Innovative Projects Realized

Explore thousands of successful projects resulting from collaboration between organizations and post-secondary talent.

29670 Completed Projects

2811
AB
4990
BC
801
MB
663
NL
825
SK
8841
ON
9197
QC
95
PE
568
NB
1088
NS

Projects by Category

Development of a wet fractionation process for producing millet protein ingredients

The quest for finding plant protein alternatives to traditional protein sources (e.g., meat, dairy, eggs and soy) used by the food industry is being driven by consumer demand for healthier choices, population growth, environmental sustainability and regulator influencers. Millet represents a staple food for developing countries, especially in Africa and Asia, and represents rich source of dietary fibre, minerals and B-complex vitamins. In terms of their protein content, levels range between 8-10% by weight. The present research focuses on developing a wet fractionation process (e.g., alkaline extraction followed by isoelectric precipitation) to obtain protein isolates with protein levels >80%, and then to characterize their functional properties (e.g., solubility, emulsification, foaming and water/oil holding capacities) and their protein quality (e.g., amino acid profile, digestibility and PDCAAS). Depending on the protein ingredients yield and functionality, the process will be scaled up at GFR Ingredients (Alberta) to gain sufficient quantities of materials for product application testing. This new ingredient could be positioned to fill the market gap in the emerging plant protein sector.

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Faculty Supervisor:

Michael Nickerson

Student:

Partner:

Rainfed Foods Ltd.

Discipline:

Life Sciences

Sector:

Manufacturing

University:

University of Saskatchewan

Program:

Accelerate

Micro Geothermal Power Generation in Depleted Wells

Engineers use the earth’s heat to create heat pumps, to store energy, and to generate power. As the world moves away from carbon fuels, geothermal resources are increasingly attractive, promising sustainable, reliable sources of energy. Fortunately, the oil industry has already provided infrastructure to access this resource—depleted horizontal oil and gas wells drilled to a minimal depth of 1500 m, where a supply a steady heat can be sourced at a minimum of about 60 °C. While much geothermal research has focused on conventional geothermal using convective heat transfer at temperatures above 150 °C, this project seeks to optimize heat extraction from the greater reaches of these abandoned wells, enabling partner GeoGen Technologies Inc. to create an operational and economic system to perform such extraction—thus developing a sustainable energy resource and mitigating the environmental costs of well closure or cleanup.

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Faculty Supervisor:

Abdulmajeed Mohamad

Student:

Partner:

GeoGen Technologies

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

University of Calgary

Program:

Accelerate

Algorithme de résolution de conflits pour l’application dynamique de règles commerciales

Les solutions d’optimisation et d’aide à la décision logicielles développées par ExPretio comportent un « moteur de règles commerciales » qui permet aux utilisateurs de configurer les modèles d’optimisation par l’ajout de contraintes, de sorte que les résultats produits en sortie respectent des stratégies commerciales de la firme utilisant les logiciels. Comme le mécanisme de règles offre beaucoup de flexibilité aux utilisateurs, il se peut que, par inadvertence, deux ou plusieurs règles soient contradictoires entre elles. Comme les règles ne sont pas évaluées au moment de leur écriture, mais plutôt de manière asynchrone dans le cadre d’un traitement d’optimisation de masse (qui typiquement est exécuté durant la nuit), le logiciel doit être en mesure, par lui-même et sans intervention humaine, de régler efficacement les conflits en relaxant de façon appropriée les règles qui contribuent au conflit.

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Faculty Supervisor:

Gilles Savard

Student:

Partner:

ExPretio Inc Technologies

Discipline:

Engineering

Sector:

Information and Communications Technology; Finance and Insurance

University:

École Polytechnique de Montréal

Program:

Accelerate

Energy assessment of AI Shading – A Smart Blind technology

The green building movement within building designs has put a spotlight on the application of green technologies to reduce the energy consumption and overall carbon footprint of residential and commercial buildings. In general, approximately half of a building’s total energy usage goes towards heating, cooling, and lighting requirements [1]. According to a recent report from the Attachments Energy Rating Council (AERC), fenestration products such as windows account for up to 30% of residential heating and cooling energy needs [9]. The application of smart blind systems such as AI Shading aim to minimize a building’s total energy consumption by reducing the heating and cooling load contributions from windows. Through the automated adjustment of blind configuration, smart blind systems reduce the indoor solar energy gain during the cooling season and limit indoor conductive energy losses during the heating season. This study works to quantitatively assess the energy-saving potential of AI Shading for application in residential and commercial buildings within Canadian climate zones. A computer-generated energy simulation model and prototype field study will be used to assess and verify the performance of AI Shading during both the Canadian heating and cooling seasons.

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Faculty Supervisor:

Lexuan Zhong

Student:

Partner:

26 Celsius;AI Shading

Discipline:

Engineering

Sector:

Information and cultural industries; Manufacturing; Professional, scientific and technical services

University:

University of Alberta

Program:

Accelerate

Leveraging native species restoration and remote sensing techniques for large-scale dust mitigation enhancement

This proposal aims to advance large-scale dust mitigation strategies in hydroelectric reservoir sites through improved revegetation outcomes, and through leveraging revegetation efforts to additionally enhance local biodiversity and cultural values. Our team has developed a strategic partnership between university researchers, BC Hydro, environmental industry partners, and the local First Nations community to provide an interdisciplinary and stakeholder-informed approach for decision-making that builds on existing reservoir mitigation programs and knowledge. Focusing on the Williston Reservoir in British Columbia, we seek to determine which plant species are able to provide the best within-year dust control and between-year soil stability, with species selection processes that prioritize native species with potential cultural value to local communities. We will use lab and greenhouse trials to test growth rates and success of a range of potential plant species, with and without soil amendments that may enhance the growing conditions of the harsh reservoir drawdown zone. Lab results will guide large-scale field trials at Williston Reservoir. These trials will cross the complex topographic landscape of the drawdown zone, allowing continuous assessment of plant performance changes based on location and local conditions.

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Faculty Supervisor:

Nancy Shackelford;Christopher Bone

Student:

Partner:

Chu Cho Industries

Discipline:

Earth science

Sector:

Professional, scientific and technical services

University:

University of Victoria

Program:

Accelerate

Supply chain sustainability and risk management in the post-Covid context: Assessing and deploying industry 4.0 solutions

The COVID-19 pandemic has exacerbated the challenges Canadian manufacturers face in managing increasingly complex, multi-tiered global supply chains, including a wide rang of sustainability issues ranging from forced labor to carbon emission management. Industry 4.0 technologies – including the internet of things (IoT), blockchain, and machine learning – can potentially address these issues via enhanced transparency, predictability, and reliability. There remains, however, considerable uncertainty as to how these technologies can be deployed, integrated, and managed. This project internship will support the “Accelerating the economic recovery by building resilience in manufacturing” Open Innovation Challenge, hosted by Bonjour Startup Montréal in collaboration with Desjardins bank. Drawing on a variety of research resources and networks, the Intern will ensure that tech start-up proposals are successfully assessed and matched to Canadian manufacturers seeking solutions to their SSR challenges, helping to foster valuable partnerships between these companies.

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Faculty Supervisor:

Jose Carlos Marques

Student:

Partner:

Bonjour Startup Montreal

Discipline:

Business

Sector:

Other services (except public administration)

University:

University of Ottawa

Program:

Business Strategy Internship

Hybrid modeling framework for flood prediction

A hybrid computational framework for short-term flood prediction in urban watersheds (characterized by overland runoff) will be developed to improve prediction accuracy. The framework aims to accurately predict an event, e.g. flood or no-flood, as opposed to traditional methods which estimate water flow characteristics, e.g. 6 feet above flood stage. Successful early prediction of these events can help authorities to take appropriate mitigation measures and to minimize losses from the flooding. The framework requires only current and historical data on water levels and precipitation in the area of interest (such as those collected by TRCA’s flood monitoring gauge network). The development of the new framework will complement existing hydrologic simulation systems to improve and enhance services provided by TRCA to local agencies and public.

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Faculty Supervisor:

Marina Erechtchoukova

Student:

Partner:

Toronto and Region Conservation Authority (Toronto, ON)

Discipline:

Computer science

Sector:

Arts, entertainment and recreation; Professional, scientific and technical services; Public administration

University:

York University

Program:

Accelerate

Assessment of current vision-based machine learning modelling efforts in directed energy deposition on defect prediction

Metal-based direct energy deposition processes ideally require feedback sensing of the deposition quality using camera detectors as they provide spatial and temporal state signatures of the process. Image processing algorithms are challenging to develop due to changing process operating conditions. Machine learning models have recently gained in popularity due to their ability to predict process defects using various monitoring technologies, and in particular vision-based cameras. The existing machine learning model architectures in literature are however trained on image-based datasets acquired through in-process recording of a single AM machine, a single camera setup and a limited set of process influencing parameter combinations. Comparison of the existing models is therefore challenging. Furthermore, the generalizability of these models is questionable, since none of the models evaluate model performance on unseen data, acquired from a different DED machine setup. A thorough investigation and comparison of the current state-of-the-art machine learning architectures on a large diverse dataset, acquired through vision camera based monitoring of several different DED machines and camera setups is however currently missing. The objective of this research is to compare the state-of-the-art machine learning architectures which use vision-based cameras to predict defects during metal-based material deposition using DED machines.

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Faculty Supervisor:

Mihaela Luminita Vlasea

Student:

Partner:

I-INC Foundation for Business Development

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

University of Waterloo

Program:

Accelerate

La créativité numérique au service des arts de la scène: Le cas du Quartier des spectacles de Montréal

La ville de Montréal est reconnue pour sa dynamique culturelle et festive. Aujourd’hui et afin d’appuyer cette réputation, de nombreuses opportunités voient le jours et de nouveaux modes de création et de diffusions émergent. Le but de cette recherche est de rendre compte de la place accordée aux arts numériques au sein des arts de la scène comme forte valeur ajoutée touristique pour la ville de Montréal. Cette étude se réalisera conjointement avec le Partenariat du quartier des spectacles, acteur majeur du développement culturel et ayant pour mandat de favoriser la création, l’innovation, la production et la diffusion culturelle au sein de ce quartier du centre-ville.

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Faculty Supervisor:

Paul Arseneault

Student:

Partner:

Quartier des Spectacles

Discipline:

Sociology

Sector:

Arts, entertainment and recreation

University:

Université du Québec à Montréal

Program:

Accelerate

Understanding shortcut learning in training deep neural networks for computer vision

Recent years have seen a great amount of success in using deep learning in publicly available and standardized datasets such as ImageNet. However, deep learning methods fail to perform as well in a lot of real-world applications such as medical imaging datasets. One potential reason for this challenge is that deep learning models tend to learn superficial features (aka spurious features) such as texture, color, etc, which merely correlate with the labels instead of discriminative features that can explain the labels. In medical imaging in particular, shortcut learning could potentially have fatal implications. Therefore, understanding this phenomenon and taking measures to prevent them can improve neural networks in terms of performance, generalizability to unseen demographics and interpretability. In this project, we will explore representation learning techniques for learning generalizable features. We will study the efficacy of these techniques on benchmarks that better reflect properties of real-world medical imaging problems.

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Faculty Supervisor:

Samira E Kahou

Student:

Partner:

Imagia

Discipline:

Computer science

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

École de technologie supérieure

Program:

Accelerate

Bio-Aggregate Sustainable Concrete panels for Farm Buildings

This research aims in utilizing agriculture wastes in production of sustainable eco-friendly concrete panels for floors and walls in farms building. The mechanical and durability performance of the concrete panels exposed to the aggressive agriculture environment will be evaluated. The research will investigate the effects of different factors mainly related to physical properties of used wastes and binding materials. This leading-edge research on reusing/recycling agro-wastes in construction materials will allow IRDA to economically and sustainably transform such wastes into a high-value product. IRDA will lead the research in this area focusing on developing and innovating sustainable building materials for farm buildings and various construction applications, with an anticipated measurable impact on the Canadian specifications for concrete.

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Faculty Supervisor:

Ahmed Soliman

Student:

Partner:

Institut de Recherche et de Développement en Agroenvironnement

Discipline:

Engineering

Sector:

Agriculture; Education; Professional, scientific and technical services

University:

Concordia University

Program:

Accelerate

Exploring the Utility of TVISFD with MLSE LaunchPad in Moss Park

MLSE LaunchPad is a sport for development (SFD) organization in Moss Park, Toronto that uses sport and physical activity to build healthy communities. A trauma- and violence- informed approach is a treatment practice used to provide safe and inclusive spaces for traumatized youth, however, it is rarely incorporated in SFD programs. In collaboration with MLSE Launchpad, this research will explore how trauma- and violence- informed approaches are used in SFD to prevent gender-based violence and intersecting health inequities. Research participants will engage in arts-based methods and interviews to discuss their experiences in sports programming, including how their gender identities have impacted their experiences, and how these programs have impacted their health and well-being.

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Faculty Supervisor:

Lyndsay Hayhurst

Student:

Partner:

MLSE LaunchPad

Discipline:

Sociology

Sector:

Arts, entertainment and recreation

University:

York University

Program:

Accelerate

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