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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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103 records · Page 6

Comparison of a Full-Scale and a 1:10 Scale Low-Speed Two-Stroke Marine Engine Using Computational Fluid Dynamics

International marine shipping is a growing component of international trade; a vast majority of all the world’s goods are being transported on large ocean-going vessels. The International Maritime Organization (IMO) introduced the Energy Efficiency Design Index in 2013, a regulatory framework of associated metrics for reducing emissions of CO 2 per tonne-mile from shipping by approximately 10% each decade. Therefore, decarbonizing the maritime sector requires the development of new fuel sources. Because of the extremely large physical size of the internal combustion engines present in shipping vessels, experimental iterative development of the engine and fuel system is cost-prohibitive. Thus, the ability to perform combustion system development in a scaled platform that can be more easily operated and modeled computationally is of interest. To that end, scaling relationships are needed to translate the results from a smaller engine to a larger counterpart. Scaling studies to date have been restricted to low scaling ratios, four-stroke light-duty engines, and under-resolved computational fluid dynamic simulations that likely do not accurately capture the physics of scaling. In this work, computational models of a 1:10 scale and a full-scale two-stroke crosshead low-speed marine engine were created and validated against experiments obtained in a real 1:10 scale engine installed at Oak Ridge National Laboratory. Further, due to the large size of the full-scale engine, the model required large high-performance computing resources to be evaluated. The availability of high-performance computing resources at the Department of Energy’s Leadership Computing Facilities is an enabler of the current work. The results of the small- and large-scale engine simulations were compared to analyze the effectiveness of the appropriate scaling laws under these extreme scaling ratio conditions.

33 ADVANCED PROPULSION SYSTEMS↗

Optical Imaging for Understanding of Thermal Barrier Coated Piston Engine Performance

Thermal barrier coatings (TBC) applied to pistons have been a recently renewed research topic in the field of internal combustion engines. Single cylinder testing of a conventional C15™ Tier 4 final pro-duction steel piston and a TBC coated piston showed that the Yttrium stabilized Zirconia (YSZ) TBC did not significantly alter engine performance, with the TBC piston having slower combustion rates and higher criteria emissions. For deeper understanding, research continued by studying 3D-piston sections optically in a constant pressure vessel using photodiodes, four high-speed cameras (natural luminosity, CH*, and OH*), and visualization of the flame from two orthogonal directions. Particle Im-aging Velocimetry (PIV) algorithms were adapted to perform Combustion Imaging Velocimetry (CIV) to aid in quantification of the observed visual combustion flow field differences. The optical work showed that the TBC piston flame development is slower, spatially different, with lower mixing energy for the TBC variant. Geometric profiles of engine pistons and optical vessel 3D-piston sections were measured using a Coordinate Measurement Machine (CMM), and surface roughness was measured with a stylus surface profilometer. CMM results showed that the TBC piston bowl had reasonable macroscopic accuracy, but the rim had a larger radius with less reentrancy. A discussion of the results and analyses in relation to future TBC effort and direction concludes the work.

33 ADVANCED PROPULSION SYSTEMS↗

Ethanol to diesel: a sustainable alternative for the heavy-duty transportation sector

The combustion of middle distillates (diesel and jet fuel) is responsible for the emission of more than 2 GTon of CO 2 per year worldwide. While sustainable alternatives exist for gasoline and jet fuel, we still lack sustainable alternatives for diesel. This fact is especially relevant if we consider that electrification of sectors where diesel is used is challenging. One sustainable approach for diesel production is the catalytic upgrading of ethanol. While most work in this field has focused on the dehydration/oligomerization of ethanol, this approach is limited to producing fuels that have a high degree of branching and low cetane number. Another approach is the sequential use of ethanol Guerbet coupling, leading to higher alcohols, followed by etherification, leading to large ethers which, importantly, result in a product with high cetane number. In this work, we explore the catalytic upgrading of ethanol into diesel following an approach based on the initial Guerbet coupling of ethanol followed by etherification. The results presented are a collaborative and synergic effort among process and systems engineers, experimentalist in the area of catalysis, and fuel property modelers. Here, we demonstrate experimentally the feasibility of upgrading ethanol into a diesel fuel with properties that surpass its fossil counterpart. The diesel produced has a predicted cetane number of ~70 and outstanding cold flow properties, while maintaining other properties (viscosity, density, and flash point) within expected ranges. A technoeconomic analysis performed based on a detailed biorefinery model shows that the MFSP is ~5.89$ per Gal in 2021 dollars when lignocellulosic ethanol is used, with the most relevant economic driver the cost of the ethanol feedstock. The upgrading process can be performed with a net energy gain (EROI = 1.49 > 1). An LCA analysis of greenhouse gas (GHG) emissions reveals that the use of lignocellulosic ethanol may lead to more than 50% reduction in GHG emissions compared to fossil diesel. Depending on the CO 2 emissions associated with the production of ethanol we show that in some instances the production of carbon neutral diesel fuel is possible.

09 BIOMASS FUELS↗

Effects of Port Angle on Scavenging of an Opposed Piston Two-Stroke Engine

Opposed-piston 2-stroke (OP-2S) engines have the potential to achieve higher thermal efficiency than a typical diesel engine. However, the uniflow scavenging process is difficult to control over a wide range of speeds and loads. Scavenging performance is highly sensitive to pressure dynamics, port timings, and port design. This study proposes an analysis of the effects of port vane angle on the scavenging performance of an opposed-piston 2-stroke engine via simulation. A CFD model of a three-cylinder opposed-piston 2-stroke was developed and validated against experimental data collected by Achates Power Inc. One of the three cylinders was then isolated in a new model and simulated using cycle-averaged and cylinder-averaged initial/ boundary conditions. This isolated cylinder model was used to efficiently sweep port angles from 12 degrees to 29 degrees at different pressure ratios. Results indicate that scavenging performance is correlated with the bulk swirl ratio generated by these port angles. Scavenging performance is also sensitive to the pressure ratio across the engine. It was concluded that, for a given pressure ratio, the smallest port angle produces the best scavenging; however, previous work indicates lower bulk swirl ratio during combustion can have an impact on emissions formation.

33 ADVANCED PROPULSION SYSTEMS↗

Role of Inlet Boundary Conditions on Fuel-Air Mixing at Supercritical Conditions

Advanced engine design and alternative fuels present the possibility of fuel injection at purely supercritical conditions in diesel engines and gas turbines. The complex interactions that govern this phenomenon still need significant research, particularly the boundary conditions for fuel injection are critical for accurate simulation. However, the flow inside the injector itself is often omitted to reduce the computational efforts, and thus, velocity, mass flux, or total pressure is specified at the injector exit (or domain inlet), often with simplified velocity profiles and turbulence levels. This simplified inlet boundary treatment has minimal effects on results for conventional fuel injection conditions, however, the validity of this approach at supercritical conditions has not been assessed. Comprehensive real-gas and binary fluid mixing models have been implemented for computational fluid dynamics (CFD) analysis of fuel-air mixing at supercritical conditions. The model is verified using prior CFD results from the literature. The model is used to investigate the effects of the shape of axial velocity and mass fraction profiles at the inlet boundary with the goal to improve the comparison of predictions to experimental data. Results show that the boundary conditions have a significant effect on the predictions, and none of the cases match precisely with experimental data. Furthermore, the study reveals that the physical location of the inlet boundary might be difficult to infer correctly from the experiments and highlights the need for high-quality, repeatable measurements at supercritical conditions to support the development of relevant high-fidelity models for fuel-air mixing.

02 PETROLEUM↗

Gas and Propane Combustion from Stoves Emits Benzene and Increases Indoor Air Pollution

Exposure pathways to the carcinogen benzene are well-established from tobacco smoke, oil and gas development, refining, gasoline pumping, and gasoline and diesel combustion. Combustion has also been linked to the formation of nitrogen dioxide, carbon monoxide, and formaldehyde indoors from gas stoves. To our knowledge, however, no research has quantified the formation of benzene indoors from gas combustion by stoves. Across 87 homes in California and Colorado, natural gas and propane combustion emitted detectable and repeatable levels of benzene that in some homes raised indoor benzene concentrations above well-established health benchmarks. Mean benzene emissions from gas and propane burners on high and ovens set to 350 °F ranged from 2.8 to 6.5 μg min –1 , 10 to 25 times higher than emissions from electric coil and radiant alternatives; neither induction stoves nor the food being cooked emitted detectable benzene. Benzene produced by gas and propane stoves also migrated throughout homes, in some cases elevating bedroom benzene concentrations above chronic health benchmarks for hours after the stove was turned off. Combustion of gas and propane from stoves may be a substantial benzene exposure pathway and can reduce indoor air quality.

54 ENVIRONMENTAL SCIENCES↗

Detailed mixing measurements from single-hole converging ECN injectors using Rayleigh scattering

Time resolved liquid and vapor fields of dodecane and oxymethylene ethers are measured from Spray A-3 and Spray D using high speed Rayleigh scattering and diffuse back illumination at the Engine Combustion Network (ECN) Spray A condition of 900 K and 22.8 kg/m 3 . Global quantities including mixture fraction, vapor and liquid penetration, as well as spreading angle are measured. The mixture fraction fields and vapor penetration profiles are well predicted by the 1-D Musculus-Kattke model. The mixture fraction field and vapor penetration from Spray A-3 are similar to those measured from Spray A in previous works. Spray D exhibits higher mixture fraction fields and vapor penetration due to its larger nozzle diameter. The quasi-steady mixture fraction fields from these injectors scale well when distance from the injector is normalized by the nozzle diameter. The turbulent dissipation structures were also analyzed based on the orientation, thickness, and magnitude of the mixing layers. Furthermore, the orientation and thickness are similar to other measurements in atmospheric gas jets, while the magnitude is lower. The thickness and magnitude are subject to uncertainties due to limitations in the imaging resolution of the system but still provide an order of magnitude as a useful reference for comparison against computational fluid dynamic simulations.

33 ADVANCED PROPULSION SYSTEMS↗

Evaluation of thermal processing and properties of 422 martensitic stainless steel for replacement of 4140 steel in diesel engine pistons

The thermal and mechanical properties of martensitic stainless steel 422 were evaluated for suitability as a drop-in replacement for 4140 steel in next generation heavy-duty diesel engine (HDDE) pistons. The time and temperature of the austenitization and tempering steps were studied to achieve optimum materials performance in piston applications, including the balance of thermal and mechanical properties and resistance to long-term thermal aging. Reducing the tempering temperature from 700 to 600 °C caused a substantial increase in elevated temperature strength from 25 to 600 °C, but had no significant influence on thermal conductivity, suggesting that thermal conductivity in 422 is dominated largely by composition and distribution of alloying elements and mostly independent of the sub-grain structure size and precipitate size. Compared to the current HDDE piston alloy 4140, 422 exhibits substantially higher elevated temperature strength and lower thermal conductivity, the latter which will cause 422 to operate at higher temperatures in pistons, possibly requiring a piston redesign to take advantage of the improved high temperature strength of 422. Piston material selection and alloy design strategies with potential to mitigate some of the shortcomings of martensitic stainless steels, such as 422, as drop-in replacements are discussed.

36 MATERIALS SCIENCE↗

Hydrogen Fuel Cell Vehicles in Tunnels

There are numerous vehicles which utilize alternative fuels, or fuels that differ from typical hydrocarbons such as gasoline and diesel, throughout the world. Alternative vehicles include those running on the combustion of natural gas and propane as well as electrical drive vehicles utilizing batteries or hydrogen as energy storage. Because the number of alternative fuels vehicles is expected to increase significantly, it is important to analyze the hazards and risks involved with these new technologies with respect to the regulations related to specific transport infrastructure, such as bridges and tunnels. This report focuses on hazards presented by hydrogen fuel cell electric vehicles that are different from traditional fuels. There are numerous scientific research and analysis publications on hydrogen hazards in tunnel scenarios; however, compiling the data to make conclusions can be a difficult process for tunnel owners and authorities having jurisdiction over tunnels. This report provides a summary of the available literature characterizing hazards presented by hydrogen fuel cell electric vehicles, including light-duty, medium and heavy-duty, as well as buses. Research characterizing both worst-case and credible scenarios, as well as risk-based analysis, is summarized. Gaps in the research are identified to guide future research efforts to provide a complete analysis of the hazards and recommendations for the safe use of hydrogen fuel cell electric vehicles in tunnels.

33 ADVANCED PROPULSION SYSTEMS↗

Data-driven selection of stiff chemistry ODE solver in operator-splitting schemes

Most computational fluid dynamics simulations of practical combustion applications employ operator-splitting schemes, where chemistry and transport are separated and integrated with distinct numerical methods. The changes in composition due to chemistry are evaluated by solving ordinary differential equations (ODE) in each cell of the computational domain, which typically dominates the computational cost when detailed chemistry is considered. In this work, a data-driven approach for the selection of chemistry ODE solvers in operator-splitting schemes is presented. Neural networks are used to predict the ODE solvers CPU times and errors for a given thermochemical state. This allows the selection of an optimal ODE solver on a cell-by-cell, timestep-by-timestep basis. The models are trained using a wide set of thermochemical states generated through partially-stirred reactors and flames simulations. The methodology is validated by quantifying the prediction errors, the classification accuracy, and the computational speedup. The model predicts the optimal ODE solver for 70 to 95% of the validation cases and decreases the computional cost by a factor of 3 or more. The generalizability of the methodology to different chemical mechanisms and different fuels is assessed and it is shown that the model’s performance is only slightly degraded and its applicability is significantly enhanced if the inputs to the neural networks are restricted to a small set of thermochemical state variables present in most chemical mechanisms. In conclusion, the models are used in an homogeneous reactor case and a multi-dimensional CFD simulation of a diesel spray at high pressure where a speedup of more than 3 is achieved.

42 ENGINEERING↗

Development of simplified model for injection rate prediction of diesel injectors during transient and steady operation

Determining the injection rate of the diesel injector during the transient short injection operations is a critical matter to enhance the combustion process by improving air/fuel mixture formation. In this regard, numerous injection models have been suggested in the literature to predict the injection rate accurately. However, such algorithms have not precisely predicted the rate of injection (ROI) in transient short injection events. In addition, most of those models require many input parameters to characterize hydraulic and mechanical subsystems in the injector, which can make them inconvenient to tune for different injector specifications, in other words not suitable for universal usage. Therefore, our study aims to develop a simple injection model that can predict injection rate precisely in the short and long injection durations considering relatively fewer input parameters. The model traces the pressure variation inside the sac based on the bulk modulus theory to predict nozzle exit velocity, thus the injection rate of the injector. In the algorithm, the bulk modulus of fuel (E) and discharge coefficients of the nozzle hole and needle seat (C d,H and C d,N ) have been considered to vary rather than set constant during the injection process since such flow parameters would have a critical impact on the flow development at the transient stage, thus affecting the injection rate of diesel injectors. Here with this modelling strategy, the accuracy of the new model on ROI prediction was confirmed with acceptable model error for both short and long injection durations by validating against experimental results. To understand the effect of the new model scheme on ROI prediction, ROI results with the new model have been discussed by comparing it with the ROI results predicted with constant flow parameters.

42 ENGINEERING↗

High-dimensional Data-driven Energy optimization for Multi-Modal Transit Agencies (HD-EMMA) (Final Technical Report)

Public bus transit services in the U.S. are responsible for at least 19.7 million metric tons of CO 2 emission annually. Electric vehicles (EVs) can have a much lower environmental impact than comparable internal combustion engine vehicles (ICEVs), especially in urban areas. Unfortunately, EVs are also much more expensive than ICEVs. As a result, many public transit agencies can afford only mixed fleets of transit vehicles, consisting of EVs, hybrids (HEVs), and ICEVs. Transit agencies that operate such mixed fleets of vehicles face a challenging optimization problem: these agencies need to decide which vehicles are assigned to serving which transit trips. Since the advantage of EVs over ICEVs varies depending on the route and time of day (e.g., the benefit of EVs is higher in slower traffic with frequent stops and lower on highways), the assignment can have a significant effect on energy use and, hence, environmental impact. Through this project, we have developed reference data about energy collections and constructed a set of machine learning models that can accurately predict the energy consumption for the whole fleet at the level of each trip. We have used these models to develop a scheduling and assignment strategy that can rotate the different vehicle types across the transit agencies’ routes. The optimization algorithm ensures that the vehicles are matched to trips considering weather patterns, expected congestion, and road gradients to minimize the overall energy usage. We list the key observations from our project for other practitioners below. Details are available in the report, and the list of source code and our publications are included in the appendix. 1. We have demonstrated the feasibility of collecting, merging and analyzing large volumes of high-resolution real-world telemetry data from a mixed vehicle fleet. To mitigate the inherent noise of the recorded GPS points, the team developed an algorithm that filters data and maps the points onto a street. The algorithm considers previous and subsequent location measurements and different characteristics of nearby streets to determine how likely the vehicle travels on them. Then, the team segmented the time series into disjoint contiguous samples based on adjacent road segments and repeated the outlier detection and removal. For each data point, the team added features corresponding to elevation changes within the samples, weather features, such as temperature, and traffic data, such as speed ratio between actual speed and free-flow speed. 2. We have developed two forms of machine learning models that be used to understand and analyze the energy operations of a mixed vehicle transit fleet. The micro prediction model provides estimates of instantaneous energy prediction for all types of buses (diesel, hybrid, and electric). Such a model is important in evaluating the energy impacts of real-time bus operation strategies, but it is challenging due to diversified driving cycles of transit buses. The model can help the drivers understand the impact of their driving behaviors and short-term congestions. The macro prediction models estimate average energy consumption across the whole trip considering the features: distance traveled, various road-type features, elevation change, day of the week, time of day, various weather features (temperature, humidity, etc.), and traffic features (speed ratio and jam factor). 3. We have demonstrated that it is possible to transfer the machine learning models we have developed in this project to other teams and cities by using inductive transfer learning. We also showed that the performance of the macro energy prediction models can be improved using a multi-task learning approach where the learning parameters are shared between the models being developed for different vehicle types. The advantage of this approach is improved learning performance as the models can exploit common spatio-temporal and environmental characteristics. 4. Finally, we have developed trip and vehicle assignment and scheduling algorithms that use the energy prediction models and develop a trip to vehicle type (diesel, electric, hybrid) assignment for the whole operation to reduce overall emissions and cost. We have shown through simulations that the proposed algorithms can save $\$$ 48,910 in energy costs and 175 metric tons of CO 2 emission annually for CARTA.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Physical, Chemical, and Mineralogical Characterizations of MSWI Ash Product and Recommendations for Downstream Processing

The primary objectives of this project are to (1) systematically characterize MSWI ash, and (2) based on characterization findings, design preliminary flowsheets for downstream processing. To achieve these objectives, a total of ten tasks were completed, including sample collection, physical separation tests, liberation tests, synthetic MSWI ash preparation, elemental composition analysis, sequential chemical extraction, mineralogical characterization, pozzolanic activity characterization, thermal stability characterization, processing flowsheet design, TEA and T2M, and project performance reporting. Many useful findings and conclusions were obtained from the exhaustive efforts of this project from several different aspects, including: a) Valuable Metals in MSWI Ash: MSWI ash contains a diverse array of valuable metals. Based on potential recoverable values, the most valuable metals present in MSWI ash include Fe, Ti, Mn, Cu, Zn, V, Co, Ni, Sr, Sn, Ag, Mo, and Sc. Some of these metals have been identified as critical minerals by DOE and DOI, suggesting that MSWI is a promising feedstock for critical mineral recovery. Noticeable graphical and seasonable variations in the valuable metal content of MSWI ash were observed. Nevertheless, it was challenging to discern any clear, definitive patterns for conclusions from those observations. Compared with bottom ash, fly ash contains more volatile metals, such as Zn and Sn, but less nonvolatile metals, such as Fe, Mn, Cu, Zn, Co, and Ni. Mineralogical analyses showed that MSWI ash contains a substantial amount of calcium minerals, such as portlandite, lime, gypsum, and calcite. In addition, it was found that different types of valuable metals often exist in the same particles. b) Physical Separation of MSWI Ash: Both dry sieving and wet sieving were performed on MSWI ash. A notable disparity in the size distribution of the same material was observed when using the two different sieving methods. The disparity is due to the agglomeration of small particles. For the valuable metals investigated, no significant enrichment in a specific size fraction was observed, suggesting that it is challenging to preconcentrate the valuable metals through size fractionation. Due to the presence of ferromagnetic materials, such as Fe, most of the materials reported to the magnetic products obtained by dry magnetic separation. However, the enrichment effect is minimal due to the existence of particle agglomerates. Density separation at a cut-off density of 2.7 SG or higher led to noticeable enrichment of selected valuable metals, particularly Ti. The unburned carbon present in MSWI ash was effectively removed by flotation using diesel as the collector. A novel reagent scheme, Na2S plus cationic collectors, that can efficiently beneficiate nonferrous metals plus Co was developed. c) Liberation Tests: The particle size of MSWI ash was effectively reduced by grinding, and as a result, the encapsulated valuable metal particles (if any) were liberated to a certain degree. However, particle size reductions did not noticeably enhance the beneficiation performance using the physical separation methods, primarily due to the inefficiency of these methods in processing fine particles and/or a possibility that insufficient liberation is not a limiting factor for achieving satisfactory physical separation performance. Valuable metals were classified into water leachable, ion-exchangeable, acid soluble, reducible, oxidable, and insoluble forms. It was found that the distributions in the different categories, i.e., the occurrence modes of the valuable metals, were not affected by the particle size. d) Leaching Characteristics of Metals from MSWI Ash: Most of the valuable metals were extracted from the fly ash samples when using 1 M HCl or HNO3 as the lixiviant. The leaching reaction is a very fast process, which can reach equilibrium within the first 5 min. The releasing of Co, Ni and Ag are sensitive to leaching temperature, a higher recovery value could be obtained when using relatively higher leaching temperatures. The leachability of the valuable metals present in MSWI bottom ash is relatively lower than that of fly ash. Leaching recoveries increased with elevations in the acid concentration. Relatively high leaching recoveries were obtained for REEs, Mn, Co, Ni, Cu, and Zn using 1 M HCl or HNO3 as the lixiviant. Elevations in the reaction temperature noticeably increased the leachability of the valuable metals, whereas the leachability was barely influenced by oxidizing and reducing agents. Similar to fly ash, leaching valuable metals from bottom ash is a rapid process, with most of the leaching reaction completed within the first 5 minutes. e) Combusted iPhones: The original structure of iPhones was remained after treating at 400 ºC and 600 ºC, while after being treated at 800℃, the screen bent, and the back cover of iPhone melted. Increasing the combustion temperature to 1000℃, the screen scattered, and most of the components turned into ashes. Combustion enhanced the leachability of REEs, while the leachability of the other valuable metals, except for Zn, was barely affected. Most of the REEs present in the original iPhones occurred as oxidizable forms. With elevations in the combustion temperature up to 600 ºC, the oxidizable REEs were transformed to acid soluble forms. However, further elevations in temperature resulted in decreases in the acid soluble fraction and corresponding increases in the reducible and oxidizable forms. Additionally, combustion temperature also significantly altered the occurrence modes of other metals present in the iPhones. f) Synthetic MSWI Ash: It was found that in the absence of hydrogen peroxide, all the elements except for Si were leached to certain degrees. It is noteworthy that approximately 80% of Zn was leached with 1.2 M HCl. When hydrogen peroxide was added to the reaction system, noticeable increases in the leaching recovery of Fe, Mn, Co, Ni, and Cu were observed. The leaching recovery of Al and Si was barely affected by adding hydrogen peroxide. These results suggested that the majority of Zn in the synthetic MSWI ash existed as metal oxide, a portion of Fe, Mn, Co, Ni, and Cu existed as metal oxide, and Al and Si are associated with glasses which are difficult to leach. Additionally, the remaining Fe, Mn, Co, Ni, and Cu in the metallic form were efficiently oxidized in the presence of hydrogen peroxide. g) Pozzolanic Activity and Thermal Stability of MSWI Ash: MSWI fly ash has higher pozzolanic activity compared to the bottom ash sample, which indicates that the fly ash sample consumed more portlandite because of its smaller particle size as reactivity fundamentally relates to reaction surface area. However, after the recovery of valuable elements, the pozzolanic activity of both the valuable elements fraction and the less valuable elements-rich products decreased significantly, which means that the valuable elements recovery lowers the Ca(OH)2 consumption, thus leading to the low activity of SCM. h) Flowsheet Design for Metal Recovery from MSWI Ash: Based on the results of the comprehensive physical separation and acid leaching tests, circuits that enable the beneficiation of the valuable metals were developed. In these circuits, the valuable metals are recovered into nonferrous, ferrous, and other valuable metal concentrates, which are processed separately in the acid leaching step. The subsequent separation and purification steps are simplified due to the physical beneficiation step. In addition, the overall recovery cost is reduced since physical beneficiation is much cheaper compared with chemical processing. Using different technologies, such as selective precipitation and solvent extraction, a comprehensive hydrometallurgical circuit was designed, and compounds of Cu, Zn, Mn, Co, and Ni with a purity close to or even higher than 95% were successfully generated.

36 MATERIALS SCIENCE↗