Anode effect prediction based on collaborative two-dimensional forecast model in aluminum electrolysis production

Abstract

In this study, a new prediction algorithm is proposed, based on the collaborative two-dimensional forecast model (CTFM) that combines the traditional method and similarity search technique. The main idea of the algorithm is that the prediction of the change trend of the slope and the accumulated slope of the cell resistance as well as the useful knowledge obtained using the similarity search technique are used as the main criteria to calculate anode effect (AE)-prediction reliability. The accumulated mass deviation value is used as an auxiliary criterion to adjust the AE-prediction reliability. Finally, the current AE-process is marked according to the current AE-prediction reliability. The prediction model based on CTFM is tested on a real situation, in which multiple samples are extracted from the production of a 400 kA aluminum electrolysis cell. We observe that when the time advance of AE-prediction is within 20 ∼ 40 min, the accuracy rate of the CTFM algorithm is greater than 95% and the applicability of the method is excellent, showing a high prediction accuracy for different aluminum electrolysis cells.

A comprehensive review of aluminium electrolysis and the waste generated by it

“…When the AE occurs, it increases the energy consumption of electrolysis and reduces the current efficiency. To avoid the occurrence of AE, Chen et al (2018), Cui et al (2020) and Zhang (2010b) used different methods to develop AE prediction models, respectively. Ershov et al (2012) and Zhang et al (2018) proposed alumina feeding systems for reducing the energy consumption of electrolysis cells, respectively.…”

“…To reduce the energy consumption of aluminium electrolysis cells and improve the current efficiency of electrolysis, many studies have been carried out on the occurrence and prediction of the AE in recent years. Chen et al (2018) combined the traditional method with a two-dimensional similarity search prediction model. The main criterion for the reliability of the AE prediction was calculated using the prediction results with the similarity search results.…”

Online Prediction Method of Molten Aluminium Height in Electrolytic Cell Based on Extreme Learning Machine with Kernel Function

“…It is an industry that consumes a lot of energy, 95% of which comes from electricity [1][2][3]. In order to improve the energy efficiency of the aluminium electrolysis production process, core parameters such as alumina concentration [4][5][6], anode effect [7][8][9], cell resistance [10,11], and electrolyte temperature [12,13] have been extensively and deeply studied using mechanism, soft sensor, and digital simulation methods.…”

Anode Effect Prediction in Hall-Héroult Cells Using Time Series Characteristics

“…Mulder et al [5] analysed the root causes of AEs and categorised them into three groups: Pot controllers feed strategy, maintenance practices and alumina quality. With the introduction of individual anode current measurement (ACM) new methods and models for predicting AEs were proposed [8][9][10][11]. Since this paper deals with data of pots where no ACM is implemented, thus it is based on classic process data, these papers are not discussed in detail.…”

“…Z. Zhang et al [16] developed a machine learning model with a lead time of 30 min and an F 1 score of 99.8%. Chen et al [9] used a collaborative two-dimensional forecast model with a lead time of 20 min to 40 min and an accuracy of 95%.…”