Feven Desta is a PhD researcher in the section of Resource Engineering. Her research topic is “Sensing and data fusion for raw material characterization”. She has a BSc. degree in Geology, an MSc. degree in Geo-environmental systems analysis and an MSc. degree in Geoinformatics. Prior to TUD, Feven worked for 8 years in the area of geosciences as a geotechnical engineer, geospatial analyst, GIS/RS and environment specialist, geologist, and geophysicist. Her research areas of interest include machine learning, data fusion, sensor technologies, automation, remote sensing, geoinformation, geostatistics, environmental systems modelling, geology, mining, and geotechnical engineering.
Utilize sensor-derived data for material characterization in mining operations. The sensor technologies include visible near-infrared (VNIR), short-wave infrared (SWIR), mid-wave infrared (MWIR) and long-wave infrared (LWIR), hyperspectral imaging, RGB imaging, RAMAN spectroscopy
- Apply machine learning methods to generate knowledge from sensors data
- Data acquisition, data pre-processing, information extraction and validation
- Data fusion – integration of sensors output
- Develop data fusion concepts for a near-complete description of materials
- MSc. In Geo-Information Science and Earth Observation (Majoring – Geoinformatics)
- MSc. In Geo-Environmental Systems Analysis
- BSc. In Geology
Geology, Mining , Sensor technologies, Remote sensing, GIS, Geoinformation, Geostatistics, Environmental system analysis, Geotechnical engineering , Geophysics
Sensors for Raw Material Characterization
Sensors for Raw Material Characterization is part of Real Time Mining Project. The overall aim of Real-Time-Mining is to develop a real-time framework to minimize environmental impact and maximize resource efficiency in the European raw material extraction industry. The project will carry out research and demonstration activities which integrate automated sensor based material characterization, online machine performance measurements, underground navigation and positioning, rapid and sequential resource model update and underground mining system simulation and optimization of planning decisions.
My Phd project - “Sensors for Material Characterization” aims to define, develop and test potential sensor combination concepts for raw material characterization to provide relevant data for real-time online process control and optimization in small scale mining applications.
Her PhD project is part of the Real-Time-Mining (RTM) Project. European Union Horizon 2020 program funded the RTM project. The overall aim of RTM was to develop a real-time framework to minimize environmental impact and maximize resource efficiency in the European raw material extraction industry. The key concept of the research was to promote the change in paradigm from discontinuous intermittent process monitoring to a continuous process and quality management system in highly selective mining operations. The project started in April 2015 and completed in May 2019.
Feven supervised the following MSc. and BSc. projects
- EGEC-D/IP Integrated Project (Environmental Management / Mine Closure)
- The influence of environmental conditions on the use of sensor technologies for in-situ material characterization - A review
- The applicability of Raman spectrometry for real-time characterization of sulphide ore
- The influence of sample moisture content on infrared spectral data