Aluminum Electrolysis Process Research Articles

Chemical leaching − Flexible precipitation for sustainable recovery of fluoride from aluminum electrolysis multisource hazardous waste

The spent carbon materials, including anode and cathode, generated from the aluminum electrolysis process contain high fluoride levels and are unavoidable hazardous solid wastes. Chemical leaching, as a promising strategy for large-scale industrial treatment, must address the challenge of secondary recovery of the solution. The co-recovery mechanism preparing various fluorides from acidic and alkaline fluoride-aluminum solutions was investigated. In this process, the F/Al molar ratio and the pH value of the substrate environment are critical factors for achieving synergistic recovery. Different acid and alkali adjustment modalities resulted in a diverse array of products. For instance, adding an alkali to an acid solution generated aluminum hydroxyfluoride hydrate, whereas the reverse sequence yielded cryolite. The precipitation mechanism of acidic solutions is characterized by a complex interplay of multiple substances, whereas the process in alkaline solutions is comparatively straightforward. Specifically, as the pH value increases, the sequence of phase transformations progresses through aluminum hydroxyfluoride hydrate, chiolite, aluminum hydroxide, and cryolite. In addition to pH, localized over-alkalinity was the primary factor contributing to impurity generation. The precipitation phase in an alkaline substrate environment is significantly better controlled. During this process, pure cryolite can be synthesized by adjusting the F/Al molar ratio to 6.0 using the neutralization solution. The synergistic synthesis of fluoride from both acidic and alkaline fluorine-containing solutions is not only feasible but also anticipated to effectively address the significant issues of water swelling and secondary solution recovery that are commonly encountered in industrial applications.

Study on Inter-electrode Process of Aluminum Electrolysis: An Insight into Inter-electrode Phenomena Under Current Fluctuations

Study on Inter-electrode Process of Aluminum Electrolysis: An Insight into Inter-electrode Phenomena Under Current Fluctuations

Development of data-knowledge-driven predictive model and multi-objective optimization for intelligent optimal control of aluminum electrolysis process

Operational optimization of the Hall-Héroult cell is essential for achieving high efficiency and cost-effectiveness in the aluminum electrolysis process. Due to the complicated mechanism and variable working conditions, manual operational decision-making is extensively used in practice. They challenge the reliable and optimal operation of aluminum electrolysis process. In this paper, we develop a data-knowledge-driven decision-making support system (DMSS) to achieve operational optimization for the aluminum electrolysis process. DMSS consists of a prediction model, a multi-objective optimizer, and a knowledge-guided decision-making module. Specifically, we propose a working-conditions-based attention with the exogeneous inputs auto-regressive neural network (WCA-NARX) to construct a data-driven heat balance indicator (HBI) prediction model, where the working condition-related variables serve as covariates to enhance predictability. In addition, the designed structure of introducing working condition information through an attention mechanism can decouple covariates from operational variables and autoregressive variables, facilitating subsequent operational optimization. Then, a novel knowledge-assigned reference vector evolutionary algorithm (KRVEA) is designed to solve the multi-objective optimization problem of the aluminum electrolysis process, in which Pareto front solutions can be solved in the preferred region. Finally, we utilize the knowledge base that stores historical optimization cases to make decisions regarding the selection of a practical-requirement-based control scheme from the Pareto set. Real-world industrial experiments demonstrate that DMSS can effectively enhance control performance and achieve superior results compared to other competitive methods. The source code is available at https://github.com/wjiecsu/WCA-NARX.

Memory Shapelet Learning for Early Classification of Streaming Time Series.

Early classification predicts the class of the incoming sequences before it is completely observed. How to quickly classify streaming time series without losing interpretability through early classification method is a challenging problem. A novel memory shapelet learning framework for early classification is proposed in this article. First, a memory distance matrix is introduced to store the historical characteristics of streaming time series, which can alleviate repetitive calculations caused by the growing length of time series. Second, early interpretable shapelets are extracted in the proposed method by optimizing both accuracy objective and earliness objective simultaneously. The proposed method employs end-to-end learning, which allows the model to directly learn early shapelets without the necessity of searching for numerous candidate shapelets. Third, an objective function of memory shapelet learning is proposed by overall considering accuracy and earliness, which can be optimized by gradient descent algorithm. Finally, experiments are conducted on benchmark dataset UCR, Tennessee Eastman process, and real-world aluminum electrolysis process in China. Comparable results with other state-of-the-art methods demonstrate the superior performance of the proposed method in interpretability, accuracy, earliness, and time complexity.

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.

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.

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.

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.

Prior knowledge-augmented unsupervised shapelet learning for unknown abnormal working condition discovery in industrial process

Unknown abnormal working condition discovery is the key of refinement industrial production. Clustering industrial time series is an effective way to discover unknown working condition types. However, it is challenge for existing time series cluster methods to discover unknown abnormal working condition from industrial time series. In this study, a novel prior knowledge-augmented unsupervised shapelet learning method is proposed to discover abnormal and meaningful working condition through interpretable subsequences. A prior feature extracting module is proposed to change prior knowledge into a recognizable form for the data model. The prior knowledge contains abnormal working condition information. The knowledge-augmented clustering module can learn informative shapelets which stand for abnormal working condition by combining prior features with data features. Furthermore, the preference of prior knowledge and data are self-adjusted in the learning phase. Numerical test results on the real-world aluminum electrolysis process, simulated Tennessee Eastman process, and continuous stirred tank heater process verify the superior performances of the proposed method. The proposed method provides a new perspective for the fusion of prior knowledge and data model. It also provides a new way to solve the problem of abnormal unknown working condition discovery in industrial process.

Failure mode and effect analysis with ORESTE method under large group probabilistic free double hierarchy hesitant linguistic environment

Failure Mode and Effect Analysis (FMEA) is a prospective and systematic analytical tool widely used to improve the reliability and safety of various industries. However, the existing FMEA methods have received criticism for their inherent deficiencies that limit their flexibility and effectiveness. To address these issues, we propose a novel FMEA model that combines Probabilistic Free Double Hierarchy Hesitant Linguistic Term Set (PFDHHLTS), Extended Grey Relation Analysis (EGRA), and ORESTE to determine the priorities of Failure Modes (FMs). Firstly, the PFDHHLTSs are proposed to represent experts’ assessments, improving the flexibility and accuracy of experts’ expressions. Secondly, the PFDHHLTS-EGRA is proposed to determine the evaluation matrix and the weights of risk factors in a large group environment. Furthermore, the ORESTE method is extended to the PFDHHLTS environment. Lastly, we verify the effectiveness of our method through a case study on the aluminum electrolysis process. The results demonstrate the effectiveness and flexibility of our method in expressing experts’ assessments and obtaining more accurate and reliable risk priorities.

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.

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.

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.

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.

Piggybacking on past problem for faster optimization in aluminum electrolysis process design

In process industry, it is a habitual phenomenon that the production efficiency is improved via replacing the obsolete devices with the advanced ones. To achieve an optimal performance, these devices are always required for heavily empirical adjustments, which is very time-consuming and inefficient. Though outdated, invaluable experiences for adjusting devices towards a more efficient industrial production have been accumulated by those replaced facilities. Thereby, new devices are expected to adapt to perform the industrial task without many modulations if the aforementioned experiences can be appropriately utilized. Inspired by the fact that evolutionary multitasking is capable of exploiting latent similarities and commonalities among multiple optimization tasks so as to improve the overall convergence of multi-task optimization, in this paper, we propose a novel framework to automatically search for the optimal settings for new devices based on the knowledge accumulated by the old. The framework, dubbed Piggybacking on Past Problem for Faster Optimization (PPPFO), is able to piggyback on the past optimization problem for a faster convergence of the targeted. By means of automatically transferring search experiences (i.e. genetic and cultural characteristics) from source task to the target, PPPFO can assist an engineering optimizer to improve its search exercises. PPPFO has been tested with a number of widely used benchmark functions and has been successfully adopted to an important real application, i.e., aluminum electrolysis process design. The remarkable results verify the efficacy and efficiency of the proposed framework.

Effect of CaO on Fluorine in Spent Cathode Carbon during Nickel Laterite Reduction

Large amounts of solid waste-spent cathode carbon (SCC) was produced in aluminum electrolysis process. At present, landfill and storage methods are widely used, which will not only cause great harm to the environment, but also cause a lot of waste of resources. Nickel and iron were recovered from laterite nickel ore by reduction roasting-magnetic separation using SCC as reducing agent. The fluorine in SCC was fixed in roasted ore by adding CaO. The feasibility of the method was verified by thermodynamic analysis and experiments, and the optimum conditions for CaO addition were determined. The recovery of nickel and iron reached 91.23% and 89.63% respectively when the addition of SCC was 14 wt. % and CaO was 8 wt. %. Adding 8 wt.% CaO under the condition of SCC as reducing agent can change the viscosity of roasted ore and promote the aggregation of ferronickel particles. The concentration of soluble F- in roasted ore was reduced and fixed in roasted ore in the form of insoluble calcium fluoride. Under the condition of adding 8 wt.% CaO, the fluorine fixation rate was 95.99%, and the concentration of F- in roasted ore was only 31 mg·L−1.

Experimental and numerical analysis on electrolytic bubble group dynamics in the aluminum electrolysis process using the slotted anode

The CuSO4 aqueous solution electrolysis experiment is conducted to investigate the electrical bubble dynamics using the slotted anode to reveal the gas removal mechanism. A three-dimensional transient mathematical model coupled discrete phase model (DPM) − discrete-continuum transition model (DCTM) − volume of fluid (VOF) method is developed to track the dispersed bubble trajectory, bridge the dispersed bubble to continuous gas, and resolve the deformed bubble surface, respectively. The slotted anode decreases the bubble diameter compared with the traditional anode because the slot shortens the bubble motion distance to decrease the bubble residence time. The slot width smaller than the detached bubble diameter can limit the bubble deformation. Increasing current plays a dual role in bubble motion in the experiment: it accelerates the bubble coalescence process but decreases the bubble collision probability. The longitudinal slot is more conducive to reducing the maximum bubble coverage and increasing the bubble release frequency than the transverse slot.

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.