Innovative Projects Realized

Model Calibration and Optimization of Oil Sands Tailings Electrokinetic Dewatering

Alberta oil sands mining operations have produced about 1 billion m3 of fluid fine tailings (FFT) stored in large “ponds” up to 10 km long and 4 km wide. FFT is a stable colloidal suspension that is forecasted to take decades to dewater if left undisturbed. The EKS-DT process, developed by ElectroKinetic Solutions, has the potential to be a cost-effective technology for dewatering FFT; understanding how the FFT properties respond to the EKS-DT process is a critical step in commercializing the technology. The EKS Model is a computer simulation program designed for this purpose. The proposed research will generate the data needed to improve the accuracy of the simulation. This information will be used to improve the efficiency of the EKS-DT process, bringing the technology closer to commercialization. Once commercialized, the EKS-DT process will enable Canada to reduce the significant environmental liability presented by the large FFT inventory.

Automatic Generation of Career Advice Articles

Indeed.com is a well-known employment-related search engine for job listings worldwide and is the number one job site in Canada. In addition to job listings, Indeed provides other job seeker content like career advice articles about jobs and required skills for different professions. Indeed hires freelance human writers to produce these articles. However, human writers are slow, expensive, and can produce content that varies drastically in quality. Furthermore, as Indeed approaches content saturation of popular/generic JobSeeker search queries, the return on investment of content writers will decrease by orders of magnitude as they begin to serve less popular search queries.
To expedite this process and reduce costs of human writers, Indeed requires to build an Artificial Intelligence (AI) system that produces career-related content automatically. In this research project, we will build on state-of-the-art neural natural language generation models to automatically generate relevant content given a JobSeeker’s Google search query. Our goal is to generate articles that humans qualitatively evaluate as comparable to existing articles (i.e. preserving coherency, context, and correctness). The performance of the model, both from coherence and correctness perspectives, will be tested automatically and manually, and new evaluation systems will be developed.

Towards joint inversion of seismic and muon tomography data for reservoir monitoring

Hydrocarbon production methods extract mass from the subsurface and consequently change the density distribution. Steam Assisted Gravity Drainage (SAGD) uses steam to enhance the heavy oil in the subsurface which mobilizes it and allows it to be pumped to the surface. Today, we do not know very accurately, where oil is produced and where steam is moving in the subsurface. This leads to waste of steam and inefficient production. This project proposes a new method to monitor the density distribution continuously through muon tomography, which uses sensors placed in boreholes and measures the incoming muons, which image the density distribution. Together with seismic imaging, we will attempt a joint inversion to exploit the advantages of both methods, the innovative muon tomography and the established 4D seismic method. This method could optimize production efficiency and mitigate environmental risks.

The Diagnostic, PCR-based Test to Detect SARS-CoV2 RNA (COVID-19): Solving the Global Shortage of the Key Organic Building Block Using Flow Chemistry

Testing for SARS-CoV2, the virus that caused the condition known as COVID-19, is done using a RT (reverse transcriptase) PCR-based test to detect the target viral RNA. These probes are prepared by conventional solid phase synthesis on controlled pore glass using O-DMT-(2-N-FMOC-4’-aminobutyl)-1,3-propanediol (OFP) as the linker. Approximately 1 kilogram of OFP has been manufactured per year for the last decade to meet all diagnostic and academic research needs; since the pandemic outbreak, the need for this material has increased 20-fold. Consequently, there is a critical global shortage of COVID-19 test kits. The main step in the production of OFP is a hydride reduction step that is very dangerous to perform, especially on a large scale. The Organ group will develop a flow chemistry route for the preparation of OFP using proprietary technology invented by them. This technology will then be transferred from the Organ lab in Ottawa to the production facility in Toronto at TRC.

Sour and Sweet Corrosion Resistance of Alloys and Coatings

The oil and gas industry often contain a certain amount of CO2, H2S and chlorine compounds. H2S can cause sour corrosion and sulfide stress cracking (SSC) of stainless steels. Failures due to H2S are usually sudden with no warning. SSC is the worst type of corrosion in the presence of H2S. Many methods have been suggested to mitigate SSC and other corrosion issues in wet and sour service. Considering the design and the operation environments, the most practical solution is to change the materials of construction and select alternative material and/ coating to suit the specific need. Therefore, this proposal aims at evaluating the sour gas resistance of stainless steels and other potential corrosion-resistant alloys and coatings in sour service. This project will also increase the competitiveness of RGL in oil & gas industry by offering valuable data and experience to enhance the performance of its products for downhole applications.

Microemulsion technologies for the extraction and delivery of herbal oils and oleoresins

Herbal oils and oleoresins are components extracted from flowers, bark, leaves, roots, or fruits. These extracts are used as fragrances, flavoring agents, antimicrobials, and therapeutic agents with a market value of more than USD 8.5 billion and 7% annual growth. Steam and solvents are used to extract oils and oleoresins (respectively), but they have limited extraction efficiency, are energy-intensive, and emit greenhouse gases (GHG). Solvent extraction can also emit volatile organic components (VOCs) and produce flammable environments. This project seeks to develop aqueous extraction and delivery systems for herbal oils and oleoresins using food-grade surfactants and emulsified solvents. The example herbal oil, clove oil, and oleoresin, capsaicin, are both used as flavoring agents, as antimicrobials, and as medicinal compounds. Micellae delivery systems (partner organization) will use the findings from this work to develop a safe aqueous extraction process and delivery systems for cannabinoids.

Large Scale Simulations of Photonic Quantum Computers

Current quantum computers are in the “NISQ”, or Noisy-Intermediate-Scale-Quantum regime. The true potential of quantum computing will only be realized when noise levels are reduced or controlled, and large scale is achieved. Xanadu’s approach is to use photonic technology as the building blocks of their machines. This project addresses two related questions concerning the future development of these machines: A – In which conditions does a photonic quantum computer reach quantum advantage (demonstrating large speedups compared to today’s most powerful conventional computers)? B – What are the resources required to build a scalable, fault-tolerant photonic quantum computer? This project will provide the team with a better understanding of the requirements and tradeoffs involved in the future, much larger-scale generations of quantum photonic hardware that must be built in order to fully realize the potential of quantum computing.

Development of simulation-based training modules for the SGI graduated licencing program

Proper education and training are necessary to ensure young drivers have the appropriate knowledge and skills to drive safely. The current Saskatchewan Government Insurance (SGI) graduated licensing program has education components and some in-car practice sessions, however, data shows that many young drivers are still involved in accidents due to a lack of experience driving in difficult or challenging situations. Working with both SGI and young drivers, the purpose of this project is to develop and pilot test new driving scenarios on a driving simulator that can be used to train young drivers to improve their real-world driving skills. These new driving scenarios will eventually be included as part of a revised SGI graduate licensing program for young drivers.

Computer Vision Algorithms for USV mounted Real-Time Marine Vessel Detection Systems

This is a feasibility study for designing an automatic marine vessel detection system that can be used by an Unmanned Surface Vehicle. Following a thorough exploration of the challenges related to the video date acquired on the boat by our partner, we will come up with recommendations for the best camera hardware setup and preprocessing techniques that improve video quality. Next, state-of-art machine learning techniques for target detection will be applied to spot the marine vessels. We will design prototypes for both training our machine learning models on the server side and for testing it on the boat processor. The output of this system, along with the collected data from the other sensors of the boat may be interpreted and integrated by our partner to create a robust, real-time, boat-embedded autonomous marine vessel detection and tracking system.

Interfacial Engineering of High Energy Density and Safe Solid-State Li Metal Batteries for Electric Vehicle Applications – Year two

Lithium-ion batteries (LIBs) have become a key player in the growing need for electric vehicles (EVs). State-of-the-art LIBs, using liquid electrolytes, still have significant challenges in their safety, lifespan, and energy density. Accordingly, solid-state lithium metal batteries (SSLBs) have recently been attracting increasing research and industrial attention due to their ability to overcome intrinsic disadvantages of flammable liquid electrolytes used in current LIBs. The objective of this proposed research is to engineer the electrode/solid-state electrolyte interface by atomic/molecular layer depositions (ALD/MLD) to achieve the safe and high-performance SSLBs. The project includes two main directions: (1) gradient interface engineering on cathode/sulfide-based SSEs; and (2) design of multi-protective layers for Li metal anode by ALD/MLD. GLABAT SOLID-STATE BATTERY INC. as an industrial partner will support and be involved in this project. The innovative research will help both GLABAT and Canada increase their global competitiveness and create new economic ventures.

Silicon Quantum Dot Trace Explosive Sensor – Year two

The rapid detection of high energy materials (i.e., explosives) and chemical, biological and radioactive (CBR) agents have received substantial attention because of its obvious importance to security and forensics. Applied Quantum Materials Inc. (AQM) is developing a straightforward luminescent quantum dot paper- and/or cloth-based detection system that shows instantaneous optical detection of nitro-based explosives in solution and solid phases at nanogram levels by monitoring the luminescence quenching after being exposed to explosive residues.

The issue at hand is the current quantum dot (QD) sensor cannot distinguish between different nitro-based explosive groups (i.e., nitroaromatics, nitramines, and nitrate esters). Furthermore, the current technology cannot detect explosive inorganic salts (i.e., nitrate- and chlorate-based). The Research Intern will be responsible for working with AQM and its partners to develop and test the capabilities of the AQM QD sensor for its selectivity and the initial basic research for the development of new sensors for the detection of inorganic salts and chemical warfare agents. The sensors will be tested for their applications for first responders in the field, border control, and aviation security.

Localization, Monitoring, and Motion Coordination of Autonomous Indoor Service Robots

The proposed project will tackle the following inter-related research topics regarding localization, monitoring, and motion coordination of autonomous indoor service robots: (1) adaptive coverage-planning of arbitrary and uncertain non-convex indoor regions, (2) accurate and robust indoor localization of service robots using vision-based technologies, (3) deep learning based depth estimation and high-precision path-tracking control of service robots, (4) intelligent detection of certain robot system and environmental states. The tools to be used in devising solutions for these problems include geometric and graph theoretical optimal path planning algorithms, various sensor data fusion techniques, model-predictive optimal control, artificial intelligence and machine learning approaches including reinforcement learning. The goal is to produce solutions to the four general problem topics above and to apply these solutions to provide reliable autonomous motion of Avidbots robots cleaning semi-structured non-convex indoor areas, with precise on-line monitoring, maximal area and quality of the cleaning, guaranteed collision avoidance, without requiring frequent calibration and fine-tuning.