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| 005 |
20260327175331.2 |
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cr cnu|||||||| |
| 008 |
231230s2023 cau o ||| 0 eng d |
| 040 |
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|a EBLCP
|b eng
|c EBLCP
|d N$T
|d OCLCO
|d OPELS
|d OCLCQ
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| 020 |
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|a 0443155259
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| 020 |
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|a 9780443155253
|q (electronic bk.)
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| 035 |
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|a (OCoLC)1412620651
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| 050 |
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|a TD797
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|a 363.72/85
|2 23/eng/20240109
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| 049 |
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|a TXAM
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| 100 |
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|a Qi, Chongchong.
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| 245 |
1 |
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|a Machine Learning Applications in Industrial Solid Ash.
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| 260 |
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|a San Diego :
|b Elsevier,
|c 2023.
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| 300 |
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|a 1 online resource (315 p.).
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 490 |
1 |
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|a Woodhead Publishing Series in Civil and Structural Engineering Series
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| 500 |
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|a Description based upon print version of record.
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| 505 |
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|a Front Cover -- Machine Learning Applications in Industrial Solid Ash -- Copyright Page -- Dedication -- Contents -- Preface -- Acknowledgments -- 1 Industrial solid ashes generation -- 1.1 Introduction -- 1.2 Making and types of industrial solid ashes -- 1.2.1 Coal ashes -- 1.2.2 Municipal solid waste -- 1.2.3 Biomass -- 1.3 Production amounts of industrial solid ashes -- 1.3.1 Coal ashes -- 1.3.2 Municipal solid waste -- 1.3.3 Biomass -- References -- 2 Properties of industrial solid ashes -- 2.1 Introduction -- 2.2 Characteristics of coal ashes -- 2.2.1 Coal fly ash
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| 505 |
8 |
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|a 2.2.1.1 Physical features -- 2.2.1.2 Mineral features -- 2.2.1.3 Chemical features -- 2.2.1.4 Classification -- 2.2.2 Coal bottom ash -- 2.2.2.1 Physical features -- 2.2.2.2 Chemical/mineralogical features -- 2.3 Characteristics of MSW bottom/fly ash -- 2.3.1 MSW bottom ash -- 2.3.1.1 Physical properties -- 2.3.1.2 Chemical features -- 2.3.1.3 Mineralogical features -- 2.3.2 MSW fly ash -- 2.3.2.1 Physical properties -- 2.3.2.2 Chemical features -- 2.3.2.3 Mineralogical features -- 2.4 Characteristics of biomass bottom/fly ash -- References -- 3 Ash management, recycling, and sustainability
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| 505 |
8 |
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|a 3.1 Introduction -- 3.2 Management of CFA and CBA -- 3.2.1 Hazards of CFA and CBA -- 3.2.2 Application and utilization of CFA and CBA -- 3.2.2.1 Cement and concrete -- 3.2.2.2 Ceramic industry -- 3.2.2.3 Brick production -- 3.2.2.4 Road construction -- 3.2.2.5 Soil amelioration -- 3.2.2.6 Zeolites -- 3.2.2.7 Environmental protection (absorbent) -- 3.3 MSW bottom ash and fly ash -- 3.3.1 Cement production -- 3.3.2 Concrete -- 3.3.3 Road pavement -- 3.3.4 Embankment -- 3.3.5 Soil stabilization -- 3.3.6 Ceramic and glass -- 3.4 Biomass bottom/fly ashes -- References
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| 505 |
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|a 4 Emerging innovative techniques for ash management -- 4.1 Introduction -- 4.2 Geopolymer -- 4.3 Recovery of REEs -- 4.4 Carbon nanotubes -- 4.5 Catalysis -- 4.6 Silica aerogel -- References -- 5 Legal framework for ashes -- 5.1 Introduction -- 5.2 Review of coal fly/bottom ash regulations -- 5.2.1 Legislations for Chinese coal ashes -- 5.2.2 India -- 5.2.3 United States -- 5.2.4 European Union -- 5.2.5 Australia -- 5.3 MSW/biomass ash regulations -- 5.3.1 Legislations for ashes in China -- 5.3.2 Legislations for ashes in the European Union -- 5.3.3 Legislations for ashes in the United States
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| 504 |
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|a References -- 6 Background of machine learning -- 6.1 History of machine learning -- 6.2 Machine learning categories -- 6.2.1 Supervised learning -- 6.2.2 Unsupervised learning -- 6.2.3 Semisupervised learning -- 6.2.4 Reinforcement learning -- 6.3 Deep learning -- 6.4 Introduction to machine learning techniques -- 6.4.1 Decision tree -- 6.4.2 Random forest -- 6.4.3 Extreme Gradient Boosting -- 6.4.4 Support vector machine -- 6.4.5 Convolutional neural network -- 6.4.6 Recurrent neural network -- 6.4.7 K-means -- 6.5 Implementation of machine learning
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| 500 |
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|a 6.5.1 Programming language for machine learning
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| 520 |
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|a Machine Learning Applications in Industrial Solid Ash begins with fundamentals in solid ash, covering the status of solid ash generation and management. The book moves on to foundational knowledge on ML in solid ash management, which provides a brief introduction of ML for solid ash applications. The reference then goes on to discuss ML approaches currently used to address problems in solid ash management and recycling, including solid ash generation, clustering analysis, origin identification, reactivity prediction, leaching potential modelling and metal recovery evaluation, etc. Finally, potential future trends and challenges in the field are discussed. Offering the ability to process large or complex datasets, machine learning (ML) holds huge potential to reshape the whole status for solid ash management and recycling. This book is the first published book about ML in solid ash management and recycling. It highlights fundamental knowledge and recent advances in this topic, offering readers new insight into how these tools can be utilized to enhance their own work.
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| 650 |
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0 |
|a Coal ash
|x Recycling.
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| 650 |
|
0 |
|a Machine learning.
|
| 650 |
|
6 |
|a Apprentissage automatique.
|
| 655 |
|
7 |
|a Electronic books.
|2 local
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| 700 |
1 |
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|a Chen, Qiusong,
|d 1959-
|
| 700 |
1 |
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|a Yılmaz, Erol.
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| 710 |
2 |
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|a ScienceDirect (Online service)
|
| 776 |
0 |
8 |
|i Print version:
|a Qi, Chongchong
|t Machine Learning Applications in Industrial Solid Ash
|d San Diego : Elsevier,c2023
|z 9780443155246
|
| 830 |
|
0 |
|a Woodhead Publishing Series in Civil and Structural Engineering Series.
|
| 856 |
4 |
0 |
|u http://proxy.library.tamu.edu/login?url=https://www.sciencedirect.com/science/book/9780443155246
|z Connect to the full text of this electronic book
|t 0
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| 936 |
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|a BATCHLOAD
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|a Elsevier ScienceDirect 2026-2027
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| 952 |
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|a Texas A&M University
|b College Station
|c Electronic Resources
|s www_evans
|d Available Online
|t 0
|e TD797
|h Library of Congress classification
|
| 998 |
f |
f |
|a TD797
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|l Available Online
|