Estimating Ore Grade Using Evolutionary Machine Learning Models
This book examines the abilities of new machine learning models for predicting ore grade in mining engineering. A variety of case studies are examined in this book. A motivation for preparing this book was the absence of robust models for estimating ore grade. Models of current books can also be use...
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| Autori principali: | , , , |
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| Ente Autore: | |
| Natura: | Elettronico eBook |
| Lingua: | inglese |
| Pubblicazione: |
Singapore :
Springer Nature Singapore : Imprint: Springer,
2023.
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| Edizione: | 1st ed. 2023. |
| Soggetti: | |
| Accesso online: | https://doi.org/10.1007/978-981-19-8106-7 |
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Sommario:
- Explains the importance of ore grade estimation
- Reviews machine learning models for ore grade estimation
- Explains the structure of different kinds of machine learning models
- Explains different training algorithms and optimization algorithms. This chapter also explains the structure of evolutionary machine learning models
- Explains the Bayesian model averaging and multilayer perceptron networks for estimating AL2O3 grade in a mine
- Explains the structure of inclusive multiple models and optimized radial basis function neural networks for estimating Sio2 grade in a mine
- Explains the application of hybrid kriging and extreme learning machine models for estimating copper ore grade in a mine
- Explains the application of optimized group machine data handling, support vector machines, and Adaptive neuro-fuzzy interface systems for estimating iron ore grade in mines
- Presents the conclusion, general comments, and suggestions for the next books.