Aluminum Electrolysis Research Articles

Research on Capacity Configuration for Green Power Substitution in an Isolated Grid Containing Electrolytic Aluminum

The deployment of a green power alternative within an isolated network, powered by renewable energy sources, in the “Three North” region of China can facilitate the substitution of high-energy-consuming industrial loads with green power. However, an inadequate power supply configuration may lead to economic and reliability issues. To address this problem, effective capacity allocation within the green power alternative isolated network is proposed. The capacity allocation process starts with the design of a network structure that aligns with local conditions. Subsequently, a capacity allocation model is developed, considering economic factors, renewable energy utilization efficiency, and system reliability. The gray wolf optimizer is enhanced to establish a capacity allocation method for the green power alternative isolated network. This method is then employed to simulate and assess the performance of the network. The results indicate that the green alternative isolated grid can successfully facilitate green power substitution, satisfying the energy requirements of the loads.

A large-scale graph clustering method for cell conditions spatio-temporal localization in aluminum electrolysis

Sensor nodes (industrial nodes) clustering is an efficacious method for gaining local working conditions real-timely in the production process. The existing deep graph neural networks-based industrial nodes clustering studies construct static graphs to describe the potential spatial correlation among industrial nodes, but the temporal correlation among industrial nodes is ignored, resulting in inferior real-time performance. Meanwhile, these deep learning models fail to obtain good clustering results due to the large-scale and noise-containing characteristics of industrial nodes. In this paper, a large-scale graph clustering method (LsGCM) is proposed for cell conditions spatio-temporal localization in aluminum electrolysis to address the above issues. Specifically, a spatio-temporal graph construction method based on industrial process mechanism knowledge and local working condition boundary types statistical feature fusion is proposed for capturing effective spatio-temporal correlations between industrial nodes. Then, hysteresis knowledge of industrial production system is used to guide the nodes merging, thereby avoiding the incorrect merging of time-asynchronous nodes when performing the spatio-temporal region detection of local working conditions. Finally, combined with the actual spatio-temporal location information of industrial nodes, a novel spatio-temporal information expansion method (StIEM) of the sub-spatio-temporal graph describing local working condition is proposed for obtaining spatio-temporal feature information of the neighborhood, which is beneficial to improve the performance of industrial node clustering. Validation studies on a numerical simulation example and multiple real aluminum electrolysis industrial datasets certificate the effectiveness and superiority of the proposed method. In particular, compared with existing graph clustering methods, our method improves the clustering accuracy by 7.79%.

PKG-DTSFLN: Process Knowledge-guided Deep Temporal–spatial Feature Learning Network for anode effects identification

In the aluminum electrolysis process, the accurate identification of anode effect (AE) can improve production efficiency. However, the existing methods fail to effectively capture the features of the anode current signal (ACS) due to its complex dynamic characteristics and temporal–spatial dependence. To address this issue, we propose a Process Knowledge-guided Deep Temporal–spatial Feature Learning Network (PKG-DTSFLN). We believe that knowledge and production data are complementary. Knowledge has potential to deduce beyond observational conditions. Data can be used to detect unexpected patterns. The combination of data and knowledge is potential to improve the performance. Specifically, knowledge is utilized to construct the adjacency matrix to represent the spatial structure of ACS. Then, a deep learning model is constructed by integrating the 1D-CNN and GAT, which is used to capture the temporal–spatial features of ACS. The experimental results on ACS dataset show that the accuracy is more than 99% with low computational cost.

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.

Study on Recovery of Lithium from Lithium-Containing Aluminum Electrolyte

The current process of recovering lithium from wasted aluminum electrolyte mostly entails extracting lithium from lithium-containing aluminum electrolyte by acid leaching and dissipation. Aiming at the disadvantages of the existing treatment method, such as the long process flow, environmental pollution, poor working environment, etc., we propose a new technology to extract lithium from the wasted aluminum electrolyte and systematically investigate the effects of raw material particle size, holding time, temperature and other factors on the recovery of lithium. The results show that the better process conditions for the recovery of lithium are as follows: the raw material particle size is 75~150 μm, the additive is CaCl2, the mass ratio of calcium chloride to lithium-containing aluminum electrolyte is 3:5, the reaction temperature is 1473 K, and the holding time is 3 h. After the product of the reaction is crushed and leaching is carried out by using deionized water (pH = 6.8), the temperature of the leaching is 368 K, the leaching time is 3 h, and the solid–liquid ratio is 1/3, and the leaching rate of Li can be up to 75.1%. In addition, the purity of the recovered AlF3 is more than 92.7%. This process realizes the comprehensive and efficient use of lithium-containing aluminum electrolyte and provides a new idea for the development of lithium extraction technology from lithium-containing aluminum electrolyte.

A method of evaluating cell state based on data augmentation and ViT16

In this paper, based on the model of data augmentation and Vision Transformer 16 (ViT16), a method of assessment for electrolysis cell state is presented to get the real-time information of the current cell state, so as to improve current efficiency of process. Firstly, in order to solve the issue of the small sample data and improve classification accuracy, the method of data augmentation is performed on the flame hole images by using convolutional block attention module to improve auxiliary classifier generativhyhee adversarial network. Secondly, the deep feature data of the flame hole images is extracted by the method of ViT16, and the genetic algorithm is applied to eliminate the redundant feature data to improve the accuracy. Thirdly, the support vector machines model is employed to classify the feature data, and the aluminum cells are classified into cold, hot, and normal. Finally, the actual data are applied to the experiments of the above method, the results of experiments show that this method is better than other methods, and the accuracy of classifying the cell state is as high as 98.677%. This is of great significance for the guidance of aluminum electrolysis production process.

Multi-objective optimization driven by preponderant individuals and symmetric sampling for operational parameter design in aluminum electrolysis process

Developing advanced population-based multi-objective optimization algorithms to explore optimal operating parameters is a crucial means of achieving energy saving and consumption reduction in aluminum electrolysis process (AEP). Many techniques, such as NSGA-II/NSGA-III, rely on dominance relationships among population members to prioritize evolving individuals. However, this mechanism assumes equal relationships among selected candidates, overlooking the potential of preponderant individuals to guide rapid population evolution. To tackle this issue, we propose a multi-objective optimization algorithm driven by preponderant individuals and symmetric sampling (MOPISS). This algorithm first establishes an individual selection strategy driven by preponderant individuals to expedite population evolution. Subsequently, it employs the count of preponderant individuals to calculate the interaction learning probability (ILP) for adaptively adjusting the genetic intensity of population. ILP is then integrated into the evolutionary process to create a novel genetic mechanism based on symmetric sampling, thereby enhancing the randomness of the solution space and genetic efficacy. Finally, MOPISS is validated across 17 benchmark functions and operational parameter optimization cases of AEP. The results indicate that the proposed method exhibits a significant competitive advantage over other mainstream approaches in terms of evolutionary speed and optimization performance. Specifically, it can increase current efficiency by approximately 3% and reduce energy consumption by 276 kW⋅h/t-Al for AEP.

A new approach for improving the quality of the carbon anode for aluminum electrolysis – An impregnation-baking process

Prebaked carbon anode is the primary consumable material in the aluminum reduction cell, and its quality directly affects the energy consumption of aluminum production. An impregnation-baking process of the anode was proposed to reduce the anode porosity, improve the quality of the anodes, and reduce additional carbon consumption. The theoretical analysis and industrial testing on the impregnation-baking process were carried out. Firstly, the coal tar pitch was immersed into the anode carbon block through the impregnation process. An anode impregnation model was developed and used for theoretical calculations to analyze the effects of pressure and temperature on the impregnation process. The accuracy of the model was verified experimentally. Secondly, the impregnated anodes were placed in an electromagnetic induction heating furnace for baking to carbonize the immersed pitch. The thermogravimetric and pyrolysis kinetics methods were used to analyze the baking process of the impregnated anodes. Finally, the impregnated-baked anodes were compared with common anodes regarding their impact on the technical and economic indicators of the aluminum electrolysis process in industrial tests. The results showed that the physicochemical properties of the impregnated-baked anodes were significantly improved, with the bulk density increase of 0.1 g/cm3, air permeability reduction of 0.54 nPm, compressive strength enhancement of 9.43 MPa, and electrical resistivity decrease of 10 uΩ·m. The anode service life was increased from 31 to 35 days, the current efficiency was increased by 0.44 %, and the operating voltage was reduced by 17 mV, which improved economic benefit significantly.

Purification of graphite from spent carbon cathodes through the reduction of the mechanical entrainment of cryolite using polyaluminium chloride as a flocculant

Spent carbon cathodes (SCCs) generated during electrolytic aluminum production pose significant environmental hazards. To optimize resource utilization, flotation is used as an early process for the clean recovery of valuable graphite from waste cathodes. However, the mechanical entrainment in flotation restricts the further improvement of product purity, resulting in an increase in waste gas and wastewater discharge in the subsequent purification process. In this study, to increase the fixed carbon content of flotation products, flocculant was first applied to the flotation separation of SCCs, and polyaluminum chloride (PAC) was used to flocculate cryolite with high impurity content to reduce mechanical entrainment in the flotation process. Zeta potential measurements, particle size analysis, optical microscopy, and scanning electron microscopy confirmed the agglomeration of cryolite under the action of PAC. Additionally, the DLVO (Derjaguin, Landau, Verwey, and Overbeek) theory was used to estimate the interaction energy to further explain the mechanism underlying cryolite agglomeration. Interaction energy calculation revealed that the attraction energy between particles was dominant at the PAC concentration of 2000 mg/L. Flotation experiments indicated that under a constant pulp pH of 7.8, the absence of sodium silicate, and a stirring speed of 1200 rpm, mechanical entrainment decreased from 91.53 ± 1.53 g/L to 69.80 ± 1.90 g/L, and the carbon grade of the product increased from 83.07 ± 0.28% to 88.94 ± 0.30%. Overall, the proposed purification method provides a new approach for the efficient recovery of graphite in waste cathodes, which is of great significance for the sustainable development of graphite resources.

Use of Industrial Waste to Improve the Performance Properties of Ceramic

Industrial waste, with an in-depth study of its resource capacity, physico-chemical and technological properties, is a valuable raw material for construction ceramics. The possibility of replacing high-quality clays in technologies for producing clarified ceramic bricks and effective wall ceramics using high-calcium waste generated during water purification by liming in thermal power engineering, chemical and other enterprises has been established. The obtained physicochemical patterns of formation on their basis of the structure and properties of a ceramic composite provide a scientific basis for the use of typical waste, in these studies, dust from electric precipitators of cement production. The role of aluminum-containing waste generated during the electrolysis of molten aluminum in regulating the technological properties of the masses and firing properties of clinker bricks has been revealed, which allows the use of low-grade clays for its production. Scientific research and recommendations on the use of various industrial wastes not only for the purpose of recycling, but also for the use of their valuable properties associated with the chemical composition, behavior in heat treatment, reactivity, the possibility of strengthening due to the formation of a controlled structure of the material, help slow down the process of decline reserves of high-quality clay raw materials.

Label Propagation With Contrastive Anchors for Deep Semi-Supervised Superheat Degree Identification in Aluminum Electrolysis Process

Accurate identification of multimodal Superheat Degree (SD) plays a critical decision-making role in Aluminum Electrolysis Process (AEP). Because the labeled SD data are scarce and annotation is expensive in real-world AEP, it is a challenge to develop a well-behaved SD identification model. In this paper, a Contrastive Anchors-based Label Propagation (CALP) algorithm is proposed to construct a Deep Semi-Supervised Learning (DSSL) model, called CALP-DSSL, for better identifying SD by utilizing large-scale unlabeled data and limited labeled data. Specifically, to improve the reliability of affinity graph and its affinity matrix, positive anchors and negative anchors are generated by estimating the uncertainty of label predictions, which can guide the correct direction of inferring pseudo-labels. For tackling the unsupervised domain adaptation problems existing in AEP, we propose a Variational Information Domain Adaptation (VIDA) module using the pseudo-labels generated by CALP to fine-tune the deep Variational Information Bottleneck (VIB) network. Finally, the overall CALP-DSSL model is trained by the Mini-Batch Incremental Learning (MBIL) technique in local level. It matches the nearest neighbors based on batch embedded features, which provides more distinct information flow during subsequent label propagation to construct the affinity graph. Benchmark datasets verify the superiority of CALP algorithm. Case study on a real-world AEP shows that CALP-DSSL model improves the accuracy of SD identification over other state-of-art DSSL methods. Our source code is available at https://github.com/wjiecsu/CALP-DSSL. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The focus of this paper is to develop a well-behaved SD identification model based on the improved deep semi-Supervised learning (CALP-DSSL) model. In this model, a Contrastive Anchors based-Label Propagation (CALP) algorithm is used to predict pseudo-labels to construct DSSL model for improving the reliability of the generated pseudo-labels. In this way, a Variational Information Domain Adaption (VIDA) module is constructed to solve the domain shift problems existed in the unstable working conditions. Moreover, the overall model is trained by Mini-Batch Incremental Learning (MBIL) strategy to build better underlying representation for supporting effective label propagation.

Analysis of the magnetohydrodynamic parameters of S-8BM (S-8B) electrolyzers in the modernization of aluminum smelters

The article aims to determine the magnetohydrodynamic parameters of an aluminum electrolyzer in order to compare different types of bus arrangements used in baths with a Soderberg anode. The electrical parameters of the electrolyzer (current distribution across blooms and anode studs) and magnetic field parameters were computed using the Blums V5.07 program (Polyfem, Russia). Data on circulation rates and skew of the metal in the electrolyzer were obtained using the MHD-Valdis program (developed by V. Boyarevich, University of Greenwich, UK). In the course of the studies, mathematical models of the C-8BM (C-8B) electrolyzer with various bus arrangements were built. Three bus arrangements were selected for testing the installation of a bridge designed to close the current distribution in the anode bus arrangement of the electrolyzer. These types of bus arrangements were realized in two variants: with and without a bridge. The obtained circulation rates and skew of the metal were used to evaluate the possibility of modernizing baths with a Soderberg anode without significant capital costs. When using the first type of bus arrangement, the best current distribution was achieved for the blooms with limits of ~757 A (for the variant without a bridge) and ~656 A (for the modernized variant with closed series), as well as for the anode studs having limits of ~1754 A and ~1609 A, respectively. With the use of the third bus arrangement variant, the current distribution was shown to slightly decrease following the installation of a bridge between the anode bus bars. The obtained results suggest that in the modernization of a C-8BM (C-8B) electrolyzer with different types of bus arrangements, current distribution across blooms and anode studs, magnetic field characteristics (By and Bz components), as well as circulation rates and skew of the metal, have no significant impact on the efficiency of this electrolyzer, which contributes to a faster transition to EcoSoderberg bath electrolysis without significant economic costs.

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.

Study on Preparation Technology and Heat Conduction Mechanism of High-Purity &amp; Ultra-Fine Alumina Powder from Scrap Aluminum Cans

In view of the increasing scarcity of bauxite resources in China, the high energy consumption and high pollution of electrolytic aluminum, and the requirements for energy conservation and environmental protection, aluminum recycling and high-value utilization of its derivatives have evolved into a crucial development requirement for the aluminum industry in the future. As an important part of the development of recycled aluminum resources, the high-value application of scrap aluminum cans has always been a hot research topic in various recycled aluminum processing enterprises and scientific research units. The traditional regeneration system of waste cans includes a series of complex technological processes such as pretreatment, paint removal, smelting system and casting system, which is difficult to control in the middle of the process. Most of the recycled scrap aluminum cans are cast and downgraded for later use, except for a part of them used as alloy materials for new cans. In this paper, combined with the research on the preparation of metal aluminum alkoxide, combined with recrystallization heat conduction to further study the effective dissolution or adsorption how to remove impurity elements to obtain high-purity aluminum alcohol salt mechanism research, and thermal effect of alcohols with different carbon chains on the synthesis of high-purity aluminum alkoxide was further investigated. Moreover, the changes in morphology and pore size distribution of hydrolyzed alumina precursor materials under different hydrothermal temperature conditions were discussed by means of the alkoxide hydrolysis-sol-gel process. Eventually, the aluminum alkoxide was obtained by the reaction of waste cans with isopropanol and heavy crystal thermal conductivity, and the high-purity aluminum alkoxide was purified by vacuum distillation. Under the hydrothermal condition of 160°C, the high-purity alumina material with a purity of 99.99% and an original crystal size of 200nm was prepared.

MgAl Oxide Coatings Modified with CeO2 Particles Formed by Plasma Electrolytic Oxidation of AZ31 Magnesium Alloy: Photoluminescent and Photocatalytic Properties

MgAl oxide coatings composed of MgO and MgAl2O4 phases were doped with CeO2 particles via plasma electrolytic oxidation (PEO) of AZ31 magnesium alloy in a 5 g/L NaAlO2 water solution. Subsequently, particles of CeO2 up to 8 g/L were added. Extensive investigations were conducted to examine the morphology, the chemical and phase compositions, and, most importantly, the photoluminescent (PL) properties and photocatalytic activity (PA) during the photodegradation of methyl orange. The number of CeO2 particles incorporated into MgAl oxide coatings depends on the concentration of CeO2 particles in the aluminate electrolyte. However, the CeO2 particles do not significantly affect the thickness, phase structure, or surface morphology of the coatings. The PL emission spectrum of MgAl oxide coatings is divided into two bands: one in the 350–600 nm range related to structural defects in MgO, and another much more intense band in the 600–775 nm range attributed to the F+ centres in MgAl2O4. The incorporated CeO2 particles do not have a significant effect on the PL intensity of the band in the red spectral region, but the PL intensity of the first band increases with the concentration of CeO2 particles. The PA of MgAl/CeO2 oxide coatings is higher than that of pure MgAl oxide coatings. The MgAl/CeO2 oxide coating developed in aluminate electrolyte with a concentration of 2 g/L CeO2 particles exhibited the highest PA. The MgAl/CeO2 oxide coatings remained chemically and physically stable across multiple cycles, indicating their potential for applications.

Recovery of aluminum oxide and iron oxide from aluminum electrolysis iron-rich cover material and preparation of aluminum fluoride.

In aluminum electrolysis, the iron-rich cover material is formed on the cover material and the steel rod connecting the carbon anode. Due to the high iron content in the iron-rich cover material, it differs from traditional cover material and thus requires harmless recycling and treatment. A process was proposed and used in this study to recovery F, Al, and Fe elements from the iron-rich cover material. This process involved aluminum sulfate solution leaching for fluorine recovery and alkali-acid synergistic leaching for α-Al2O3 and Fe2O3 recovery were obtained. The optimal leaching rates for F, Na, Ca, Fe, and Si were 93.92, 96.25, 94.53, 4.48, and 28.87%, respectively. The leaching solution and leaching residue were obtained. The leaching solution was neutralized to obtain the aluminum hydroxide fluoride hydrate (AHFH, AlF1.5(OH)1.5·(H2O)0.375). AHFH was calcined to form a mixture of AlF3 and Al2O3 with a purity of 96.14%. The overall recovery rate of F in the entire process was 92.36%. Additionally, the leaching residue was sequentially leached with alkali and acid to obtain the acid leach residue α-Al2O3. The pH of the acid-leached solution was adjusted to produce a black-brown precipitate, which was converted to Fe2O3 under a high-temperature calcination, and the recovery rate of Fe in the whole process was 94.54%. Therefore, this study provides a new method for recovering F, Al, and Fe in iron-rich cover material, enabling the utilization of aluminum hazardous waste sources.

Microwave hydrothermal sulfuric acid leaching of spent cathode carbon from aluminum electrolysis for high efficiency removal of insoluble calcium fluoride

Toxic substances, like fluoride salts present in spent cathode carbon (SCC), have been a great risk to the environment and public health. Our approach involves alkali leaching to eliminate soluble fluoride, followed by microwave hydrothermal acid leaching to efficiently remove insoluble CaF2 from SCC. The optimized conditions, including a temperature of 353 K, a solid–liquid ratio of 1:20, and a 60-minute reaction time, resulted in an impressive 95.6 % removal of fluoride from SCC. Various characterization techniques were employed to analyze the composition, micro-morphology, and elemental content of the materials before and after the leaching process. Furthermore, critical process parameters on the leaching separation of insoluble CaF2 during microwave hydrothermal acid leaching were systematically investigated. The study removal mechanism revealed the transformation of insoluble CaF2 in the process of microwave oxidation insertion-hydrothermal acid leaching for SCC. The kinetic characteristics of the two-stage leaching process of CaF2 at different temperatures were analyzed according to the shrinkage kernel model. The results indicate that the two-stage leaching process of CaF2 is affected by mixing control and by diffusion control, severally. The expansion of the graphite flake layer of SCC through oxidative intercalation was identified as a critical process for the thorough removal of CaF2. Microwave hydrothermal acid leaching demonstrated a 17 % improvement over traditional hydrothermal acid leaching within the same reaction time, showcasing a noteworthy enhancement in fluoride removal. Consequently, the microwave oxidizing intercalation-hydrothermal acid leaching treatment of SCC, as explored in this study, offers an effective approach for achieving deep defluoridation of SCC.

Enhanced flotation separation of aluminum electrolysis anode slag via microemulsification of diesel collector

Aiming at the defects such as poor dispersion and high consumption of conventional diesel collector in the flotation process of aluminum electrolysis anode carbon slag (CS), a microemulsion diesel collector was prepared, and its performance and mechanism in CS flotation were investigated by means of flotation experiments as well as modern analysis methods (surface tension, laser particle size, zeta potential, FTIR, etc.). A complex of sorbitan monooleate (Span 80) and cetyltrimethylammonium bromide (CTAB) was used as the primary surfactant, and the optimal formulation of the microemulsion collector was HLB of 8, co-surfactant of n-butanol, Km of 1.7, and oil-surfactant-water mass ratio of 27.80:27.8:44.40, and the droplet size of the obtained microemulsion collector was 62.22 nm. The flotation results showed that a carbon recovery of 74.48 % was obtained at a microemulsion collector dosage of 400 g/t, which resulted in a collector saving of 66.67 % with similar flotation indexes. The flotation process of microemulsion diesel for CS is in accordance with the classical first-level kinetic model, and its flotation rate is faster than that of conventional diesel. Analysis test results showed that microemulsification resulted in smaller droplet size and better dispersion of diesel collector in water, which enhanced its adhesion to carbon particles even at low reagent dosage. At the same time, the microemulsion diesel reduces the electronegativity of the CS particles and enhances the coverage of oxygenated functional groups on the carbon surface, thus enhancing the hydrophobicity of the carbon particles and improving its flotation recovery performance. This research can provide new theoretical basis and technical support for efficient waste disposal and resource recovery for CS.

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.