The performance of non-precious metal catalysts (NPMCs) for proton exchange membrane fuel cell (PEMFC) has now reached a stage at which they can be considered as possible alternatives to expensive Pt, especially for low power applications. However, despite significant efforts on catalyst development in the past, only limited studies have been performed on NPMC-based electrode designs. Thus, it is required to develop an effective NPMC-based electrode that can correctly balance the complex parameters to maximize the performance it can bring.
This project will focus on researching Cell Broadcast solutions for Machine-to-Machine (M2M) communications (command and control operations). As an alternative to the Internet, Cell Broadcast is expected to offer great
advantages to both Utility providers as well as Commercial/Residential HVAC consumers due to its characteristics and itâs broadcasting nature over cellular control channels. Adopting Cell Broadcast will generate several communications-related challenges that will be studied and researched throughout the grant period.
Energy companies are in the business of turning energy from one form into another. For example, a gas-fired power station turns chemical potential energy stored in the natural gas into electrical energy. A natural gas storage facility allows energy (held in the form of natural gas) to be stored at one point in time and recovered at a later time. A gas pipeline moves energy from one location to another. The result is that the financial risks faced by an energy company involve a large portfolio of spreads â differences between energy prices.
The goal of the research is to implement different data mining algorithms in order to improve the prediction on a userâs electricity consumption. The research will be dedicated to improve the existing algorithms or implementing new algorithms for the improvement of the prediction accuracy. Besides application of the prediction algorithms, different data pre-processing methods will be used. Research will include supervised and unsupervised modelling of the dataset by using the R programming language.
Natural gas is one of the cleanest fuels for heat and power generation. But in China, coal is still the dominant fuel
but burning coal has caused severe and damaging air pollutions. The partner organization of this project, Seven
Generations Energy Ltd., is a significant Canadian producer of natural gas. This project will comprehensively
assess the overall environmental performance of natural gas production (by Seven Generations) and exporting it
to China to replace coal.
The proposed research focuses on developing a secure, reliable and real-time heterogeneous communication system to monitor and protect utility assets such as metering infrastructure, pumps and underground pipelines. The proposed system will be able to collect and aggregate data and upload the data through a cloud gateway to allow remote monitoring and control. The proposed system will interface the transmitted data with Lexcomâs Capital Infrastructure Management Systems (CIMS).
This project seeks to understand how a next-generation nuclear reactor, a molten salt reactor, behaves under various conditions. Particular attention is paid to aspects of the reactorâs performance that could have an impact on its safe operation. This research focuses on how the various properties of the reactor evolve in time after a change to the reactorâs operating configuration is made. The analysis is done using state-of-the-art computer codes and a multi-physics approach that model both the nuclear and thermal behaviour of the reactor.
With the increased demand for electric power and the need to reduce green house gas emission, newer power plants (fossil fuel or nuclear based) are being designed with elevated turbine inlet temperature to improve thermodynamic efficiencies and achieve other benefits. The much severe operating conditions (than that in the existing power plants) presents great challenges to material selections.
Air-core dry-type electrical reactors are integrated into power system infrastructures to limit current and regulate voltage in transmission lines. These reactors, are designed and built to facilitate customer specific requirements using an elementary noise prediction model, which was developed almost 30 years ago. With increasingly stricter noise emission guidelines set by the environmental regulatory bodies, the need to better predict and meet specific noise requirements has become more important to the design and manufacturing of the reactors.
The objective of this project is to develop an analytics tool for REALPAC to use to better classify buildings using the â20 x â15â dataset collected by REALPAC since 2009. Preliminary analysis has been conducted of this data in past years, but this has been limited to a simple retrospective analysis.