KSUTraffic: A Microscopic Traffic Simulator for Traffic Planning in Smart Cities

Simulation is a powerful tool for improving, evaluating and analyzing the performance of new and existing systems. Traffic simulators provide tools for studying transportation systems in smart cities as they describe the evolution of traffic to the highest level of detail. There are many types of traffic simulators that allow simulating traffic in modern cities. The most popular traffic simulation approach is the microscopic traffic simulation because of its ability to model traffic in a realistic manner. In many cities of Saudi Arabia, traffic management represents a major challenge as a result of expansion in traffic demands and increasing number of incidents. Unfortunately, employing simulation to provide effective traffic management for local scenarios in Saudi Arabia is limited to a number of commercial products in both public and private sectors. Commercial simulators are usually expensive, closed source and inflexible as they allow limited functionalities. In this project, we developed a local traffic simulator “KSUtraffic” for traffic modeling, planning and analysis with respect to different traffic control strategies and considerations. We modeled information specified by GIS and real traffic data. Furthermore, we designed experiments that manipulate simulation parameters and the underlying area. KSUTraffic visualizes traffic and provides statistical results on the simulated traffic which would help to improve traffic management and efficiency.     

Cu-assisted induced atomic-level bivalent Fe confined on N-doped carbon concave dodecahedrons for acid oxygen reduction electrocatalysis

Atomically dispersed transition metals anchored on N-doped carbon have been successfully developed as promising electrocatalysts for acidic oxygen reduction reaction (ORR). Nonetheless, how to introduce and construct single-atomic active sites is still a big challenge. Herein, a novel concave dodecahedron catalyst of N-doped carbon (FeCuNC) with well confined atomically dispersed bivalent Fe sites was facilely developed via a Cu-assisted induced strategy. The obtained catalyst delivered outstanding ORR performance in 0.5 M H₂SO₄ media with a half-wave potential (E_1/2) of 0.82 V (vs reversible hydrogen electrode, RHE), stemming from the highly active bivalent Fe-N_x sites with sufficient exposure and accessibility guaranteed by the high specific surface area and curved surface. This work provides a simple but efficient metal-assisted induced strategy to tune the configurations of atomically dispersed active sites as well as microscopy structures of carbon matrix to develop promising PGM-free catalysts for proton exchange membrane fuel cell (PEMFC) applications.     

An unsupervised approach for fault diagnosis of power transformers

Electrical utilities apply condition monitoring on power transformers (PTs) to prevent unplanned outages and detect incipient faults. This monitoring is often done using dissolved gas analysis (DGA) coupled with engineering methods to interpret the data, however the obtained results lack accuracy and reproducibility. In order to improve accuracy, various advanced analytical methods have been proposed in the literature. Nonetheless, these methods are often hard to interpret by the decision-maker and require a substantial amount of failure records to be trained. In the context of the PTs, failure data quality is recurrently questionable, and failure records are scarce when compared to nonfailure records. This work tackles these challenges by proposing a novel unsupervised methodology for diagnosing PT condition. Differently from the supervised approaches in the literature, our method does not require the labeling of DGA records and incorporates a visual representation of the results in a 2D scatter plot to assist in interpretation. A modified clustering technique is used to classify the condition of different PTs using historical DGA data. Finally, well-known engineering methods are applied to interpret each of the obtained clusters. The approach was validated using data from two different real-world data sets provided by a generation company and a distribution system operator. The results highlight the advantages of the proposed approach and outperformed engineering methods (from IEC and IEEE standards) and companies legacy method. The approach was also validated on the public IEC TC10 database, showing the capability to achieve comparable accuracy with supervised learning methods from the literature. As a result of the methodology performance, both companies are currently using it in their daily DGA diagnosis.     

Life-cycle assessment of hydrogen technologies with the focus on EU critical raw materials and end-of-life strategies

We present the results of a life-cycle assessment (LCA) for the manufacturing and end-of-life (EoL) phases of the following fuel-cell and hydrogen (FCH) technologies: alkaline water electrolyser (AWE), polymer-electrolyte-membrane water electrolyser (PEMWE), high-temperature (HT) and low-temperature (LT) polymer-electrolyte-membrane fuel cells (PEMFCs), together with the balance-of-plant components. New life-cycle inventories (LCIs), i.e., material inputs for the AWE, PEMWE and HT PEMFC are developed, whereas the existing LCI for the LT PEMFC is adopted from a previous EU-funded project. The LCA models for all four FCH technologies are created by modelling the manufacturing phase, followed by defining the EoL strategies and processes used and finally by assessing the effects of the EoL approach using environmental indicators. The effects are analysed with a stepwise approach, where the CML2001 assessment method is used to evaluate the environmental impacts. The results show that the environmental impacts of the manufacturing phase can be substantially reduced by using the proposed EoL strategies (i.e., recycled materials being used in the manufacturing phase and replacing some of the virgin materials). To point out the importance of critical materials (in this case, the platinum-group metals or PGMs) and their recycling strategies, further analyses were made. By comparing the EoL phase with and without the recycling of PGMs, an increase in the environmental impacts is observed, which is much greater in the case of both fuel-cell systems, because they contain a larger quantity of PGMs.     

Effect of water distribution in gas diffusion layer on proton exchange membrane fuel cell performance

Water management of proton exchange membrane fuel cells remains a prominent issue in research concerning fuel cells. In this study, the gas diffusion layer (GDL) of a fuel cell is partially treated with a hydrophobic agent, and the effect of GDL hydrophobicity on the water distribution in the fuel cell is examined. First, the effect of the position of the cathode GDL hydrophobic area relative to the channel on the fuel cell performance is investigated. Then, the water distribution in the fuel cell cathode GDL is observed using X-ray imaging. The experimental results indicate that when the hybrid GDL's hydrophobic area lies on the channel, water tends to accumulate under the rib, and the water content in the channel is low; this improves the fuel cell performance. When the hydrophobic area is under the rib, the water distribution is more uniform, but the performance deteriorates.     

Efficient fault diagnosis method of PEMFC thermal management system for various current densities

The temperature of a fuel cell has a considerable impact on the saturation of a membrane, electrochemical reaction speed, and durability. So thermal management is considered one of the critical issues in polymer electrolyte membrane fuel cells. Therefore, the reliability of the thermal management system is also crucial for the performance and durability of a fuel cell system. In this work, a methodology for component-level fault diagnosis of polymer electrolyte membrane fuel cell thermal management system for various current densities is proposed. Specifically, this study suggests fault diagnosis using limited data, based on an experimental approach. Normal and five component-level fault states are diagnosed with a support vector machine model using temperature, pressure, and fan control signal data. The effects of training data at different operating current densities on fault diagnosis are analyzed. The effects of data preprocessing method are investigated, and the cause of misdiagnosis is analyzed. On this basis, diagnosis results show that the proposed methodology can realize efficient component-level fault diagnosis using limited data. The diagnosis accuracy is over 92% when the residual basis scaling method is used, and data at the highest operating current density is used to train the support vector machine.     

Graduate Student Literature Review: Current recommendations and scientific knowledge on dairy goat kid rearing practices in intensive production systems in Canada,the United States,and France

Dairy goat kid rearing is the foundation of future milk production, yet little is known on this topic. References available to producers are limited, making it more difficult for dairy goat farms to reach their full production potential. This review paper aimed to identify the current recommendations on dairy goat kid rearing practices for intensive production systems and to assess whether the different recommendations were based on scientific literature. Recommendations on dairy goat kid rearing practices, from birth to weaning inclusively, were presented and compared between countries under similar intensive production systems, including Canada, the United States, and France. The different areas of rearing investigated included kidding management, colostrum management, liquid and solid feeding, health management, disbudding, housing, weaning, and growth monitoring. Gaps in the literature were identified in all areas except for disbudding. More research on the topic of goat kid raising practices would be beneficial to refine and validate current recommendations.