Effects of particle sizes on performances of the multi-zone steam generator using waste heat in a bio-oil steam reforming hydrogen production system

Hydrogen production by bio-oil steam reforming is an advanced production technology. It is a good method of coupling waste heat utilization with bio-oil steam reforming to produce hydrogen, which increases the cleaning ability of the bio-oil steam reforming system. A multi-zone steam generator using waste heat has been proposed, which can produce the heat source and steam source of the hydrogen system. The DEM model of the multi-zone steam generator was set up. The model has been used to investigate the effects of particle sizes (40 mm–80 mm). With increasing particle size, the flow index and the flow uniformity gradually decrease, the vertical velocity gradient increases in the area on both side with the zone steam generator, and the vertical velocity fluctuation amplitude gradually increases. So, the hydrogen production decreases from the particle size increasing.     

Predicting elastic modulus of porous La0.6Sr0.4Co0.2Fe0.8O3-δ cathodes from microstructures via FEM and deep learning

In this work, a deep learning accelerated homogenization framework is developed for prediction of elastic modulus of porous materials directly from their inner microstructures. The finite element method (FEM) and the homogenization theory are used to obtain the macroscopic properties of materials based on their microstructures. Based on a large dataset consisting of various microstructures and corresponding elastic properties via FEM, a deep convolutional neural network (CNN) is trained to capture the nonlinear functional relationship between the microstructure features and their macroscopic elastic properties. The deep learning model is finally well validated against extra new samples with excellent predictive performances. This demonstrates that the CNN deep learning model can be trusted as a surrogate model for the FEM based homogenization method, with the computation time being reduced by several orders of magnitude. The proposed deep learning framework is highly extendable for prediction of various macroscopic properties from microstructures.     

Biohythane production from organic waste: Recent advancements,technical bottlenecks and prospects

The availability of fossil fuels is a major factor that determines the economy of a country. However, possible exhaustion of fossil fuel deposits as well as increased pollution, and other adverse effects on the environment has prompted us to search for alternative fuels. This resulted in the development of hythane, a blend of hydrogen with methane, at concentrations of 10%–30%. The breakdown of organic substrates using sequential dark fermentation (DF) and anaerobic digestion (AD) leads to biohythane production. The quality and quantity of biohythane can be improved by altering the following aspects: selection, development, and/or genetic engineering of suitable microbial consortium; the use of cheap, appropriate substrates; improved design of bioreactors; and the implementation of two-stage fermentation system. This review focusses on the mechanism of biohythane production and the different aspects involved in increasing both its production rate and quality. A comparative study has also been done to demonstrate the superiority of biohythane over other biofuels.     

Chromium evaporation and oxidation characteristics of alumina-forming austenitic stainless steels for balance of plant applications in solid oxide fuel cells

The chromium (Cr) evaporation behavior of several different types of iron (Fe)-based AFA alloys and benchmark Cr₂O₃-forming Fe-based 310 and Ni-based 625 alloys was investigated for 500 h exposures at 800 °C to 900 °C in air with 10% H₂O. The Cr evaporation rates from alumina-forming austenitic (AFA) alloys were ~5 to 35 times lower than that of the Cr₂O₃-forming alloys depending on alloy and temperature. The Cr evaporation behavior was correlated with extensive characterization of the chemistry and microstructure of the oxide scales, which also revealed a degree of quartz tube Si contamination during the test. Long-term oxidation kinetics were also assessed at 800 to 1000 °C for up to 10,000 h in air with 10% H₂O to provide further guidance for SOFC BOP component alloy selection.     

Modified La0.6Sr0.4Co0.2Fe0.8O3-δ cathodes with the infiltration of Er0.4Bi1.6O3 for intermediate-temperature solid oxide fuel cells

Aiming to lower the activation energy and expedite the oxygen reduction reaction (ORR) process of La_0.6Sr_0.4Co_0.2Fe_0.8O_3-δ (LSCF) cathodes for application in intermediate-temperature solid oxide fuel cells (IT-SOFCs), Er_0.4Bi_1.6O₃ (ESB) modified LSCF was prepared by infiltrating using organic solvents. The infiltration of ESB dramatically reduces the polarization resistances of LSCF cathodes (from 0.27 to 0.11 Ω cm² at 700 °C, from 0.58 to 0.25 Ω cm² at 650 °C), and lowers their activation energy (from 100.28 to 97.15 kJ mol^?1). Also, ESB makes the rate-limiting step of LSCF cathodes at high frequency change from the charge transfer process on the cathode to the adsorption and diffusion of oxygen on cathode surface. The single cell with ESB infiltrated LSCF cathodes shows a peak power density of 469 mW cm^?2 at 700 °C using humid hydrogen and air as fuels and oxidants, respectively, as well as a good short-term stability for 50 h.     

Perovskite materials for highly efficient catalytic CH4 fuel reforming in solid oxide fuel cell

SOFC (solid oxide fuel cell, SOFC) is recognized to be efficient green energy technology in the 21st century. However, when hydrocarbons are directly used as fuel, carbon deposition is easy to occur in Ni-based anode, thus losing electrochemical catalytic activity. Fuel pre-reforming is also called on-cell reforming of hydrocarbons, which has been a promising solution for alleviating the carbon deposition problem in cermet anodes to varying degrees. And the key factor is to find an efficient and stable fuel reforming catalyst. Perovskite oxides have stable structure, highly catalytic activity and adjustable thermal expansion coefficient for using on the cells, showing great potentials of application for fuel reforming. In this paper, we summarize the application of perovskite catalyst in CH₄ fuel reforming based on the research of our group and other scholars, and puts forward the corresponding views and perspective, especially in perovskite catalyst with Ni exsolution.     

Performance improvement of microbial fuel cell through artificial intelligence

The current work introduces an enhancement in the performance of the microbial fuel cell through estimating the optimal set of controlling parameters. The maximization of both power density (PD) and the percentage of chemical oxygen demand (COD) removal were considered as the enhancement in the cell's performance. Three main parameters in terms of performance as well as commercialization are the system's inputs; the Pt which takes the range of 0.1‐0.5 mg/cm², the degree of sulphonation in sulfonated‐poly‐ether‐ether‐ketone that changes in the range of 20‐80%, and the rate of aeration of cathode which varies between 10 and 150 mL/min. From the experimental dataset, two robust adaptive neuro‐fuzzy inference system models based on the fuzzy logic technique have been constructed. The comparisons between the models' outputs and the experimental data showed well‐fitting in both training and testing datasets. The mean squared errors of the PD model, for testing and whole datasets, were found 2.575 and 0.909 while for the COD model it showed 19.242 and 6.791, respectively. Then, based on the two fuzzy models, a Particle Swarm Optimization algorithm has been used to determine the best parameters that maximize both of the PD and the COD removal of the cell. The optimization process was utilized for single and multi‐object optimization processes. In the single optimization, the resulting maximums of the PD and the COD removal were found 62.844 (mW/m²) and 99.99 (%), respectively. Whereas, in the multi‐object optimization, the values of 61.787 (mW/m²) and 96.21 (%) were reached as the maximums for the PD and COD, respectively. This implies that, in both cases of optimization processes, the adopted methodology can efficiently enhance the microbial fuel cell performances than the previous work.     

Effect of grain size on the electrical performance of BaZr0.1Ce0.7Y0.1Yb0.1O3-δ solid electrolytes with addition of NiO

In order to clarify the effect of grain size on the electrical performance of BaZr_0.1Ce_0.7Y_0.1Yb_0.1O_3-δ (BZCYYb) solid electrolytes with addition of NiO, microcrystalline (~1.5?µm) and ultrafine-grained (~280?nm) BZCYYb electrolytes (with 1?wt% NiO) were fabricated by the conventional and two-step sintering method, respectively. The results show that compared with microcrystalline electrolytes, the ultrafine-grained electrolytes have similar grain-interior conductivities, but much lower grain-boundary conductivities, illustrating that the grain boundary is not conducive for ionic transport. As a result, the electrical conductivity of microcrystalline electrolytes (1.9?×?10^?2 S?cm^?1 at 600?°C in wet air) is higher than that of ultrafine-grained electrolytes (1.1?×?10^?2 S?cm^?1 at 600?°C in wet air). In addition, the OCV (open-circuit voltage) values of electrolyte-supported single cells show that the undesired electronic conduction exists in the electrolytes due to the BaY₂NiO₅ impurity formed by the reaction of NiO and BZCYYb. The ultrafine-grained electrolytes show lower OCV values than that of microcrystalline ones, due to the prolonged electronic transport paths. Therefore, large-grained or grain boundary-free microstructure are necessary for improving the electrical performance of BZCYYb electrolytes.     

Recent progress in LiF materials for safe lithium metal anode of rechargeable batteries: Is LiF the key to commercializing Li metal batteries?

The lithium metal battery has attracted considerable attention as the ultimate lithium secondary battery for high energy density. However, safety issues and battery performance deterioration due to the growth of lithium dendrites have hampered the practical use of lithium metal batteries. Recently, lithium fluoride has been considered as a lithium metal protective layer to solve this problem. In this review, firstly, the results of the studies on dendrites and SEI that have been carried out to date are reviewed. Secondly, the results of studies on lithium fluoride are divided into additive, artificial SEI, and other methods and the possibilities of their practical use are discussed. Finally, the significance and limitations of the lithium fluoride studies are summarized, and general conclusions and prospects for recommended research directions to accelerate the commercialization of lithium metal batteries are presented.     

Utilization of linseed cake as a postagricultural functional filler for poly(lactic acid) green composites

Linseed cake (LC), a byproduct of linseed oil extraction, is used as a functional filler for production of biodegradable composites. To determine the influence of residual linseed crude oil contained in lignocellulosic filler on the properties of the poly(lactic acid) (PLA)-based composites with 5–30% filler content, two types of LC were analyzed: a defatted and an unmodified one. Complex analysis of the composites' properties change was conducted in relation to their structure modification caused by the addition of a waste filler. It was found that the addition of LC resulted in simultaneous plasticization and improved crystallization of PLA. Lignocellulosic particles and crude linseed oil contained in the LC powder provided a modifying effect, influencing the level of crystallinity and mechanical and thermomechanical properties. Using LC may thus overcome one of the main drawbacks of PLA, which is brittleness and low crystallinity. © 2018 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 47152.