Prediction of Low-Voltage Tetrafluoromethane Emissions Based on the Operating Conditions of an Aluminium Electrolysis Cell

Abstract

Greenhouse gas (GHG) generation is inherent in the production of aluminium by a technology that uses carbon anodes. Most of those GHG are composed of CO 2 produced by redox reaction that occurs in the cell. However, a significant fraction of the annual GHG production is composed of perfluorocarbons (PFC) resulting from anode effects (AE). Multiple investigations have shown that tetrafluoromethane (CF 4 ) can be generated under low-voltage conditions in the electrolysis cells, without global anode effect. The aim of this paper is to find a quantitative relationship between monitored cell parameters and the emissions of CF 4 . To achieve this goal, a predictive algorithm has been developed using seven cell indicators. These indicators are based on the cell voltage, the noise level and other parameters calculated from individual anode current monitoring. The predictive algorithm is structured into three different steps. The first two steps give qualitative information while the third one quantitatively describes the expected CF 4 concentration at the duct end of the electrolysis cells. Validations after each step are presented and discussed. Finally, a sensitivity analysis was performed to understand the effect of each indicator on the onset of low-voltage PFC emissions. The standard deviation of individual anode currents was found to be the dominant variable. Cell voltage, noise level, and maximum individual anode current also showed a significant correlation with the presence of CF 4 in the output gas of an electrolysis cell.

Simulator of Non-homogenous Alumina and Current Distribution in an Aluminum Electrolysis Cell to Predict Low-Voltage Anode Effects

“…Previous research by the current authors [18] demonstrated that the standard deviation among individual anode currents is the strongest indicator of LVAE. Therefore, it is easily possible to calculate the standard deviation of the individual anodic currents resulting from the simulator to predict the risk of LVAE.…”

“…However, as the alumina concentration is rarely measured during normal operation of an electrolysis cell, such a model can hardly be used to quantify PFC emissions resulting from LVAE. To counter this problem, the authors of this article previously developed a complex multivariate model [18] that uses seven, monitored cell indicators to account for the level of PFC emitted during LVAE. The results of this analysis demonstrated that the dominant cell indicator that can reveal information on the presence of LVAE in the electrolysis cell is the standard deviation among all individual anode currents.…”

“…Experimental measurements demonstrated that the standard deviation threshold was equivalent to 3.65 kA. [18] To correctly implement this threshold in the simulator, it was necessary to determine if the resulting standard deviation from the simulator was within the same order of magnitude as the real electrolysis cell. Under normal operations, the simulation results are more than three times lower in terms of standard deviation among individual anode currents than the real measurements for similar conditions.…”

“…The gas was extracted using stainless steel sampling probes directly from each of the five inner ducts that route the gas from the different parts of the cell up to the main exhaust duct in order to well represent the overall cell. [36] The flow rate of each probe was regulated to 1 LPM and the total gas flow (5 LPM) was routed to a GASMETä DX-4000 FTIR (Fourier Transformed Infra-Red) spectrometer using a Peltier-cooled ). The gas stream was sent sequentially through the desiccant, activated alumina, a 5-mm filter, and finally a 2-mm filter to remove traces of water, hydrogen fluoride, and dust, respectively, for the protection of the measuring equipment.…”

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

“…But when an AE occurs, it reduces the cleaning efficiency and lowers the quality of aluminium products. In addition, AE is also accompanied by a high cell temperature, extensive material waste, high energy consumption, and harmful fluoride gas emission [4]. Thus, the frequency with which AE occurs must be controlled to an acceptable level.…”