Development of an effective process with aid of micro-organisms and fungus for reduction of environmentally destructive effect of spill oil toward ocean ecosystem protection.

The risk of an oil spill in the marine environment always exists during offshore oil production and transportation. The oil spill can affect the marine ecosystem, including seabirds, aquatic organisms and even humans health, both directly and indirectly. The best solution to the oil spill incident is collecting the spilled oil, extracting that from the marine environment and potentially taking it back to the energy cycle. However, depending on the situation and severity of the oil spill incident, it can be partially possible or even impossible.

Design and optimization of low-frequency piezoelectric energy harvesters

Portable electronic applications are typically powered by batteries, which have limited lifespan and size constraints. Energy harvesting from the spatial environment is a promising solution to sustainable power supplies for low-power portable devices and sensor networks. Vibration-based energy harvesting has received much attention due to the recent advances in microfabrication of piezoceramic materials. These smart materials can convert mechanical parasitic vibrations to electric charge through the direct piezoelectric effect.

Sustainable functionalized magnetic particles for efficient treatment of marine oil pollution

Oily wastewater production and discharge from different sources such as industries and daily human activities are the main sources of marine oil pollution. More importantly, accidental oil spills occurred in oil extraction/production, refining, and transportation stages can cause detrimental impacts on the aquatic ecosystems and marine environments.

Automated real-time inspection for robotic arc welding operations

Welding has widespread use in manufacturing, and its quality often determines the overall performance of the manufactured part. Nevertheless, due to the complex nature of the process, weld quality inspection remains manual with the standard approach of QA for defects after production completes. This introduces significant costs of downtime and reworks associated with finding defects at the late stages of production. This project leverages advanced data analytics and machine learning to develop and validate an automated real-time quality inspection system for industrial welding operations.

Updating Laubscher’s empirical method to estimate subsidence limits

The increasing global demand for mineral resources and the depletion of significant of high grade near-surface deposits is driving mining companies to consider cave mining as the ideal method to exploit large low-grade deposits at depth. A key characteristics of cave mining is the formation of a significant surface subsidence crater, which may impact nearby infrastructures, as well as have important environmental impacts. The objective of this research is to update empirical subsidence charts using new cases from recent cave mining operations.

Smart empathetic speaker based on real-time EEG-based music therapy

During COVID-19 outbreak, many people are suffering the negative emotions, causing anxiety, fear, and depression in daily life. To improve the individuals’ mental health, musical therapy will be employed due to its high value of treatment in the mental health field. In our project, a smart empathetic speaker based on real-time emotion recognition system will be developed. Through electroencephalography (EEG) measurement, the types of signals reflecting the listener’s emotion will be extracted. The deep learning method will be used to recognize the emotions of the user in real-time.

Microchannel Plate & Shell Heat Exchanger for Flue Gas Heat Recovery

Natural gas power generation is a cost-effective method of generating electricity. NG power generators have been successfully installed in a wide variety of energy intensive facilities in Canada, including district energy systems, wastewater treatment facilities, schools, nursing homes, hospitals, office buildings, and residential buildings. However, more than 60% of the energy is wasted in the form of heat loss, and major heat loss is carried by the hot flue gas.

Mineral carbonation for global warming mitigation and concurrent nickel and cobalt extraction from laterites

Both global warming and increasing supply of nickel and cobalt are urgently world-wide issues to be addressed. Mineral carbonation is a promising method to permanently store greenhouse gas CO2 into stable mineral carbonates but energy-intensive requirements and low-value products limit the successful application. Meanwhile, with the increasing demand on global nickel and cobalt supply for electrical vehicles and gradual decrease of nickel sulphides deposit, it becomes more important to extract nickel and cobalt from dominant laterites of nickel resources.

Atmospheric stirred tank leaching of chalcopyrite concentrate in ferric sulfate media catalysed by iodine with air/oxygen injection

Leaching of primary copper sulfide (chalcopyrite) using ferric as the oxidant at ambient temperature and pressure exhibits slow kinetics and poor leaching efficiency. In collaboration with LeadFX, this project aims at developing an atmospheric tank leaching process for copper extraction from chalcopyrite concentrate with iodine as the catalyst. The temperature used will be higher than a typical heap leach process. Air/oxygen will be injected to the reactor to maintain the solution potential through continuously oxidizing ferrous to ferric.

Performance prediction and fault diagnosis in photovoltaic systems for optimal energy management

In this project, we will develop software-based models to monitor and to predict the performance of Perovskites-based
PVBlindsTM for optimal energy management and optimal integration into buildings, as well as to diagnose faults of PV cells for
safe, efficient, and reliable operation. The PVBlindsTM are developed by Solaires Inc. and will be deployed at various locations
within the Greater Vancouver area, and in various types of buildings. To develop the software-based models, machine learning
approaches will be studied and implemented.

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