Alucell: A Unique Suite of Models to Optimize Pot Design and Performance

Renaudier, Steeve; Langlois, Steve; Bardet, Benoît; Picasso, Marco; Masserey, Alexandre

doi:10.1007/978-3-319-72284-9_71

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…In the Al smelting process, ANN has been used in a minor way to simulate and model processes [56,57,58], while in parallel other techniques like clustering help to identify pots with common behaviors to enhance the knowledge derived from the data [59]. In major part, mathematical techniques have been used to create models to emulate the Al production process [60,61,62,63,64].…”

Section: Introductionmentioning

confidence: 99%

Soft Sensors in the Primary Aluminum Production Process Based on Neural Networks Using Clustering Methods

Souza

Soares

Castro³

et al. 2019

Sensors

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Primary aluminum production is an uninterrupted and complex process that must operate in a closed loop, hindering possibilities for experiments to improve production. In this sense, it is important to have ways to simulate this process computationally without acting directly on the plant, since such direct intervention could be dangerous, expensive, and time-consuming. This problem is addressed in this paper by combining real data, the artificial neural network technique, and clustering methods to create soft sensors to estimate the temperature, the aluminum fluoride percentage in the electrolytic bath, and the level of metal of aluminum reduction cells (pots). An innovative strategy is used to split the entire dataset by section and lifespan of pots with automatic clustering for soft sensors. The soft sensors created by this methodology have small estimation mean squared error with high generalization power. Results demonstrate the effectiveness and feasibility of the proposed approach to soft sensors in the aluminum industry that may improve process control and save resources.

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Section: Introductionmentioning

confidence: 99%

Soft Sensors in the Primary Aluminum Production Process Based on Neural Networks Using Clustering Methods

Souza

Soares

Castro³

et al. 2019

Sensors

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“…Как правило, расчет МГД выполняется в приближении мелкой воды и без учета теплообмена и формы рабочего пространства (ФРП). В работах [7,8] приводятся результаты трехмерного моделирования электролизеров APXe и AP6X с помощью разработанной программы ALUCELL, основной целью которой является возможность сопряженного расчета МГД стабильности ванны, растворения и переноса в электролите глинозема, а также тепловых полей.…”

Section: Introductionunclassified

Mathematical model of magnetic hydrodynamics and heat transfer in aluminium reduction cell

Pianykh

Arkhipov

Tretyakov

2019

Izv.VUZ. Tsvet. Met.

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The paper presents a new nonstationary three-dimensional mathematical model of an aluminium reduction cell which makes it possible to perform coupled thermoelectric and magnetohydrodynamic calculations taking into account sideledge formation. The model considers the nonlinear dependence of material electrical conductivity and thermal conductivity coefficients on temperature, and the nonlinear dependence of magnetization on the magnetic field strength for ferromagnetic materials. Heat transfer coefficients on outer surfaces included the radiant and convective components of heat transfer and were functions of the ambient temperature and the local surface temperature. The energy equation took into account internal heat sources due to the electric current flow, exothermic reactions and additional thermal effects associated with the raw material loading and phase transitions. The control volume method was used to obtain a numerical solution. The developed mathematical model was experimentally tested at the S8BME aluminium reduction cell. The paper presents the calculated and experimental data of magnetic, electric, thermal and hydrodynamic fields. The comparison of calculation results with the results of industrial experiments showed that the developed model reflects physical processes taking place in the aluminium reduction cell with accuracy sufficient for engineering calculations. The calculated values of electrical voltage, magnetic induction and temperature practically coincide with the measured ones. Velocity directions in the metal and the sideledge profile shape obtained by calculation have insignificant differences from experimental values. The developed model can be used to estimate operation specifications and design parameters for new and modernized aluminium reduction cells. Further studies will be aimed at refining the calculated results by improving the developed mathematical model.

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Improvements to a Mathematical Model Used to Reproduce the Wave and Stream at the Bath-Metal Interface and Assess Their Impact on the Movement of Alumina Rafts

Richer

Dion

Kiss

et al. 2023

The Minerals, Metals &Amp; Materials Series

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Alucell: A Unique Suite of Models to Optimize Pot Design and Performance

Cited by 4 publications

References 5 publications

Soft Sensors in the Primary Aluminum Production Process Based on Neural Networks Using Clustering Methods

Soft Sensors in the Primary Aluminum Production Process Based on Neural Networks Using Clustering Methods

Mathematical model of magnetic hydrodynamics and heat transfer in aluminium reduction cell

Improvements to a Mathematical Model Used to Reproduce the Wave and Stream at the Bath-Metal Interface and Assess Their Impact on the Movement of Alumina Rafts

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