Robust optimal operation of continuous catalytic reforming process under feedstock uncertainty

The continuous catalytic regenerative (CCR) reforming process is one of the most significant sources of hydrogen production in the petroleum refining process. However, the fluctuations in feedstock composition and flow rate could significantly affect both product distribution and energy consumption. In this study, a robust deviation criterion based multi-objective optimization approach is proposed to perform the optimal operation of CCR reformer under feedstock uncertainty, with simultaneous maximization of product yields and minimization of energy consumption. Minimax approach is adopted to handle these uncertain objectives, and the Latin hypercube sampling method is then used to calculate these robust deviation criteria. Multi-objective surrogate-based optimization methods are next introduced to effectively solve the robust operational problem with high computational cost. The level diagram method is finally utilized to assist in multi-criteria decision-making. Two robust operational optimization problems with different objectives are solved to demonstrate the effectiveness of the proposed method for robust optimal operation of the CCR reforming process under feedstock uncertainty.     

超高效液相色谱-串联质谱法同时检测糕点中6种甜味剂

目的建立超高效液相色谱-串联质谱法同时测定糕点中6种常用合成甜味剂的分析方法。方法选用超纯水作为提取溶剂,涡旋和超声提取后,低温离心,取部分上清液加入正己烷除脂,Waters Atlantis■T3色谱柱、甲醇-5 mmol/L甲酸铵(含0.1%甲酸)作为流动相、亲水亲脂平衡型固相萃取柱HLB(hydrophile-lipophile balance)净化。结果6种甜味剂在质量浓度为10~200 ng/mL的曲线范围内呈良好线性关系,相关系数r均大于0.999,平均加标回收率在85.0%-98.2%之间,相对平均偏差(relative standard deviation,RSD)为1.3%~6.7%。结论该方法具有前处理简单、灵敏度高、检测速度快等优点,适合糖精钠、甜蜜素、三氯蔗糖、阿斯巴甜、阿力甜、纽甜的检测,但不适用于安赛蜜的检测。     

Enhanced electrochemical performance of LiNi0.8Co0.1Mn0.1O2 via titanium and boron co-doping

Titanium and boron are simultaneously introduced into LiNi_0.8Co_0.1Mn_0.1O₂ to improve the structural stability and electrochemical performance of the material. X-ray diffraction studies reveal that Ti⁴⁺ ion replaces Li⁺ ion and reduces the cation mixing; B³⁺ ion enters the tetrahedron of the transition metal layers and enlarges the distance of the [LiO₆] layers. The co-doped sample has spherical secondary particles with elongated and enlarged primary particles, in which Ti and B elements distribute uniformly. Electrochemical studies reveal the co-doped sample has improved rate performance (183.1 mAh·g^-1 at 1 C and 155.5 mAh·g^-1 at 10 C) and cycle stability (capacity retention of 94.7% after 100 cycles at 1 C). EIS and CV disclose that Ti and B co-doping reduces charge transfer impedance and suppresses phase change of LiNi_0.8Co_0.1Mn_0.1O_2.     

Optimized microwave absorption properties by tailoring the morphology of carbon coated TiC nanoparticles by N2 pressure

Increasing the dielectric loss capacity plays an important role in enhancing the electromagnetic absorption performance of materials. It remains a challenge to simultaneously introduce multiple types of dielectric losses in the material. In this work, we show that the atomic and interfacial dipole polarizations can be simultaneously enhanced by substituting N species into both carbon coating layers and bulk TiC lattices of a core-shell TiC@C material. Additionally, substitution of N species results more exposed TiC(111) facets and refines the TiC grain sizes in the bulk material, which is beneficial for enhancing the scattering of the external electromagnetic waves. The maximum reflection loss of the N substituted TiC@C material is measured as ?47.1 dB with an effective absorbing bandwidth of 4.83 GHz at 1.9 mm, which illustrates a valuable way to further tuning the electromagnetic absorption performance of this type of materials.     

Development of an SFMM/CGO composite electrode with stable electrochemical performance at different oxygen partial pressures

This paper carefully evaluates the electrocatalytic activity of Sr₂FeMo_0.5Mn_0.5O₆ (SFMM) double perovskite as a candidate to substitute the state-of-the-art Ni/YSZ fuel electrode. The electrochemical performance of a 40% SFMM/CGO composite electrode was studied in CO/CO₂ and H₂ with different oxygen partial pressure. Two different cell configurations are prepared at a relatively low temperature of 800 °C to increase the electrochemically active surface area. The cell was supported with a 150 μm 10Sc1CeSZ electrolyte in the first configuration. The cell in the second configuration was made by applying a 400 nm thin 8YSZ layer on 150 μm CGO electrolyte to improve the electrolyte ionic conductivity. Improving catalytic activity with increasing oxygen partial pressure is a key characteristic of the developed electrode. The polarization resistance of about 0.34 and 0.56 Ω cm² at 750 °C in 3%H₂O + H₂ and 60% CO/CO₂ makes this electrode a promising candidate for SOCs application.     

Effect of MoS2/PTFE coatings on performance of Si3N4/TiC ceramics in dry sliding against WC/Co

To enhance the tribological performance of Si₃N₄/TiC ceramics, MoS₂/PTFE composite coatings were deposited on the ceramic substrate through spraying method. The micrographs and basic properties of the MoS₂/PTFE coated samples were investigated. Dry sliding friction experiments against WC/Co ball were performed with the coated ceramics and traditional ones. These results showed that the composite coatings could significantly reduce the friction coefficient of ceramics, and protect the substrate from adhesion wear. The primary tribological mechanisms of the coated ceramics were abrasive wear, coating spalling and delamination, and the tribological property was transited from slight wear to serious wear with the increase of load because of the lower surface hardness and shear strength. The possible mechanisms for the effects of MoS₂/PTFE composite coatings on the friction performance of ceramics were discussed.     

Identifying catalyst layer compositions of proton exchange membrane fuel cells through machine-learning-based approach

Membrane electrode assembly (MEA) is considered a key component of a proton exchange membrane fuel cell (PEMFC). However, developing a new MEA to meet desired properties, such as operation under low-humidity conditions without a humidifier, is a time- and cost-consuming process. This study employs a machine-learning-based approach using K-nearest neighbor (KNN) and neural networks (NN) in the MEA development process by identifying a suitable catalyst layer (CL) recipe in MEA. Minimum redundancy maximum relevance and principal component analysis were implemented to specify the most important predictor and reduce the data dimension. The number of predictors was found to play an essential role in the accuracy of the KNN and NN models although the predictors have self-correlations. The KNN model with a K of 7 was found to minimize the model loss with a loss of 11.9%. The NN model constructed by three corresponding hidden layers with nine, eight, and nine nodes can achieve the lowest error of 0.1293 for the Pt catalyst and 0.031 for PVA as a good additive blending in the CL of the MEA. However, even if the error is low, the prediction of PVA seems to be inaccurate, regardless of the model structure. Therefore, the KNN model is more appropriate for CL recipe prediction.     

Energy conversion performances during biomass air gasification process under microwave irradiation

Biomass gasification technology under microwave irradiation is a new and novel method, and the energy conversion performances during the process play a guiding role in improving the energy conversion efficiencies and developing the gasification simulation models. In order to improve the energy utilization efficiency of microwave biomass gasification system, this study investigated and presented the energy conversion performances during biomass gasification process under microwave irradiation, and these were materialized through detailing (a) the energy conversion performance in the microwave heating stage, and (b) the energy conversion performance in the microwave assisted biomass gasification stage. Different forms of energies in the biomass microwave gasification process were calculated by the method given in this study based on the experimental data. The results showed that the useful energy (energy in silicon carbide (SiC), 18.73 kJ) accounted for 31.22% of the total energy input (electrical energy, 60.00 kJ) in the heating stage, and the useful energy (energy in the products, 758.55 kJ) accounted for 63.41% of the total energy input (electrical and biomass energy, 1196.28 kJ) in the gasification stage. During the whole biomass gasification process under microwave irradiation, the useful energy output (energy in the products, 758.55 kJ) accounted for 60.38% of the total energy input (electrical and biomass energy, 1256.28 kJ), and the energy in the gas (523.40 kJ) product played a dominate role in product energy (758.55 kJ). The energy loss mainly included the heat loss in the gas flow (89.20 kJ), magnetron loss (191.80 kJ) and microwave dissipation loss (198.00 kJ), which accounted for 7.10%, 15.27% and 15.76% of the total energy, respectively. The contents detailed in this study not only presented the energy conversion performances during microwave assisted gasification process but also supplied important data for developing gasification simulation models.