Navegando por Assunto "Aluminum reduction"

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Acesso aberto (Open Access)
Agrupamento de fornos de redução de alumínio utilizando os algoritmos Affinity Propagation, Mapa auto–organizável de Kohonen (som), Fuzzy C–Means e K–Means
(Universidade Federal do Pará, 2017-10-11) LIMA, Flávia Ayana Nascimento de; CARDOSO, Diego Lisboa; http://lattes.cnpq.br/0507944343674734; OLIVEIRA, Roberto Célio Limão de; http://lattes.cnpq.br/4497607460894318
The continuous development of technology accounts for measures that provide industries benefits to grant them profitability and competitive advantage. In the mineralogy field, aluminum smelting usually requires substantial number of cells, also known as reduction pots, to produce aluminum in a continuous and complex process. Analytical monitoring is essential for those industries’ competitive advantage, given that during operation some cells show behavior similar to others, thereby forming clusters of cells. These clusters depend on data patterns usually implicit or invisible for the operation, but can be found by data analysis techniques. In this work four clustering techniques are presented to that end: the Affinity Propagation; the Kohonen Self Organizing Map; the Fuzzy C–Means; and the K–Means Algorithm. These techniques are used to find and group cells that share similar behavior, by analysing seven variables which are closely related to the aluminum reduction process. This work aims at addressing the benefits of clustering, especially by simplifying the aluminum potline analysis, once a large group of cells might be summarized in one sole group, what can provide more compact yet rich information for data driven modeling and control. Moreover, the identification of similar data patterns in clusters makes the task of those who is going to be in charge of analyzing these dats. This work also identifies the ideal cluster size for each technique applied.
Acesso aberto (Open Access)
Atenuação de oscilações magnetohidrodinâmicas em cuba de redução de alumínio usando estruturas periódica
(Universidade Federal do Pará, 2023-04-27) ANDERE, Thais Pascoal de Oliveira; OLIVEIRA, Rodrigo Melo e Silva de; http://lattes.cnpq.br/4768904697900863
Magnetohydrodynamic instability (MHD) in an aluminum reduction cell is due to the interactions between the conductive liquid currents and the magnetic field generated by very high currents flowing through current feeding circuit buses. Such phenomenon creates oscillations in this fluid, compromising the efficiency of the process aluminum reduction. The reduction cells consist, in their usual configuration, of a container with flat walls that accommodates the liquid. In this work, a new geometry is proposed for the container based on periodic structures, with the aim of to mitigate such oscillations. The analysis of oscillations of fluid in both configurations is made with a software developed in this work to numerically simulate the process in two dimensions. The numerical formulation employed is based on the finite-difference time-domain method to solve the Navier-Stokes equations (N-S) explicitly through the Chorin projections method. The volume and surface of the fluid are mapped using the MAC method, (marker and cell). The liquid is treated as incompressible and viscous, in addition to being electrically conductive. The accelerations caused by magnetic field and the electric currents are coupled to N-S by calculating the Lorentz Force. The results are analyzed and comparisons through the difference between the variation of the height of the conductive fluid in two scenarios, considering the flat bottom of the cell, current configuration, and by applying periodic structures at the bottom of the cell.
Acesso aberto (Open Access)
Modelagem neural da resistência elétrica dos fornos de redução do alumínio
(Universidade Federal do Pará, 2015-10-16) CONTE, Thiago Nicolau Magalhães de Souza; OLIVEIRA, Roberto Célio Limão de; http://lattes.cnpq.br/4497607460894318
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.