Search Results (78)

In the aluminum electrolysis production process, the traditional cell control method based on cell voltage and series current can no longer meet the goals of energy conservation, consumption reduction, and digital-intelligent transformation. Therefore, a new digital cell control technology that is centrally dependent on various process parameters has become an urgent demand in the aluminum electrolysis industry. Among them, the real-time online measurement of alumina concentration is one of the key data points for implementing such technology. However, due to the harsh production environment and limitations of current sensor technologies, hardware-based detection of alumina concentration is difficult to achieve. To address this issue, this study proposes a soft-sensing model for alumina concentration based on a long short-term memory (LSTM) neural network optimized by a weighted average algorithm (WAA). The proposed method outperforms BiLSTM, CNN-LSTM, CNN-BiLSTM, CNN-LSTM-Attention, and CNN-BiLSTM-Attention models in terms of predictive accuracy. In comparison to LSTM models optimized using the Grey Wolf Optimizer (GWO), Harris Hawks Optimization (HHO), Optuna, Tornado Optimization Algorithm (TOC), and Whale Migration Algorithm (WMA), the WAA-enhanced LSTM model consistently achieves significantly better performance. This superiority is evidenced by lower MAE and RMSE values, along with higher R² and accuracy scores. The WAA-LSTM model remains stable throughout the training process and achieves the lowest final loss, further confirming the accuracy and superiority of the proposed approach. Full article

Textile dyeing wastewater is one of the most challenging industrial effluents to treat due to its high concentrations of persistent organic compounds and nitrogenous substances. Conventional treatment methods often fall short in achieving both sufficient removal efficiency and environmental safety. In this study, we aimed to remove the total nitrogen (T-N) and total organic carbon (TOC) of dyeing wastewater from an industrial complex in D City, Korea, by applying bipolar and packed bipolar electrolysis using aluminum (Al) electrodes and activated carbon (AC). The system was operated for 60 min under varying conditions of applied voltage (5–15 V), electrolyte type and concentration (non-addition, NaCl 5 mM, NaCl 10 mM, Na₂SO₄ 5 mM, Na₂SO₄ 10 mM), and AC packing amount (non-addition or 100 g/L). The highest T-N and TOC removal efficiencies were observed at 15 V, reaching 69.53% and 63.68%, respectively. Electrolyte addition significantly improved initial treatment performance, with NaCl 10 mM showing the best results. However, Al leaching also increased, from 549.83 mg/L (non-addition) to 623.06 mg/L (NaCl 10 mM). When AC was used without electrolysis (control experiment), the T-N and TOC removal efficiencies were limited to 30.24% and 29.86%, respectively. In contrast, AC packing combined with 15 V electrolysis under non-addition achieved 86.04% T-N and 77.98% TOC removal, while also reducing Al leaching by 40.12%. These results suggested that electrochemical treatment with AC packing under non-addition conditions offers the best balance between high treatment efficiency and low environmental impact. These findings demonstrate that the synergistic use of packed activated carbon and electrochemical treatment under additive-free conditions can overcome the limitations of conventional methods. This study contributes to the development of more sustainable and effective technologies for treating high-strength industrial wastewater. Full article

The aluminum electrolysis industry faces significant challenges due to the high consumption and environmental impact of carbon anodes, which are prone to oxidation in high-temperature and strongly oxidizing environments. This study innovatively introduces aluminum fluoride (AlF₃) as an additive to enhance the oxidation resistance of carbon anodes for aluminum electrolysis. By systematically exploring microstructural evolution through SEM, XRD, Raman spectroscopy, and permeability analyses, it reveals that AlF₃ inserts fluorine atoms into carbon interlayers, forming F-C bonds that reduce interlayer spacing while promoting graphitization. Simultaneously, AlF₃-derived α-Al₂O₃ particles densify the anode and make it more compact, reaching the optimum when 7 wt.% AlF₃ is doped. The bulk density of the carbon anode increased to 2.08 g/cm³, porosity decreased to 0.315, and air permeability reached a minimum of 2.3 nPm. In addition, the fluorine intercalation reduces the electrical resistance to 2.12 Ω via conductive F-C clusters. The demonstrated efficacy of AlF₃ additives in enhancing the oxidation resistance and conductivity of carbon anodes suggests strong potential for industrial adoption, particularly in optimizing anode composition to reduce energy consumption. Full article

This paper is devoted to the problem of magnetohydrodynamic stability (MHDS) in the energy-intensive process of primary aluminum production by electrolysis. Improving MHDS control is important because of the high costs and reduced efficiency caused by the instability of magnetic and current fields. In this work, a methodological analysis of modern theoretical and numerical methods for studying MHDS was carried out, and approaches to optimizing magnetic fields and control algorithms aimed at stabilizing the process and reducing energy costs were considered. This review identified key challenges and proposed promising directions, including the application of computational methods and artificial intelligence to monitor and control electrolysis in real time. In this paper, it was revealed that wave MHD instability at the metal–electrolyte phase boundary is a key physical obstacle to further reducing specific energy costs and increasing energy stability. The novelty of this paper lies in an integrated approach that combines modeling and practical recommendations. The purpose of this study is to systematically summarize scientific data, analyze the key physical factors affecting the energy stability of electrolyzers, and determine promising directions for their solution. The results of this study can be used to improve the energy efficiency and environmental friendliness of aluminum production. Full article

Superheat degree (SD) is an important indicator for identifying the status of aluminum electrolytic cells. The fire hole video of the aluminum electrolytic cell captured by an industrial camera is an important basis for identifying SD. This article proposes a novel method that VideoMamba enhances with attention and learnable Fourier transform (CFVM) for SD identification. With a lower computational complexity and feature extraction capabilities comparable to transformers, VideoMamba offers the CFVM model a stronger feature extraction basis. The channel attention mechanism (CAM) block can achieve information exchange between channels. Through matrix eigenvalue manipulation, the learnable nonlinear Fourier transform (LNFT) block may guarantee stable convergence of the model. Furthermore, the LNFT block can efficiently use mixed frequency domain channels to capture global dependency information. The model is trained using the aluminum electrolysis fire hole dataset. Compared with recent fire hole identification models that primarily rely on neural networks, the method proposed in this paper is based on the concept of state space modeling, offering lower model complexity and enhanced feature extraction capability. Experimental results demonstrate that the proposed model achieves competitive performance in fire hole video identification tasks, reaching an identification accuracy of 85.7% on the test set. Full article

The Hall–Héroult aluminum production process generates lithium-rich waste barrier materials, which are challenging to process using conventional acid leaching due to the environmental risks posed by hydrofluoric acid (HF) emissions. This research introduces a two-stage water–HCl sequential leaching (WHSL) approach to recover lithium while reducing these environmental impacts. The method evaluates key factors, such as the liquid–solid ratio, temperature, duration, rotation speed, and HCl concentration, and compares its efficacy with traditional HCl leaching using XRD, FTIR, DBP, and SEM techniques. The findings indicate that initial water leaching dissolves NaF salts, creating surface grooves and cracks. Subsequent HCl leaching selectively extracts lithium from aluminum and silicon, forming silica gel while preserving the nepheline phase due to its structural integrity. The process produces a porous residue with smaller particles, reduced surface potential, and promotes colloidal aggregation. This two-step process achieves efficient lithium recovery while reducing acid consumption and minimizing hydrogen fluoride (HF) emissions. Full article

The problem of the adhesion of aluminum slag to the inner wall of a vacuum ladle is essential but has not been addressed. Using a high-temperature wettability experimental setup, this paper investigates the mechanism of interfacial wettability, adhesion, and penetration between Na₃AlF₆-Al₂O₃-CaF₂ cryolite-based molten salt and SiC refractory substrate. The composition of the slag was determined based on the slag obtained in the transfer ladle during the aluminum electrolysis process. We mainly study the effects of different Al₂O₃ contents in cryolite-based molten salt and temperatures on the contact angle and surface tension. The results indicate that as the Al₂O₃ content in the slag increases, the contact angle decreases, enhancing the slag’s wettability on the SiC substrate. Additionally, a higher Al₂O₃ content leads to higher slag melting temperatures and surface tension, which improves the slag mobility and enhances the mass transfer and diffusion capabilities of molecules or ions within the slag. The work of adhesion, calculated using the Mills model, also increases with the increasing Al₂O₃ content. The increased Al₂O₃ concentration activates the activity of Na₃AlF₆ in the slag, facilitating the dissolution reactions and improving the wettability between the slag and SiC. Moreover, the wetting behavior of the Na₃AlF₆-Al₂O₃-CaF₂ slag is primarily influenced by the initial Al₂O₃ content and its compositional changes. The results show that the slag penetration resistance and mechanical erosion resistance of the ladle lining can be improved by using an SiC-based refractory with an optimized Al₂O₃ content. This will have important guiding significance for the development, design, and application of inner wall materials for aluminum electrolysis industrial vacuum ladles. Full article

The comprehensive recycling of aluminum electrolysis cell waste barrier material is urgent. This study focuses on the sulfuric acid leaching of waste barrier material, systematically examining the effects of factors such as reaction temperature, liquid-to-solid ratio, sulfuric acid concentration, and reaction time on the leaching of elements like lithium, aluminum, sodium, and silicon. The experimental results show that under the conditions of 0.9 mol/L sulfuric acid concentration, a liquid-to-solid ratio of 20:1, a reaction temperature of 90 °C, and a reaction time of 1.5 h, the leaching rates were 84.5% for lithium, 85.6% for aluminum, 98.5% for sodium, and 4.8% for silicon. The sulfuric acid leaching process of the waste barrier material follows a shrinking core model and is controlled by internal diffusion. The apparent activation energies for the leaching reactions of lithium, aluminum, and sodium were 4.29 kJ/mol, 8.99 kJ/mol, and 9.11 kJ/mol, respectively. The selective leaching of lithium, sodium, and aluminum from silicon was successfully achieved in the sulfuric acid leaching of the waste barrier material. Full article

In the aluminum electrolysis production workshop, heavy-load overhead cranes equipped with multi-functional operation terminals are responsible for critical tasks such as anode replacement, shell breaking, slag removal, and material feeding. The real-time monitoring of these four types of operation terminals is of the utmost importance for ensuring production safety. High-resolution cameras are used to capture dynamic scenes of operation. However, the terminals undergo morphological changes and rotations in three-dimensional space according to task requirements during operations, lacking rotational invariance. This factor complicates the detection and recognition of multi-form targets in 3D environment. Additionally, operations like striking and material feeding generate significant dust, often visually obscuring the terminal targets. The challenge of real-time multi-form object detection in high-resolution images affected by smoke and dust environments demands detection and dehazing algorithms. To address these issues, we propose the YOLOv8n-Al-Dehazing method, which achieves the precise detection of multi-functional material handling terminals in aluminum electrolysis workshops. To overcome the heavy computational costs associated with processing high-resolution images by using YOLOv8n, our method refines YOLOv8n through component substitution and integrates real-time dehazing preprocessing for high-resolution images, thereby reducing the image processing time. We collected on-site data to construct a dataset for experimental validation. Compared with the YOLOv8n method, our method approach increases inference speed by 15.54%, achieving 120.4 frames per second, which meets the requirements for real-time detection on site. Furthermore, compared with state-of-the-art detection methods and variants of YOLO, YOLOv8n-Al-Dehazing demonstrates superior performance, attaining an accuracy rate of 91.0%. Full article

To address the challenge of the real-time monitoring of alumina concentrations during the production process, this paper employs a Deep Belief Network (DBN) within the framework of deep learning to predict alumina concentration. Additionally, the improved Grey Wolf Optimizer (IGWO) is utilized to optimize key parameters of the DBN model, including the number of hidden layer nodes, reverse iteration count, and learning rate. An IGWO-DBN hybrid model is then constructed and compared against DBN models optimized by other techniques, such as the Sparrow Search Algorithm (SSA) and Particle Swarm Optimization (PSO), to evaluate the predictive performance. The comparative analysis reveals that, in terms of predictive accuracy, the IGWO-DBN model outperforms both the SSA-DBN and PSO-DBN models. Specifically, it achieves lower root mean square errors (RMSE) and mean absolute errors (MAE), alongside a higher coefficient of determination (R²). Furthermore, the IGWO-DBN model exhibits a faster convergence rate and a lower final convergence value, indicating superior generalization ability and robustness. Furthermore, the IGWO-DBN model not only demonstrates significant advantages in prediction accuracy for alumina concentration but also substantially reduces model training time through its efficient parameter optimization mechanism. The successful implementation of this model provides robust support for the intelligent and refined management of the aluminum electrolysis industry, aiding enterprises in reducing costs, improving production efficiency, and advancing the green and sustainable development of the industry. Full article

Life cycle assessment (LCA) is used to quantitatively analyze the energy consumption and environmental impact of primary aluminum production in China, the United States, and Europe, as well as global average. The results indicate that electricity and fuel contribute more than 60% of the environmental impact of bauxite mining; steam is the greatest contributor to the environmental impact of alumina production by the Bayer process, with a result exceeding 35%; and electricity contributes >50% of the environmental impact of aluminum electrolysis. The environmental impact of primary aluminum production in China is 1.2 times the global average. The contributions of the three stages of primary aluminum production to the total environmental impact of the process in China are, in descending order, aluminum electrolysis (64.33%), alumina production (33.09%), and bauxite mining (2.58%). If the proportion of thermal power in the electricity source structure is reduced from 60% to 0%, the contribution of electricity to the environmental impact of primary aluminum production will decrease from 38% to 2%, and the total environmental impact will decrease by 73%. Therefore, energy conservation and emissions reduction can be realized through the optimization of the power generation structure, adoption of clean energy production, and improvement of the heat utilization rate in production processes. Full article

To solve the problem of low segmentation model accuracy due to the complex shape of carbon slag in the aluminum electrolysis fire-eye image and the blurring of the boundary between the slag and the surrounding electrolyte, this paper proposes a segmentation model of the fire-eye image based on an improved U-Net. The model reduces the depth of the traditional U-Net to four layers and uses the multiscale dilated convolution module (MDCM) in the down-sampling stage. Second, the Convolutional Block Attention Module (CBAM) is embedded in the skip connection part of the network to improve the ability of the model to extract contextual features from images of multiple scales, enhance the guidance of high-level features to low-level features, and make the model pay more attention to the critical regions. To alleviate the negative impact of the imbalance of positive and negative examples in the dataset, the weighted binary cross-entropy loss and the Dice loss are used to replace the traditional cross-entropy loss. The experimental results show that the segmentation accuracy of the improved model on the fire-eye dataset reaches 88.03%, which is 5.61 percentage points higher than U-Net. Full article

The use of reduction leaching in the production of alumina from bauxite by the Bayer process in order to decrease the amount of waste (bauxite residue) by adding elemental iron or aluminum, as well as Fe²⁺ salts and organic compounds in the stage of high-pressure leaching, requires the purchase of relatively expensive reagents in large quantities. The aim of this study was to investigate the possibility of the use of electrolytically reduced bauxite residue (BR) as a substitute for these reagents. Reduced BR was obtained from Al-goethite containing BR using a bulk cathode in alkaline suspension. The degree of deoxidation of Fe³⁺ compounds was 55% after 2 h of electrolysis with a current yield of more than 73%. The addition of reduced BR according to the shrinking core model leads to a change in the limiting stage of the high-pressure boehmitic bauxite leaching from a surface chemical reaction to internal diffusion. The activation energy decreased from 32.9 to 17.2 kJ/mol by adding reduced red mud. It was also shown that the addition of reduced BR increased the rate of thickening of the slurry after leaching by a factor of 1.5 and decreased the Na₂O losses by 15% without the addition of lime. The solid residue was examined by means of X-ray diffraction analysis and scanning electron microscopy to confirm the presence of magnetite and elemental iron. A preliminary techno-economic analysis was carried out to assess the applicability of the proposed process. Full article

The objective of this study is to examine the effectiveness of the electrocoagulation (EC) process in treating real produced water (PW). The impact of the EC process on water quality parameters (pH and conductivity, turbidity, and oil content) was studied using bench-scale 5 L PW for this process. The findings indicate that prolonged EC leads to the release of metal ions and secondary electrode reactions, which resultantly increase the pH of the outlet water. The EC process decreased in several water quality parameters, including Chemical Oxygen Demand (COD), Total Organic Carbon (TOC), and oil and grease (O&G). COD decreased by roughly 1300 mg/L, resulting in a 33% removal. In the same manner, TOC dropped from an initial value of 1300 mg/L to approximately 585 mg/L, exhibiting a maximum removal efficacy of nearly 60%. Oil and gas (O&G) decreased to a value below 10 mg/L, accompanied by a remarkable removal efficacy of up to 99.6%. The turbidity, which was initially recorded at an average of 160 NTU, was reduced to approximately 70 NTU, which is a 44% reduction. The application of centrifugation after EC treatment resulted in a turbidity reduction above 99%. EC treatment removed BTEX (benzene, toluene, ethyl benzene, and xylenes) from PW by more than 99%. The inorganic constituents, specifically heavy metals, exhibited minimal changes following the application of EC, emphasizing the necessity for additional treatment methods to effectively address their presence. In summary, EC demonstrates an acceptable level of efficacy in the removal of turbidity and pollutants from PW, with a special emphasis on organic compounds such as BTEX, but it does not address the elimination of inorganic compounds. Subsequent investigations should prioritize the optimization of EC parameters and the integration of supplementary interventions to effectively address the removal of inorganic elements and insoluble metals from treated PW. The study evaluates the pollutant removal efficiency using iron and aluminum electrodes and the effects of the applied current and electrolysis time on the EC process. Full article

To improve the accuracy of calculating the aluminum electrolysis pre-close wind volume, this study focused on optimizing the two main factors that influence its magnitude: the aluminum output speed and the pre-close wind volume coefficient. First, the Unscented Kalman Filter (UKF) algorithm was used to estimate the aluminum output speed, and its application in real production was verified through simulation experiments. The results demonstrate that UKF provides more accurate speed estimates when handling the non-linear dynamic system of aluminum electrolysis. When there was a sudden change in speed, the UKF achieved a relative error of only 0.0373%, significantly lower than the 2.52% error of the traditional Kalman Filter (KF). At the same time, the UKF exhibited a shorter runtime in the simulation. Additionally, this research introduces a self-correction mechanism for the pre-close wind volume coefficient for the first time. By dynamically adjusting the parameter based on aluminum output deviations and applying the Average Filter (AF) to improve the compensation accuracy, the pre-close wind volume coefficient can be precisely calculated. The combination of these methods significantly enhances the accuracy and robustness of pre-close wind volume calculations, providing solid theoretical foundations and the technical support needed to achieve high-precision aluminum output control. Full article