C. I. Nwoye^1,, C. U. Nwoye², S. O. Nwakpa¹, E. C. D. Nwoye², O. C. A. Nwoye², B. C. Chukwudi³, N. E. Idenyi⁴

Predictability of sulphur removal efficiency of iron ore (designated for production of orthopedic devices) has carried out based treatment temperature and mass-input of KClO₃ used as oxidizing agent. Results generated from experiment, derived model and regression model show that sulphur removal efficiencies increases with increase in both treatment temperature and mass-input of KClO₃ up to 800°C and 12g of KClO₃ respectively. A two- factorial empirical model was derived, validated and used for the predictive analysis. The validity of the derived model expressed as: ₰ = 1.5 x 10^-11 ɤ^4.3318 + 5.6655 ϑ - 25.237 was rooted in the model core expression ₰ + 25.237 = 1.5 x 10^-11 ɤ^4.3318 + 5.6655 ϑ , where ₰, ɤ and ϑ are the sulphur removal efficiency, treatment temperature and mass-input of KClO₃ respectively. Both sides of the core expression are correspondingly approximately equal. This research presents the possibility of limiting the Processed Iron Ore Remnant Sulphur (PIORS) through strategized input of ɤ and ϑ during processing of iron ore designated for orthopedic devices. This was geared towards enhancing the durability and biocompatibility of medical devices made of steel since PIORS is deleterious to the mechanical properties and functional performance of the steel-made medical devices. Sulphur removal efficiency per unit rise in treatment temperature & per unit mass-input of KClO₃ as well as standard error incurred in predicting the sulphur removal efficiency for each value of the treatment temperature & mass-input of KClO₃ as obtained from experimental, derived model and regression model predicted results were 0.2422, 0.2659 and 0.2493 % / °C & 11.0109, 12.0865 and 11.3315 % /g as well as 6.5587, 6.3878 and 3.3787 x 10^-5 & 3.2057, 2.6827 and 3.5936% respectively. The correlations between sulphur removal efficiency and treatment temperature & per unit mass-input of KClO₃ as obtained from experiment, derived model and regression model indicated were all > 0.98. Deviational analysis revealed that the maximum deviation of model-predicted sulphur removal efficiency from the experimental results is 12.33%. This invariably translated into over 87% operational confidence for the derived model as well as over 0.87 dependency coefficients of sulphur removal efficiency on treatment temperature and KClO₃ addition.

prediction, sulphur removal efficiency, iron ore processing, orthopedics devices production