Data Analytics Applied to the Mining Industry

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. Les mer
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Innbundet
Legg i
Vår pris: 2531,-

(Innbundet) Fri frakt!
Leveringstid: Sendes innen 21 dager
På grunn av Brexit-tilpasninger og tiltak for å begrense covid-19 kan det dessverre oppstå forsinket levering.

Om boka

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:











Explains how to implement advanced data analytics through case studies and examples in mining engineering







Provides approaches and methods to improve data-driven decision making







Explains a concise overview of the state of the art for Mining Executives and Managers







Highlights and describes critical opportunity areas for mining optimization







Brings experience and learning in digital transformation from adjacent sectors

Fakta

Innholdsfortegnelse

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.

Om forfatteren

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.