• Energy Research
• CN close
• DE close
• IN close

Cold-start (subzero startup) capability of polymer electrolyte fuel cell (PEFC) is of great importance. In this paper, the effects of the micro-porous-layer (MPL), varying start-up temperatures, start-up current density variation on the cold-start operation are investigated. We found PEFC with anode micro-porous-layer (AMPL), compared with one with CMPL, has higher possibility of successful cold start. By analyzing the anode and cathode pressure revolution of PEFC, the effect of MPL on the super cooled water removal and ice formation at different temperatures (-7,-10,-15 and-20℃) are discussed . In addition, we investigate the ice distribution in MEA through the X-ray device, by comparing the initial image when fuel cells shut down (before the produce the water) and final image after failed cold-start. We also explore the negative voltage phenomenon in the initial process of cold-start operation.

Abstract The increasing energy demand in developing countries has jeopardised energy security, necessitating the employment of solar energy to augment conventional energy sources. It is important to assess the annual performance parameters of solar power plants to understand its place in energy generation. This study analyses the performance and economic viability of a large-scale solar power park located in India. A 50 MWp Solar Photovoltaic Power Park (SPPP) located at Sakunala, in the State of Andhra Pradesh, is one of the largest solar power park in India, and the site receives an average solar radiation of 5.5–6.0 kWh/m2/day. The design, performance analysis, economic feasibility, and greenhouse gas mitigation of the 50 MWp SPPP is presented. The energy yields, performance ratio (PR), capacity utilization factor (CUF), and losses are assessed based on the IEC 61724 standards for two consecutive years (2018–2019 and 2019–2020). The performance results obtained are compared with the PVsyst simulation results. The PR, CUF, and energy yields are estimated as 0.779, 0.24, and 107,326.4 MWh in 2018–2019 and 0.691, 0.22, and 96,707.336 MWh in the year 2019–2020, respectively. The PVsyst simulator evaluates the PR, CUF, and energy yields as 0.80, 0.24, and 106,022 MWh, respectively. Further, the effect of power curtailment policies on solar PV projects is also discussed and the effect of curtailment policy on the economy of the solar power park in terms of payback period and emissions are analysed. The performance of the SPPP is also compared with those of other PV plants installed all over the world.

Chromium (Cr) exposure during gestation causes malformations in animal experiments. In this multicenter case-control study, we initially involved 130 orofacial clefts (OFCs) and 260 controls to assess the association between Cr concentration and risk for OFCs. Then, umbilical cord serum (49 vs. 119) and cord tissue (84 vs. 142) were used to validate the association between Cr and OFCs. We found that maternal serum Cr concentrations in OFC cases were significantly higher than those in controls. Compared with the lowest tertile of maternal serum Cr concentration, the highest tertile of Cr increased the risk for OFCs [OR = 2.14 (1.14-4.05)]. In the validation cohort of umbilical cord serum and tissue, higher concentrations of Cr were associated with increased risks for OFCs in a dose-dependent manner (all Ps for trends <0.05). Cr concentrations in maternal serum and cord serum showed a positive correlation. The Cr concentration in cord serum was inversely correlated with egg and milk consumption frequencies, and the Cr concentration in cord tissue was positively associated with indoor coal burning. In conclusion, prenatal Cr exposure is a risk factor for OFCs, and indoor coal burning during pregnancy may be one of the sources of Cr exposure.

Straw and other biomasses can cause severe problems when used as fuel in combustion systems. Some of the major problems include high emission of alkalis, HCl, and especially SO2 to the gas phase. T...

Abstract Background : To design an effective workflow scheduling optimization of web servers that will bring good trade-offs to meet the workflow task delay prerequisite and performance requirement. Methods: This study presents a delayaware and performance-efficient energy optimization (DAPEEO) technique for workflow execution in a heterogeneous environment (i.e., edge-cloud environment). This technique provides a workflow execution model which meets the application delay prerequisite and performance requirement. Findings: Our model has been designed to reduce the energy consumption, increase throughput, reduce computational cost and computational time to provide a delay-aware and performance-efficient workflow model for the web servers in hybrid cloud computing. Our model Delay Aware and Performance Efficient Workflow Scheduling of Web Servers in Hybrid Cloud Computing Environment (DAPEEO) has reduced the energy consumption by 4.217%, increased the throughput by 19.51%, and reduce the computational cost by 62.38% when compared with the existing Deadline-Constrained Cost Optimization for Hybrid Clouds (DCOH) models. Furthermore, the average energy consumption showed a reduction of 40.993% and 90.384% when compared with the DCOH and Self-Configuring and Self-Healing of Cloud-based Resources (RADAR) workload model respectively. Experiment outcome shows the DAPEEO technique achieves much superior energy efficiency, throughput and computation cost reduction when compared with the existing workflow execution model. Novelty: Existing model failed to balance reducing cost, and meeting workflow execution deadlines under a heterogeneous environment. On the other side, the DAPEEO is efficient in bringing trade-offs in reducing energy dissipation and meeting task deadlines with reduced cost under the edge-cloud computing model. Keywords: Cloud Computing; EdgeCloud; DAPEEO; Energy Efficiency; Throughput; Cost

<p>This paper presents cuckoo search algorithm (CSA) for solving non-convex economic load dispatch (ELD) problems of fossil fuel fired generators considering transmission losses and valve point loading effect. CSA is a new meta-heuristic optimisation technique inspired from the obligate brood parasitism of some cuckoo species by laying their eggs in the nests of other host birds of other species. The strength of the proposed meta-heuristic optimization technique CSA has been tested and validated on the standard IEEE 14-bus, 26-bus and 30-bus system with several heuristic load patterns. The results have indicated that the proposed approach is able to obtain significant economic load dispatch solutions than those of Firefly Algorithm (FFA) and other soft computing techniques reported in the literature.</p>

Pedestrian inertial navigation technology plays an important role in indoor positioning technology. However, low-cost inertial sensors in smart devices are affected by bias and noise, resulting in rapidly increasing and accumulating errors when integrating double acceleration to obtain displacement. The data-driven class of pedestrian inertial navigation algorithms can reduce sensor bias and noise in IMU data by learning motion-related features through deep neural networks. Inspired by the RoNIN algorithm, this paper proposes a data-driven class algorithm, RBCN-Net. Firstly, the algorithm adds NAM and CBAM attention modules to the residual network ResNet18 to enhance the learning ability of the network for channel and spatial features. Adding the BiLSTM module can enhance the network’s ability to learn over long distances. Secondly, we construct a dataset VOIMU containing IMU data and ground truth trajectories based on visual inertial odometry (total distance of 18.53 km and total time of 5.65 h). Finally, the present algorithm is compared with CNN, LSTM, ResNet18 and ResNet50 networks in VOIMU dataset for experiments. The experimental results show that the RMSE values of RBCN-Net are reduced by 6.906, 2.726, 1.495 and 0.677, respectively, compared with the above networks, proving that the algorithm effectively improves the accuracy of pedestrian navigation.

Abstract To alleviate the shortage of natural gas resource and ease carbon emissions, a novel solar-driven combined cooling, heating and power (CCHP) system is designed and optimized using the genetic algorithm in the work. Different from the process of direct combustion in a conventional CCHP system, natural gas is firstly converted into syngas by a solar-driven natural gas reforming step, which is consumed in an efficient tri-generation system. Energy, economic and environmental evaluations on five office buildings in different climate zones in China are implemented to validate the advantages of the proposed system. Results show that the annual maximum primary energy saving, total cost saving, and CO2 emission reduction are 69.76%, 49.80%, and 71.55%, respectively. The system located in severe cold zones, where solar energy is abundant and building requires more heat load in whole year, achieves the highest benefits in comparison with separate systems. Furthermore, the sensitivities on the price fluctuations of electricity, natural gas and solar field to the system profits are investigated, which indicates that the influence of electricity price on the system performance is the most significant. Thus, a promising method for reducing the natural gas consumption and improving the utilization efficiency of solar energy is provided.