Anode Current for Aluminum Electrolysis Cell Condition Identification Based on Improved Temporal Convolutional Network

Aimed to improve accuracy rate and time advance of the Anode Effect (AE) prediction in aluminum electrolysis cell, a new prediction method by means of the Generalized Regression Neural Network (GRNN) is proposed. The structure and advantages of the GRNN are introduced, then modeling the anode effect system of aluminum electrolysis cell by means of system identification based on the GRNN. The structure of samples is analyzed emphatically in the process of modeling, and the influence of sample size to model prediction accuracy is analyzed by experiment. The prediction model based on GRNN is trained and tested by sufficient samples extracted from the production data of the 400kA aluminum electrolysis cell. It's proved that the accuracy rate of the AE prediction is more than 90% on average in advance 30 minutes. In addition, results tested for different aluminum electrolysis cells show that the method has extensive applicability while keep high prediction accuracy and its effective.

The paper addresses the basic mechanisms leading to the low-voltage anode effect (LVAE) as well as the high-voltage anode effect (HVAE) in aluminium electrolysis cells. The root cause of any anode effect is too low alumina concentration at the anode. By statistical treatment based on a Gaussian distribution of the alumina concentration in the bath, it was found that the onset of a HVAE takes place at higher average alumina concentration if there is a large standard deviation. By calculating the anode potential using a pragmatic numerical model, it was found that a LVAE can take place at a part of the anode as small as 0.001 m2, while the rest of the anode is at the normal anode reaction, which makes it extremely difficult to discover. It was suggested that the on-and-off current pattern observed during LVAE can be explained by alternation between blocking of the surface (AE) and normal electrolysis, induced by non-stationary mass transfer of alumina.KeywordsAnode effectPFCAlumina distributionStatisticsCOF2

In this work, neural networks are built and trained to be used in a predictive control scheme for the aluminum electrolytic cell. An efficient control of the cell requires the knowledge of predicted future values of the decision variables in order to enable the standard (nonpredictive) control logic to take anticipated actions to prevent the anode effect, a destabilizing event occurring during cell operation. The networks are first trained on data obtained from a computer simulator of the cell prior to undergoing further on-line learning. Trained to predict the cell resistance and the resistance’s trend indicators, the networks are applied to the control of cells in different cell states, with a view to preventing anode effects, the latter being deliberately induced by reducing the alumina feed rate or reducing the feeding frequency and duration. Results show that, with neural-predictive control, anode effects can be avoided, which should result in increased thermal stability, decreased power consumption, and reduced fluoride emissions. Further, the on-line learning capacity of the networks offers a good perspective for their application to other complex industrial processes as well.

Detection of abnormal alumina feed rate in aluminium electrolysis cells using state and parameter estimation

Aluminum electrolytic cell voltage signal is the most important real-time data in the process of aluminum electrolysis. Under normal circumstances, the traditional LMD decomposition of the original voltage signal containing noise in the aluminum electrolytic cell will produce a large error and affect the diagnosis of the failure of the aluminum electrolytic cell. In this paper, an improved LMD decomposition method is used to decompose the cell voltage signal based on wavelet packet denoising. At the same time, according to the energy analysis, the extracted features are classified. The accuracy and reliability of feature extraction can be improved, and the faults existing in the aluminum electrolytic cell can be accurately diagnosed.

Proof of concept to recover thermal wastes from aluminum electrolysis cells using Stirling engines

This article describes the importance of temperature monitoring in aluminum electrolysis cells. The challenge of getting long-term performance from thermocouples is discussed, and appropriate examples are given of information of value to cell operations derived from bath temperature measurements.

A phenomenological model for the creation and transport of anodic gas bubbles in Hall-H´eroult cells has been developed, following a multiscale approach. The essential features of the modelling framework are reviewed and discussed in the present paper. The model covers the generation of molecular gas species through Faradays law, subsequent bubble nucleation, and the evolution of macroscopic bubbles which are treated by a volume of fluid model. Recently, the modelling framework has been extended to include several complex phenomena such as surface tension and wetting, bath chemistry, and variable flow properties. The modelling framework has currently been applied to a laboratory scale electrolysis cell setup. The results demonstrate that the essential properties are well represented over a large range of experimental conditions by the proposed approach.

The purpose of modeling thermal stresses in TiB{sub 2}-G cathode elements was to aide in the design of the final TiB{sub 2}-G cathode element shape to be used in an industrial reduction cell based on knowledge of thermal stress values and an estimate of the probability of element failure. Five finite element models of four TiB{sub 2}-G cathode element shapes were constructed (Figures 1-5) to model thermal stresses resulting from step changes in temperature of 25{degree}C--960{degree}C and 300{degree}C--960{degree}C. The models were developed to represent worst case'' conditions, that is, conditions representing the greatest anticipated values of thermal stress likely to be encountered. ANSYS was used to perform the thermal stress modeling. ANSYS is a general purpose finite element code available from Swanson Analysis Systems, Incorporated. Modeling thermal stress in ANSYS for a step change in temperature requires an initial transient thermal analysis followed by a structural analysis. After the geometry of each model was defined, material properties were specified, and heat transfer boundary conditions developed. The finite element models were constructed using convective heat transfer boundary conditions as a means of specifying heat flow into the models. Convection faces and convective heat transfer coefficients were defined at the TiB{sub 2}-G -- molten metal interfaces. A uniform starting temperature of either 25{degree}C or 300{degree}C was assigned and the models were solved for transient temperature distribution and thermally induced stress. LCHEM codes were utilized to model voltage distribution and current density in reduction cells utilizing TiB{sub 2}-G cathode elements. 4 refs., 119 figs., 9 tabs.

Gas generated beneath anodes in aluminum electrolytic cells play an important role for the circulation of the bath, alumina mixing, and heat balance. Those bubbles cause an extra voltage drop, which is strongly affected by the amount and shape of the bubbles beneath anodes. Consequently, understanding the dynamic behavior of bubbles in aluminum electrolytic cells has been a major research focus worldwide in recent decades.

Numerical simulations of the effects of different anode slot configurations on the gas–liquid multiscale flow characteristics in an aluminum electrolysis cell have been conducted based on a recently developed mathematical model. The results clearly showed that use of anode slots can significantly promote the bubble evacuation behavior and thus affect the overall flow pattern. Both the gas volume fraction and bubble size decreased obviously when transversal or especially longitudinal slots were used. Moreover, the greater the number of both kinds of slot, the lower the mentioned parameters. With increasing anode slot width, the gas volume fraction decreased slightly while there was almost no effect on the bubble size distribution (BSD). Reducing the anode slot height caused a higher gas volume fraction. Both the overall gas volume fraction and BSD in industrial-scale cells are apparently influenced by two large circulation vortices caused by electromagnetic forces (EMFs).

Improved CFD modeling of full dissolution of alumina particles in aluminum electrolysis cells considering agglomerate formation

At present, the aluminium electrolytic cell is developing in the direction of large-scale, but it still adopts the past ‘overunder’ discharging method for the control of alumina concentration, which has low current efficiency and frequent occurrence of anode effect. The development of fiber optic measurement technology provides new possibilities for the measurement of anode current in electrolytic tanks. Based on fiber optic current measurement technology and modern detection technology, this paper develops a real-time monitoring system for aluminium electrolytic tanks based on regional current, realizes accurate online measurement of regional current and tank voltage in aluminium electrolytic tanks and uploads data to the cloud, realizes fine feedback of alumina concentration, and provides data support and technical guarantee for the independent discharging of aluminium electrolytic tanks in different regions.

An open-set fault diagnosis framework for MMCs based on optimized temporal convolutional network

The surface effect is becoming apparently significant as the miniaturization of fluidic devices. In the micro/nanochannel fluidics, the electrode surface effects have the same important influence on the current signals as the channel surface effects. In this paper, when aqueous solution are driven with non-continuous DC electric field force, the characteristics of current signals of the fluid transferring through microfluidic channel are systematically studied. Six modes of current signal are summarized, and some new significant phenomena are found, e.g. there exists a critical voltage at which the steady current value equals to zero; the absolute value of the steady current decreases at first, however, it increases with the external voltage greater than the critical voltage as the electrode area ratio of cathode and anode is 10 and 20; the critical voltage increases with the enhancing of electrode area ratio of cathode and anode and solution pH, while it decreases with the raising of ion concentration. Finally, the microscopic mechanism of the electrode surface charge effects is discussed preliminarily. The rules will be helpful for detecting and manipulating single biomolecules in the micro/nanofluidic chips and biosensors.