Innovative Projects Realized

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

30508 Completed Projects

2882
AB
5105
BC
825
MB
681
NL
860
SK
9051
ON
9491
QC
97
PE
586
NB
1141
NS

Projects by Category

Data Mining and Statistical Analysis of Hydraulic Fracture Performance in the Eagle Ford Formation

As the global supply of oil and gas from conventional reservoirs (i.e., porous rock formations) continues to diminish, it becomes increasingly important to produce these fluids from unconventional (“tight”) reservoirs. Hydraulic fracturing is generally required in order to achieve sufficient production rates from these tight reservoirs. Key questions to be addressed in hydraulic fracture design include the following: How much fluid and proppant (sand) should be injected? How many fractures should be created, and at what spacing? How is the effectiveness of the design affected by the depth, thickness, fluid pressure and temperature of the reservoir? This project will take data from over 1000 wells and use neural network (artificial intelligence) techniques to identify patterns between design parameters, reservoir properties and oil production rates. The outputs of this research will enable the partner organization (Baytex Energy) to design more effective hydraulic fracture treatments, hence increasing oil production and/or reducing well completion costs.

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Faculty Supervisor:

Christopher Hawkes

Student:

Partner:

Baytex Energy Corp

Discipline:

Engineering

Sector:

Mining

University:

University of Saskatchewan

Program:

Accelerate

Improving Resource Estimation and Reconciliation with Machine Learning

Models quantifying the grade and tonnage of mineral deposits form the basis of important and costly decisions for planning, optimization and extraction of a natural resource. Models are initially generated from sparse exploration sampling; however, information is continuously collected until resource extraction. Predicted values that reconcile well with true values following extraction instill confidence in the production forecasts. Failure to meet production forecasts can have crippling effects on cash flow and ultimately result in failure of the project.
In this research a neural-network-based prediction framework is proposed that incorporates production information to the predictive algorithm to improve forecasts of future production, thereby improving reconciliation at a mining project. The proposed method could be used to continually update resource models to improve decisions being made at all scales. This research will benefit the partner company since the incorporation of a wide array of data in manual reconciliation is complex. The proposed research will simultaneously simplify the workflow for the practitioner and improve reconciliation by improving predicted values in unmined areas. This will generate value through increased operational efficiency.

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Faculty Supervisor:

Jeff Boisvert

Student:

Partner:

Teck Resources Ltd (Calgary, AB)

Discipline:

Engineering

Sector:

Mining

University:

University of Alberta

Program:

Accelerate

The Development of Novel Third Generation (3G) Advanced High Strength Steels Steels for Tank Car Applications

Railway tank cars are constructed from TC 128 steel plates, a design that has not changed for more than 50 years. The Lac Megantic rail disaster in 2013 refocused the attention of Canadians on the safety aspects of tank car design and operation, but not so much on the actual properties of the steels used to build them. In this project, we will explore the potential of modern advanced high strength steels as a replacement for TC 128. In particular, significant improvements to the tank wall puncture resistance will be targeted. New alloys will be designed using the latest scientific knowledge. Casting and processing will be done using the state of the art pilot facilities at the CanmetMATERIALS (CMAT) laboratory in Hamilton. Analysis and testing will involve experts at CMAT and at McMaster University. Promising new alloys will be tested in computer simulated accident events by industrial partner Trinity Rail. The aim is to achieve full scale industrial trial status for the new alloys within the next 5 years, ultimately leading to a new class of better performing and safer tank car steels.

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Faculty Supervisor:

Hatem Zurob

Student:

Partner:

Trinity Rail Leasing Inc

Discipline:

Engineering

Sector:

Transportation and warehousing

University:

McMaster University

Program:

Accelerate

UTILISATION DES NOUVELLES TECHNOLOGIES D’ÉDITION DU GÉNOME ET DE SÉQUENÇAGE POUR AMÉLIORER LA SÉCURITÉ DES TRANSFUSIONS SANGUINES

Aux Etats-Unis, plus de 15 millions d’unités de sang sont transfusées chaque année à des patients atteints, entre autres, d’anémies ou de certains cancers. Bien que cette technique soit pratiquée depuis longtemps, plusieurs risques y demeurent associés. Le risque le plus important est l’incompatibilité entre le donneur et le receveur, pouvant résulter dans les cas les plus extrêmes à la mort du patient. Cette incompatibilité repose sur l’existence d’une grande diversité de groupes sanguins qui demeurent difficiles à identifier avec précision. Ainsi, l’identification précise de ces groupes demeure une problématique essentielle du domaine de la transfusion. Pour répondre à ce problème, nous souhaitons utiliser un séquenceur d’ADN nouvelle génération afin de rendre plus efficace l’identification des groupes sanguins et réduire les risques associés à l’incompatibilité. En parallèle, nous proposons d’utiliser les nouvelles technologies de modification génomique pour produire des globules rouges artificiels afin de faciliter la détection des incompatibilités.

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Faculty Supervisor:

Yannick Doyon

Student:

Partner:

Héma-Québec (Montreal)

Discipline:

Life Sciences

Sector:

Health and Related Sciences & Technology; Biotechnology; Pharmaceuticals

University:

Université Laval

Program:

Accelerate

Hemodynamic Impacts of Combining Axial Pumps

Heart failure is a prevalent disease effecting 250,000 people in North America alone. This disease can be treated by the transplant of a donor organ, but insufficient donor organs have led to the development of mechanical circulatory supports which now provide a reliable alternate treatment option for patients. Unfortunately, many patients that could be helped by a mechanical circulatory support are deemed ineligible due to the invasive, open-heart surgery that is required to install such devices. Puzzle Medical Inc. and the McGill group are researching novel pump designs that can deployed non-invasively to increase the availability of mechanical circulatory supports to high risk patients. An important consideration for development of this device is how it interacts with blood and its cellular components. Computational fluid dynamics will be performed on the pump to determine if different design changes will impact its biocompatibility.

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Faculty Supervisor:

Rosaire Mongrain

Student:

Partner:

Puzzle Medical Devices

Discipline:

Engineering

Sector:

Manufacturing

University:

McGill University

Program:

Accelerate

Statistical methods and their applications to biomedical studies with complex study designs

With the increasing complexity of study designs in biomedical research, biomedical scientists are often challenged with tasks of analyzing data measured by complex study design. Although most statistical analyses assume the end results are independent, the outcomes of repeated measurements taken on the same subjects are in fact dependent, which may contribute to the failure of the analysis. This Project will propose an advanced statistical technique, General Equation Estimation, to account for such dependencies and correlations. With the increasing complexity of study designs within the pharmaceutical and biotechnological research disciplines, the partner organization will gain additional expertise and insight with future statistical analysis tasks.

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Faculty Supervisor:

Irina Dinu

Student:

Partner:

Applied Pharmaceutical Innovation

Discipline:

Life Sciences

Sector:

Professional, scientific and technical services; Retail trade

University:

University of Alberta

Program:

Accelerate

The Great River Rapport

The Great River Rapport is an initiative that involves collaboration, consultation, and input from scientists, Indigenous partners, citizens and students. The goal is to provide a report on the health of the Upper St. Lawrence River ecosystem and inspire people to become engaged and aware of how ecosystem health is linked to all of us. Through public workshops, presentations, and online surveys, citizens and students will share their concerns and questions about the health of the river, and River Institute scientists and academic partners will use the public feedback to establish themes, compile scientific data, and identify ecological indicators that define the health of the ecosystem. This process will also reveal environmental trends, predict future impacts, and help identify needs for future research. Outcomes will include a Technical Report and an interactive online space with stories, Indigenous Knowledge, pictures, videos, and educational messages that reflect the concerns of the community.

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Faculty Supervisor:

Frances Pick

Student:

Partner:

St. Lawrence River Institute of Environmental Sciences

Discipline:

Life Sciences

Sector:

Professional, scientific and technical services

University:

University of Ottawa

Program:

Accelerate

Caractérisation de la photostimulation optogénétique spatialement structurée chez la souris

Plusieurs stratégies pour restaurer la vue ont été tentées depuis les années 60 en équipant les patients d’une caméra et en stimulant directement le cerveau. Toutefois aucune de ces tentatives n’a pour l’instant pu se concrétiser et ceci est en grande partie imputable à la façon dont le cerveau est stimulé. Grâce au développement de l’optogénétique, une nouvelle approche de génie génétique, il est maintenant possible d’interfacer le cerveau avec la lumière et de façon beaucoup plus réaliste ce qui devrait se traduire par une amélioration des performances de restauration de la vision. Les outils optogénétiques seront implantés grâce à des virus modifiés et la photostimulation sera effectuée grâce à des matrices de LED. Ce développement va d’abord s’effectuer chez des singes. Toutefois, les caractéristiques précises de la façon de réaliser ces photostimulations sont manquantes. Ce projet va permettre cette caractérisation chez la souris.

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Faculty Supervisor:

Matthieu Vanni

Student:

Partner:

Aix-Marseille Université

Discipline:

Life Sciences

Sector:

Education

University:

Université de Montréal

Program:

Globalink Research Award

Development of a comprehensive feminist performance evaluation framework for international Sexual-and-Reproductive-Health-and-Rights (SRHR) development programs: The case of Inter Pares

The main objective of this short-term Mitacs project is to gather evidence through research to support Inter Pares’ development of Performance Evaluation Framework for their upcoming SRHR program, which aims to improve the sexual and reproductive health and rights of women and adolescent girls in the global south. To learn from the program implementation and to show accountability to stakeholders Inter Pares requires to develop a PEF prior to the launch of the program. However, Inter Pares, as most other non-profit organizations, lack the essential research expertise, resources, and capacity to develop project performance evaluation framework. This research will conduct necessary research for the development of an effective PEF for their needs. It will conduct literature review and thematic analysis to develop a feminist evaluation framework, a set of relevant indicators and relevant data collection techniques. It will be significant for Inter Pares to develop their current and future projects, to secure future funding and donation, to effectively advocate SHR programming to improve their programming.

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Faculty Supervisor:

Annette Burfoot

Student:

Partner:

Inter Pares

Discipline:

Sociology

Sector:

Other services (except public administration)

University:

Queen's University

Program:

Accelerate

Temperature Prediction using Machine Learning

Synauta is a startup bringing the world’s best Internet of Things solutions to water utilities. Our deep industry knowledge prepares utilities for true connectivity to realize energy savings. We provide cyber security, sensors and software. In this project we will create a temperature prediction algorithm to save energy for water treatment plants. More energy can be saved if operators can plan to make more treated water when temperatures are high and less treated water when temperatures are lower. Over a week, the amount of water produced would be the same, but less energy would be used. To do this, Synauta requires a temperature prediction algorithm that can forecast the temperature of water into the future. This technology will provide customers with clear dollar savings as energy can be a major portion of opex costs.

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Faculty Supervisor:

Qing Zhao

Student:

Partner:

Synauta Inc

Discipline:

Computer science

Sector:

Utilities

University:

University of Alberta

Program:

Accelerate

Ecological risk assessment of tire wear particles in water bodies in Interior British Columbia

Microplastic pollution is becoming an emerging environmental issue in developed countries. The tire wearing particles (TWP) have also been categorized as microplastic pollution. TWP size range from a few nanometers to several hundred micrometers and are emitted into the environment. Tire industry is now inclined towards increasing knowledge of TWP origin, mechanism, transmission, and fate in the environment along with their impacts on receiving water bodies. This study aims to identify commonly occurring TWPs and investigate their characteristics, exposure mechanism, and toxicities followed by ERA, to evaluate the impacts on aquatic environment. The project will facilitate in generating a reliable knowledgebase on physicochemical properties, fate and transport, and toxicities of TWP. This project will also increase awareness in identifying the knowledge gaps to perform more comprehensive studies on TWP impacts.

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Faculty Supervisor:

Rehan Sadiq;Kasun Hewage

Student:

Partner:

Kal Tire

Discipline:

Engineering

Sector:

Retail trade

University:

The University of British Columbia - Okanagan

Program:

Accelerate

Unsupervised Anomaly detection using Deep Learning

This project aims at evaluating whether recent results in deep learning models, trained to exploit weak labels can serve to extract meaningful lesion localizations from image-level labels, either from individual scans or given a (longitudinal) sequence thereof. To this end, we will scale up existing models that have been shown to work on 2D images to a 3D context, studying labeling performance as the dataset size grows. If successful, this work will assert the usefulness of DCNNs to provide a general modeling framework to integrate imaging with other clinical patient data into a predictive system that could help support clinical decisions and ultimately improve patient care.
The proposed research project fits within the partner’s scientific roadmap, which is to develop deep learning models suitable to processing clinical data that arises in a sequential fashion at the patient level (longitudinal data), wherein the set of available clinical modalities can be highly variable (heteromodality). The industrial partner has an existing team of full-time researchers dedicated to studying these questions; the intern will attack complementary questions with the help of the team.

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Faculty Supervisor:

Pascal Vincent

Student:

Partner:

Imagia

Discipline:

Computer science

Sector:

Health and Related Sciences & Technology; Information and Communications Technology; Biotechnology

University:

Université de Montréal

Program:

Accelerate

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