Machine Learning Applications in Industrial Solid Ash.
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. Th...
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| Format: | eBook |
| Language: | English |
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San Diego :
Elsevier,
2023.
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| Series: | Woodhead Publishing Series in Civil and Structural Engineering Series.
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| Subjects: | |
| Online Access: | Connect to the full text of this electronic book |
| Summary: | 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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| Item Description: | Description based upon print version of record. 6.5.1 Programming language for machine learning |
| Physical Description: | 1 online resource (315 p.). |
| Bibliography: | 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 |
| ISBN: | 0443155259 9780443155253 |