Modeling of metal-slag equilibrium processes using neural nets

Although numerous methods have been proposed in the literature for the modeling of slag/metal equilibrium processes, these models are usually semiempirical hybrids of regression equations and thermodynamic equilibria. Fundamental models are successful only in a few cases, owing to the complexity of interaction between variables in real pyrometallurgical systems. This article shows that neural networks are suitable for modeling such ill-defined equilibrium processes, with noa priori knowledge being required about the form of any model. A three-layered perceptron or backpropagation network is used in this article. A weight matrix describes the relative strength of the interconnections between the network nodes and therefore the relative importance of variables. A number of sets of experimental data could be used to train the neural net which can then be used to predict other sets of data. Neural nets have been used successfully for the modeling of activities in metals and slags, the distribution of species between metal and slag, and slag viscosity on the basis of published data.