The scrap dissolution in an actual process like the BOF is affected both by mass transfer and heat transfer. In this paper, the mass transfer of carbon in liquid melt is considered along with heat transfer. The approaches used in this paper to model the scrap dissolution phenomenon include the application of Green's function, quasi-static, integral profile, and the finite difference approach for different Biot numbers. Mass transfer coefficients are calculated using the Chilton-Colburn's analogy for the case of forced convection. Since the quasi-static approach requires the least computational time, it is used for a detailed parametric study, including the effect of other parameters like different scrap ratios and heating rates of liquid melt. The region of control of heat transfer vs mass transfer is also identified. The dissolution of mixed scrap (light and heavy scrap) is investigated for different scrap ratios and the autogenous heating rates of liquid melt, with the help of mathematical models. The heat transfer coefficient is estimated as a function of mixing energy and the mass transfer coefficient by invoking the Chilton-Colburn analogy. The permissible limits of light scrap, which can be charged into the BOF, are also suggested from the results of this model. The Artificial Neural Network (ANN) model is trained on the dataset (patterns) generated by the coupled heat and mass transfer model. The accuracy of the results obtained using different ANN topologies is discussed followed by a recommendation for selecting the best approach. © 2013 The Minerals, Metals & Materials Society and ASM International.