Syngas methanation with municipal solid waste char supported Ni catalyst: Choice of key parameters and catalyst's response

The low quality municipal solid wastes (MSW) derived char has a potential to be used as a methanation catalyst to achieve low-cost methanation of the syngas derived from MSW, and its performance with varying reaction parameters should be explored for better application. In this study, a MSW char supported Ni-based catalyst (Ni-MSWC) was prepared by impregnation; the influences of CO₂ and CH₄ impurities in the raw syngas on methanation and the feasibility of conducting methanation in a low-pressure condition were investigated, then the catalyst's response to the changing parameters was identified. The results showed that the presence of low concentration CO₂ in the H₂-rich syngas is favorable to the gasification of the char carrier and activates Ni-MSWC catalyst. However, it also promotes F–T synthesis reaction and leads to a decrease in the methane yield (Y_CH4). The decrease in reaction pressure causes a decrease in Y_CH4 and results in coke formation; but inhibits F–T synthesis reaction and increases methane selectivity (S_CH4). A higher reaction pressure increases system complexity and accelerates char carrier consumption. Moreover, presence of methane by 2.8% (vol.) promotes the methanation of CO₂ through the methane dry reforming reaction, but it inevitably causes coke formation and affects the catalyst's stability. Based on Y_CH4 and Ni-MSWC's responses, CO:CO₂ ≥ 3:2 and reaction pressure of 1 MPa (gauge pressure) are recommended, which help to inhibit the side reactions and maintain a high Y_CH4 (>95%).     

激光熔覆SiO2/La2O3梯度生物陶瓷涂层生物活性研究

目的研究SiO_2含量对钛合金表面激光熔覆梯度生物陶瓷涂层生物活性的影响。方法利用激光熔覆技术,采用梯度成分设计思想,固定涂层中稀土氧化物La_2O_3的添加量,在钛合金TC4表面制备了掺杂不同含量SiO_2的梯度生物陶瓷涂层。采用金相显微镜(OM)、X射线衍射仪(XRD)、扫描电子显微镜(SEM)、噻唑蓝(MTT)及荧光素双醋酸酯(FDA)染色等测试手段,研究了SiO_2含量对激光熔覆制备梯度涂层的组织结构和生物活性的影响。结果 SiO_2在激光熔覆过程中可以降低梯度生物陶瓷涂层的开裂敏感性,并起到细化晶粒的作用。当SiO_2掺杂量为2.5%时,激光熔覆过程中诱导合成的HA+CaTiO_3数量最大;当SiO_2掺杂量为7.5%时,模拟体液(SBF)实验表明,涂层的矿化沉积能力最强。MTT测试表明,SiO_2掺杂量为7.5%的涂层细胞增殖数量的OD值最大,细胞能够紧贴涂层表面生长。FDA染色分析表明,SiO_2掺杂量为7.5%的涂层上细胞数量最多,且分布均匀。结论 SiO_2掺杂量深刻影响着生物活性陶瓷相HA和Ca_2SiO_4数量,进而影响生物陶瓷涂层的生物活性。SiO_2掺杂量为7.5%的涂层具有最佳的生物相容性及生物活性。     

基于信噪比和灰关联度的电火花加工Ti-6Al-4V工艺参数优化

电火花加工Ti-6Al-4V是多目标工艺参数优化问题,影响其工艺指标的主要工艺参数有电流、间隙电压、脉冲宽度和工作因子。以材料去除率、电极损耗率和表面粗糙度为工艺指标项目,采用信噪比和灰色关联度分析方法分析正交试验结果,获得最优的工艺参数组合。试验结果表明,结合信噪比和灰色关联度分析方法,极大地简化多目标优化问题,提高Ti-6Al-4V的电火花加工质量。     

电火花加工Ti-6Al-4V合金表面组织和性能

采用电火花加工Ti-6Al-4V合金,并利用SEM、EDS、XRD、表面粗糙度仪等研究电火花加工参数电流和脉冲宽度对Ti-6Al-4V合金表面层的影响.结果表明,试验条件下,Ti-6Al-4V合金加工表面非均匀分布着裂纹、孔洞、大小不一的剥落点、放电凹坑、熔融滴、球状、非球状颗粒;Ti-6Al-4V合金白层由马氏体α′组成,其成分主要是TiC,其白层平均厚度随电流和脉冲宽度增大而增厚;表面粗糙度随电流或脉冲宽度增加而增大.     

Machine learning-assisted tri-objective optimization inspired by grey wolf behavior of an enhanced SOFC-based system for power and freshwater production

In recent years paying attention to the generation of clean and sustainable power and fresh water along with having lower cost and emission has increased. In the present research, a novel scheme for generating efficient power using the flame-assisted fuel cell is introduced, which has higher efficiency than ordinary fuel cells due to increased hydrogen concentration in the flame-rich combustion chamber. The waste heat is then introduced to a multi-effect desalination unit through a heat recovery steam generation unit to generate fresh, drinkable water. In order to make the system have higher efficiency, lower cost, and lower emission, the machine learning techniques are applied to optimize the operational conditions of the system, and find out the best solution point based on the cutting-edge algorithm of the grey wolf. Also, a complete techno-economic analysis and a parametric study are necessary to figure out the best solution point based on the TOPSIS method. The results indicate that the maximum value of exergy efficiency and drinkable water generation is 67.5% and 3.4 kg/s, respectively, while the minimum energy cost is 90.1 $/MWh. Moreover, results show that for the second optimization scenario considering the drinkable water production, energy cost, and pollution index as the objectives, the net produced power, energy efficiency, exergy efficiency, and water mass flowrate improve by around 1059 kW, 5.1%, 1.3%, and 1.6 kg/s than the design condition. Besides, energy cost and emission index are reduced by about 22 $/MWh and 51.9 kg/MWh, respectively.     

Mixed-valence molybdenum ion incorporated graphitic carbon nitride with high photocatalytic H2 evolution activity

The graphitic carbon nitride (CN) incorporated with mixed-valence molybdenum ion has been prepared via in-situ copolymerization to improve the photocatalytic H₂ evolution performance. The introduced Mo species existed in mixed valence of Mo⁴⁺ and Mo²⁺ state and its content could be tuned by simply adjusting amount of added MoCl₅ in the preparation procedure. The incorporated mixed-valence Mo ions contributed to narrowed band gap, increased electron density and elevated electron motion kinetics, resulting in extended visible light response, promoted separation and transportation of photoexcited charge carriers. The obtained CN–Mo photocatalyst with an optimal content of Mo ions (0.41 wt%) exhibited a robust H₂ production activity up to 1.44 mmol h⁻¹ g⁻¹, 18 times higher than that of pristine counterpart.     

An interpretable data-driven method for degradation prediction of proton exchange membrane fuel cells based on temporal fusion transformer and covariates

The durability of proton exchange membrane (PEM) fuel cells is an obstacle to industrialization. PEM fuel cells need a robust incentive to improve their durability. Traditional data-driven methods combined with deep learning for degradation prediction methods lack interpretability, which prevents users from fully trusting the results and limits the application. Moreover, traditional data-driven methods not making full use of the valuable information in the experimental data. This study presents an easy to interpret data-driven method that improves the reliability of the model, the heterogeneity of input features, and the effects of significant covariates. The method's primary objective is to provide a long- and short-term degradation prediction of PEM fuel cells for a temporal fusion transformer. This method achieves high accuracy in predicting the degradation performance of PEM fuel cells with a root mean square error of 0.0067. The model's root means square error is reduced by 58% compared to the attention-based gated recurrent unit model. Based on the interpretability analysis, the degradation performance directly correlates with power, the cathode inlet temperature, the anode inlet temperature, time, the cathode inlet pressure, and the cathode outlet pressure. The method can also be applied to fuel cell systems for optimizing control, management, and maintenance.