ESTIMAÇÃO MULTIVARIÁVEL DO BANHO QUÍMICO DE UM FORNO DE REDUÇÃO DE ALUMÍNIO
Bath Chemistry Modeling, Simulation, Single Layer Feedforward Neural Networks
In this work we present a single layer neural network based model for bath chemistry variables in the aluminum smelting process. This model is designed to be simulated with real data as if it worked online in parallel with the process. The model is built using a very fast machine learning algorithm, the Extreme Learning Machines, which provides excellent results in regression problems in a very short time. Also we applied statistical analysis for data collection, preprocessing and filtering and for validation we performed several simulations to attest the neural model's capability to respond to new data. A comparison of this model against linear and traditional nonlinear structures is performed to show how single layer neural networks can be applied on the bath chemistry modeling.