Innovative Projects Realized

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

13270 Completed Projects

1072
AB
2795
BC
430
MB
106
NF
348
SK
4184
ON
2671
QC
43
PE
209
NB
474
NS

Projects by Category

10%
Computer science
9%
Engineering
1%
Engineering - biomedical
4%
Engineering - chemical / biological

Effects of Geomechanical Heterogeneity on Wormhole Development during Cold Heavy Oil Production – Phase 1

Canada possesses vast resources of heavy oil, which is oil that is too thick to flow through porous sandstone reservoirs and into production wells at economic rates when conventional operating practices are used. Since the mid 1980’s, heavy oil operators have demonstrated their ability to increase heavy oil production rates by encouraging the creation of porous and permeable zones (“wormholes”) within their reservoirs by allowing sand grains to detach from the reservoir rock and flow into the well (along with the oil). However, in order to improve the efficiency of these operations, a better understanding of the processes controlling wormhole growth is required. The proposed project will result in the design of a laboratory testing system that will lead to a better understanding of wormholes in heavy oil reservoirs.

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Faculty Supervisor:

Chris Hawkes

Student:

Michael Pereira

Partner:

Petroleum Technology Research Centre

Discipline:

Engineering - civil

Sector:

Oil and gas

University:

Program:

Accelerate

Balancing costs and benefits of invasive species management for endangered wetland reptiles

Invasive species can have major effects on the landscape, but sometimes their effects are assumed to be negative before they are scientifically tested. The common reed is an extremely tall and robust grass that is moving rapidly into wetlands across Canada. Common reed is believed to threaten some reptiles by reducing their access to suitable habitats, but this has not been tested. In this project, we use state-of-the-art tracking equipment to directly test whether endangered turtles and snakes are forced to change their habitat use in areas impacted by the common reed. We also test the impact of current control measures for common reed (application of the herbicide glyphosate) by assessing chemical loads in our study wetland. Our research fills critical knowledge gaps that will allow managers to make informed decisions, balancing the benefits of controlling this invasive plant against the potential costs of chemical control.

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Faculty Supervisor:

Christina Davy

Student:

Rachel Dillon

Partner:

Wildlife Preservation Canada

Discipline:

Environmental sciences

Sector:

Environmental industry

University:

Program:

Accelerate

Advanced cluster and predictive analysis tool development for corporate real estate energy usage

The objective of this project is to develop an analytics tool for REALPAC to use to better classify buildings using the “20 x ‘15” dataset collected by REALPAC since 2009. Preliminary analysis has been conducted of this data in past years, but this has been limited to a simple retrospective analysis. The tool that will be developed will incorporate “big data” techniques such as machine learning, which will allow the classification of buildings as “likely strong performers”, “likely poor performers”, “high probability for significant energy conservation”, and “low probability for significant energy conservation”. The intern will undertake data cleaning and classification tasks, as well as the development and testing of the predictive models and associated algorithms that will make up this tool. This tool, in turn, will provide REALPAC with a depth of insight previously unavailable to inform both public policy as well as corporate sustainability strategies of its member organizations. TO BE CONT.

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Faculty Supervisor:

Jenn McArthur

Student:

Carleen Lawson

Partner:

Real Property Association of Canada

Discipline:

Architecture and design

Sector:

Energy

University:

Program:

Accelerate

Investigation and development of an air-core dry-type reactor noise prediction model

Air-core dry-type electrical reactors are integrated into power system infrastructures to limit current and regulate voltage in transmission lines. These reactors, are designed and built to facilitate customer specific requirements using an elementary noise prediction model, which was developed almost 30 years ago. With increasingly stricter noise emission guidelines set by the environmental regulatory bodies, the need to better predict and meet specific noise requirements has become more important to the design and manufacturing of the reactors. The objective of the research is to identify the fundamental structural and electrical mechanisms of noise generation for the reactors and to use this information to develop a more advanced noise prediction model. Having the ability to accurately predict noise emissions at the early design stage will not only allow Trench to meet the specific noise requirements for their customers, but also give them an important competitive advantage over other manufacturers.

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Faculty Supervisor:

Colin Novak

Student:

Frank Angione

Partner:

Trench Canada

Discipline:

Engineering - mechanical

Sector:

Energy

University:

Program:

Accelerate

In-Seam Electromagnetics to Identify Anomalous Near Mine Brine-FilledGeological Layers

One of the major issues facing potash mining in Saskatchewan is the potential for water to enter
the mine from water-bearing rocks above mining operations. Rocks near-mine are normally
considered dry and low risk. However, under some conditions, in localized areas, there is the
potential for unsaturated water to have been introduced into the rock formations near the potash
ore. In this project, we will perform electromagnetic surveys at various underground PotashCorp
mine sites to determine the effectiveness and detection limits of these electromagnetic
techniques at finding water-logged areas. The purpose of this research is threefold: to determine
the size and parameters of these water-logged areas, to determine the detection limits of the
equipment underground, and make recommendations on the best performance device and
technique that is currently available. In addition to underground surveys, the project will also consist of computer modelling of potash mines, constructed at the University of Saskatchewan,
which will aid in our understanding of potash mine environments and in the interpretation of the
results gathered from the underground surveys.

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Faculty Supervisor:

Sam Butler

Student:

Todd LeBlanc

Partner:

PotashCorp

Discipline:

Geography / Geology / Earth science

Sector:

Mining and quarrying

University:

Program:

Accelerate

Phase II: Genomics and Lipid Studies for Flavour Selection in Pork

The hog industry in Manitoba is a very efficient means of converting grains and pulses into
high quality protein. Fresh pork is a healthy and nutritious source of protein, yet demand
remains static. One of the main reasons cited by consumers for not choosing pork is the
absence of good taste in modern pork. The objective of this project, which is a continuation
from Phase I of this project is to get fresh pork back on the dinner table by restoring its
flavour. In Phase I, sensory analysis to test the flavour of 1,350 pork tenderloin samples from
an array of breeds supplied by Maple Leaf was conducted, in addition to metabolomics
analysis, which gave information on precursors critical for good flavour development.
Sensory data and characterization of the metabolomics profile, coupled with genomics and lipid analysis in the final phase of this research (Phase II), will enable us to understand the
relationship between sensory outcomes and factors that play of role in influencing taste and
acceptability to determine which samples result in the most naturally flavorful meat samples.
This will allow for customized recommendations to be made to the hog industry, with
specificity regarding diets, ideal genetic strains, and conditions to yield the most desirable
pork for consumers. TO BE CONT.

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Faculty Supervisor:

Michel Aliani

Student:

Erin Goldberg

Partner:

Maple Leaf Foods

Discipline:

Food science

Sector:

Agriculture

University:

Program:

Accelerate

SRRM4 protein purification and antibody production

The SRRM4 gene has been recently identified as a key protein responsible for a subtype of highly aggressive prostate cancers, called neuroendocrine prostate cancer (NEPC). Detection of SRRM4 protein in patient tumor biopsies is therefore important to predict and diagnose NEPC, so that early and more effective therapeutic means can be in place. Unfortunately, there is no SRRM4 antibody currently available that can be used for pathological analyses. This project will join force of academic and industrial expertise to develop a SRRM4 antibody that can be used in clinic for NEPC diagnosis.

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Faculty Supervisor:

Xuesen Dong

Student:

Yinan Li

Partner:

Applied Biological Materials

Discipline:

Medicine

Sector:

Life sciences

University:

Program:

Accelerate

Digital tools for Ottawa’s Cultural Heritage Conservation: Ottawa New Edinburgh Club Boat-House (ONEC)

Heritage information plays an essential role in the adequate planning and monitoring of conservation strategies. Digital tools have revolutionized the speed and the accuracy in recording heritage places. This pilot project addresses how to integrate information gathered through digital technology into coherent graphic record (floor plans, section, and elevations) and how to understand the relationship between recording and good conservation decision-making. It will test how to integrate strategies for emerging digital technologies in the rehabilitation of architectural heritage. This documentation activity will fill the gaps between digital innovations in the recording technology and traditional heritage documentation and will contribute as a new form of information on the current state of the building that can be manipulated to explore and demonstrate a range of enhancement and adaptive reuse actions. The research motif will be one of the few examples of aquatic architecture in Canada from early 20th century: the Ottawa New Edinburgh Club (ONEC).

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Faculty Supervisor:

Mariana Esponda

Student:

Kathleen Coulthart

Partner:

Lingbawan Storer

Discipline:

Engineering

Sector:

Management of companies and enterprises

University:

Program:

Accelerate

Learning tools to predict treatment responses for schizophrenia from neuroimaging data

Schizophrenia is a chronic mental disorder associated with a significant health, social and financial burden, not only for patients but also for their families, and society. However, the current treatment methods have been only partially successful, mainly due to the inter-individual differences between patients, which means that a treatment that is successful for one patient, might not work for another. Here, we will explore ways to determine whether a treatment will be successful based on measurable features, including many derived from various modalities of magnetic resonance imaging of patient’s brain. The proposed project aims to develop systems that can learn models that will enable psychiatrists to administer “patient-specific treatment”, by using earlier clinical experience to determine which treatment is best suited for each individual patient, based on measurements from brain scans. Such an objective evidence-based approach can potentially improve patient outcome as these
clinical decisions would be less influenced by the subjective diagnostic tools that are currently used in
psychiatric practice.

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Faculty Supervisor:

Russell Greiner

Student:

Sunil Kalmady

Partner:

IBM Canada

Discipline:

Computer science

Sector:

Medical devices

University:

Program:

Accelerate

Estimation and Prediction of Censored Arrival Processes with Censoring for Replenishable Item Purchases

The aim of the project is to predict future customer demand for repeat-buying items based on available customer purchase records. However, the purchase history for a single customer may not be sufficient to base predictions on. Also, some purchase records might be missing due to sales events at competitors’ locations. Thus, treating each customer as a replicant of the average customer and averaging inter-purchase times to predict future demand will likely be an inadequate approach. For this project, a generalization of traditional models in marketing research will be studied and a more flexible model that accounts for time-varying model features will be investigated to better model the data generation process to provide accurate forecasts that will bring foreseeable benefits in logistical efficiency.

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Faculty Supervisor:

Nancy Reid

Student:

Tianle Chen

Partner:

Rubikloud Technologies Inc.

Discipline:

Statistics / Actuarial sciences

Sector:

Information and communications technologies

University:

Program:

Accelerate

Coupling event sampling to ColiMinder® high-frequency monitoring of E. coli for improved microbial risk assessment in source waters (COLIRISK)

Safe drinking water supply is a daily need but it can be seriously threatened by microbial hazards originating from fecal contamination of source water, especially following periods of intense rainfall. In order to assess drinking water intakes (DWIs) vulnerability to fecal pollution and to take cost-effective decisions in case of hazardous events, it is urgent to implement early-warning systems. A recent enzyme-based technology, ColiMinder® enables to measure E. coli in water at high temporal resolution (every 30 minutes). In order to implement it for effective microbial risk assessment at DWIs, research first needs to clarify how E. coli signals are representative of the prevalent microbial risk (i.e. of pathogen loads). The present project thus aims at developing a simple and original system that integrates the ColiMinder® with an automated sampling device for event-based sampling and analysis of pathogens and fecal source tracers. This integrated system should prove useful in the implementation of the ColiMinder® at Canadian DWIs with the purpose to improve the assessment of their vulnerability to fecal pollution.

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Faculty Supervisor:

Sarah Dorner

Student:

Jean-Baptiste Burnet

Partner:

Le Groupe AquaCion Inc

Discipline:

Engineering - civil

Sector:

Natural resources

University:

Program:

Accelerate

Characterizing topography of signal fidelity in a low-cost fNIRS device

High-performance athletes have learned that even after they have exhausted their bodies during training, they can continue to train their minds for an extra edge. Imagining your sport engages many of the same brain areas used to actually play your sport, and it has been shown that such mental practice can improve sport performance. However, simply sitting and imagining isn’t very engaging and doesn’t provide either the athlete nor their coaches with any information regarding how well they are engaging in mental imagery. Axem Neurotechnology has developed a system to monitor the brain activity of athletes engaged in mental imagery so that they can receive real-time feedback on how well they are engaging the brain areas that will help them perform their sport. This project is helping to explore how best to design the brain monitoring device that Axem will use in their first-generation product.

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Faculty Supervisor:

Shaun Boe

Student:

Michael Lawrence

Partner:

Axem Neurotechnology

Discipline:

Psychology

Sector:

Information and communications technologies

University:

Program:

Accelerate

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