Estimating Ore Grade Using Evolutionary Machine Learning Models

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Estimating Ore Grade Using Evolutionary Machine Learning Models

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Author : Mohammad Ehteram
language : en
Publisher: Springer Nature
Release Date : 2022-12-27

Estimating Ore Grade Using Evolutionary Machine Learning Models written by Mohammad Ehteram and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-27 with Science categories.

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.

Artificial Intelligence In Future Mining

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Author : Amir Razmjou
language : en
Publisher: Elsevier
Release Date : 2025-01-22

Artificial Intelligence In Future Mining written by Amir Razmjou and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-22 with Science categories.

Artificial Intelligence in Future Mining explores the latest developments in the use of artificial intelligence (AI) in mining and how it will impact the industry's future. The application of data science and artificial intelligence in future mining involves using advanced technologies to optimize operations, improve decision-making, and enhance safety and sustainability in the industry. After a brief history of AI in mining, the book's editors look closely at different AI techniques used. Chapters explore ocean mining, brine mining, and urban mining. With an eye towards sustainability, the editors then review the future of wastewater mining and green mining.The book wraps up with chapters on safety and risk, resource planning, and a larger discussion of the opportunities and challenges of mining with AI in the future. This book is a must-have for researchers and professionals who find themselves at the intersection of mining, engineering, and data science. - Provides high-level analyses as well as practical insights and real-world examples on the impact of AI on future mining - Includes case studies on the application of data processing, the Internet of Things, and artificial intelligence in environmental sensing - Provides in-depth discussion of the future implications of AI on the mining industry at the end of each chapter

Geostatistical Ore Reserve Estimation

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Author : M. David
language : en
Publisher: Elsevier
Release Date : 2012-12-02

Geostatistical Ore Reserve Estimation written by M. David and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-02 with Technology & Engineering categories.

Developments in Geomathematics, 2: Geostatistical Ore Reserve Estimation focuses on the methodologies, processes, and principles involved in geostatistical ore reserve estimation, including the use of variogram, sampling, theoretical models, and variances and covariances. The publication first takes a look at elementary statistical theory and applications; contribution of distributions to mineral reserves problems; and evaluation of methods used in ore reserve calculations. Concerns cover estimation problems during a mine life, origin and credentials of geostatistics, precision of a sampling campaign and prediction of the effect of further sampling, exercises on grade-tonnage curves, theoretical models of distributions, and computational remarks on variances and covariances. The text then examines variogram and the practice of variogram modeling. Discussions focus on solving problems in one dimension, linear combinations and average values, theoretical models of isotropic variograms, the variogram as a geological features descriptor, and the variogram as the fundamental function in error computations. The manuscript ponders on statistical problems in sample preparation, orebody modeling, grade-tonnage curves, ore-waste selection, and planning problems, the practice of kriging, and the effective computation of block variances. The text is a valuable source of data for researchers interested in geostatistical ore reserve estimation.

Predictive Performance Of Machine Learning Algorithms For Ore Reserve Estimation In Sparse And Imprecise Data

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Author : Sridhar Dutta
language : en
Publisher:
Release Date : 2006

Predictive Performance Of Machine Learning Algorithms For Ore Reserve Estimation In Sparse And Imprecise Data written by Sridhar Dutta and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with Gold ores categories.

"Traditional geostatistical estimation techniques have been used predominantly in the mining industry for the purpose of ore reserve estimation. Determination of mineral reserve has always posed considerable challenge to mining engineers due to geological complexities that are generally associated with the phenomenon of ore body formation. Considerable research over the years has resulted in the development of a number of state-of-the-art methods for the task of predictive spatial mapping such as ore reserve estimation. Recent advances in the use of the machine learning algorithms (MLA) have provided a new approach to solve the age-old problem. Therefore, this thesis is focused on the use of two MLA, viz. the neural network (NN) and support vector machine (SVM), for the purpose of ore reserve estimation. Application of the MLA have been elaborated with two complex drill hole datasets. The first dataset is a placer gold drill hole data characterized by high degree of spatial variability, sparseness and noise while the second dataset is obtained from a continuous lode deposit. The application and success of the models developed using these MLA for the purpose of ore reserve estimation depends to a large extent on the data subsets on which they are trained and subsequently on the selection of the appropriate model parameters. The model data subsets obtained by random data division are not desirable in sparse data conditions as it usually results in statistically dissimilar subsets, thereby reducing their applicability. Therefore, an ideal technique for data subdivision has been suggested in the thesis. Additionally, issues pertaining to the optimum model development have also been discussed. To investigate the accuracy and the applicability of the MLA for ore reserve estimation, their generalization ability was compared with the geostatistical ordinary kriging (OK) method. The analysis of Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Error (ME) and the coefficient of determination (R2) as the indices of the model performance indicated that they may significantly improve the predictive ability and thereby reduce the inherent risk in ore reserve estimation"--Leaf iii.

Application Of Artificial Neural Network Systems To Ore Grade Estimation From Exploration Data

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Author : Ioannis K. Kapageridis
language : en
Publisher:
Release Date : 1999

Application Of Artificial Neural Network Systems To Ore Grade Estimation From Exploration Data written by Ioannis K. Kapageridis and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999 with categories.

Neural Network Modelling Of Placer Ore Grade Spatial Variability

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Author : Jinchuan Ke
language : en
Publisher:
Release Date : 2002

Neural Network Modelling Of Placer Ore Grade Spatial Variability written by Jinchuan Ke and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with Gold ores categories.

"Traditional geostatistical methods have been used in ore reserve estimation for decades. Research in the last two decades or so has added a number of other statistical methodologies for ore reserve estimation procedures. Recent advances in neural networks have provided a new approach to solve this problem. This thesis is focused on the Neural-network modeling for the estimation of placer ore reserve. Due to the spatial variability, multiple dimensional inputs and very noisy drill hole sample data from the selected region, it requires that the neural-network be organized in a multiple-layers to handle the non-linearity and hidden slabs for smoothing the predicted results. Various neural-network architectures are investigated and the Back-propagation is selected for modeling the ore reserve estimation problem. Sensitivity analysis is performed for the following parameters: the type of neural-network architecture, number of hidden layers and hidden neurons, type of activation functions, learning rate and momentum factors, input pattern schedule, weight updated, and so on. The influences of these parameters on the predicted output are analyzed in details and the optimal parameters are determined. To investigate the accuracy and promise of neural network modeling as a tool for ore reserve estimation, the ore grade and tonnage of Neural-network output is compared with those estimated by geostatistical methods under various cut-off grades. In addition, the overall performance is also validated by the analysis of R-squared (R2), Root-Mean-Squared (RMS), and the comparison between predicted values and 'actual' values. As the final part of this study, the optimized Neural Network was used to esimate the distribution of placer gold grade and volume of gold resource in offshore Nome. The predicted results for all the mining blocks in the lease area are validated by checking the values of RMS, R2, and Scatter plots. The estimated gold grades are also presented as contour maps for visualization"--Leaf iii.

Estimating Mineral Distribution Using Machine Learning Models

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Author : Hoon Seo
language : en
Publisher:
Release Date : 2019

Estimating Mineral Distribution Using Machine Learning Models written by Hoon Seo and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Machine learning categories.

Mineral Resource And Ore Reserve Estimation

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Author : A. C. Edwards
language : en
Publisher:
Release Date : 2001

Mineral Resource And Ore Reserve Estimation written by A. C. Edwards and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with Electronic books categories.

Ore Reserve Estimation And Strategic Mine Planning

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Author : Roussos Dimitrakopoulos
language : en
Publisher: Springer
Release Date : 2015-11-15

Ore Reserve Estimation And Strategic Mine Planning written by Roussos Dimitrakopoulos and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-11-15 with Science categories.

The mining business faces continual risks in producing metals and raw materials under fluctuating market demand. At the same time, the greatest uncertainty driving the risk and profitability of mining investments is the geological variability of mineral deposits. This supply uncertainty affects the prediction of economic value from the initial valuation of a mining project through mine planning, design and production scheduling. This book is the first of its kind, presenting state-of-the-art stochastic simulation and optimization techniques and step-by-step case studies. Quantification of geological uncertainty through new efficient conditional simulation techniques for large deposits, integration of uncertainty to stochastic optimization formulations for design and production scheduling and the concurrent management of risk are shown to create flexibility, options and oportunities, increase asset value, cashflows and return on investment. New approaches introduced include resource/reserve risk quantification, cost-effective drilling programs, pit design and long-term production scheduling optimization with simulated orebodies, ore reserve classification, geologic risk discounting, waste managing and demand driven scheduling, risk assessment in meeting project production schedules ahead of mining, risk based optimal stope design, options valuation when mining. Applications include commodities such as gold, copper, nickel, iron ore, coal and diamonds.

Ore Reserve Estimation Methods Models And Reality

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Author : M. David
language : en
Publisher:
Release Date : 1986

Ore Reserve Estimation Methods Models And Reality written by M. David and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1986 with categories.