Foto: Forschungsprogramm Energieeffizienz, Materialien und Ressourcen ©Copyright: BengsResource Technologies

Energy > Energy Efficiency, Materials and Resources - All Topics

Foto: Expedition nach Maamorilik, Westgrönland mit HIF-Beteiligung - Ziel ist es, hoch aufgelöste geologische Karten der Erdoberfläche zu erstellen, um das Rohstoffpotenzial des Landes besser einzuschätzen. ©Copyright: HZDRFrom the copper cables to the lithium batteries, metal and mineral raw materials play a vital role in our everyday lives. The demand for these resources in terms of quantity and diversity is increasing, especially for use in renewable energy, electromobility, communications and other advanced technologies. At the same time, however, ensuring their continued availability poses a growing number of global challenges, as mineable deposits tend to be located in inhospitable areas or at great depths, while the ores themselves have low metal content and are often fine-grained and complex in structure. How can supplies be secured in the long term? An important contribution to the more efficient use of resources can be made by recycling (known as the Circular Economy) and by minimizing loss from the system. The Helmholtz Institute Freiberg for Resource Technology (HIF) is dedicated to providing the innovative technologies that are urgently needed.

HIF was set up in 2011 by the German government as part of its national strategy for raw materials. The research team has been drawn from multiple scientific disciplines and has been gathered together under a single roof to look into such issues as the exploration, processing, metallurgy and recycling of mineral resources. By precisely analyzing the properties of raw materials and the valuable substances they contain as well as by means of computer simulation, it is possible to quantify the material and energy efficiency of processes along the value chain and to identify new solutions for the socially responsible and commercially viable use of raw materials. The focus at HIF is on complex resources from primary and secondary sources as well as on high technology metals such as indium, gallium, germanium and rare earth elements.

HIF is a constituent part of the Helmholtz-Zentrum Dresden-Rossendorf and works in close collaboration with TU Bergakademie Freiberg. It is a core member of the European EIT RawMaterials network, having played a decisive role in its establishment.


  • Developing new technologies for safeguarding the long-term supply of mineral and metalliferous raw materials from domestic and global sources
  • Contribution to global environmental protection through material and energy efficieny
  • Establishing long-term economic relations with resource-based countries
  • Training a new generation of highly qualified scientists and engineers for German industry and for academia

Press Releases

Involved HZDR institutes




  • Hassanzadeh, A. C.; Nazari, S. A.; Shafaei, S. Z. A. et al.
    Study of effective parameters on generating submicron (nano)-bubbles using the hydrodynamic cavitation
    Physicochemical Problems of Mineral Processing 56(2020)5, 884-904 (10.37190/ppmp/126628)
  • Reuter, M. A.; Obiso, D.; Schwittala, D. H. et al.
    Dynamics of Rising Bubbles in a Quiescent Slag Bath with Varying Thermo-Physical Properties
    Metallurgical and Materials Transactions B 51(2020), 2843-2861 (10.1007/s11663-020-01947-0)
  • Duan, P.; Lai, J.; Ghamisi, P. et al.
    Component Decomposition-Based Hyperspectral Resolution Enhancement for Mineral Mapping
    Remote Sensing 12(2020), 2903 (10.3390/rs12182903)
  • Huang, R.; Xu, Y.; Hong, D. et al.
    Deep point embedding for urban classification using ALS point clouds: A new perspective from local to global
    ISPRS Journal of Photogrammetry and Remote Sensing 163(2020), 62-81 (10.1016/j.isprsjprs.2020.02.020)
  • Duan, P.; Lai, J.; Kang, J. et al.
    Texture-Aware Total Variation-Based Sun Glint Removal of Hyperspectral Images
    ISPRS Journal of Photogrammetry and Remote Sensing 166(2020), 10.1016/j.isprsjprs.2020.06.009
  • Salcedo-Sanz, S.; Ghamisi, P.; Piles, M. et al.
    Machine learning information fusion in Earth observation: A comprehensive review of methods, applications and data sources
    Information Fusion 63(2020), 256-272 (10.1016/j.inffus.2020.07.004)
  • Kang, J.; Fernandez-Beltran, R.; Ye, Z. et al.
    Deep Metric Learning Based on Scalable Neighborhood Components for Remote Sensing Scene Characterization
    IEEE Transactions on Geoscience and Remote Sensing 58(2020)12, 8905-8918 (10.1109/TGRS.2020.2991657)
  • Sheikholeslami, M. M.; Nadi, S.; Naeini, A. A. et al.
    An Efficient Deep Unsupervised Superresolution Model for Remote Sensing Images
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13(2020), 1937-1945 (10.1109/JSTARS.2020.2984589)
  • Ardabili, S. F.; Mosavi, A.; Ghamisi, Pedram et al.
    COVID-19 Outbreak Prediction with Machine Learning
    Algorithms 13(2020)10, 249 (10.3390/a13100249)
  • Pinter, G.; Felde, I.; Mosavi, A. et al.
    COVID-19 Pandemic Prediction for Hungary; A Hybrid Machine Learning Approach
    Mathematics 8(2020)6, 890 (10.3390/math8060890)
  • Rasti, B.; Ghamisi, P.
    Remote Sensing Image Classification Using Subspace Sensor Fusion
    Information Fusion 64(2020), 121-130 (10.1016/j.inffus.2020.07.002)
  • Sheykhmousa, M.; Mahdianpari, M.; Ghanbari, H. et al.
    Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13(2020), 6308-6325 (10.1109/JSTARS.2020.3026724)
  • Nosratabadi, S.; Mosavi, A.; Duan, P. et al.
    Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods
    Mathematics 8(2020), 1799 (10.3390/math8101799)
  • Dehghani, M.; Salehi, S.; Mosavi, A. et al.
    Spatial Analysis of Seasonal Precipitation over Iran: Co-Variation with Climate Indices
    ISPRS International Journal of Geo-Information 9(2020), 73 (10.3390/ijgi9020073)
  • Hang, R.; Li, Z.; Ghamisi, P. et al.
    Classification of Hyperspectral and LiDAR Data Using Coupled CNNs
    IEEE Transactions on Geoscience and Remote Sensing 58(2020)7, 4939-4950 (10.1109/TGRS.2020.2969024)
  • Choubin, B.; Abdolshahnejad, M.; Moradi, E. et al.
    Spatial hazard assessment of the PM10 using machine learning models in Barcelona, Spain
    Science of the Total Environment 701(2020), 134474 (10.1016/j.scitotenv.2019.134474)
  • Hong, D.; Wu, X.; Ghamisi, P. et al.
    Invariant Attribute Profiles: A Spatial-Frequency Joint Feature Extractor for Hyperspectral Image Classification
    IEEE Transactions on Geoscience and Remote Sensing 8(2020)6, 3791-3807 (10.1109/TGRS.2019.2957251)
  • Duan, P.; Kang, X.; Ghamisi, P. et al.
    Multilevel Structure Extraction-Based Multi-Sensor Data Fusion
    Remote Sensing 12(2020), 4034 (10.3390/rs12244034)
  • Kang, J.; Fernández-Beltrán, R.; Ye, Z. et al.
    High-Rankness Regularized Semi-Supervised Deep Metric Learning for Remote Sensing Imagery
    Remote Sensing 12(2020), 2603 (10.3390/rs12162603)
  • Li, H.; Ghamisi, P.; Rasti, B. et al.
    A Multi-Sensor Fusion Framework Based on Coupled Residual Convolutional Neural Networks
    Remote Sensing 12(2020), 2067 (10.3390/rs12122067)
  • Mueller, U.; Tolosana Delgado, R.; Grunsky, E. C. et al.
    Biplots for Compositional Data Derived from Generalised Joint Diagonalization Methods
    Applied Computing and Geosciences 8(2020), 100044 (10.1016/j.acags.2020.100044)
  • Marks, M. A. W.; Eggenkamp, H. G. M.; Atanasova, Petya et al.
    Review on the Compositional Variation of Eudialyte-Group Minerals in the Ilímaussaq Complex (South Greenland)
    Minerals 10(2020)11, 1011 (10.3390/min10111011)
  • Eggenkamp, H. G. M.; Marks, M. A. W.; Atanasova, Petya et al.
    Changes in Halogen (F, Cl, Br, and I) and S Ratios inRock-Forming Minerals as Monitors for MagmaticDifferentiation, Volatile-Loss, and HydrothermalOverprint: The Case for Peralkaline Systems
    Minerals 10(2020)995 (10.3390/min10110995)
  • Reuter, M.; Taube, M. C.; Adamczyk, B. et al.
    Tantalum recycling from pyrometallurgical residues (Tantalrecycling aus pyrometallurgischen Rückständen)
    World of Metallurgy - Erzmetall 73(2020)4, 196-205
  • Rafiezadeh Shahi, K.; Ghamisi, P.; Rasti, B. et al.
    Data Fusion Using a Multi-Sensor Sparse-Based Clustering Algorithm
    Remote Sensing 12(2020)23, 4007 (10.3390/rs12234007)
  • Kupka, N.; Kaden, P.; Jantschke, A. et al.
    Acidified water glass in the selective flotation of scheelite from calcite, part II: species in solution and related mechanism of the depressant
    Physicochemical Problems of Mineral Processing 56(2020)5, 797-817 (10.37190/ppmp/125639)
  • Hassanzadeh, A. A.; Sajjady, S. A. B.; Gholami, H. C. et al.
    An improvement on selective separation by applying ultrasound to rougher and re-cleaner stages of copper flotation
    Minerals 10(2020)7, 619 (10.3390/min10070619)
  • Götze, J.; Möckel, R.; Pan, Y.
    Mineralogy, geochemistry and genesis of agate - a review
    Minerals 10(2020)11, 1037 (10.3390/min10111037)
  • Hassanzadehmahaleh, A.; Azizi, A. A.; Masdarian, M. A. et al.
    Parametric optimization in rougher flotation performance of a sulfidized mixed copper ore
    Minerals 10(2020)8, 1-19 (10.3390/min10080660)
  • Booysen, R.; Jackisch, R.; Lorenz, S. et al.
    Detection of REEs with lightweight UAV-based hyperspectral imaging.
    Scientific Reports 10(2020)1, 17450 (10.1038/s41598-020-74422-0)
  • Braun, R.; Schönberger, N.; Vinke, S. et al.
    Application of Next Generation Sequencing (NGS) in Phage Displayed Peptide Selection to Support the Identification of Arsenic-Binding Motifs
    Viruses 12(2020)12, 1360 (10.3390/v12121360)
  • Contreras Acosta, I. C.; Khodadadzadeh, M.; Tolosana Delgado, R. et al.
    Drill-Core Hyperspectral and Geochemical Data Integration in a Superpixel-Based Machine Learning Framework
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13(2020), 4214-4228 (10.1109/JSTARS.2020.3011221)
  • Jackisch, R.; Lorenz, S.; Kirsch, M. et al.
    Integrated Geological and Geophysical Mapping of a Carbonatite-Hosting Outcrop in Siilinjärvi, Finland, Using Unmanned Aerial Systems
    Remote Sensing 12(2020)18, 2998 (10.3390/rs12182998)
  • Rafiezadeh Shahi, K.; Khodadadzadeh, M.; Tusa, L. et al.
    Hierarchical Sparse Subspace Clustering (HESSC): An Automatic Approach for Hyperspectral Image Analysis
    Remote Sensing 12(2020)15, 2421 (10.3390/RS12152421)
  • Ditscherlein, R.; Furat, O.; de Langlard, M. et al.
    Multiscale Tomographic Analysis for Micron-Sized Particulate Samples
    Microscopy and Microanalysis 26(2020)4, 676-688 (10.1017/S1431927620001737)
  • Buchmann, M.; Borowski, N.; Leißner, T. et al.
    Evaluation of Recyclability of a WEEE Slag by Means of Integrative X-Ray Computer Tomography and SEM-Based Image Analysis
    Minerals 10(2020)4, 309 (10.3390/min10040309)
  • Reuter, M. A.; Akashi, M.; Kriebitzsch, S. et al.
    CFD Modeling and Experimental Validation of Top-Submerged-Lance Gas Injection in Liquid Metal
    Metallurgical and Materials Transactions B 51(2020), 1509-1525 (10.1007/s11663-020-01864-2)
  • Ojala, A. E. K.; Mattila, J.; Middleton, M. et al.
    Earthquake-induced deformation structures in glacial sediments—evidence on fault reactivation and instability at the Vaalajärvi fault in northern Fennoscandia
    Journal of Seismology 24(2020)3, 549-571 (10.1007/s10950-020-09915-6)
  • Sharul, H.; Vahid, J.-N.; Nikolaos, K. K. et al.
    Direct Characterisation of Solute Transport inUnsaturated Porous Media using 4D X-raySynchrotron Microtomography
    Proceedings of the National Academy of Sciences of the United States of America 117(2020)38, 23443-2344 (10.1073/pnas.2011716117)
  • Smith, A. J. B.; Beukes, N. J.; Gutzmer, J. et al.
    Life on a Mesoarchean marine shelf – insights from the world’s oldest known granular iron formation
    Scientific Reports 10(2020)1, 10519 (10.1038/s41598-020-66805-0)
  • Talebi, H.; Peeters, L. J. M.; Mueller, U. et al.
    Towards Geostatistical Learning for the Geosciences: A Case Study in Improving the Spatial Awareness of Spectral Clustering
    Mathematical Geosciences 52(2020), 1035-1048 (10.1007/s11004-020-09867-0)
  • Balinski, A.; Kelly, N.; Helbig, T. et al.
    Separation of aluminium and iron from lanthanum - a comparative study of solvent extraction and hydrolysis-precipitation
    Minerals 10(2020)6, 556 (10.3390/min10060556)
  • Rasti, B.; Ghamisi, P.; Seidel, P. et al.
    Multi Optical Sensor Fusion for Mineral Mapping of Core Samples
    Sensors 20(2020)13, 3766 (10.3390/s20133766)
  • Belo Fernandes, I.; Abadias Llamas, A.; Reuter, M.
    A simulation-based exergetic analysis of NdFeB permanent magnet production to understand large systems
    JOM: The Journal of the Minerals, Metals & Materials Society 72(2020)7, 2754-2769 (10.1007/s11837-020-04185-6)
  • Jackisch, R.
    Drone-based surveys of mineral deposits
    Nature Reviews Earth & Environment 1(2020)4, 187 (10.1038/s43017-020-0042-1)
  • Bru, K.; Sousa, R.; Machado Leite, M. et al.
    Pilot-scale investigation of two Electric Pulse Fragmentation (EPF) approaches for the mineral processing of a low-grade cassiterite schist ore
    Minerals Engineering 150(2020), 160270 (10.1016/j.mineng.2020.106270)
  • Kupka, N.; Babel, B. M.; Rudolph, M.
    The Potential Role of Colloidal Silica as a Depressant in Scheelite Flotation
    Minerals 10(2020)2, 144 (10.3390/min10020144)
  • Kupka, N.; Möckel, R.; Rudolph, M.
    Acidified water glass in the selective flotation of scheelite from calcite, Part I: performance and impact of the acid type
    Physicochemical Problems of Mineral Processing 56(2020)2, 238-251 (10.37190/ppmp19101)
  • Godefroy-Rodríguez, M.; Hagemann, S.; Frenzel, M. et al.
    Laser ablation ICP-MS trace element systematics of hydrothermal pyrite in gold deposits of the Kalgoorlie district, Western Australia
    Mineralium Deposita 55(2020), 823-844 (10.1007/s00126-020-00958-w)
  • Frenzel, M.; Cook, N. J.; Ciobanu, C. L. et al.
    Halogens in hydrothermal sphalerite record origin of ore-forming fluids
    Geology 48(2020)8, 766-770 (10.1130/G47087.1)
  • Kiani, M.; Du, N.; Vogel, M. et al.
    Disturbing-free determination of yeast concentration in DI water and in glucose using impedance biochips
    Biosensors 10(2020)1, 7 (10.3390/bios10010007)
  • Tusa, L.; Kern, M.; Khodadadzadeh, M. et al.
    Evaluating the performance of hyperspectral short-wave infrared sensors for the pre-sorting of complex ores using machine learning methods
    Minerals Engineering 146(2020), 106150 (10.1016/j.mineng.2019.106150)
  • Gholami, H.; Rezai, B.; Mehdilo, A. et al.
    Effect of microwave system location on floatability of chalcopyrite and pyrite in a copper ore processing circuit
    Physicochemical Problems of Mineral Processing 56(2020)3, 432-448 (10.37190/ppmp/118799)
  • Bartie, N. J.; Abadias Llamas, A.; Heibeck, M. et al.
    The simulation-based analysis of the resource efficiency of the circular economy – the enabling role of metallurgical infrastructure
    Mineral Processing and Extractive Metallurgy 129(2020), 229-249 (10.1080/25726641.2019.1685243)
  • Abadias Llamas, A.; Bartie, N. J.; Heibeck, M. et al.
    Simulation-based exergy analysis of large circular economy systems: Zinc production coupled to CdTe photovoltaic module life cycle
    Journal of Sustainable Metallurgy 6(2020)1, 34-67 (10.1007/s40831-019-00255-5)
  • Luque Consuegra, G.; Kutschke, S.; Rudolph, M. et al.
    Halophilic bacteria as potential pyrite bio-depressants in Cu-Mo bioflotation
    Minerals Engineering 145(2020), 106062 (10.1016/j.mineng.2019.106062)
  • Atanasova, P.; Marks, M. A. W.; Frenzel, M. et al.
    Fractionation of geochemical twins (Zr/Hf, Nb/Ta and Y/Ho) and HREE-enrichment during magmatic and metamorphic processes in peralkaline nepheline syenites from Norra Kärr (Sweden).
    Lithos 372-373(2020), 105667 (10.1016/j.lithos.2020.105667)
  • Hannula, J. O.; Da Assuncao Godinho, J. R.; Abadias Llamas, A. et al.
    Simulation-Based Exergy and LCA Analysis of Aluminum Recycling: Linking Predictive Physical Separation and Re-melting Process Models with Specific Alloy Production
    Journal of Sustainable Metallurgy 6(2020), 174-189 (10.1007/s40831-020-00267-6)
  • Michaux, B.; Hannula, J.; Rudolph, M. et al.
    Study of process water recirculation in a flotation plant by means of process simulation
    Minerals Engineering 148(2020), 106181 (10.1016/j.mineng.2020.106181)
  • Kupka, N.; Tolosana Delgado, R.; Schach, E. et al.
    R as an environment for data mining of process mineralogy data: A case study of an industrial rougher flotation bank
    Minerals Engineering 146(2020), 106111 (10.1016/j.mineng.2019.106111)
  • Abd El-Rahman, Y.; Gutzmer, J.; Li, X.-H. et al.
    Not all Neoproterozoic iron formations are glaciogenic: Sturtian-aged non-Rapitan exhalative iron formations from the Arabian–Nubian Shield
    Mineralium Deposita 55(2020)3, 577-596 (10.1007/s00126-019-00898-0)
  • Contreras Acosta, I. C.; Khodadadzadeh, M.; Tusa, L. et al.
    A Machine Learning Framework for Drill-Core Mineral Mapping Using Hyperspectral and High-Resolution Mineralogical Data Fusion
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12(2020)12, 4829-4842 (10.1109/JSTARS.2019.2924292)
  • Hassanzadehmahaleh, A.; Hoang, D. H.; Stockmann, M.
    Assessment of flotation kinetics modeling using information criteria; Case studies of elevated-pyritic copper sulfide and high-grade carbonaceous sedimentary apatite ores
    Journal of Dispersion Science and Technology 41(2020)7, 1083-1094 (10.1080/01932691.2019.1656640)
  • Balinski, A.; Wiche, O.; Kelly, N. et al.
    Separation of rare earth elements from contaminants and valuable components by in situ precipitation during hydrometallurgical processing of eudialyte concentrate
    Hydrometallurgy 194(2020), 105345 (10.1016/j.hydromet.2020.105345)
  • Matys, S.; Schönberger, N.; Lederer, F. et al.
    Characterization of specifically metal-binding phage clones for selective recovery of cobalt and nickel
    Journal of Environmental Chemical Engineering 8(2020)2, 103606 (10.1016/j.jece.2019.103606)
  • Gärtner, A.; Merchel, S.; Niedermann, S. et al.
    Nature does the averaging – in-situ produced ¹⁰Be, ²¹Ne, and ²⁶Al in very young river terraces
    Geosciences 10(2020), 237 (10.3390/geosciences10060237)
  • Menzel, P.; Teichmann, J.; van den Boogaart, K. G.
    Efficient representation of Laguerre mosaics with an application to microstructure simulation of complex ore
    Mathematical Geosciences 52(2020), 731-757 (10.1007/s11004-019-09841-5)
  • Frenzel, M.; Mikolajczak, C.; Reuther, M. A. et al.
    Quantifying the relative availability of high-tech by-product metals – The cases of gallium, germanium and indium
    Resources Policy 52(2017), 327-335 (10.1016/j.resourpol.2017.04.008)
  • Rasti, B.; Ghamis, I. P.; Gloaguen, R.
    Hyperspectral and LiDAR Fusion Using Extinction Profiles and Total Variation Component Analysis
    IEEE Transactions on Geoscience and Remote Sensing 99(2017), 3997-4007 (10.1109/TGRS.2017.2686450)
  • Shuva, M. A. H.; Rhamdhani, M. A.; Brooks, G. A. et al.
    Thermodynamics of Palladium (Pd) and Tantalum (Ta) Relevant to Secondary Copper Smelting
    Metallurgical and Materials Transactions B 1(2017), 317-327 (10.1007/s11663-016-0839-y)
  • Frenzel, M.; Kullik, J.; Reuter, M. A. et al.
    Raw material "criticality" - Sense or non-sense
    Journal of Physics D: Applied Physics 50(2017)12, 123002 (10.1088/1361-6463/aa5b64)
  • Rudolph, M.; Hartmann, R.
    Specific Surface Free Energy Component Distributions and Flotabilities of Mineral Microparticles in Flotation – An Inverse Gas Chromatography Study
    Colloids and Surfaces A: Physicochemical and Engineering Aspects 513(2017), 380-388 (10.1016/j.colsurfa.2016.10.069)
  • Lederer, F. L.; Curtis, S. B.; Bachmann, S. et al.
    Identification of lanthanum-specific peptides for future recycling of rare earth elements from compact fluorescent lamps
    Biotechnology and Bioengineering 114(2017)5, 1016-1024 (10.1002/bit.26240)
  • Leißner, T.; Bachmann, K.; Gutzmer, J. et al.
    MLA-based partition curves for magnetic separation
    Minerals Engineering 94(2016), 94-103 (10.1016/j.mineng.2016.05.015)