The paper evaluates two types of Artificial Neural Networks to model dynamically the behaviour of the electrical resistance of a primary aluminum reduction furnace. The proposal is to use Direct Multilayer neural networks (RNMD) and Recurrent Neural networks (RNR) to model the electrical resistance of the oven. For each of these Neural Networks is explored its ability to model dynamic systems, either by varying the number of layers of neurons, as well as the number of neurons in each layer, varying the neural network input signals, etc. The data to be used in modeling from a Brazilian factory of primary aluminum. This modeling can be used to control the distance (up or down) between the electrodes, anodes and cathodes of the reduction that it consists primarily of carbonaceous materials. In this way the system of control has the task of maintaining the value of resistance within acceptable ranges of operation always attempting to ensure thermal stability and consequently the production of primary aluminum, high-purity, based on data available online in the control system of the plant. Through these electrodes are injected electrical currents keep that, besides the electrolysis itself cause the electrolytic bath, raising its temperature to a range up to 960° C. The motivation for the work is in high complexity of primary aluminum reduction process, whose nature is non-linear and the same suffering directly related variables influence the dynamics of the process, often imperceptible process engineers from the factory, but can be perceived by means of computational intelligence techniques reflecting about the different operating conditions of the real system.
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