| Tag |
First Indicator |
Second Indicator |
Subfields |
| LEADER |
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| 001 |
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251210s2026 ne o 000 0 eng d |
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|a YDX
|b eng
|e rda
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|a 300661870
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|a 1564499797
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|a 9780443328336
|q electronic book
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|a 0443328331
|q electronic book
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|z 0443328323
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|a CIPO000326515
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|a (OCoLC)1559783244
|z (OCoLC)1564499797
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| 050 |
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|a TPP155.7
|b .A78 2026
|
| 082 |
0 |
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|a 660/.28
|2 23/eng/20251222
|
| 049 |
|
|
|a TXAM
|
| 245 |
0 |
0 |
|a Artificial neural networks in chemical engineering processes :
|b from theory to applications.
|
| 264 |
|
1 |
|a Amsterdam, Netherlands ;
|a Cambridge, MA :
|b Elsevier,
|c 2026.
|
| 300 |
|
|
|a 1 online resource
|
| 336 |
|
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|a text
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|
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|a computer
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|
| 338 |
|
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|a online resource
|b cr
|2 rdacarrier
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| 520 |
|
|
|a Artificial Neural Networks in Chemical Engineering Processes: From Theory to Applications serves as a comprehensive resource on artificial neural networks within chemical engineering, including understanding the fundamental principles, learning about relevant algorithms and architectures, and exploring practical case studies. This book covers theoretical principles, relevant algorithms, and practical case studies, this book covers artificial neural network concepts, architectures, and algorithms, with a focus on applications in chemical engineering processes. This book also addressed common challenges by providing practical guidance through successful case studies, offering insights on data pre-processing, model selection, training strategies, and performance evaluation. The book serves as a valuable tool for bridging the gap between neural networks and their practical implementation in chemical engineering. This book will be an invaluable resource for chemical Engineers, particularly researchers and industry professionals working in Machine Learning and Artificial Intelligence. It will also be a very useful guide for Graduate and Postgraduate Students in Chemical Engineering and machine learning. Artificial Neural Networks in Chemical Engineering will also be a valuable resource for anyone working with artificial neural networks in other industries, particularly data scientists and analysts.
|
| 532 |
1 |
|
|3 EBSCOhost
|a "EBSCO evaluates our products based on the Web Content Accessibility Guidelines (WCAG) and the related Section 508 and EN 301 549 regulations in the US and EU. Most EBSCO products are substantially conformant with WCAG 2.2 level AA." Source: https://connect.ebsco.com/s/article/EBSCO-VPATs?language=en_US. Last accessed April 22, 2025.
|
| 650 |
|
0 |
|a Chemical processes
|x Data processing.
|
| 650 |
|
0 |
|a Neural networks (Computer science)
|
| 650 |
|
6 |
|a Réseaux neuronaux (Informatique)
|
| 650 |
|
7 |
|a Chemical engineering
|x Data processing
|2 fast
|
| 650 |
|
7 |
|a Neural networks (Computer science)
|x Industrial applications
|2 fast
|
| 655 |
|
7 |
|a Electronic books.
|2 local
|
| 710 |
2 |
|
|a ScienceDirect (Online service)
|
| 776 |
0 |
8 |
|i Print version:
|z 0443328323
|z 9780443328329
|w (OCoLC)1530355347
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| 776 |
0 |
8 |
|i Print version:
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4 |
0 |
|u http://proxy.library.tamu.edu/login?url=https://www.sciencedirect.com/science/book/9780443328329
|z Connect to the full text of this electronic book
|t 0
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|a Elsevier ScienceDirect 2026-2027
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| 952 |
f |
f |
|a Texas A&M University
|b College Station
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|s www_evans
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|e TPP155.7 .A78 2026
|h Library of Congress classification
|
| 998 |
f |
f |
|a TPP155.7 .A78 2026
|t 0
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