Anzeige
Erschienen in:
27.11.2019 | LCI METHODOLOGY AND DATABASES
Life cycle assessment of ferro niobium
Erschienen in: The International Journal of Life Cycle Assessment | Ausgabe 3/2020
EinloggenAktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Abstract
Purpose
Ferro niobium (FeNb) is a metallic alloy whose industrial use has been increasing steadily in the last decades. This work aims to systematize the available information on FeNb production, provide its inventory data and generate its first technologically representative publicly available life cycle impact assessment (LCIA).
Methods
The production of 1 kg of FeNb from pyrochlore in the baseline year 2017 was modelled following a cradle-to-gate approach. Primary information on mass, energy and water flows was collected when possible from the Brazilian leading FeNb supplier, CBMM (80% of the world market). The CML method (CML-IA 4.7) was applied for the impact assessment including global warming potential (GWP), acidification potential (AP), eutrophication potential (EP), ozone layer depletion potential (ODP), abiotic depletion potential (fossil and elemental) (ADPfossil and ADPelemental) and photochemical ozone creation potential (POCP).
Results and discussion
The first stage of pyrochlore processing (pyrochlore ore extraction, mechanical processing and flotation) and the last stage (aluminothermic reaction) bear the highest impact in all analyzed CML impact categories. The primary aluminium consumption has the most important contribution in five out of seven impact categories (50% in ADPfossil, 55% in AP, 35% in EP, 57% in GWP and 40% in POCP). In this sense, the industry should promote a higher share of secondary aluminium in the production process. Also, the impact from electricity consumption and processing chemicals showed to be relevant.
Conclusions
This work is the first LCIA on ferro niobium to be published with representative, high-quality data. A dataset was produced in order to enable ferro niobium to be incorporated to future LCIA-modelling.