Universal Approach to Estimate Perfluorocarbons Emissions During Individual High-Voltage Anode Effect for Prebaked Cell Technologies

Perfluorocarbon (PFC) emissions are the result of a phenomenon called anode effect (AE), when normal electrolysis is discontinued due to lack of alumina and another reaction takes place leading to generation of PFC gases. Anode effects have been characterized in the industry by measuring their frequency and duration when the cell voltage is above 8 volts. Efforts have been made by the Aluminium Industry to reduce AE frequency and duration leading to considerable reductions in PFC emissions. However, recent independent measurements reported sources of PFC emissions that are “non AE related”. Measurements have been carried out on all DUBAL cell technologies in 2010/2011 and similar occurrences have been observed. DUBAL has initiated a program to identify the possible mechanism of the “non AE related” PFC emissions and to develop new logics for treating it. In this paper, a study of low voltage PFC emissions at DUBAL will be presented.

— The perfluorocarbons (PFCs) used as etching-process and chamber-cleaning gases in the manufacture of LCD devices have a high global-warming potential and a long atmospheric lifetime. Thus, to voluntarily reduce these environmentally harmful PFCs, in 2001 the Japanese LCD association established the World LCD Industry Cooperation Committee (WLICC), together with its counterpart associations in the Republic of Korea and Taiwan. Since that time involving many discussions, the WLICC reached a consensus on reducing the aggregate absolute PFC emissions to the equivalent of less than 0.82 million metric tons of carbon by 2010. Each association has been taking whatever emission reduction steps it considers best to achieve this goal. Thanks to these measures, the PFC emissions from the Japanese LCD industry have remained at the same level, or less, than those of the year 2000, in spite of the fact that PFC purchases have continually increased. In the interim, the Japanese LCD association has been obtaining experimental data for the 2006 IPCC Inventory Guidelines, which will give the emission factors needed to estimate the PFC emissions. As a result, some data are quite different from emission factors given in the current 2000 Inventory Guidelines. All the data obtained have been submitted to the IPCC to be adopted in the new 2006 Guidelines.

The global warming potential of perfluorocarbons (PFCs) is much higher than that of CO2 in the aluminum reduction industry; hence, the release of PFCs is becoming increasingly regulated. Mitigation of PFC release by reducing anode effects (AEs) occurring during aluminum reduction is challenging. In this study, online anode current measurements were conducted at the anode beam of 400-kA cells, following which a simple local AE detection model was developed for AE prediction. A satisfactory AE true positive prediction rate of 80.3% was obtained in an industrial test. An independently controlled feeder strategy was then developed and applied to cells integrated with the local AE detection and prevention. The results show that the process provides an improved current efficiency with low direct current power consumption. Finally, the immense potential to reduce AE is confirmed with twinkling AEs and normal AEs, and daily AE duration decreased by 42.8%, 60%, and 56.9%, respectively.

The aluminium industry is today the primary source of perfluorocarbon (PFC) to the atmosphere. These gases have a lifespan of several thousand years, and a greenhouse warming potential (GWP) 6500–10000 times that of CO2. It is therefore of essential to understand, and reduce, the PFC emissions.

Influencing factors on PFC (perfluorocarbon) emission were investigated in Chinese smelters. The formation mechanism of non-anode effect related PFC emissions (short as NAE-PFC) from aluminum reduction cells were discussed. Anodic overvoltage has an obvious influence on NAE-PFC formation, CF4 will emit when anodic overvoltage is higher than 2.55 volts, C2F6 will be generated when anodic overvoltage surpasses 2.68 volts. Alumina concentration in bath with anodic current density also has a clear influence on NAE-PFC formation. Alumina fluctuates in large cells from the metal-tapping-end to the flue-end. Alumina always distributes unevenly in the bath because of bath flow driven by magnetic fields, which will also cause NAE-PFC formation. NAE-PFC will emit when there is not enough alumina under certain anode’s bottom. The limit alumina concentration is 75% of normal level which is 2.0–3.5% in Chinese smelters. Cells will generate NAE-PFC when any anode’s current density is higher than 1.5 times of normal level which is 0.72–0.80A/cm2 in Chinese smelters.

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.

In addition to the conventional electrochemical reaction in Hall-Heroult electrolysis process where the alumina is reduced to liquid aluminum, the co-evolution of anthropogenic perfluorocarbon (PFC) gases, namely tetrafluoromethane CF4 and hexafluoroethane C2F6, was occasionally detected during the electrolysis process. In Emirates Global Aluminium (EGA), along with the continuous amperage creep program, incremental increase in anode dimensions and associated reduction in cell anode-cathode distance (ACD) to satisfy cell energy requirements, the company has focused its efforts on improving the advanced cell control algorithm which has assisted EGA technologies to operate at a high current density while maintaining low PFC emissions. As part of a continuous performance enhancement program, EGA has initiated a program to understand the temporal and spatial changes in cell conditions that cause the co-evolution of PFC emissions. The work included studies to gain insights in major events where the co-evolution of PFC emissions was detected using state of art continuous individual anode current monitoring, along with continuous monitoring of PFC emissions in reduction cells.

Current Tier 2 algorithms calculate CF4 emission factors based on average anode effect minutes or average millivolts Overvoltage per cell day. Both the “slope” and “overvoltage” algorithms are based on the assumption that CF4 emission rates remain constant for anode effect durations ranging from less than one second to more than 1000 s. Detailed analysis of PFC measurements made over the past decade has shown conclusively that CF4 emission rates decrease rapidly with increasing reported anode effect duration. Average duration or average overvoltage can be skewed by changes in the mix of anode effect durations, particularly the mix of anode effects mitigated by automatic process control and those much longer anode effects requiring manual intervention. This study analyzes measurement data from individual anode effects from more than 2000 individual anode effects from more than 30 different measurements to develop a new exponential algorithm that better reflects changes in emission rate with anode effect duration. The exponential calculation should prove to be more robust resulting in more accurate calculation of PFC emissions than previous models.

It is an important part in aluminum electrolysis production to control the anode effect (AE). Since there are some shortcomings in traditional methods of anode effect prediction in aluminum electrolysis, this paper combined two methods, the slope of cell resistance and learning vector quantization (LVQ) neural network, to predict anode effect. First of all, the first prediction of anode effect will be conducted based on the slope of cell resistance. Afterwards, the inaccurate data are supposed to be re-predicted. The second prediction consists of two steps, one is to estimate the power spectrum from the signal of cell resistance by means of periodogram, the other is to re-predict the anode effect with the LVQ neural network, since the energy of frequency bands are served as the input feature variables of neural network, so as to raise the accuracy of prediction. It turned out that the success rate of ten-minute in advance prediction for anode effect can be above 85%, though just cell resistance signal is studied.

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

High-frequency in situ measurements at Gosan (Jeju Island, Korea) during November 2007 to December 2008 have been combined with interspecies correlation analysis to estimate national emissions of halogenated compounds (HCs) in East Asia, including the chlorofluorocarbons (CFCs), halons, hydrochlorofluorocarbons (HCFCs), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulfur hexafluoride (SF(6)), and other chlorinated and brominated compounds. Our results suggest that overall China is the dominant emitter of HCs in East Asia, however significant emissions are also found in South Korea, Japan and Taiwan for HFC-134a, HFC-143a, C(2)F(6), SF(6), CH(3)CCl(3), and HFC-365mfc. The combined emissions of CFCs, halon-1211, HCFCs, HFCs, PFCs, and SF(6) from all four countries in 2008 are 25.3, 1.6, 135, 42.6, 3.6, and 2.0 kt/a, respectively. They account for approximately 15%, 26%, 29%, 16%, 32%, and 26.5% of global emissions, respectively. Our results show signs that Japan has successfully phased out CFCs and HCFCs in compliance with the Montreal Protocol (MP), Korea has started transitioning from HCFCs to HFCs, while China still significantly consumes HCFCs. Taiwan, while not directly regulated under the MP, is shown to have adapted the use of HFCs. Combined analysis of emission rates and the interspecies correlation matrix presented in this study proves to be a powerful tool for monitoring and diagnosing changes in consumption of HCs in East Asia.

The stable operation of the aluminum electrolysis cell is the basis for the safe and efficient production of the electrolytic aluminum industry, and the cell condition identification technology is an important means to ensure the normal operation of the aluminum electrolysis cell. The cell condition identification technology based on anode current signal has played an increasingly important role in identify and fine control large aluminum electrolysis cells. This paper proposes an improved temporal convolutional network, which uses the time characteristics of the current signal to classify the current sequence for cell condition identification. The classification result can help people identify and monitor the conditions of the electrolysis cell. In this paper, the proposed method is verified on the real current signal, which can be used to identify various cell conditions such as anode effect and anode sliding.

The so-called anode effect, particularly important in industrial alumina electrolysis, has mostly been interpreted as the consequence of altered wettability of the electrode surface by the melt. By means of a mathematical model assuming isolated large bubbles in contact with the electrode it is shown that the anode effect is the result of the combined action of fluid dynamics and wettability. The interpretation of the incipience of the anode effect obtained by means of a previous, completely different mathematical model is confirmed. The theoretical results are compared with experimental data by various authors.

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

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.