Desta, F.S.

Profile

Feven Desta is an Assistant Professor in Raw material characterization and Spatio-temporal modelling of mine waste systems. She completed her BSc in geology and MSc in geo-environmental system analysis. She received her second MSc degree in geo-information science and earth observation. Feven completed her PhD in 2021, her dissertation title is “Sensing and data fusion opportunities for raw material characterization in mining”. She has 8 years of experience working in different companies in the area of geosciences and environmental studies. She worked as a geotechnical engineer, GIS/RS and environmental specialist, and Geospatial analyst. Feven also worked as a Postdoc in the section of Resource Engineering at TU Delft.

Education

• PhD in Resource engineering section, Delft University of Technology, The Netherlands. Thesis titled - Sensing and data fusion opportunities for raw material characterization in mining
• MSc. degree in Geo-information Science and Earth Observation (Geoinformatics), University of Twente, The Netherlands. Thesis titled “Non- Stationary Linear Mixed Modelling of Air Quality, across Europe“
• MSc. Degree in Geo-Environmental Systems Analysis, Department of Earth science, Addis Ababa University (State University), Ethiopia. Thesis titled: “Spatial and Temporal Water quality trend analysis using sediment core and water samples from Aba Samuel lake, Central Ethiopia“
• BSc. Degree in Geology, Addis Ababa University (State University), Ethiopia

Research

Feven research areas of interest include raw material characterization, sensor technologies, machine learning, data fusion, mine waste characterization, environmental systems modelling, and mining. Her main interest is to develop methodological approaches for the characterization and Spatio-temporal modelling of environmental systems. The approach focuses on the use of multi-scale multi-source sensor-derived data coupled with machine learning and data fusion techniques for enhanced characterization and modelling of environmental systems. The methodology can be applicable in geological, mining, and environmental studies. It can also be upgraded for insitu and automated material characterization.

Participation in research projects

Insitu ore grading system using LIBS in harsh environments (inSITE) project
https://www.inesctec.pt/en/projects/insite#about
inSITE is an upscaling project funded by EIT Raw Materials. LIBS is a promising tool for real-time analysis of low atomic weight critical raw materials such as Li. Solution on the market is plagued by inconsistent results and poor quantification performance. inSITE introduces a new solution where the concept of information transfer coupled with advanced AI algorithms and a knowledge database of mineral spectra enables true in-situ ore grading with a new generation of smart LIBS technology.

Real-Time Mining (RTM) Project: International consortium to develop a real-time process-feedback control loop linking online data acquired during extraction at the mining face rapidly with a sequentially up-datable resource model associated with real-time optimization of long-term planning, short-term sequencing and production control decisions. The project included research and demonstration activities integrating automated sensor-based material characterization, online machine performance measurements, underground navigation and positioning, underground mining system simulation and optimization of planning decisions, and state-of-the-art updating techniques for resource/reserve models.

Transition information modelling for the transition from coal exploitation to a re-vitalized post-mining landscape (TRIM4Post-Mining) https://trim4postmining.com/  
TRIM4Post-Mining brings together a consortium of European experts from industry and academia to develop an integrated information modelling system. This is designed to support decision-making and planning during the transition from coal exploitation to a re-vitalized post-mining landscape enabling infrastructure development for agricultural and industrial utilization, and also contributing to the recovery of energy and materials from coal mining dumps.

TRIM4Post-Mining will develop efficient methods for comprehensive Spatio-temporal data analytics, feature extraction, and predictive modelling that allow for the identification of potential contamination areas and forecasting of the waste dump dynamics. It will be founded upon a high-resolution Spatio-temporal database utilizing state-of-the-art multi-scale and multi-sensor monitoring technologies that characterize dynamical processes in coal waste dumps related to timely dependent deformation and geochemical processes.