) illustrations.<br/>Content includes a significant number of web links (printed URLs, QR codes etc.).<br/>Content includes mathematical notations, formulae.
520 ## - SUMMARY, ETC.
Summary, etc 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.<br/>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.<br/>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 ## - SUMMARY, ETC.
Summary, etc Key features<br/>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<br/>• 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<br/>• 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 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Machine learning
General subdivision Applications
650 #4 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Chemical engineering
650 #4 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Artificial intelligence
General subdivision Methods
650 #4 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Predictive models (Computer science)
650 #4 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Chemical engineering
650 #4 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Artificial intelligence
650 #4 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Physics-informed machine learning
650 #4 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Surrogate modeling in chemical processes
650 #4 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Soft sensors
650 #4 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term High-throughput screening
650 #4 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Digital twins in chemical engineering
650 #4 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Chemical process control
650 #4 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Chemical processes
650 #4 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Materials informatics or Chemoinformatics
650 #4 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Chemical reactions
650 #4 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Neural networks (Computer science)
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Ochoa-Barragán, Rogelio
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Raya-Tapia, Alma Yunuen
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Ramírez-Márquez, César
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Ponce-Ortega, José María
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Books
Holdings
Lost status Damaged status Permanent Location Current Location Date acquired Cost, normal purchase price Full call number Accession Number last updated Koha item type
    Nalanda Library Nalanda Library 25/02/2026 19438.15 660.0285 T25 LOP 22800 25/02/2026 Books