Suitability Assessment of Okelele Ilorin Clay Mineral as a Raw Material for Sustainable Cement Production
Bibliographic record
Abstract
Integrating the Sustainable Development Goals (SDGs)-particularly those related to innovation and infrastructure, responsible consumption, and climate action-into industrial practices is crucial, as the rising demand for cement depletes natural resources and poses significant environmental and social challenges.This article delves into the intricate relationship between the escalating demand for cement and the depletion of essential resources, while emphasizing the crucial need to align industry practices with the overarching principles of sustainable development as outlined by the SDGs.This article aims to shed light on the path towards a more sustainable and responsible approach to cement production.The objective of this study is to evaluate a clay mineral from Okelele, Ilorin, Kwara State, Nigeria, for its potential use as a raw material in cement production.The mineralogical and chemical compositions of the clay were analyzed using modern characterization techniques, including XRF, XRD, SEM/EDX, and DTA-TG.Cement was produced using the clay mineral and the limestone as raw materials with gypsum as admixture.Raw mix was prepared from clay and limestone to produce cement clinker.Sample A consisted of 70% limestone and 30% clay, sample B contained 75% limestone and 25% clay, while sample C was composed of 80% limestone and 20% clay.The cement produced from raw mixes A, B, and C was used to produce mortar specimens.The compressive strength of mortar samples A, B, and C was tested after curing periods of 7 and 28 days.XRF results indicated that the clay mineral contained silica (SiO) and alumina (AlO) as the major chemical constituents, with percentages of 70.226% and 20.815%, respectively-values typical of kaolinitic clay.Consistent with the XRF results, XRD analysis indicated that quartz and kaolinite were relatively abundant in the clay sample.EDX result shows a high proportion of silicon (48.74%) and aluminum (12.78%) in the clay.DTA-TG analysis of the raw mixes (samples A, B, and C) prepared from the clay and limestone showed an endothermic peak at 870C and an exothermic peak at 1200.Sample B exhibited the highest compressive strength, with a value of 21.7 N/mm , compared to samples A and C.This compressive strength is in reasonable agreement with the minimum requirements of the British Standard.These results indicate that the clay mineral can be considered a suitable alumino-silicate raw material for cement production, and its composition is consistent with that of typical kaolin clay.The clay meets the minimum quality standards required for its use as a raw material in cement production.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".