Evaluating the performance of hyperspectral short-wave infrared sensors for the pre-sorting of complex ores using machine learning methods

Autor: Tuşa, L., Kern, M., Khodadadzadeh, M., Blannin, R., Gloaguen, R., Gutzmer, J.

Publikováno v: In Minerals Engineering 15 January 2020 146

A self-adaptive particle-tracking methodology for minerals processing

Autor: Pereira, L., Frenzel, M., Khodadadzadeh, M., Tolosana Delgado, R., Gutzmer, J.

Publikováno v: Journal of Cleaner Production 279(2021), 123711

Resource and energy efficiency are essential for the raw-materials industry to secure a sufficient and economically feasible supply of minerals and metals for society in the coming decades. This task becomes more challenging as the complexity of prim

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::409f64e911f9297ee9b4ace324321c27
https://www.hzdr.de/publications/Publ-30727-1

Data publication: Mineral quantification at deposit scale using drill-core hyperspectral data: A case study in the Iberian Pyrite Belt

Autor: La Rosa Ferna, Khodadadzadeh, M., Tusa, L., Kirsch, M., Gisbert, G., Tornos, F., Tolosana Delgado, R., Gloaguen, R.

Publikováno v: Publication date: 2021-12-23 Open accessDOI: 10.14278/rodare.1350Versions: 10.14278/rodare.1351License: CC-BY-4.0

We present a semi-automated workflow for large scale interpretation of Hyperspectral data, founded on a novel approach of mineral mapping based on a supervised dictionary learning technique. This approach exploits the complementary information from s

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______4577::1af8950f4122691ee19da7911c5dfa58
https://www.hzdr.de/publications/Publ-33801-1

Rethinking ore sorting - The search for the optimal sensor

Autor: Kern, M., Tusa, L., Khodadadzadeh, M., Leißner, T., Gloaguen, R., Boogaart, K. G., Gutzmer, J.

Publikováno v: AT Minerals Processing 61(2020)3, 54-64

Zusammenfassung: Sensorgestützte Sortierung ist eine Technologie, die in zunehmendem Maße zur Aufbereitung von Primärrohstoffen verwendet wird. Mit dem hier vorgestellten simulations-basierten Ansatz ist es möglich, den optimalen Sensor durch qua

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______4577::50fbdfa9710c41b203453ae1ff8540ca
https://www.hzdr.de/publications/Publ-30826-1

Supervised machine learning for the quantification of mineral phases in drill-core hyperspectral data

Autor: Tusa, L., Khodadadzadeh, M., Contreras Acosta, I. C., Fuchs, M., Gloaguen, R., Gutzmer, J.

Publikováno v: EGU General Assembly, 07.-12.04.2019, Vienna, AustriaProceedings of the EGU General Assembly
EGU General Assembly, 07.-12.04.2019, Vienna, Austria

Discovery and delineation of new ore deposits require substantial investment into diamond-drilling. Traditionally, the extracted drill-cores are visually analysed by site geologists and subjected to geochemical analyses for metal grade evaluation. Fr

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::abb29686725e51a520263b197e5ca703
https://www.hzdr.de/publications/Publ-30243-1