Abstract
The reliability of an optimum predictive model is dependent on its consistency with experimental data. Hence, this study aimed at determining the error margin between developed optimum predictive models and experimental data from the pretreatment of a waste copper dust. The aim was achieved by computing the root-mean-square error for each point on the dataset generated from the constrained interpolant and polynomial curve fitting models. These were compared with the experimental outputs and error computations carried out. The results obtained generally showed low error margin between developed models and experiments for both oxidative roasting (OR) and density separation (DS) pretreatments. It was therefore concluded that the results obtained for the developed models are in good conformance with the experimental outputs with a high degree of accuracy and confidence level over 97% whereas for OR and DS, the developed constraint interpolant models were well aligned with experimental outputs. A maximum percentage error of 0.07% and 0.06%, respectively, were recorded in the predictive outputs for both pretreatment methods.
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Acknowledgements
The authors would like to thank the following institutions for allowing access to their facilities:
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1.
Tshwane University of Technology (TUT), Pretoria, Republic of South Africa,
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2.
Vaal University of Technology (VUT), Vanderbijlpark, Republic of South Africa,
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3.
Gravity concentrator Africa (GCA), Johannesburg, Republic of South Africa.
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Okanigbe, D.O., Ayomoh, M.K., Van Der Merwe, S.R. (2023). Pre-treatment of Waste Copper Dust (II): Optimum Predictive Models and Experimental Data Error Margin. In: Ogochukwu Okanigbe, D., Popoola, A.P. (eds) Resource Recovery and Recycling from Waste Metal Dust. Springer, Cham. https://doi.org/10.1007/978-3-031-22492-8_4
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DOI: https://doi.org/10.1007/978-3-031-22492-8_4
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