Cost-effective, sustainable and responsible extraction routes
for recovering distinct critical metals and industrial minerals
as by-products from key European hard-rock lithium projects

EXCEED publication on the concept of “characterization and categorization” of industrial byproducts for use in construction materials

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EXCEED presents the idea of “characterization and categorization” of industrial byproducts for use in construction in the form of a dedicated paper focused on the utilization of residues from lithium extraction.

Exploring the potential of lithium tailings in construction materials” by A. Mishra, P. Vevaldi, N. Binte Kabir, A. Yushark Fosu, Q. Dehaine, P. Heikkilä, N. Kanari, H. Chambart, M. Räisänen and P. Perumal, is published in Minerals Engineering.

Abstract:

Lithium mining generates substantial quantities of tailings, posing environmental and resource recovery challenges. This study evaluates the potential reuse of lithium tailings from three prospective lithium mine projects as alternative construction materials, including alkali-activated materials (AAMs), supplementary cementitious materials (SCMs), lightweight aggregates (LWAs), and ceramics (CMS) through characterization and categorization investigation. Mineralogical analysis indicates the presence of silicate minerals, hydroxide-bearing silicates, sulfates, and carbonates. Particle size analysis shows that slimes (M3S) have a finer distribution (D50 = 5 µm), favouring AAMs and SCMs use, while coarser M1S and M2S (D50 = 100 µm) are better suited for LWAs. Among the samples, M3M exhibits the highest BET surface area (1.74 m2/g), enhancing its reactivity for SCMs and CMS applications. Leaching tests confirm that all samples comply with EU landfill directive limits, indicating environmental safety. Thermogravimetric analysis shows mass losses of 0.5 wt% and 1.1 wt% for M1S and M2S, respectively, suggesting insignificant differences in phase transformations. Isothermal calorimetry reveals that M3D has the highest heat release (57 J/g), indicating potential for early-age hydration; however, overall pozzolanic activity remains limited. A machine learning approach using XGBoost, Random Forest, Histogram-based Gradient Boosting, and AdaBoost was applied to predict promising applications based on characterization data. While most models agreed on classification, AdaBoost frequently identified CMS as a promising use. Overall, the findings indicate that lithium tailings are environmentally benign and technically promising for proposed construction applications, contributing to a circular, resource-efficient, and zero-waste approach to lithium mining.

Read the full paper here.

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