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Estimating ore grade using evolutionary machine learning models

Record Type:	Electronic resources : Monograph/item
Title/Author:	Estimating ore grade using evolutionary machine learning models/ by Mohammad Ehteram ... [et al.].
other author:	Ehteram, Mohammad.
Published:	Singapore :Springer Nature Singapore : : 2023.,
Description:	xiii, 101 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Ores - Sampling and estimation -
Online resource:	https://doi.org/10.1007/978-981-19-8106-7
ISBN:	9789811981067

Estimating ore grade using evolutionary machine learning models
Estimating ore grade using evolutionary machine learning models[electronic resource] /by Mohammad Ehteram ... [et al.]. - Singapore :Springer Nature Singapore :2023. - xiii, 101 p. :ill., digital ;24 cm.

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.

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 used for the different sciences because they have high capabilities for estimating different variables. Mining engineers can use the book to determine the ore grade accurately. This book helps identify mineral-rich regions for exploration and exploitation. Exploration costs can be decreased by using the models in the current book. In this book, the author discusses the new concepts in mining engineering, such as uncertainty in ore grade modeling. Ensemble models are presented in this book to estimate ore grade. In the book, readers learn how to construct advanced machine learning models for estimating ore grade. The authors of this book present advanced and hybrid models used to estimate ore grade instead of the classic methods such as kriging. The current book can be used as a comprehensive handbook for estimating ore grades. Industrial managers and modelers can use the models of the current books. Each level of ore grade modeling is explained in the book. In this book, advanced optimizers are presented to train machine learning models. Therefore, the book can also be used by modelers in other fields. The main motivation of this book is to address previous shortcomings in the modeling process of ore grades. The scope of this book includes mining engineering, soft computing models, and artificial intelligence.

ISBN: 9789811981067

Standard No.: 10.1007/978-981-19-8106-7doiSubjects--Topical Terms:

3625461
Ores
--Sampling and estimation

LC Class. No.: TN560

Dewey Class. No.: 622.0285631

Estimating ore grade using evolutionary machine learning models
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245 0 0 $a Estimating ore grade using evolutionary machine learning models $h [electronic resource] / $c by Mohammad Ehteram ... [et al.].
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2023.
300 $a xiii, 101 p. : $b ill., digital ; $c 24 cm.
505 0 $a 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.
520 $a 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 used for the different sciences because they have high capabilities for estimating different variables. Mining engineers can use the book to determine the ore grade accurately. This book helps identify mineral-rich regions for exploration and exploitation. Exploration costs can be decreased by using the models in the current book. In this book, the author discusses the new concepts in mining engineering, such as uncertainty in ore grade modeling. Ensemble models are presented in this book to estimate ore grade. In the book, readers learn how to construct advanced machine learning models for estimating ore grade. The authors of this book present advanced and hybrid models used to estimate ore grade instead of the classic methods such as kriging. The current book can be used as a comprehensive handbook for estimating ore grades. Industrial managers and modelers can use the models of the current books. Each level of ore grade modeling is explained in the book. In this book, advanced optimizers are presented to train machine learning models. Therefore, the book can also be used by modelers in other fields. The main motivation of this book is to address previous shortcomings in the modeling process of ore grades. The scope of this book includes mining engineering, soft computing models, and artificial intelligence.
650 0 $a Ores $x Sampling and estimation $x Data processing. $3 3625461
650 0 $a Machine learning. $3 533906
650 1 4 $a Mineralogy. $3 516743
650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a Data Science. $3 3538937
650 2 4 $a Artificial Intelligence. $3 769149
700 1 $a Ehteram, Mohammad. $3 3625460
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-981-19-8106-7
950 $a Earth and Environmental Science (SpringerNature-11646)