Aluminum Electrolysis Cells Research Articles

Pre‐oxidized Recycled MgO–Steel Composite Material for Possible Application in Cryolitic Melts

Recycled MgO–C lining bricks and 316L stainless steel are used to manufacture composite material for inert anode samples for aluminum electrolysis cell. The microstructure of the composite material is characterized after preoxidation thermal treatments at 800, 900, and 1000 °C as well as in its sintered state. Preoxidation (PO) process is designed to enhance the material's corrosion resistance in molten cryolite environments by developing robust Fe–Mg–O, Fe–Cr–O‐ containing phases. Analytical techniques including scanning electron microscopy, electron backscatter diffraction, and energy dispersive X‐ray spectrometry are applied to characterize the phase formation, revealing the potential of these composites for use as inert anodes in aluminum electrolysis cells. PO at 800 °C is not sufficient to form adequate protective oxide layers. Whereas, PO at 900 and 1000 °C leads to the formation of protective oxide layers containing Mg–O Fe–O halite‐like solid solutions and (Cr,Fe)3O4 spinel phase. Sample, preoxidized at 1000 °C is sealed in Mg–Fe–O spinel phase.

Simulation and Application of a New Type of Energy-Saving Steel Claw for Aluminum Electrolysis Cells

Aluminum electrolysis is a typical industry with high energy consumption, and the energy saving of aluminum electrolysis cells is conducive to the sustainable development of the ecological environment. The current density distribution on the steel claws of conventional aluminum electrolysis cells is uneven, resulting in a large amount of power loss. Therefore, a new type of current-equalized steel claw (CESC) is designed in this paper. The ANSYS simulation study shows that the CESC can achieve a more uniform current density distribution and reduce the voltage drop by about 36 mV compared with the traditional steel claw (TSC). In addition, the use of CESC optimizes the temperature distribution of the steel claws and reduces the risk of cracking and deformation. The results of the industrial application tests are highly consistent with the simulation results, confirming the accuracy of the simulation results. The economic benefit analysis shows that using CESC saves 114.1 kWh of electricity per ton of aluminum produced. If this technology can be promoted throughout China, it is expected to save up to 4.75 billion kWh of electricity annually. The development of CESC is promising and of great significance for improving the overall technical level of the aluminum electrolysis industry.

Fully-Coupled Electric-Thermal-Flow Modeling and Investigation of Dynamic Thermal-Ledge Behavior in Aluminum Electrolysis Cell

Aluminum electrolysis cells (AECs) require effective thermal regulation to operate flexibly alongside renewable energy sources. Prior to implementing thermal regulation strategies, it is essential to predict the dynamic variations in the thermal field and ledge characteristics of a full-scale cell. This study introduces a transient electro-thermal-flow coupling model for a full-scale AEC, aimed at investigating the interactions between ledge distribution and various operational fields, including thermal, electric, and flow fields. The model facilitates the calculation and assessment of the dynamic properties of the ledge and thermal balance under ±15% flexible current variations. Results indicate that during a current increase, ledge melting predominantly occurs in the electrolyte layer, while ledge solidification is primarily observed in the metal layer during a current reduction. Regions with a thicker ledge and faster velocity tend to melt more during current increases and are less likely to return to their original shape and thickness during current reductions, complicating the rapid restoration of thermal equilibrium. To achieve uniform ledge distribution and real-time adaptation to flexible current variations, it is recommended to install distributed cooling devices on the sides of the AEC to enable differential ledge regulation at various locations.

Effects of Sintering Aids and Thickness on Densification and Properties of Magnesia-Aluminum Spinel Transparent Ceramics in the Aluminum Electrolysis Cells

Effects of Sintering Aids and Thickness on Densification and Properties of Magnesia-Aluminum Spinel Transparent Ceramics in the Aluminum Electrolysis Cells

Numerical Simulation and Test Analysis of Flow Field in an Aluminum Electrolytic Cell with Graphitized Cathode Carbon Block

Numerical Simulation and Test Analysis of Flow Field in an Aluminum Electrolytic Cell with Graphitized Cathode Carbon Block

The recycling of carbon-rich solid wastes from aluminum electrolytic cells: a review

The recycling of carbon-rich solid wastes from aluminum electrolytic cells: a review

Multi-Objective Optimization of Cell Voltage Based on a Comprehensive Index Evaluation Model in the Aluminum Electrolysis Process

In the abnormal situation of an aluminum electrolysis cell, the setting of cell voltage is mainly based on manual experience. To obtain a smaller cell voltage and optimize the operating parameters, a multi-objective optimization method for cell voltage based on a comprehensive index evaluation model is proposed. Firstly, a comprehensive judgment model of the cell state based on the energy balance, material balance, and stability of the aluminum electrolysis process is established. Secondly, a fuzzy neural network (FNN) based on the autoregressive moving average (ARMA) model is designed to establish the cell-state prediction model in order to finish the real-time monitoring of the process. Thirdly, the optimization goal of the process is summarized as having been met when the difference between the average cell voltage and the target value reaches the minimum, and the condition of the cell is excellent. And then, the optimization setting model of cell voltage is established under the constraints of the production and operation requirements. Finally, a multi-objective antlion optimization algorithm (MOALO) is used to solve the above model and find a group of optimized values of the electrolysis cell, which is used to realize the optimization control of the cell state. By using actual production data, the above method is validated to be effective. Moreover, optimized operating parameters are used to verify the prediction model of cell voltage, and the cell state is just excellent. The method is also applied to realize the optimization control of the process. It is of guiding significance for stabilizing the electrolytic aluminum production and achieving energy saving and consumption reduction.

A dynamic graph structure identification method of spatio-temporal correlation in an aluminum electrolysis cell

The dynamic correlation analysis of cell-spatial information (distributed anode current signal, DACS) is of great significance in the regional-refined control of industrial aluminum electrolysis cell. Due to the strong-dynamic spatio-temporal correlation of DACS and the complex dynamic cell noise, the existing methods are difficult to effectively obtain the spatio-temporal correlation analysis results of aluminum electrolysis cell. To solve these problems, a dynamic graph structure identification method of spatio-temporal correlation (DGSI-StC) in an aluminum electrolysis cell is proposed. The identified dynamic graph structure is used to describe the dynamic correlation analysis results of cell-spatial information. Specifically, a novel strongly robust distance function Edit Distance on Real sequences with Adaptive threshold (At-EDR) is proposed to weaken the impact of complex dynamic cell noise on the dynamic correlation analysis of cell-spatial information. Under the guidance of aluminum electrolysis process mechanism knowledge, the matrix-representation of dynamic cell noise information obtained from production data is used as the adaptive threshold of At-EDR to construct the dynamic graph structure. Then, based on the single-layer classical graph convolutional network, a framework for optimizing the dynamic graph structure is designed to capture the strong-dynamic spatio-temporal correlation of DACS. The framework obtains the optimal dynamic graph structure by optimizing the hyper-parameters. Finally, the experimental results on the public datasets show that the robustness of At-EDR is superior to existing methods. Meanwhile the experimental results on multiple sets of industrial aluminum electrolysis production datasets show that DGSI-StC improves the classification accuracy by 2.82% compared with existing classification methods.

Preparation of aluminum fluoride from carbon residue in aluminum electrolysis cell by roasting-leaching method

The carbon residue is a hazardous waste generated by the aluminum electrolysis production process, which contains a large amount of electrolyte fluoride salts. In order to recover the electrolyte components in carbon residue, this paper proposes a new method of using carbon residue to prepare aluminum fluoride by converting Na5Al3F14 and Na3AlF6 in carbon residue into aluminum fluoride to realize the recycling of fluoride in carbon residue. In the initial step, optimal parameters for carbon removal during roasting have been determined, including an oxygen flow rate of 7 g/min, a roasting temperature of 670 °C, and a roasting time of 80 min. The roasted clinker is obtained, and the removal rate of carbon reaches 99.95 % while the loss rate of fluorine is 0.48 %. In the second step, the mix anhydrous Al2(SO4)3 with roasted clinker for roasting. The optimal roasting temperature of 670 °C, the time of 10 min, and the mass ratio of anhydrous Al2(SO4)3 to roasted clinker of 5:5 could convert fluoride to AlF3 products, with a conversion rate of 99.76 % and a loss rate of fluorine of 1.05 %. Finally, by removing sulfate impurities through water leaching for 15 min at 35 °C and with a liquid-solid ratio of 6 ml/g, the loss rate of fluorine is 3.98 %. This process yields AlF3 with a purity of 94.30 %. The recovery rate of fluorine in the whole process is 94.56 %, which successfully achieves the recycling of fluorine in the carbon residue to obtain AlF3 products.

Multi-objective optimization of locomotive engines hybridized with fuel cells using selected fuel blends

Locomotive engines serve as the powering heart of rail transportation which is widely used for massive cargo over long distances. The traditional locomotive engines have depended on the internal combustion engine for decades, operated using fossil fuels that negatively impact the environment. Since more and more commodities are transported by trains, the prime engines need to be improved where the fuels are expected to enhance the engine's performance and alleviate the environmental impact. This paper presents a comparison of three developed locomotive engines with different configurations, such as an internal combustion engine combined with a gas turbine and molten carbonate fuel cell (ICE-GT-MCFC), a gas turbine combined with a solid oxide fuel cell and onboard hydrogen production using aluminum electrolysis cell (GT-SOFC), and a GT combined with sold oxide and proton exchange membrane fuel cells (GT–SOFC–PEMFC). The paper then identifies one hybridized engine, which is GT–SOFC–PEMFC, according to its low weight and better performance to conduct an optimization study to enhance its efficiency, reduce the cost, and lower the environmental impact. The present optimization study is undertaken through a multi-objective particle swarm optimization, involving thermodynamic, exergoeconomic, and exergoenvironmental analyses. The optimized GT–SOFC–PEMFC locomotive engine is improved in power from 5.9 MW to 7.3 MW and improved in performance to 49.1% energetic efficiency and 47.0% exergetic efficiency. Additionally, the optimized GT–SOFC–PEMFC engine has less specific fuel and product cost of 24.9 $/GJ and 34.7 $/GJ, respectively, and less specific exergetic environmental impact of fuel and product of 13.2 Pt/GJ and 16.2 Pt/GJ, respectively. Also, the relative cost difference and relative environmental impact difference are significantly reduced by more than 20% to 39.4% and 22.8%, respectively. The operational conditions of the optimized GT–SOFC–PEMFC engine are improved to achieve better efficiency and better cost effectiveness in a more environmentally-benign manner.

Explicit Evolutionary Framework With Multitasking Feature Fusion for Optimizing Operational Parameters in Aluminum Electrolysis Process.

Collaboratively optimizing operational parameters through leveraging accumulated production experience is an innovative approach to reducing energy consumption in aluminum electrolysis cells (AECs). Due to the dynamic heterogeneity of various AECs, an explicit evolutionary multitasking (EMT) framework capable of incorporating different optimizers, has the potential to tackle this challenge effectively. However, there is a notable gap in theoretical research on multitasking collaborative evolutionary algorithms specifically applied to AECs. Meanwhile, existing explicit EMT algorithms often overlook the intertask correlation of feature information extracted in isolation from individual tasks. These issues significantly limit the development of synergistic effects in multitasking optimization for addressing parameter design in AECs. To address these limitations, this work proposes an explicit evolutionary framework with multitasking feature fusion (EMFF). This framework thoroughly considers the potential connections among feature information from different tasks. It achieves effective knowledge transfer by the design of a unique multitasking feature fusion mechanism, which enhances the information value of source tasks for target tasks. Furthermore, a transfer individual derivation (TID) strategy is introduced to ensure the rapid evolution of critical knowledge. Finally, comprehensive components and designed process are presented. Experimental results demonstrate EMFF's exceptional performance in various benchmark tests and real-world AEC parameter optimization cases.

Error correction for temperature measurement in insulating material: Application to the aluminum electrolysis cell

Error correction for temperature measurement in insulating material: Application to the aluminum electrolysis cell

Quality diagnostics of the baked anodes for aluminum electrolytic cells by non-destructive control method

Quality diagnostics of the baked anodes for aluminum electrolytic cells by non-destructive control method

Consensus-based probabilistic hesitant intuitionistic linguistic Petri nets for knowledge-intensive work of superheat degree identification

Superheat degree identification of aluminum electrolysis cell is a typical knowledge-intensive work which is the precondition of efficient production. However, existing methods have application limitations, and the large cognitive differences among experts are ignored in the existing methods. To address these issues, we propose the consensus-based probabilistic hesitant intuitionistic linguistic Petri nets (CPHILPNs). The truth values of places in CPHILPNs are represented by probabilistic hesitant intuitionistic linguistic term sets (PHILTSs), which can enrich the experts’ expression and improve the efficiency of dealing with uncertainties. The proposed probabilistic hesitant intuitionistic linguistic consensus optimization model can reduce the deviation of individual preferences to reach a consensus. Then the obtained experts’ weights and decision assessments will be more accurate and comprehensive with objective evaluations. The order weighted averaging PHILTSs concurrent reasoning algorithm is proposed to enhance the inference efficiency. Finally, the usefulness and validity of the proposed CPHILPNs are verified by conducting comparisons and actual experiments in a real-world plant.

Performance Evaluation of Low-Temperature KF-NaF-AlF3 Electrolytes for Aluminum Electrolysis Using Vertical Inert Cu–Ni–Fe Alloy Anodes

In this study, the performance of low-temperature electrolytes in a 40 A laboratory oxygen-evolving aluminum electrolysis cell with vertical inert electrodes for two compositions of NaF-KF-AlF3 based electrolyte is investigated. Two compositions of Cu–Ni–Fe alloy, single-phase homogenized, 20Cu-42Ni-38Fe wt% and 5Cu-48Ni-47Fe wt%, are evaluated as anodes. Titanium diboride (TiB2) is used as a wetted cathode. The characteristics of a protective oxide layer, pre-formed on the anode through high-temperature air oxidation, are examined using X-ray diffraction (XRD) and scanning electron microscopy (SEM). It is found that a multilayered oxide morphology, revealing Fe2O3 and NiFe2O4 as major constituents, is dense and adherent to the base alloy. The effects of electrolyte compositions, anode alloys, and electrolysis conditions on cell voltage evolution, current efficiency, contamination levels in the electrolyte and produced aluminum, and anode wear rate are systematically studied. A protective oxide layer, formed through oxidation treatment on the 20Cu-42Ni-38Fe wt% alloy, not only remains compact post-electrolysis but also performs significantly better in a K-rich electrolyte in terms of wear rate and the purity of the aluminum obtained. Influences of electrolyte composition on anode degradation behavior are discussed.

Study on preparation of aluminum fluoride from cryolite

As China's aluminum electrolysis industry expanded its production capacity, it produced a large amount of bath materials– cryolite based molten salt, which is not utilized fast enough and piled up in aluminum factories. It is of great significance to find a proper way to process the accumulated bath materials. Through the method introduced in this paper, aluminum fluoride can be produced from cryolite-based electrolyte and fed back into the aluminum electrolysis cell as a necessary additive. This method includes four steps: Calcination of crystalline aluminum nitrate with cryolite. Cryolite and crystalline aluminum nitrate are mixed and preheated. The main chemical reaction that occurs in this step will produce sodium nitrate (NaNO3), β-aluminum fluoride (β-AlF3), chiolite (Na5Al3F14) and the aluminum hydroxyfluoride hydrate (AHF). After calcination, the products are washed with water to separate soluble NaNO3 from the other products. The insoluble products obtained after washing are calcined with ammonium bifluoride to convert AHF into AlF3. The mixture obtained after preheating is then washed with an aqueous solution of aluminum salt (such as Al(NO3)3) to remove Na5Al3F14. Aluminum fluoride of high purity is obtained in the end. Calcination step is the most important among the four steps. The effects of calcination temperature (85 – 135 ℃) and molar ratio of crystalline aluminum nitrate to cryolite (0.5 – 2) on the conversion rate of cryolite were studied. The experimental results show that the highest conversion rate of cryolite can reach 97.03%. Through SEM, EDS, ICP and other analysis methods, it was found the final products contained more than 98.8 wt% aluminum fluoride and less than 0.4 wt% sodium, and the average particle size of aluminum fluoride is approximately 15 – 20 µm.

Evaluation model of aluminum electrolysis cell condition based on multi-source heterogeneous data fusion

Evaluation model of aluminum electrolysis cell condition based on multi-source heterogeneous data fusion

Numerical Investigation of Electro-Thermal Field Distribution Law of Busbar under Different Operating Conditions

The electro-thermal state of a busbar system of electrolysis cells for aluminum production represents the main factor affecting hydromagnetic stability and current distribution. Based on the busbar system of a 500 kA aluminum electrolytic cell, an overall busbar electro-thermal field coupling calculation model was established based on ANSYS. The characteristics of busbar temperature, current density, and voltage drop distribution were analyzed. In addition, the electro-thermal distribution of the busbar system was simulated under different current intensities, ambient temperatures, and heat transfer coefficients. The results show that the temperature distribution of the riser busbar and the cathode busbar is higher in the middle location and tends to decrease along the two sides. Differences in heat conduction and heat dissipation environment are the main factors affecting the distribution of the busbar system’s electro-thermal field, while the Joule heat of the current is not the major factor. Increasing the current intensity will increase the average temperature and average voltage drop of the busbar. With an increase in the ambient temperature, the average busbar temperature increases significantly, and the voltage drop of the busbar also increases. With an increase in heat transfer coefficient, the average temperature and voltage drop of the busbar decreases.

A dynamic spatial distributed information clustering method for aluminum electrolysis cell

Distributed anode current (DAC) is a high-dimensional spatial-distributed signal that can be measured online in the industrial aluminum electrolysis process. The difference of physicochemical properties in different spatial regions in an aluminum electrolysis cell can be obtained by spatial clustering analysis of DAC data. In this study, a dynamic spatial distributed information clustering method (DSDIC) for aluminum electrolysis cell is proposed. This method can effectively capture the complex dynamic spatio-temporal correlations in DAC. Firstly, the dynamic graph is identified to capture the complex dynamicity of the DAC. Then, the anode-spatial structure information (ASSI) extends the one-dimensional current signal generated by each carbon anode into a feature matrix to achieve the fusion of data and spatial structure knowledge. Finally, the adjacency matrix of dynamic graph performs low-pass filtering on the feature matrix to obtain low-frequency information that is beneficial to downstream learning tasks. Meanwhile, a fixed graph structure based on process mechanism knowledge is designed to capture the spatial correlation caused by external periodic operations in industrial process. The experimental results on the actual industrial aluminum electrolysis datasets show that our method improves the clustering accuracy by 3.96% compared with existing clustering methods.

Deep learning assisted physics-based modeling of aluminum extraction process

Modeling complex physical processes such as the extraction of aluminum is mainly done using pure physics-based models derived from first principles. However, the accuracy of these models can often suffer due to a partial understanding of the process, uncertainty in the input parameters, and numerous modeling assumptions. More recently, with the ever-increasing availability of data, there has been an explosion of interest in applying modern machine learning methods because of their ability to learn complex mappings directly from data. Unfortunately, these models tend to be black boxes, require an enormous amount of data, and do not utilize existing domain knowledge. In this work, we develop a novel approach combining physics-based and data-driven modeling approaches while eliminating some weaknesses. We use a data-driven model to correct a misspecified physics-based model of the Hall–Héroult process in an aluminum electrolysis cell using a corrective source term added to the set of governing ordinary differential equations. Our approach ensures that the existing knowledge is utilized to the maximum extent possible while relying on the data-driven models only to model those aspects which the physics-based model does not represent well. We compare this approach with an end-to-end learning approach and an ablated physics-based model, showing that the proposed hybrid method is more accurate, consistent, and stable for long-term predictions.