@article{ajmm2014233,
author={{Nwoye, C. I. and Obuekwe, I. and Mbah, C. N. and Ede, S. E. and Nwangwu, C. C. and Abubakar, D. D.},
title={Empirical Evaluation of Slag Cement Minimum Setting Time (SCMST) by Optimization of Gypsum Addition to Foundry Slag during Production},
journal={American Journal of Mining and Metallurgy},
volume={2},
number={3},
pages={51--56},
year={2014},
url={http://pubs.sciepub.com/ajmm/2/3/3},
abstract={An empirical evaluation of slag cement minimum setting time (SCMST) has been successfully carried out through optimization of gypsum addition to foundry slag in the course of the cement production. A model was derived and used as a tool for predictive analysis of the cement setting time based on gypsum input. The model aided optimization of gypsum addition indicates a minimum setting time of 14.1054 minutes at an optimum gypsum input concentration of 6.4847%. Beyond 6.4847% gypsum addition, the slag cement setting time increases drastically; a situation typifying immiscibility and lack of homogeneity between the cement slurry and the extra gypsum addition. This is because increased gypsum addition (above a specified quantity) is unlikely to forms a coherent mass with a specified and fixed liquid volume, resulting to delayed and differential setting. The derived model expressed as;<img src=image/abs1.png></img> is quadratic and single factorial in nature. The slag cement setting time per unit gypsum addition are as obtained from experiment and derived model are 4.75 and 4.996 mins./% respectively. Statistical analysis of the experimental and derived model-predicted results for each value of the gypsum input concentration considered shows standard errors of 1.8974 and 1.5485% respectively. Deviational analysis indicates 10.46% as the maximum deviation of the model-predicted slag cement setting time from the corresponding experimental value. The validity of the model was rooted on the expression 0.0226 ? + 0.2101 ¦Á = 0.0162 ¦Á2 + 1.0001 where both sides of the expression are correspondingly approximately equal. The validity of the derived model-predicted results also was ascertained using SPSS 17.0. The results indicate check variance = 0.001, standard deviation = 0, and model operational confidence of 95.0% at a significant level: 0.05.},
doi={10.12691/ajmm-2-3-3}
publisher={Science and Education Publishing}
}