Investigation of Clay Deposits as Supplementary Cimentitious Materials (Scm): Case Study of Songololo Deposit, Kongo Central/Dr Congo

Nowadays Cement industry is relying on the Supplementary Cementitious Materials (SCMs) to reduce the clinker factor enabling then the reduction of carbon dioxide (CO 2 ) emission into the atmosphere but this will be a safer solution only if it’s using available and abundant material as SCMs. Discovery of alternative to the conventional SCMs is a big challenge for sustainability.
The thick clay deposits (namely YC, RC and LC) aged Neoproterozoic of West- Congolian group and widely spread in the Songololo area (DR Congo) where 5 cement plants are erected, are examined in this paper in order to produce the calcined clay (metakaolin) which can be then mixed with limestone to partially substitute to the clinker in the cementitious system and produce an eco-cement called Limestone Calcined Clay Cement (LC3).
The initial clay assessment based on chemical composition carried out using the XRF method and then the phase composition using the XRD method. The kaolinite equivalent (KEQ) parameter was used to determine the kaolinite content of each clay. The composite cement (LC3) was prepared by mixing calcined clay, limestone, clinker and gypsum at different clinker’s substitution rate. The standard mortars were prepared. Physical analysis performed and finally the strength development measured at 1, 2, 7, 28 and 90 days in order to monitor the pozzolanic activity of the calcined clay – limestone mixture and select the suitable clay.
The results showed that among 3 types of clay of Songololo deposit, YC clay was deemed fit after calcination at 850°C for use as SCM. The strength development of the mortar complies the standard and was better than the reference cement. The clay YC can be considered immediately for future development of LC3 while further studies are recommended for LC clay. The RC clay has low calcination prospects.

The authors declare no conflict of interest.

Not applicable

The datasets used in this study are openly available at [repository link] and the source code is available on GitHub at [GitHub link].

This work did not receive any external funding.