Abstract
In aluminium production, anode effects occur when the alumina content in the bath is so low that normal fused salt electrolysis cannot be maintained. This is followed by a rapid increase of pot voltage from about 4.3 V to values in the range from 10 to 80 V. As a result of a local depletion of oxide ions, the cryolite decomposes and forms climate-relevant perfluorocarbon (PFC) gases. The high pot voltage also causes a high energy input, which dissipates as heat. In order to ensure energy-efficient and climate-friendly operation, it is important to predict anode effects in advance so that they can be prevented by prophylactic actions like alumina feeding or beam downward movements. In this paper a classification model is trained with aggregated time series data from TRIMET Aluminium SE Essen (TAE) that is able to predict anode effects at least 1 min in advance. Due to a high imbalance in the class distribution of normal state and labeled anode effect state as well as possible model’s weaknesses the final F1 score of 32.4% is comparatively low. Nevertheless, the prediction provides an indication of possible anode effects and the process control system may react on it. Consequent practical implications will be discussed.
Highlights
In the early days of aluminium electrolysis there was no automatic feed control
The operators had to wait until an anode effect (AE) occured, which is always manifested by a rapid increase of pot voltage
The TRIMET Aluminium SE Essen (TAE) plant consists of 360 pots in three production potrooms with EPT-14 PreBaked Point Feeder (PBPF) cells in the first two potrooms and EPT-17 PBPF cells in the third potroom
Summary
In the early days of aluminium electrolysis there was no automatic feed control. The operators had to wait until an anode effect (AE) occured, which is always manifested by a rapid increase of pot voltage. Due to the availability of automatic feeders and computer controlled feeding and automatic AE termination routines the aluminium industry was able to eminently reduce the frequency and duration of AEs [1]. In the last decades there was further improvement and research—Haupin and Seger [1] present different methods for predicting approaching AEs (hysteresis in volt-amp curve, rate of voltage rise, measuring high frequency electrical noise, measuring acoustic noise and pilot anodes) and reduce. Thorhallsson [6] suggests an improved maintenance, more focus on pot tending to reduce AE frequency and a stepwise downward shift of the anode beam with specific delays between separate downward movements and an adjusted feeding strategy during termination to reduce the duration of AEs. Fardeau [7] et al found that mainly a proper bath height control can reduce AE frequency. Preprocessing, training and testing steps including evaluation criteria are presented with high degree of transparency
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
