Aluminum Electrolysis Research Articles

Challenges and Strategies of Aluminum Anodes for High-Performance Aluminum-Air Batteries.

Aluminum-air battery (AAB) is a promising candidate for next-generation energy storage/conversion systems due to its cost-effectiveness and impressive theoretical energy density of 8100Wh kg-1, surpassing that of lithium-ion batteries. Nonetheless, the practical applicability of AABs is hampered by the occurrence of serious self-corrosion side reactions and substantial capacity loss, resulting in suboptimal anode utilization. Consequently, improving the anode utilization to facilitate the construction of high-performance AABs have attracted widespread attention. Herein, the fundamentals and strategies to enhance aluminum anode utilization are reviewed from modifications of aluminum anodes and electrolytes. This comprehensive review may provide a scientific tool for the development of novel AABs in the future.

Synthesis of gel crosslinked composite material from spent cathode carbon blocks and its application in modified asphalt binders

The utilization of spent cathode carbon blocks (SCCB) from aluminum electrolysis has become challenging due to their content of highly toxic cyanides and environmentally harmful fluorides. To achieve reliable recovery and utilization of hazardous waste, targeted research has been conducted on the treatment of waste cathode carbon blocks and their potential application in asphalt pavement. Hydrogen peroxide (H2O2) is employed to remove cyanides from the waste cathode carbon blocks, meeting the construction requirements for transportation areas. Anhydrous calcium chloride is used as a calcium source to precipitate soluble fluorides in the solution. Finally, the waste cathode carbon blocks are gel-crosslinked using polyvinyl alcohol-polyvinyl pyrrolidone, addressing the release of fluorides beyond environmental tolerance. This results in the synthesis of a composite material consisting of calcium fluoride deposits and gel-encapsulated waste cathode carbon blocks (PSB), which exhibits good thermal stability in the temperature range of 0–217 °C. Through DSR and MSCR testing, it has been demonstrated that the gel-crosslinked waste cathode carbon block composite material improves the asphalt's sensitivity to temperature and enhances its resistance to rutting at high temperatures. The 1% PSB/SBS modified asphalt shows the best performance, compared to SBS, the complex modulus (G*) of the modified asphalt increased by 117.4% at 46 °C, and the recovery indices for creep at 64 °C improved by 39.28% (at 0.1 kPa) and 55.65% (at 3.2 kPa), and thermal gravimetric analysis of the asphalt confirms that the addition of PSB improves the asphalt's thermal stability. Fluorescence microscopy analysis reveals that the inclusion of PSB does not affect the morphological development of SBS swelling and plays a stabilizing and supporting role in the asphalt mixture.

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.

Structure characteristics and adsorption performance of graphene oxide prepared by spent carbon cathode-an ultra-low temperature graphitized carbon material.

Spent carbon cathode (SCC) is a hazardous waste from the aluminum electrolysis industry. It is commonly used as a carbon source in the current disposal and recovery strategies, such as combustion, or as a reductant for smelting. The novelty of this study is to propose a strategy for recycling SCC as a graphite resource and to investigate the unique structural characteristics and adsorption properties of graphene oxide produced from this low temperature graphitized carbon. The adsorption kinetics and isotherms of SCC-GO on methylene blue (MB) were studied and compared with the GO prepared from natural flake graphite (NFG) and artificial graphite (AG). The results show that SCC-GO exhibits the highest adsorption rate and adsorption capacity (647.83 mg/g) for MB, which is much higher than NFG-GO (451.22 mg/g) and AG-GO (533.12 mg/g). The analysis of the spectroscopy and morphology confirmed that SCC-GO has a high degree of crystal defects, oxidation, and surface wrinkle. Overall, this study reveals the unique structure of SCC-GO and highlights its significant scientific and application potential as an ultra-low temperature graphitized carbon. This research is also significant for recycling aluminum electrolytic cathode solid waste in the form of a graphite source.

Characterization of the electrochemical deposition of aluminum from an AlCl3:N-methylacetamide eutectic solvent modified with nicotinamide

Aluminum currently sees extensive use in a wide range of applications that include cars, the aviation industry, consumer appliances, container vessels, and electronics. Aluminum electroplating is typically carried out using conventional ionic liquids, but these are typically toxic, costly, and relatively sensitive to air. This research will investigate the electrodeposition of aluminum for the first time from a (1:1 M ratio of) AlCl3:N-methylacetamide-based eutectic solvent on different substrates at room temperature in the presence and absence of nicotinamide as an additive. The electrochemical behavior of the aluminum in N-methylacetamide electrolyte was studied via chronocoulometry and cyclic voltammetry. The redox peak current was found to decrease relative to the pure Al solution when nicotinamide is added. The surface morphologies and composition of any aluminum films produced have been investigated via SEM/EDX, while the crystal structures of the products and their roughnesses were examined using XRD and AFM, respectively. The roughness of the Al film produced from the bath containing 0.02 M nicotinamide decreased to approximately 312 nm. The corrosion resistance of the aluminum coatings obtained from the systems containing 0.1 M and 0.2 M nicotinamide were found to be improved.

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.

Recovery of carbon from spent carbon cathode by alkaline and acid leaching and thermal treatment and exploration of its application in lithium-ion batteries.

The spent carbon cathode (SCC) is a hazardous solid waste from aluminum production. It has an abundant carbon source and a unique graphitic carbon layer structure, making it a valuable waste for recycling. This paper uses alkaline and acid leaching methods to report a straightforward way of extracting recovered carbon (RC) from SCC as anode material for lithium-ion batteries (LIBs). The results show that alkaline and acid leaching conditions at 70 °C with 1 M NaOH and HCl solution individually in 6 h and a liquid-solid ratio of 20:1 can result in RC with up to 94.63% carbon content than 49.38% in SCC, exhibiting a typical graphite structure. SCC and RC materials are obtained after calcination at 400 °C in an inert atmosphere and used as anode materials (SCC-400 and RC-400). In this paper, The initial charging specific capacities are 490.0 mA h g-1, 195.4 mA h g-1, and 423.2 mA h g-1and initial coulombic efficiencies (ICE) are 67.8%, 78.9%, and 72.0% of RC-400, SCC, and SCC-400. RC-400 also shows excellent capacity retention and impedance values. This exciting finding provides a viable, non-hazardous, and resourceful method for treating and disposing of SCC from aluminum electrolysis.

Unsupervised heat balance indicator construction based on variational autoencoder and its application to aluminum electrolysis process monitoring

Heat balance plays a significant role in reflecting the health state of aluminum electrolysis process (AEP). However, current methods hardly take into consideration the quantitative Heat Balance Indicator (HBI) construction by using the unlabeled data. In addition, it is limited to construct HBI by learning the complex relationship between degraded features and large-scale HBI labels in a supervised manner, because the labeled data are scarce and annotations are expensive in practice. To quantitatively construct HBI by using the unlabeled data, this paper proposes an unsupervised HBI construction method based on variational autoencoder (VAE). Firstly, we propose fuzzy evaluation strategy to estimate the tendency of cell temperature to highlight the trend of heat balance. Rather than simply using the latent features, we extract the feature representation of the heat balance state considering not only the latent features but also the reconstruction error. Finally, HBI is constructed by calculating the distance between the features representation of normal heat balance and degraded state. The applications of heat balance monitoring in a real-world aluminum electrolysis plant are performed to verify its effectiveness. The experimental results demonstrate that our proposed HBI construction method can better represent heat balance state of AEP, the average fault detection rate can achieve 80% for the monitoring electrolytic cells, increasing by more than 3% compared with these traditional monitoring statistics.

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

Sustainable recycling of aerospace-grade ultra-clean 7050 aluminum alloy melts through argon refining without secondary aluminum dross generation

The refining of aluminum scraps commonly employs nitrogen-fluxes to eliminate impurities. However, the aluminum nitride (AlN) generated by the reaction between nitrogen and the aluminum melt, along with residual fluxes, contribute to low cleanliness of the recycled aluminum melt and pose difficulties for the disposal of secondary aluminum dross (SAD). Our innovative research has resulted in the recycling of aerospace-grade clean aluminum alloy melts. The results indicate that the argon bubble floating process adsorbs inclusions and hydrogen, achieving an aerospace-grade ultra-clean melt. The solid-liquid interface between the tube wall and the melt provides a diffusion channel for hydrogen atoms, and the bubbles rising up along the wall lead to a higher hydrogen content in the melt. The argon refining dross is primarily composed of Al and Al2O3, which can be recycled as a raw material for aluminum electrolysis. Argon refining can decrease the hydrogen content and the number of inclusions with particle size ≥40 μm in the recycled aluminum melt to the level of aerospace aluminum alloy melt. Nonetheless, the particle size ≥20 μm remains 1.5–2.5 times that of primary aluminum.

A graphical model for formalizing health maintenance activities in the context of the whole equipment lifecycle

Formalizing health maintenance activities for the whole equipment lifecycle is significantly challenging in Maintenance, Repair, and Overhaul (MRO). It can support computer-aided managing and tracking maintenance activities, reducing the frequency of equipment failures and ensuring stable production operation, thereby reducing production costs and increasing enterprise profit. There are two problems formalizing health maintenance activities for the equipment lifecycle. The common formalizing modeling approaches, such as the Unified Modeling Language (UML), are readable and writable for computers but not friendly for human beings. There are various maintenance strategies adopted to assure equipment reliability in a real production scenario, and it is complex to formalize massive maintenance activities under different strategies. Thus, the research aims to propose a model for formalizing health maintenance activities that is friendly to both computer and human reading and writing and can manage and track massive maintenance activities under different strategies. In this regard, an event-state triggering mechanism-based graphical model for formalizing health maintenance activities in the context of the whole equipment lifecycle is proposed. First, the meta model of the Maintenance Graphical Model (MGM) based on the event-state triggering mechanism is introduced, clarifying the model unit and lifecycle model. Second, the Bill of Material (BOM) based configuration mechanism of the graphical model for specific equipment is presented. Third, the operation mechanism of the graphical model for managing and tracking the maintenance activities of equipment is illustrated. Finally, a real case of kneader, the production equipment of prebaked anode for aluminum electrolysis, is applied to verify our theory. The case demonstrates that the proposed model can reduce the frequency of equipment failures, maintenance time, and maintenance costs.

A Federated Dictionary Learning Method for Process Monitoring With Industrial Applications

Edge computing is an indispensable technology in the Industry 4.0 era. Data is stored at the edge and transmission is prohibited due to concerns about data privacy. Thus, the traditional centralized data-driven modeling methods will face many difficulties and can hardly be used in practice. Federated learning aims to let local nodes learn a global model cooperatively while keeping their data localized. Motivated by the pioneering framework of federated learning, a federated dictionary learning method is proposed for process monitoring. In detail, local nodes train local data by K-SVD method and learn local dictionaries. Then, local dictionaries, instead of local data, are transferred to the fusion center for calculation of the global dictionary. In order to guarantee an optimal global dictionary, a novel federated dictionary average strategy is introduced. Finally, the reconstruction errors of local data and the control limit are calculated based on the global dictionary for process monitoring. The performance of the proposed method is verified on a numerical simulation, the CSTH benchmark process, and the industrial aluminum electrolysis process.

Molecular Dynamics Simulation of the Effect of Zr, B, C, W, Ti, Hf, Nb, and Ta Elements on the K3AlF6-Na3AlF6-AlF3 Molten Salt System

The criticality of the physicochemical properties of the K3AlF6-Na3AlF6-AlF3 molten salt system to electrolytic aluminum production is a topic of great interest. In this study, molecular dynamics simulations were conducted to investigate the impact of Zr, B, C, W, Ti, Hf, Nb, and Ta on various properties including the radial distribution function, coordination number, viscosity, and conductivity of the 23.4K3AlF6-54.6Na3AlF6-22AlF3 molten salt electrolyte under conditions of 1173 K and standard atmospheric pressure. Significant and noteworthy discoveries were yielded by the simulations. A decrease in the coordination between Al-F and conductivity was brought about by the inclusion of B2-. Likewise, higher Zr4+ content resulted in a decrease in the coordination number between Al-F and viscosity. The impact of carbon was insignificant, while the presence of tungsten reduced the coordination number between Al-F. On the other hand, titanium, hafnium, niobium, and tantalum facilitated the decomposition of Al-F ligands, leading to an increased coordination between Al-F and the ions in the electrolyte. These findings provide valuable insights for future investigations on inert anodes containing Zr, B, Ti, Hf, Nb, and Ta. By understanding the effects of these elements on the physicochemical properties of the molten salt electrolyte, this work lays a solid foundation for further research in the field.

Modified Recovered Carbon from Aluminum Electrolysis by Expansion Treatment as an Anode for Sodium-Ion Batteries

Modified Recovered Carbon from Aluminum Electrolysis by Expansion Treatment as an Anode for Sodium-Ion Batteries

Growth characteristics and properties of plasma electrolytic oxidation coatings produced in different electrolytes on SiCp/Al composite

PEO coatings on SiCp/Al composites generated in phosphate, silicate, and aluminate electrolytes are compared to reveal the effect of the electrolyte composition on the coating formation process and coating performance. The observed significant disparity in spark discharge highlights the considerable influence of the electrolyte composition on the coating growth of the SiCp/Al composite surface. Within the plasma discharge zone, the local temperature ranges are approximately 6000 K–12000 K for silicate electrolyte, 6000 K–10000 K for aluminate electrolyte, and 4000 K–5000 K for phosphate electrolyte. Regarding spark discharge intensity, silicate electrolyte exhibits the highest discharge, followed by aluminate electrolyte, while phosphate electrolyte displays the weakest discharge. Consequently, the resulting surface porosity reaches up to 9.1% for PEO-Si coating, 6.5% for PEO-Al coating, and 4.9% for PEO-P coating. Phase analysis reveals that coatings generated in aluminate and silicate electrolytes tend to consist of crystalline alumina and mullite, whereas the coating formed in phosphate electrolyte predominantly contains amorphous phases and some crystalline mullite. The electrochemical tests demonstrate that the anti-corrosive ability of the coatings follows the order of PEO-Al > PEO-Si > PEO-P. The electrical insulation protection performance of the three coatings is PEO-Al > PEO-Si > PEO-P under comprehensive consideration.

Greenhouse gas emissions embodied in the U.S. solar photovoltaic supply chain

Solar photovoltaic (PV) electricity is considered to be an important source of electricity generation in the quest for net-zero carbon emissions. However, the growth of solar electricity is creating both increased material demands and increased greenhouse gas (GHG) emissions from silicon and PV manufacturing (also referred to as embodied GHG emissions of solar electricity). Here we analyze the silicon and solar PV supply chain for the United States (U.S.) market and find that the embodied GHG emissions of solar PV panel materials (such as silicon), manufacture, logistics, and installation in the U.S. given the current supply chain are 36 g CO2e kWh−1 of solar electricity generated. Eighty-five percent of the embodied GHG emissions are from PV panel production processes in China and other Asia–Pacific countries. Moving the silicon and PV manufacturing to the U.S. would reduce the embodied GHG emissions of solar electricity by 16% from its current level, primarily because of the lower GHG emission intensity of the U.S. electrical grid and the lower GHG emissions for aluminum electrolysis in North America. Future scenario analysis shows that by 2030, with the U.S. PV domestic supply chain and its decarbonized grid electricity and aluminum production, as well as improving PV conversion efficiency, the embodied GHG emissions of solar electricity in the U.S. will be reduced to 21 g CO2e kWh−1.

Intelligent Online Inspection of the Paste Quality of Prebaked Carbon Anodes Using an Anomaly Detection Algorithm

Prebaked carbon anodes are a critical consumable in the aluminum electrolysis industry. Prebaked carbon anode paste is the intermediate product of the prebaked carbon anode, and its quality significantly impacts the prebaked carbon anode. Therefore, inspecting the quality of the prebaked carbon anode paste is essential. Currently, the quality inspection of the paste still relies on laboratory analysis or manual experience. A laboratory inspection cannot obtain results in real time, while manual inspection poses potential risks. To address these issues, an online intelligent inspection method for prebaked carbon anode paste based on an anomaly detection algorithm was proposed. Firstly, we acquired the temperature of the paste and the power of the kneading motor. Secondly, we transformed these time-series data into images using the Gramian Angular Field (GAF) technique and joined them to create the paste anomaly detection dataset. Thirdly, we trained a matched anomaly detection model based on the PatchCore algorithm. Finally, we compared two advanced models: HaloAE and TSRD. PatchCore performs best on our dataset with an AUC-ROC score of 0.9943, followed by HaloAE (0.9906) and TSRD (0.9811). Our proposed method enables on-time intelligent inspection of prebaked carbon anode paste quality. This eliminates the need for manual inspection, reduces labor requirements, and ensures worker safety.

ИССЛЕДОВАНИЕ МИНЕРАЛИЗУЮЩЕГО ЭФФЕКТА КРИОЛИТА И ЕГО ВЛИЯНИЯ НА ПРОЦЕССЫ КЛИНКЕРООБРАЗОВАНИЯ

The article discusses the possibility of using cryolite Na3AlF6, the main fluorine-containing compound of a man-made product of electrolytic aluminum production, as a mineralizing additive when burning clinker. A comparative assessment of the effectiveness of cryolite Na3AlF6, as a mineralizer, and calcium fluoride CaF2, a substance that has the most effective mineralizing effect, is provided. The influence of mineralizer additives on the processes of dissociation of calcium carbonate, assimilation of free calcium oxide with the formation of silicates, aluminates and calcium ferrites, liquid-phase sintering and clinker formation with the synthesis of basic clinker minerals is considered. The physical and chemical processes that occur when heating raw material mixtures without additives and with the addition of mineralizers – CaF2 and Na3AlF6 – to a clinker sintering temperature of 1450ºC are examined in detail. The dynamics of changes in the qualitative phase composition of firing products at various temperatures, ranging from 700ºC to the clinker sintering temperature of 1450ºC, as well as a quantitative characteristic of the intensity of formation of the main clinker minerals are presented. It has been determined that cryolite has a mineralizing effect and can be used as a mineralizer when burning clinker. The effectiveness of cryolite is comparable to the effectiveness of calcium fluoride, and the burning temperature of clinker can be reduced to 1400ºC

Optimizing the marketing of flexibility for a virtual battery in day-ahead and balancing markets: A rolling horizon case study

Industrial electricity consumers with flexible demand can profit from adjusting their load to short-term prices or by providing balancing services to the grid. Markets which support this kind of short-term position adjustment are the day-ahead market and balancing markets. We propose a formulation for a combined optimization model that computes an optimal distribution of flexibility between the balancing and day-ahead markets. The optimal solution also includes the specific bids for the day-ahead and balancing markets. Besides the expected profits of each market and their different rules for bidding, our model also takes their different roles in a continuous marketing of flexibility into account. To prevent overrating short-term profits we introduce a variable penalty term that adds a cost to unfavorable load schedules. We evaluate the optimization model in a rolling horizon case study based on the setting of a virtual battery at TRIMET Aluminum SE, which is derived from a flexible aluminum electrolysis process. For such a battery we compute a daily optimal split of flexibility and trading decisions based on data in the period 04/2021–03/2022. We show that the optimal split is more profitable than using only one market or a fixed split between the markets.