Estimating Ore Grade Using Evolutionary Machine Learning Models

Ehteram, Mohammad|Khozani, Zohreh Sheikh|Soltani-Mohammadi, Saeed|Abbaszadeh, Maliheh

ISBN 10: 9811981051 / ISBN 13: 9789811981050

Published by Springer, Berlin|Springer Nature Singapore|Springer, 2023

About this Item

Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 .

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Bibliographic Details

• Title: Estimating Ore Grade Using Evolutionary Machine Learning Models
• Publisher: Springer, Berlin|Springer Nature Singapore|Springer
• Publication Date: 2023
• Language: English
• ISBN 10: 9811981051
• ISBN 13: 9789811981050
• Binding: Hardcover
• Condition: New

About this title

Synopsis

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 oregrade 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.

About the Author

Mohammad Ehtearm is a Researcher in the field of artificial intelligence. He has a Ph.D. in civil engineering. His research interests generally lie in the areas application of remote sensing in water resources, water, energy, and food nexus, extreme hydrological events, river engineering, remote sensing in water resources, dam and hydropower operation, geotechnical engineering, mining engineering, artificial intelligence, and remote sensing in mining engineering.

Zohreh Sheikh Khozani is a Scientific Researcher in the field of civil engineering and mining engineering. The scope of her current research is covering hydraulic structures, hydrology, water resources engineering, environmental engineering, and the implementation of data analytics, geotechnical engineering, mining engineering, and artificial intelligence models.

Saeed Soltani-Mohammadi is Associate Professor at Department of Mining Engineering, University of Kashan, Iran. He holds a PhD on mining engineering from the Amirkabir University of technology in 2009. His research spans application of artificial intelligence and optimization methods in geosciences and mining problems. He developed many soft computing models based on practical applications for mining engineering.

Maliheh Abbaszadeh is Assistant Professor at Department of Mining Engineering, University of Kashan, Iran. She holds a Ph.D. in mining engineering from the Amirkabir University of technology in 2014. She developed her teaching activity in the areas of geochemical exploration, remote sensing and machine learning algorithms. Her primary research interest is application of machine learning algorithms in exploratory data analysis.