Ali Soofastaei

Data Analytics Applied to the Mining Industry (eBook, PDF)

• Format: PDF

Produktbeschreibung

The aim of the book is to provide practical help for executives, managers and research and development teams to identify where and how to apply advanced data analytics in mining engineering. Extensive case studies worked examples and details of how to develop and use an Analytics Maturity Matrix, and associated Analytics Roadmap has been provided.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 272
• Erscheinungstermin: 12. November 2020
• Englisch
• ISBN-13: 9780429781773
• Artikelnr.: 60545210

Autorenporträt

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.

Inhaltsangabe

1. Digital Transformation of Mining. 2. Data Analytics and the Mining Value
Chain. 3. Data Collection, Storage and Retrieval. 4. Making Sense of Data.
5. Analytics Toolset. 6. Making Decisions based on Analytics. 7. Process
Performance Analytics. 8. Process Maintenance Analytics. 9. Data Analytics
for Energy Efficiency and Gas Emission Reduction. 10. Future Skills
Requirements.