The industrial sector is one of the main responsible for the serious environmental situation on the planet and also for increasing legal requirements, in relation to the waste generated. On the other hand, many companies have reacted proactively, based on the implementation of management strategies such as: clean production, environmental certification, reduction of toxic waste, recycling, sustainable consumption and reuse, mainly. It is worth mentioning that the aluminum reduction process is responsible for a large amount of greenhouse gas emissions and, thus, promotes environmental impacts and serious climate changes. During the aluminum reduction process, the occurrence of the anodic effect causes an extreme increase in the tub tension and, consequently, an increase in the bath temperature, with very high temperatures, resulting in a thermal disturbance, with the possibility of melting the insulating layer of the vat and the final consequences are the loss of production in the entire vat line, its shortened service life and the production of PFC gases. Seeking a strategy based on sustainability, I try to take into account the problem of the worsening of the Greenhouse Effect, the extreme increase in kiln tension and, consequently, the loss of production in the entire line of vats, this work proposes the use of an Artificial Neural Network together with Clustering algorithms to automatically create anodic pre-Effect labels, and thus predict the nonlinear dynamic behavior of the primary aluminum reduction industry oven anodic pre-effect, based on actual vat data electrolytic. With the use of these Machine Learning models, it is possible to predict the occurrence of the anodic pre-effect, allowing process operators to take mitigating measures to suppress the anodic effect, avoiding the loss of aluminum production in the vat and decreasing the emission of gases from the greenhouse effect.
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