The devastating effects of tailings storage facilities (TSF) failures cannot be overemphasized. Hence, every means that can ensure TSF stability, safe operations, and closure should be explored and exploited to the fullest. In the past, TSF safety has largely been reliant on historic information of similar facilities, manual monitoring and measurements, and interpretation of data based on their face values. This has led to disastrous TSF failures. In this data age, this should not be allowed to happen. Diligent collection of real-time data, application of machine learning methods to analyze the data, and appropriate intervention/remediation can make TSFs operations and closure safer. The use of machine learning for data analytics can reveal very useful hidden insights in complex TSF datasets. Engineers with this information can then proactively stabilize TSFs and make them safer. To be able to exploit the full potential of data for TSF safety, accurate collection of relevant data is the first step. The next step is the application of the appropriate data analysis procedure (machine learning methods) to the datasets. Proper interpretations of the analysis results will reveal valuable information on TSF safety for possible intervention. This talk will highlight the importance of accurate real-time data collection and analysis in safe TSF operations and closure.

About the Speaker

Prosper Ayawah has a Ph.D. in Geological Engineering from the Missouri Univ. of Science and Tech. (USA), having previously obtained an MSc. in Petroleum Engineering from the African Univ. of Science and Tech. (Nigeria), and a BSc. in Geological Engineering from the Univ. of Mines and Tech., Tarkwa (Ghana). His Ph.D. research focused on numerical and experimental studies of mechanical rock excavation using tunnel boring machines. Dr. Ayawah currently works with Stantec Consulting Services Inc. (Denver, Colorado) as a Geotechnical Engineer – EIT where he focuses on mine waste and tailings facility management. Prosper is interested in geomechanical modeling, geotechnical investigations, and data analysis using machine learning methods. His career goal is to become an exceptional professional, disrupting the geotechnical industry through relevant breakthroughs and contributions. Prosper is a member of the Society of Mining, Metallurgy, and Exploration (SME), American Rock Mechanics Association (ARMA), Geological Society of America (GSA), and National Association of Black Geoscientist (NABG).