Rare Earth Extraction Process Research Articles

Establishing forward mixing model to mass transfer overview in multi-impeller agitated column for rare Earth extraction process

The current study develops comprehensive mass transfer models to optimize the rare earth extraction. A plug flow, axial dispersion, backflow, forward mixing-based mass transfer model was created and solved numerically using the fitting technique. The investigated process is a multi-impeller agitated column designed to provide proper contact between organic and aqueous phases to extract rare-earth ions. Taking Sm(III)–Gd(III) separation as an application case, extraction efficiency in the agitation speed of 200 rpm was obtained equal to 95.14%, 76.67% by this column for Gd(III), and Sm(III) ions, respectively. The model's findings were compared with experimental data, and a significant agreement was achieved with the forward mixing model. The results indicated that the high agitation speed is beneficial to increasing the interfacial area while reducing the mass-transfer coefficient. On the contrary, the circulation within the larger droplet improves the transfer of mass, albeit at the expense of reducing the interfacial area. The results showed that the drop size distribution is a crucial factor as the droplet sizes significantly affect the droplet mass transfer. The mathematical models’ values of Ec for mass transfer parameters showed that the operational variables significantly affect the mass transfer rate and can cause deviations from the ideal flow path. A reasonable and appropriate estimation of the organic-side volumetric overall mass transfer coefficient was provided, which can be applied to this contactor’s design and scale-up.

Full recycling of MgCl2 wastewater generated in rare earth extraction and separation process by spray pyrolysis

Wastewater recycling is the desired goal of the rare earth separation plants for current ecological requirements and economic benefit. The MgCl2 wastewater generated during rare earth extraction and separation process was treated by spray pyrolysis for the first time to achieve full recycling. The kinetic of spray pyrolysis process and the mechanism affecting the activity of MgO powder were investigated. The results showed that the rate determining step of MgCl2 solution spray pyrolysis was transformed from a chemical reaction control to a diffusion process control after the temperature exceeded 1073 K. The MgO powder prepared by spray pyrolysis was rough and porous, the embedding of Cl made MgO grains severely distorted, producing more defects. Meanwhile, polycrystalline basic magnesium chloride likewise had more defects. These two points resulted a higher density of defects of MgO powders, and causing higher activity. Moreover, at a temperature of 1173 K, MgCl2 concentration of 300 g/L and N2 flow rate of 40 L/min, the thermal decomposition rate of MgCl2 reached 99.54%, the alkalinity of the powder reached 0.045 mol/g, and the iodine absorption value reached 102.72 mg I2/g MgO. Meanwhile, the high purity hydrochloric acid with a concentration of 6.70 mol/L was obtained.

Soft Measurement of Rare Earth Multi-Element Component Content Based on Multi-LightVGG Modeling

The current hardware equipment used to detect the content of each element component in the rare earth extraction process has a complex structure and high maintenance cost. A modeling method for the soft measurement of rare earth multi-element component content is proposed to address this issue. This method uses the Multi-LightVGG multi-tasking learning model and the Multi Gradient Descent Algorithm based on Optimized Upper Bound (MGDA-OUB) to optimize the model for each prediction task and find the Pareto optimal solution. After conducting several experiments, the Multi-LightVGG model loaded with MGDA-OUB has lower MRE, RMSE for Pr, Nd prediction, and MAX(|error|) for Nd prediction than the Multi-LightVGG model without MGDA-OUB by 0.3778%, 0.5208%, 0.0015, 0.0015, and 0.1985%, respectively; and the MRE and RMSE of the Multi-LightVGG model for Pr and Nd prediction under the same optimization conditions are lower than those of Multi-ResNet18 by 0.3297%, 0.5423%, 0.0019, and 0.002, respectively, thus indicating that MGDA-OUB can effectively solve multiple task-specific Pareto solutions to avoid possible conflicts between specific tasks, while the Multi-LightVGG model, compared to the Multi-Resnet18 model, has a backbone network that can effectively capture the abstract representations in the images of the rare earth-extraction mixed solution, which in turn improves the prediction accuracy of the content of each elemental component.

Constrained Model Predictive Control Based on Event Triggering for the Rare Earth Extraction Process.

Since the reagent dosage is manually adjusted according to work conditions, an event-triggered constrained model predictive control is proposed for rare earth extraction. First, the linear predictive system, based on a state space model, is established. Subsequently, the feedback correction link is fine-tuned to reduce the prediction error. Following this, an objective optimization function, incorporating input and output constraints, is introduced to calculate the appropriate reagent dosage. Finally, an event-triggering mechanism, underpinned by a designated threshold, is designed to update the controller. Simulation outcomes substantiate the efficacy of the proposed approach.

Multi-Delay Identification of Rare Earth Extraction Process Based on Improved Time-Correlation Analysis.

The rare earth extraction process has significant time delay characteristics, making it challenging to identify the time delay and establish an accurate mathematical model. This paper proposes a multi-delay identification method based on improved time-correlation analysis. Firstly, the data are preprocessed by grey relational analysis, and the time delay sequence and time-correlation data matrix are constructed. The time-correlation analysis matrix is defined, and the H∞ norm quantifies the correlation degree of the data sequence. Thus the multi-delay identification problem is transformed into an integer optimization problem. Secondly, an improved discrete state transition algorithm is used for optimization to obtain multi-delay. Finally, based on an Neodymium (Nd) component content model constructed by a wavelet neural network, the performance of the proposed method is compared with the unimproved time delay identification method and the model without an identification method. The results show that the proposed algorithm improves optimization accuracy, convergence speed, and stability. The performance of the component content model after time delay identification is significantly improved using the proposed method, which verifies its effectiveness in the time delay identification of the rare earth extraction process.

Yttrium separation by phosphorylcarboxylic acid and the underlying tetrad effect along lanthanide unveiled from different microscopic interactions

Due to similar physical and chemical properties, separating and purifying rare earth elements is challenging. Considering the environmental issue, the design and synthesis of efficient and green extractants are significant for this purpose. To this end, the underlying complexation between rare earth and the extractant should be clarified, and the long-historically existing tetrad effect along with the lanthanide family from La to Lu due to the different 4f orbital occupations should be figured out. Thus, within this study, taking our newly experimentally synthesized extractant, phosphorylcarboxylic acid, as an example and utilizing accurate quantum mechanical calculation, we comprehensively investigated the complexation behavior between rare earth and the extractant via the quantum theory of atoms in molecules (QTAIM), Mayer bond order (MBO), molecular orbital (MO) energy level, natural bond orbital (NBO), charge decomposition analysis (CDA), electron localization function (ELF) and extended transition state-natural orbitals for chemical valence (ETS-NOCV). It has been found that yttrium can be separated from the lanthanide series by the phosphorylcarboxylic acid, attributed to the poor ability of Y3+ to accept electrons and the weak orbital interactions compared to other rare earth cations. This paper also reveals the tetrad effect caused by the different occupations of 4f orbital electrons, providing a theoretical approach to comprehending the diverse extraction behaviors in the rare earth extraction process.

Minerals recovery from a rare earth extraction wastewater by a combined chemical precipitation and membrane distillation process

Rare earth extraction wastewater (REEW) derived from rare earth (RE) extraction contains high value-added resources, such as magnesium. Therefore, it is highly desirable to recover the minerals from REEW to reuse in the RE extraction process. This study investigated the combined chemical precipitation and direct contact membrane distillation (DCMD) process to treat REEW for minerals recovery. Results showed that 99.7% Mg2+ in REEW was precipitated as Mg(OH)2 by adjusting pH of the solution. The MgO was obtained through the calcination of Mg(OH)2. The specific surface area (SSA) and reactivity of the calcined MgO decreased with the increase of calcination duration. MgO calcined at 500 °C for 2 h exhibited higher reactivity and SSA of 188.2 m2/g. DCMD performance decline was mainly caused by gypsum during treatment of the Mg-separated stream and membrane fouling was effectively mitigated by almost complete removal of Ca2+, resulting in the concentrated Na2SO4 solution. Subsequently, Na2SO4 was successfully recovered via freeze crystallization by controlling its solubility. The recovered Na2SO4 exhibited narrow crystal size distribution with mean size of 43.3 μm. With this combined process, it was possible to successfully recover 20.1 g/L MgO, 49.7 g/L Na2SO4 along with 72.6% of the water from REEW. The recovered MgO and Na2SO4 with purity of 98.5% and 99.2%, respectively, achieving industrial grade. The results suggest that the combined technologies, chemical precipitation and DCMD process, can be used as a viable engineering solution for minerals recovery from REEW.

Effect of Oxidation Behavior of (Nd,Dy)-Fe-B Magnet on Heavy Rare Earth Extraction Process

Rare earth magnets with excellent magnetic properties are indispensable in the electric device, wind turbine, and e-mobility industries. The demand for the development of eco-friendly recycling techniques has increased to realize sustainable green technology, and the supply of rare earth resources, which are critical for the production of permanent magnets, are limited. Liquid metal extraction (LME), which is a type of pyrometallurgical recycling, is known to selectively extract the metal forms of rare earth elements. Although several studies have been carried out on the formation of intermetallic compounds and oxides, the effect of oxide formation on the extraction efficiency in the LME process remains unknown. In this study, microstructural and phase analyses are conducted to confirm the oxidation behavior of magnets pulverized by a jaw crusher. The LME process is performed with pulverized scrap, and extraction percentages are calculated to confirm the effect of the oxide phases on the extraction of Dy during the reaction. During the LME p rocess, Nd i s completely e xtracted a fter 6 h, w hile D y remains as D y2Fe17 and Dy-oxide. Because the decomposition rate of Dy2Fe17 is faster than the reduction rate of Dy-oxide, the importance of controlling Dy-oxide on Dy extraction is confirmed.

Adaptive Sampling Dual Heuristic Dynamic Programming for Rare Earth Extraction Process

Event-triggered control is applied to adaptive dynamic programming (ADP) as an effective method to reduce the computational cost, in which sampling only happens when a specific event occurs. In several cases of industrial applications, however, the sampling rate should be reduced in the given ranges where the prior knowledge of the process has been obtained. Correspondingly a greater sampled data are needed to observe the change of the control system more carefully if the controlled variables lie outside the boundary of the desired region. In response to this industrial demand, a novel adaptive sampling strategy according to a priori knowledge for different system states is proposed to reduce the sensitivity of the ADP-based methods. To implement the strategy into nonlinear continuous-time systems, an adaptive sampling condition is given. Furthermore, the stability analysis for the close-loop system is explicitly provided by using Lyapunov approach. The experimental results on the rare earth extraction process (REEP) verify the effectiveness of the proposed method.

Prediction of component content in rare earth extraction process based on ESNs-Adaboost

Aiming at the problem that the component content of elements in REEP (rare earth extraction process) is difficult to detect, a modeling method for component content in REEP based on ESNs-Adaboost is proposed. It uses ESN (echo state network) with fast training speed and high stability to establish multiple identification model of REEP. And then combined with the improved Adaboost algorithm, multiple models are integrated into a final ESNs-Adaboost model of component content of REEP according to the combination rule. The improved Adaboost algorithm has an advantage that its threshold can be adjusted adaptively with the training error. By collecting data from CePr/Nd extraction process, the simulation results show that the proposed method has high prediction accuracy, strong generalization ability, good robustness and better performance than single ESN model, which can meet the requirements of rapid detection of component content in extraction field.

Multiple-model predictive control for component content of CePr/Nd countercurrent extraction process

A new multiple-model predictive control scheme is proposed for the control of the component content in the rare earth extraction process. Multiple local linear models with two-input and two-output of the rare earth countercurrent extraction process are constructed, each of which is established under an operation condition. A component content predictive controller is designed for a local linear model, based on the dynamic compensation of the extraction liquid flow and the scrubbing liquid flow. A switching algorithm based on the minimum accumulative error is formed to select the most appropriate model and controller to meet the productive purity requirements. Simulation results for the CePr/Nd countercurrent extraction process are presented to show the desired performance of the proposed approach.

Testing Methods of Component Contents during Rare-earth Extraction Process Based on Image Feature Retrieval

One online monitoring method based on the component contents of image feature retrieval is proposed to conduct the extracting and separating process for the rare-earth ion’s feature color. By analyzing different component contents of rare-earth ion color features, it is exposed with the relevance between component contents and the color feature H component. By combining the image retrieval technique based on contents, two retrieval algorithms, H component histogram and H component coherence vector, are proposed to help with the rare-earth component contents’ testing. In order to improve the soft sensing accuracy, the sensing accuracy correction algorithm based on PSO is suggested. To verify the effects of the above algorithm, the rare-earth component contents soft sensing experimenting platform based on image retrieval has been developed with VC6.0 and SQL Server. After doing imitation experiment with the component contents from the subjects of the rare-earth’s two components Pr/Nd extraction separation system, this method is proved with a fairly high testing accuracy.

Study on preparation and application of novel saponification agent for organic phase of rare earths extraction

In view of the problem of ammonia-nitrogen wastewater pollution in rare earths extraction and separation, the novel saponification agent of organic phase, which is magnesium bicarbonate solution, was prepared with the natural rich and cheap dolomite as raw material through carbonation process. The behavior and purification of main impurities ions in the carbonation process as well as the application effect of the novel saponification agent in the extraction and separation was researched. The results showed that the concentration of Fe, Al, Si impurities ions was less than 5 ppm in the saponification agent through the development of effective removal technology, respectively. When the novel saponification agent was used in the extraction and separation, magnesium utilization rate was more than 95%, and rare earths extraction rate above 99.5% has achieved. Therefore, the technology could replace ammonia-water to saponify the organic phase in rare earth extraction and separation process.

Multiple Model Predictive Control of Component Content in Rare Earth Extraction Process

Aiming at the complicated characteristic of rare earth extraction process and combining the material balan- ce model, a multiple models modeling and control method is proposed. Based on the data collected in an industrial field, an improved subtractive clustering algorithm is employed to obtain steady operation points for the process; the recuresi- ve least squares algorithm is adopted to identify submodel parameters and establish multiple linear models. According to the model switching index, an online optimal predictive model is obtained. And the efficiency of the model is verified by taking a certain rare earth company extraction as an ex- ample. In the end, generalized predictive controller of the corresponding sub-model is designed, so that component content is controlled in real time and accurately. Simulation results show the effectiveness of the method above.

A novel hybrid neural network for modeling rare-earth extraction process

Although neural networks are universal approximators, they are black-box models and it is difficult to obtain a suitable neural structure for a special unknown nonlinear system. Since many physical systems can be modeled by mechanistic models, we use these information to modify the normal neural identifiers and propose a novel neural modeling approach in this paper. This model includes a linear model which is linearized from the mechanistic model and a multilayer neural network. By Lyapunov stability approach, we prove that this hybrid neuro is stable in present of parameter and structure uncertainties. Then we apply these theory results on rare-earth extraction process. The results of application show that this new method can be used as soft-sensor of complex nonlinear systems.

Simulation of the development automatization control system for rare earth extraction process: Combination of ESRECE simulation software and EDXRF analysis technique

Recently, the requirement of developing the automatic control for rare earth extraction processes has been attracting a lot of attention. However, due to the similarity of chemical physical properties of rare earth, the separation of these elements requires a great number of stages of mixer-settles. The multistage countercurrent extraction system, consisted of two immiscible flowing phases—the organic phase and the aqueous phase and the variable composition take place stage by stage. This plant generally requires a continuous control the status of the separation process and its efficacy, in order to prevent appearing an ‘out-of-specification product’ or a high raffinate value. For studying the steady or dynamic performance of stage-wise processes, and establishing the relative controlling system, the on-line EDXRF analytical technique and ESRECE simulation system were used in our laboratory.