Experimental investigation on the voltage uniformity for a PEMFC stack with different dynamic loading strategies

The operating life of the proton exchange membrane fuel cell stack is mainly decided by performances of its weakest single cell because of the “Buckets effect”, thus high voltage uniformity during a dynamic loading process is key to the stack durability. In this work, a 3-kW stack is examined experimentally on its voltage uniformity (voltage coefficient variation (Cv)) under conditions of loading from open-circuit state (0 A) to nominal current (165 A) and stack temperatures of 30 °C, 45 °C and 65 °C. Different dynamic loading strategies, namely constant loading rate strategy, decreasing loading rate strategy, and increasing loading rate (square/cube increasing loading rate) strategy, are examined and compared. Results display that during the loading process, (a) the voltage uniformity rises abruptly and goes down quickly when the loading current is small (e.g. from 0 A to 22 A), (b) the voltage uniformity under a small loading current is better than that under the open-circuit state, and (c) voltage uniformity decreases as the loading current increases from a small value to the nominal current. Comparisons of different current loading strategies show that as the stack temperature rises from 30 °C to 65 °C, the stack Cv value under the open-circuit state increases from 1.12 to 1.84 and decreases from 3.85 to 2.45 in the nominal current state. The maximum Cv for the decreasing loading rate strategy decreases from 16.25 to 9.49 and that of the constant loading rate strategy also decreases from 5.85 to 4.96. Cv values of the square current increasing loading rate strategy keep below 3.85 under conditions of the three stack temperatures and display a slight fluctuation during the whole current loading process, which indicates that the strategy can effectively make the stack being of an excellent voltage uniformity during the instantaneous response process.     

Real-time analysis and prediction of shield cutterhead torque using optimized gated recurrent unit neural network

An accurate prediction of earth pressure balance (EPB) shield moving performance is important to ensure the safety tunnel excavation. A hybrid model is developed based on the particle swarm optimization (PSO) and gated recurrent unit (GRU) neural network. PSO is utilized to assign the optimal hyperparameters of GRU neural network. There are mainly four steps: data collection and processing, hybrid model establishment, model performance evaluation and correlation analysis. The developed model provides an alternative to tackle with time-series data of tunnel project. Apart from that, a novel framework about model application is performed to provide guidelines in practice. A tunnel project is utilized to evaluate the performance of proposed hybrid model. Results indicate that geological and construction variables are significant to the model performance. Correlation analysis shows that construction variables (main thrust and foam liquid volume) display the highest correlation with the cutterhead torque (CHT). This work provides a feasible and applicable alternative way to estimate the performance of shield tunneling.     

电压降法在水平定向钻穿越管道外涂层评价中的应用研究

介绍了电流-电位法、电流密度法在水平定向钻穿越管道外涂层评价中的应用,联合电压降法计算管内电流值,并结合实际案例对穿越管道两端连头之后外涂层性能进行评价。     

基于物质平衡方程的页岩气井产能预测方法

页岩气井产能受地质和压裂改造参数综合影响,在开发初期,难以确定可靠的地质模型来预测气井产能。为了快速、准确地预测气井产能,在研究页岩气物质平衡方程和产能方程的基础上,考虑吸附气和异常高压气藏岩石弹性能量影响,建立了基于物质平衡方程的页岩气井产能预测方法,该方法需要参数较少,可在开发初期快速对页岩气井产能进行预测。涪陵地区X1井实例计算表明:该方法综合考虑了页岩气吸附气解吸、异常高压气藏岩石弹性能量的影响,产能预测结果准确、可靠;吸附气对气井累产气量影响明显,约占总累产气量17%,对定产生产气井的稳产期影响不大;吸附气的贡献在2.5~4年逐渐体现出来,定压放喷生产初期产量高,地层压力下降快,吸附气的产出贡献比控制产量的定产生产方式要早;考虑岩石弹性能量的影响,气井稳产时间和累产气有所增加,但影响不明显。     

High performance multi-layer varistor (MLV) from doped ZnO nanopowders by water based tape casting: Rheology,sintering, microstructure and properties

In this work, we report the fabrication of a high performance multi-layer varistor (MLV) via water based tape casting method using novel compositions of nanomaterials. Bi₂O₃, CaO and Co₃O₄ doped ZnO nanopowders were prepared by solution combustion synthesis (SCS) route, calcined at different temperatures (550, 650, 750 and 850?°C) and characterized by TEM, XRD, SEM and AFM. The nanopowder (crystallite size ~30?nm) calcined at 650?°C for 1?h was used as the starting material for MLV fabrication. Compositions of the slurry containing doped ZnO nanopowders, binder and plasticizer in water solvent were optimized for the fabrication of thick film. The rheological properties of the slurries having different solid loadings were analysed and thick films of various thicknesses (50–500?µm) were prepared by varying the feeding rate of tape casting. The film roughness of 38.3?nm for the thick film made from 40?wt% solid slurry was found to be superior compared to other samples due to the presence of reduced crack and shrinkage. MLV fired at 950?°C for 1.5?h exhibited a coefficient of nonlinearity of 18 and breakdown voltage of 291.5?V that yields superior properties compared to commercial MLVs.     

Direct model-based predictive control scheme without cost function for voltage source inverters with reduced common-mode voltage

This paper presents a direct model-based predictive control scheme for voltage source inverters (VSIs) with reduced common-mode voltages (CMVs). The developed method directly finds optimal vectors without using repetitive calculation of a cost function. To adjust output currents with the CMVs in the range of –V_dc/6 to +V_dc/6, the developed method uses voltage vectors, as finite control resources, excluding zero voltage vectors which produce the CMVs in the VSI within ±V_dc/2. In a model-based predictive control (MPC), not using zero voltage vectors increases the output current ripples and the current errors. To alleviate these problems, the developed method uses two non-zero voltage vectors in one sampling step. In addition, the voltage vectors scheduled to be used are directly selected at every sampling step once the developed method calculates the future reference voltage vector, saving the efforts of repeatedly calculating the cost function. And the two non-zero voltage vectors are optimally allocated to make the output current approach the reference current as close as possible. Thus, low CMV, rapid current-following capability and sufficient output current ripple performance are attained by the developed method. The results of a simulation and an experiment verify the effectiveness of the developed method.     

Identifying poor metabolic adaptation during early lactation in dairy cows using cluster analysis

Currently, cows with poor metabolic adaptation during early lactation, or poor metabolic adaptation syndrome (PMAS), are often identified based on detection of hyperketonemia. Unfortunately, elevated blood ketones do not manifest consistently with indications of PMAS. Expected indicators of PMAS include elevated liver enzymes and bilirubin, decreased rumen fill, reduced rumen contractions, and a decrease in milk production. Cows with PMAS typically are higher producing, older cows that are earlier in lactation and have greater body condition score at the start of lactation. It was our aim to evaluate commonly used measures of metabolic health (input variables) that were available [i.e., blood β-hydroxybutyrate acid, milk fat:protein ratio, blood nonesterified fatty acids (NEFA)] to characterize PMAS. Bavarian farms (n = 26) with robotic milking systems were enrolled for weekly visits for an average of 6.7 wk. Physical examinations of the cows (5–50 d in milk) were performed by veterinarians during each visit, and blood and milk samples were collected. Resulting data included 790 observations from 312 cows (309 Simmental, 1 Red Holstein, 2 Holstein). Principal component analysis was conducted on the 3 input variables, followed by K-means cluster analysis of the first 2 orthogonal components. The 5 resulting clusters were then ascribed to low, intermediate, or high PMAS classes based on their degree of agreement with expected PMAS indicators and characteristics in comparison with other clusters. Results revealed that PMAS classes were most significantly associated with blood NEFA levels. Next, we evaluated NEFA values that classify observations into appropriate PMAS classes in this data set, which we called separation values. Our resulting NEFA separation values [<0.39 mmol/L (95% confidence limits = 0.360–0.410) to identify low PMAS observations and ≥0.7 mmol/L (95% confidence limits = 0.650–0.775) to identify high PMAS observations] were similar to values determined for Holsteins in conventional milking settings diagnosed with hyperketonemia and clinical symptoms such as anorexia and a reduction in milk yield, as reported in the literature. Future studies evaluating additional clinical and laboratory data, breeds, and milking systems are needed to validate these finding. The aim of future studies would be to build a PMAS prediction model to alert producers of cows needing attention and help evaluate on-farm metabolic health management at the herd level.     

Parallel Multipopulation Differential Evolutionary Particle Swarm Optimization for Voltage and Reactive Power Control

This paper presents parallel multipopulation differential evolutionary particle swarm optimization (DEEPSO) for voltage and reactive power control (VQC). The problem can be formulated as a mixed integer nonlinear optimization problem and various evolutionary computation techniques have been applied to the problem including PSO, differential evolution (DE), and DEEPSO. Since VQC is one of the online controls, speed‐up of computation is required. Moreover, there is still room for improvement in solution quality. This paper applies parallel multipopulation DEEPSO in order to speed up the calculation and improve solution quality. The proposed method is applied to IEEE 30, 57, and 118 bus systems. The results indicate that the proposed method can realize fast computation and minimize more active power losses than the conventional evolutionary computation techniques.