Data Analytics Applied to the Mining Industry
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A01=Ali Soofastaei
Advanced data analytics method
ARIMA Model
ARMA Model
Author_Ali Soofastaei
BD Era
BDA Technique
Big Data
Business Processes
Category=KNAT
Category=UN
Cycle Time
Data Discovery
Data Governance Programs
Data Models
Data Quality Programs
Data storage
Data-driven decision making
Decision Making
Dt
Energy Efficiency
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eq_business-finance-law
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Established Ann Model
Firefly Algorithm
Hadoop Distributed File System
Industrial internet of things
Mineral Processing Plants
Mining Companies
Mining engineering
Mining optimization
Mining Value Chain
Ml Algorithm
Operational Data Infrastructure
Process Performance Analysis
Retrieval
RNN
Robotic Process Automation
SVM
Transmission
Unsupervised Ml
Vice Versa
Product details
- ISBN 9781138360006
- Weight: 453g
- Dimensions: 156 x 234mm
- Publication Date: 13 Nov 2020
- Publisher: Taylor & Francis Ltd
- Publication City/Country: GB
- Product Form: Hardback
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Data Analytics Applied to the Mining Industry describes the key challenges facing the mining sector as it transforms into a digital industry able to fully exploit process automation, remote operation centers, autonomous equipment and the opportunities offered by the industrial internet of things. It provides guidelines on how data needs to be collected, stored and managed to enable the different advanced data analytics methods to be applied effectively in practice, through use of case studies, and worked examples. Aimed at graduate students, researchers, and professionals in the industry of mining engineering, this book:
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- Explains how to implement advanced data analytics through case studies and examples in mining engineering
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- Provides approaches and methods to improve data-driven decision making
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- Explains a concise overview of the state of the art for Mining Executives and Managers
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- Highlights and describes critical opportunity areas for mining optimization
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- Brings experience and learning in digital transformation from adjacent sectors
Ali Soofastaei is a Data Analyst at Vale and a Professorial Research Fellow at the University of Queensland (UQ) Australia. Vale is a Brazilian multinational corporation engaged in metals and mining and one of the largest logistics operators in Brazil. Vale is the most significant producer of iron ore and nickel in the world. Dr Soofastaei uses new models based on Artificial Intelligence (AI) methods to increase productivity, energy efficiency and reduce the total costs of mining operations. In the past 14 years, Dr Soofastaei has conducted a variety of research studies in academic and industrial environments. He has acquired an in-depth knowledge of Energy Efficiency Opportunities (EEO), VE and advanced data analysis. He is also proficient at using AI methods in data analysis to optimize the number of effective parameters in energy consumption in mining operations. Dr Soofastaei has been working in the oil, gas and mining industries and he has academic experience as an assistant professor. He has been in School of Mechanical and Mining Engineering at UQ since 2012 involved in many research and industrial projects, and I have been a member of the supervisory team for PhD and Master Students. Dr Soofastaei has completed many research projects and published their results in a lot of journal and conference papers. He also has developed few patents and five software packages.
