Innovative Projects Realized

Production of synthesis gas via Dry Reforming of Methane: Fundamentals and Applications

The aim of this proposed research is the development of an efficient technology to convert carbon dioxide (CO2) via Dry Reforming of Methane (DRM) to produce value-added products. DRM, one the promising CO2 utilization technologies, has gained much attention as not only it reduces greenhouse gases (GHG), but also converts them to a valuable product, syngas. There are two main knowledge gaps associated with DRM process, which hinders the industrial application: 1. Catalyst deactivation, and 2. The high energy requirement for the CO2 conversion reaction. This proposed research is designed to bridge these gaps by using novel catalysts to mitigate coke formation, followed by high-level heat recovery and plant-wide optimization. These innovations will ensure that the technology will advance towards commercialization. This study will allow us to meet major milestones required to reduce GHGs emissions and transition to a lower carbon economic system.

Conservation de la cohérence physique inter-variable dans la correction de biais multivariée des simulations climatiques

Les scénarios climatiques utilisés dans les projets d’impact, de vulnérabilité et d’adaptation aux changements climatiques sont issus d’une combinaison des simulations numériques du climat avec des observations. Les méthodes impliquées dans cette combinaison visent à optimiser l’équivalence statistique avec les observations, et doivent garantir la cohérence physique entre les variables du scénario (p. ex., température, humidité, pression, etc.). L’objectif du projet est d’étudier comment certaines méthodes récemment développées et de type multivarié affectent la cohérence physique inter-variable. Les résultats permettront d’améliorer les stratégies de combinaison des séries temporelles (simulations et observations) dans les contextes climatologiques où les corrélations statistiques importent, et ce d’une manière qui préserve la cohérence physique contenue dans les simulations numériques. Ceci concerne notamment les risques liés à la santé des travailleurs en période de canicule, où la combinaison entre température et humidité importe, ainsi qu’aux feux de forêt, où de nombreuses variables entrent en jeu simultanément.

Sustainable concrete incorporating low-grade metakaolin

The production of cement and concrete is estimated to account for 5-8% of globally man-made carbon dioxide emissions. The use of supplementary cementitious materials (SCMs) such as metakaolin to partially replace Portland cement in concrete is the most effective method to reduce the carbon footprint of cement industry. Pure metakaolin, however, is expensive and not widely available. This research focuses on exploring the potential of using low-grade and locally available metakaolin in Canada as SCMs in concrete. Synthetic calcium silicate hydrate (C-S-H) will be used to accelerate the hydration of cement and enhance the reactivity of low-grade metakaolin, ultimately improving the properties of concrete. The expected results will advance our knowledge in terms of the synergistic effects of low-grade metakaolin and C-S-H seeds. The successful study of this research will also promote wider application of low-grade metakaolin in concrete, which will contribute to the reduction of the carbon emissions associated with the construction industr

ELSTec : Efficient lightweight structures made by automated fiber placement (AFP) of thermoplastic composites

Led by Prof. Steve Tsai from Stanford University, Palo Alto, USA, new methods to efficiently design, analyze, optimize and manufacture high-performance light weight panels made of carbon fiber reinforced composites and thermoset resins have been developed by an international consortium. The ideas already available give indication to massively reduce cost, maximize light weight efficiency and increase the strength and stiffness of traditional (legacy) design of fiber reinforced composites. Ideas were presented in an overview at a conference organized by Concordia University in 2019 which has led to the idea to transfer this also to more environmentally-friendly materials made by automated manufacturing technique (i.e., thermoplastic composites made by automated fiber placement).

The planned project deals with the transfer of these ideas to sustainable materials, especially thermoplastic resins at a first step. Later, natural fibers might be added. Especially the simplified procurement of material data but also automated manufacturing of skin-grid structures should be investigated within the proposed project. There is a strong indication that environmentally-friendly high performance low cost lightweight structures can be obtained to be used by many industries, e.g. aerospace, railway, automotive, shipbuilding, wind turbines, investment goods and more.

Conservation Policy for Effective Local Resource Governance: A Case Study in the Kailash Sacred Landscape Conservation and Development Initiative

This research project is part of a comparative study on conservation governance in two landscape conservation initiatives: the Tallurutiup Imanga National Marine Conservation Area in Nunavut, Canada and the Kailash Sacred Landscapes Conservation and Development Initiative in Uttarakhand, India. Hosted by the Wildlife Institute of India, the Uttarakhand component of this project aims 1) to understand the role of local resource users in managing protected areas 2) to identify conditions which support effective governance; and 3) to make recommendations for long-term management which empower local resource governance institutions to work effectively and contribute to conservation goals. The methods will consist of legal analysis, semi-structured interviews, participatory mapping, and focus groups with policymakers, forest managers, and local community members. This research follows community-based participatory research protocols, involving community input throughout the research process. The expected outcomes of this internship are a series of culturally appropriate policy briefs on resource governance, and a set of case-specific recommendations to improve the implementation of forest governance policies in alpine villages within the Kailash project area in Pithoragarh District, Uttarakhand, prepared and issued jointly with local partners.

Contrasting Predictors of Regional versus Local Rarity Among Bryophytes of Haida Gwaii, BC

My masters thesis is on contrasting predictors of regional versus local rarity among the bryophytes of Haida Gwaii, B.C. This work involves sequencing regions of the DNA from bryophyte species collected from the field in Haida Gwaii as well as herbarium specimens to reconstruct bryophyte evolutionary relationships – their phylogeny – and to construct a ‘DNA barcode library’. This information can then be used to determine the correlation between key life history traits and rarity. Through the Mitacs Globalink Research Award, I will be able to collaborate with Dr. Yang Liu, an expert on bryophyte genomics at the Chinese Academy of Sciences, Fairy Lake Botanical Garden, and will have access to the state-of-the-art DNA sequencing facilities at the Botanical Garden. This opportunity will provide me with training in sequencing techniques for DNA barcoding, new sequencing protocols, and the analysis of next generation sequence data.

An in vitro Platform of Antigen-Presenting Cells to Evaluate Critical Quality Attributes in Vaccine Formulations

Sanofi-Pasteur is developing new vaccine formulations that need to be evaluated on their efficacy and potency. Traditionally, the use of animal models to predict human immunity has been accepted as the best way to select vaccine formulations. However, animal models can be costly and time-prohibitive, and the assays employed to assess vaccine efficacy and potency are not ideal for rapid screening and optimization of multiple formulations. To overcome these limitations, we propose to test new vaccine formulations utilizing laboratory cultured macrophage cells. Vaccine interactions with macrophages ultimately determine the effectiveness of a given formulation.

Operationalizing Medical AI: Intelligent Health Support and Devices

There is more to Artificial Intelligence than the algorithm. Intelligent systems must also consider how data is acquired, trade-offs between algorithmic approaches, and what action is taken based upon algorithmic decisions. Lunar Medical, Inc. performs research and development in the area of intelligent and operational space medicine. This internship will investigate the full range of necessary components that make up intelligent health support and devices for the objective of operationalizing medical artificial intelligence. The intern will investigate medical advisory systems, decision support algorithms, and external defibrillators. Outcomes will include verification and validation of advisory medical algorithms and a prototype intelligent defibrillator. The overall objective is to provide experience and insight to medical AI developers to improve the algorithms for systems and operations.

Centre for Operations Excellence Industry Projects 2019

The Centre for Operations Excellence Industry Projects 2019 consists of seven sub-projects sponsored by five different industry partners. Each sub-project represents an important challenge for its sponsor. These sub-projects include Extracting data from unstructured text of NOTAMs to assess relevance (Boeing 1), Developing a fix effectiveness engine to perform data-driven predictive maintenance (Boeing 2), Initial Claim Routing Rules Optimization for Worksafe BC (WSBC), , Optimizing Production of Clean Coal Products for Teck Resources (Teck), A Data Driven approach to Comparing Site Costs and Evaluating Cost Performance for Teck Resources (Teck), Developing personalized rewards to increase emotional loyalty among London Drugs LD Extras members (London Drugs), and Authority: Rate Setting and Workforce Planning Scenario Analyser (Pacific Pilotage Authority)

Program to Advance the Treatment of Opaque Fluids using Ultraviolet Technologies (Project extension)

This research project is intended to assess the efficacy of thin film reactors in achieving disinfection of opaque fluids (juices, milk, and wine). These opaque fluids present new challenges for reactor testing and modeling. A thin film reactor will be implemented and its performance will be evaluated against opaque fluids. Consequently numerical modeling will be conducted using computational fluid dynamics for the thin film reactor to predict the reactor performance. This will enable Trojan Technologies to understand the reactor performance and hydraulic efficiency of the reactor. It will also give insights if thin film reactors could be used for commercial relevant fluids.

La Grande Tournée

Dans ce projet de recherche, l’organisme partenaire, le Regroupement des Maisons de Jeunes du Québec vise à dresser, avec le soutien de la chercheure principale et de la stagiaire, un portrait des instances régionales de gouvernance dans les 16 régions desservies et à qualifier l’intervention qui se déroule dans les maisons de jeunes afin de démontrer dans quelle mesure les intervenants communautaires contribuent au développement de jeunes actifs, critiques et responsables. Dans les efforts de prévention du décrochage scolaire, le développement de l’autonomie et des sentiments de compétence et d’appartenance a une influence positive sur la persévérance et la réussite. Il s’agit ici de démontrer la complémentarité de l’intervention communautaire avec les efforts de prévention qui ont cours dans le domaine scolaire.

Edge-Twin based Framework for Real-Time AI Applications for Vehicular Scenarios

Edge computing is expected to play a transformative role for future AI applications in 5G networks by bringing cloud-style resource provisioning closer to the devices that have the data. Instead of running resource-intensive AI applications at the end devices, we can consolidate their execution at the edge, which brings many benefits, such as eliminating the redundant task processing, running machine learning (ML) tasks with sizeable data sets and running ML tasks in a spatial context that is shared by many devices. The downside of using edge computing for AI is the latency of transporting the data from the end device (e.g., a vehicle) to the edge servers and processing it there and getting the results back to the end device. This project will focus on developing a novel edge twin concept that extends the digital twin idea to maintain computing state at edge computing systems. The objective of edge twin is to model and control a complex system of IoT in a highly responsive manner. To achieve this objective, we will investigate many ideas, and one of them is time shifting.