Projets novateurs réalisés

Feasibility and Benchmarking of Quantum Random Number Generation for Distributed Ledger Technologies

Quantum Random Number Generation (QRNG) is a promising technology to provide true randomness as a service for many computing uses, such as cryptography. The company Quantum eMotion develops an affordable product called QNG2 which offers quantum random number generation in a compact device (i.e., PCIe card) which can easily be used in any standard PC with their accompanying software. Blockchain and distributed ledger technologies (DLTs) are quickly becoming a popular utilization of
cryptography for a wide variety of domains, such as cryptocurrencies, digital art, and supply chain management. A source of truly random numbers is often desirable to enable application logic, harden security, or improve the efficiency of the system. The objective of this project is: to investigate the convergence of two cutting-edge technologies: quantum random number generation and blockchain based distributed ledger technologies, and to assess the feasibility and evaluate the potential of QRNG powered DLT systems and applications.

Influence of slag characteristics on dephosphorization and refractory wear in steel electric arc furnace

The Canadian steel industry experiences fierce global competition. It is also under an increasing pressure to decrease its environmental foot-print. Continuous process optimization is, therefore, necessary for a sustainable operation. To produce high quality steel and concurrently reduce costs, trial and error cannot be effective. This proposal aims to advance the knowledge of electric arc furnace steelmaking process. The students, handling the project, will establish methodologies to simulate the industrial operation at lab scale to study the effects of interplaying operational variables on the steel quality and find the optimum set of process parameters for production of steel with utmost properties. The research results will be then validated at real scale. The major impact of the project on the industry will be to produce steel with superior quality meeting the national and global market demand, operate more efficiently with least disruption, consume lower amount of energy, and reduce costs.

Outils numériques pour l’étude des avions à propulsion hybride-électrique

La propulsion hybride-électrique a le potentiel de réduire la consommation en carburant des aéronefs et constitue une alternative plus réaliste par rapport à l’option tout électrique. Avec l’utilisation de sources d’énergies différentes, le processus de dimensionnement est complexifié et couplé à l’opération de l’aéronef. Cela nécessite alors une approche d’exploration par analyse de mission et d’optimisation au niveau du système au complet. Ainsi, des environnements de simulation et d’optimisation ont été développés lors de projets académiques précédents afin d’évaluer les bénéfices potentiels des propulsion hybrides. Les outils et modèles numériques ainsi développés seront implémentés dans le système d’analyse du partenaire afin d’accroitre ses capacités d’étude des technologies de propulsion hybride pour l’aviation.

Upcycling of Winemaking Waste By-Products into Sustainable Cosmetic Product Formulas

Cheekbone Beauty Inc. is an Indigenous Canadian-owned beauty company with a mission to transform the lives of all its
consumers through sustainable products and business innovation. The proposed project involves repurposing wine-making
waste (grape pomace) from local Niagara Region wineries into high-value added ingredients with anti-aging, anti-pollution, and sun protection properties, for cosmetic applications. This circular and sustainable economy project directly aligns with the company’s mission, and it integrates and honours the Indigenous Ways of Knowing and Doing.

The project interns will gain useful skills including technical advanced analytics (e.g. HPLC, GC-MS), and professional skills through the interaction with entrepreneurs as well as to expand their knowledge in the field of cosmetic science and technology. With this project, Cheekbone Beauty hopes to aide in the reconciliation between Canada and Indigenous people while allowing Indigenous people to lead themselves to a better path of healing and prosperity. The upcycle of high-valued active ingredients from winery waste will contribute to Canada’s growing circular and sustainable economy.

A Data-Driven Modeling Approach for Estimating Methane Emission Sources from Satellite, Environmental, and Oil/Gas Well Data

There has been an increased commitment to capping GHG emissions from oil/gas (O/G) operations. However, reliable monitoring and quantification of methane emissions from particular sources are difficult for several reasons: (1) high-resolution sampling data over an extensive geographical area is rare; (2) identifying emission sources from large-scale monitoring satellite data is extremely challenging, often yielding inconsistent and highly varying results.
Satellite retrievals of near-surface concentrations of atmospheric methane, together with other environmental (weather), and oil/gas operational/production data, can be used to infer potential emission sources. This project aims to develop practical workflows for quantifying methane emission sources from O/G operations by (1) building machine-learning models for correlating production data and well activity to methane emission rates, and (2) understanding the relationships between O/G activities and emission source characteristics. The outcomes would be useful for identifying better monitoring opportunities.

Performance of Cast Steel Replaceable Yielding Links in Steel Eccentrically Braced Frames

This research project is aimed at the development and experimental validation of a new generation of cast steel replaceable yielding links for use in steel eccentrically braced frames (EBF). The cast steel links can be used to make EBF more resilient than the conventional links. Through the collaboration with the industry partner, CastConnex, in the past four years, the performance of several sizes of cast steel links was experimentally tested. Currently, the performance of an EBF with the proposed cast steel links is being studied using an advanced simulation method. The proposed research will be focused on the experimental validation of an alternative design of the cast steel replaceable links to further enhance the performance of the links, proposing a method to improve the reusability of EBFs after an earthquake event, and detailed post-processing of the experimental results. This will ensure that little to no structural repair would be required for budlings using the proposed system, after major earthquakes. The performance of the newly proposed self-centering eccentrically braced frames will be studied through numerical and experimental simulations.

Methane Assisted Catalytic Upgrading of Extra Heavy Crudes under Moderate Conditions

Vacuum residue (VR) is the heaviest part of crude oil accounting for a large proportion in modern refineries, while its utilization routes are generally of low efficiency and high energy intensity, and thus economically and environmentally unfavorable. Similarly, bitumen recovered from Steam-assisted gravity drainage (SAGD) process experiences high viscosity and density as well as high content of impurities and thus diluent is often needed for its transportation with additional cost and environmental concerns. Both the diluent containing naphtha fraction recovered SAGD crude and VR are considered as extra heavy crude which are required to be further processed before end use. The goal of the current project is to develop a cost-effective upgrading technology to greatly enhance the quality of extra heavy crudes for the potential productions of clean light fuel and high-value added petrochemicals. This will be done using a technology based on the directly catalytic incorporation of natural gas into the heavy oil at mild temperatures and pressures, leading to enhanced productivity and reduced production cost and thus improved profitability. Furthermore, the incorporation of natural gas into the transportable oil product will minimize greenhouse gas emission from upgrading process, making it more environmentally friendly.

Two-Step super-resolution method to enhance orbital imagery of celestial bodies using ground truth from rovers and drones.

Exploration of other planetary bodies poses multiple challenges. Notably, how can we obtain the most information about a planet or a moon while avoiding risky and costly crewed or robotic missions to the surface? Satellite imagery can partly solve this problematic by providing a relatively cheap alternative to cover massive areas of a planet. Sounds great, what’s the catch? Well, these satellite images have a limited resolution and therefore, limited information can be extracted from them. That being said, recent advances in artificial intelligence could offer us a way to enhance the resolution of satellite imagery. This projects consequently aims to test various artificial intelligence algorithms to help enhance the resolution of Martian orbital imagery. In term, this method could help the Canadian Space Agency and private partners such as Mission Control Space Services select region of particular interest for further investigation.

Feasibility of a pressure retarded osmosis process for Quebec electricity generation

Osmotic power or salinity gradient energy is one type of the renewable energies which is produced by mixing fresh water with salt water. This new environmental friendly energy based on the advantages such as carbon dioxide free and generating power continuously seems to be a good option in which to invest as it will be highly beneficial to Quebec. This project looks to develop expertise related to osmotic power. A pressure retarded osmosis (PRO) power test unit will be designed and installed in Quebec. This will be the first osmotic power system in North America. The project will consist of evaluation and selection of sites for potential pilot testing, determination of pretreatment and cleaning requirements for the membranes, and subsequent design of the test unit. This will provide hands on experience related to the design and operation of a PRO power system in Quebec and enable the evaluation of the viability of this technology for future development for the interns.

Traceability Systems of the Next Generation: Towards Transparency, Agility and Sustainability in Quebec’s Seafood Supply Chain (SSC)

This project brings together university researchers, a Collegial Center for Technology Transfer – CCTT (Merinov), two young companies working in the sector of IT management tools (EOS Nation and reelyActive) and a Non-For-Profit Organization (Cercle-i) in order to address important research and development challenges facing the Seafood Supply Chain of the Gaspésie and Magdalen Islands region. The research team will study the possibility of adopting emerging technologies (including Blockchain, Internet of Things and Analytics) to improve the traceability of seafood, thus increasing the transparency of information, supply chain security and agility. Ultimately, this project may also have a positive impact on the sound management of the natural resources of the seafood industry in Quebec. To face this complex, multidimensional and multidisciplinary issues, the objective of this project is to develop a simulation platform integrating business aspects (supply chain management and data management) and technical aspects (integration of different emerging technologies) in order to be able to assess the potential gains and risks of an innovative traceability system.

Machine Learning Based Classification of Atomization from a Rotating Bell Nozzle

Spray painting in the automotive manufacturing industry is a tough technical challenge. Due to a poor paint job, approximately 25% of cars coming off the assembly line must be reworked or scrapped. Previous research has shown that the droplet size distribution of the paint applied to the vehicle is important for the final coating finish and colour. Paint droplets are formed in an atomization process, and changes to this process can cause considerable differences in the coating finish and quality. In this project, experiments using an automotive paint spray robot will be done to collect atomization data of the paint as it leaves the nozzle. A machine learning based model will be developed to detect when the atomization changes, which can be used to flag defects on the assembly line. This project will further validate Mazlite’s technology in optimizing the automotive paint spray process and accelerate the commercialization of its product line.

Developing a Data Science Tool for Open Government Datasets

In an effort to build public trust, the Government of Canada has made a commitment to move toward an Open Government, where government data will be easily accessible and shared broadly. For the public to benefit from Open Government sources, they will need tools that will allow them to analyze and interpret the data accurately.
Dacture is an alpha-released platform where users can find openly accessible datasets, compute statistical analyses, and build engaging visualizations without needing any expertise in coding or statistics. As a byproduct of giving people the power to conduct data science themselves accurately and reliably, we hope that our platform will contribute to the public’s statistical literacy and trust in data from government sources