Innovative Projects Realized

Development of Energy-Efficient Means for Intensifying Mass Transfer Rates in Multiphase Reactors

The proposed project mainly focuses on developing an innovative gas/liquid contacting technology that is of critical importance to a wide range of process industries and environmental-management operations. Successful
development and implementation of this project are expected to:

? Reduce the environmental impact of a variety of operations that are needed to meet human needs and welfare (e.g. water/wastewater treatment, aquaculture, environmental management),
? Strengthen Alpha Tau’s position in the areas such as process intensification, advanced wastewater treatment, mineral processing, biotechnology and green chemistry,
? Expand Alpha Tau’s sustainable business opportunities in new areas (e.g. selective separation processes) and enhance job creation for high-tech personnel.

COVID-19: Indoor light-activated, self-cleaning surfaces for continuous decontamination of air in HVAC systems

The risk of airborne disease transmission is high for essential workers even while wearing PPE. Since SARS-CoV-2 can remain airborne after a sneeze or cough for several hours, ideally, there would be effective methods to remove these infectious particles directly from the air. In this work, we develop a membrane-based technology capable of serving the two-in-one function of dehumidification and decontamination of the indoor air, which can be coupled to room A/C systems. This will be achieved using nanomaterial-based membranes which incorporate a light activated antiviral coating expected to kill viruses and other microbes as they pass through the system Through development of these coatings and demonstrating their applicability and efficacy, Evercloak will significantly benefit. Access to scientific expertise, high-technical human capital, and access to a well-established lab facility at the University of Waterloo will accelerate development and fuel innovation.

Effect of carboxylated cellulose nanocrystals on the properties of water-based latex coatings

This project proposes the use of carboxylated cellulose nanocrystals (CNC) – developed and manufactured by Anomera Inc. from Canadian forest – as a nanosized ingredient to tailor a set of properties in composites made from water-based latexes used for coating applications such as sealants and paints. Incorporation of the CNCs will enhance the performance of the coating and prolong its lifetime. Due to its unique properties, CNC can significantly enhance the durability of coatings, making them more resistant to abrasion, scratches and impact, thereby extending their useful lifetime. Project results are expected to promote the use of Anomera’s CNC material in the coatings industry, as well as to promote Canadian forest sector as Canada is considered among the global leaders in the exportation of forest products.

Finding an optimized contact material for interconnect layers in reversible solid oxide fuel cells for the conversion of CO2 and H2O to syngas

SeeO2 Energy has developed world-leading catalysts for reversible solid oxide fuel cell (RSOFC) systems with promising performance to produce syngas from H2O:CO2 feeds. The company has scaled-up the technology and has moved closer towards commercialization by building larger cells. However, assembling the RSOFC stack presents challenges due to issues associated with contact materials especially at the oxygen side of interconnects. The contact material connects the respective electrodes and interconnect and provides high electrical conduction paths between the interconnect and electrodes. To this end, SeeO2 Energy has been working towards optimizing contact materials for use in assembling a RSOFC stack. Finding an optimized contact material for RSOFC stack is an important step towards commercialization. Proposed project aims to develop a highly electrically conductive, chemically and thermally stable contact material for RSOFC stack, which can facilitate long-term operation. The optimized contact material will fulfill following requirements: high electronic conductivity and matching thermal expansion coefficient values with other cell components, and appropriate sintering activity.

Investigation of effectiveness of sewage-based activated carbon (SBAC) for removing poly- and perfluoroalkyl substances (PFASs) from aqueous environmental media

Poly- and perfluoroalkyl substances (PFASs) are groups of contaminants that have received much attention due to their frequent detection in the environment and potential adverse health effect on humans and animals. PFASs can be removed from water using adsorption processes. Adsorbent produced by pyrolysis of sewage sludge (SBAC) offers a promising sustainable solution for removing PFAS contamination in environmental water (e.g., stormwater, wastewater). This research will investigate the effectiveness of SBAC in removing mixture of PFASs from environmental water. The partner organization will use the research findings to incorporate SBAC as a pollution mitigation strategy in water resource management infrastructures (e.g., wastewater treatment plant, rain garden).

Nanostructured ceramic catalysts for fuel cells

Currently, burning fossil fuels such as coal, oil, and natural gas as a primary source of energy is a contributor to rising atmospheric carbon dioxide and climate change. Fuel cells are a promising environmentally friendly technology for sustainable clean energy, where the only byproduct is water. An important component of fuel cells for generating electricity is the ‘catalyst’, a material that allows for key chemical reactions to happen. Current difficulties in fuel cell technology are the cost-effectiveness and stability of these catalysts which deteriorate after long term use, these factors have hampered their widespread application in vehicles and homes. The proposed research aims to use the beneficial properties of polymers to make stable hard materials (ceramics) with lessexpensive and highly active catalysts embedded within. The potential benefits of the research are highly active, lower cost, and stable catalysts, furthering the commercial viability of fuel cells and a greener future.

Real-time surrogate safety analysis solution using Lidar technology powered by artificial intelligence

The safety of intersections, interchanges, and other traffic facilities is most often assessed by tracking and analyzing police-reported motor vehicle crashes over time. Given the infrequent and random nature of crashes, this process is slow to reveal the need for remediation of either the roadway design or the flow-control strategy. This process is also not applicable to assess the safety of roadway designs that have yet to be built or flow-control strategies that have yet to be applied in the field. In this project, we are building an automated surrogate safety analysis platform that uses real-time traffic data generated by BCT Lidar-based traffic monitoring system and converts them into safety metrics such as near-misses, time to collision, etc. These metrics will be available to the city planners using a dashboard in real-time. This project speeds up the development of BCT’s SaaS platform that generated a new stream of revenue along with its standalone sensor sale. Additionally, this analysis will automate the process that currently BCT is doing manually to generate safety reports for its customer.

Performance mapping, modelling, and development of membrane-based energy recovery ventilation exchangers

Membrane based energy recovery ventilation (ERV) is now a key component of most energy efficient modern buildings. ERVs improve the indoor air quality of buildings through ventilation and reduce the energy cost of ventilation by using building exhaust air to pre-condition fresh building supply air. In cooling conditions heat and humidity are removed from the incoming air and in heating condition heat and moisture are added to the incoming air. The membrane in the device allows the transport heat and moisture without transport of other gases and contaminants from the exhaust air to the supply air. Better models are required for predicting performance of exchangers of different geometry, materials, and assembly methods. In this project students will assist in testing, prototype assembly, and analysis of exchangers and develop improved performance models for membrane-based ERVs.

Novelty Detection in Lunar Analogue Terrains

Lunar and planetary rovers are faced with a high volume of data from their sensors and cameras, and yet decisions must be made rapidly to prioritize to which crater it should drive up to and of which rock it should take a closer look. In Canada’s intended upcoming exploration of the Lunar surface aboard short-timescale commercial missions, these considerations are of utmost importance. By leveraging recent advances in artificial intelligence (AI), this research will equip rovers with the ability to survey its surroundings and discover which elements of the terrain ahead are novel and potentially interesting to scientists. The rover will then be able to prioritize which data to pass on to the science team or even select its next point of investigation autonomously.

Multilingual Semantic Search Engine using Multilingual Semantic Similarity

Multiple situations require cross-lingual searching: lawyers reviewing litigation documents; intelligence analysts data mining open source data; and patent attorneys investigating technical documents. To imitate cross-lingual search, people use online translation platforms to find the equivalent terms laboriously and then re-execute the query multiple times in various languages. The commercial search industry hasn’t seen much demand for crosslingual search. Search is always monolingual and very English-centric. However, to communicate with end users, businesses regularly produce written documents in various languages. Therefore, a set of rules are required to ensure that information in these documents is ‘correct’ and consistent across languages and communication channels. This project aims at creating algorithms capable of performing semantic search within a very large pool of multilingual unstructured enterprise contents with less overhead regardless of the natural language being used for each document. The proposed algorithms must scale with the size of the corpus being used.

Multilingual Semantic Similarity of Unstructured Enterprise Content

To communicate with their end users, businesses regularly produce written documents such as letters, notices, statements, etc. in various languages. A set of rules are usually used to ensure that information in these documents is ‘correct’ and consistent across languages and communication channels. However, with the increasing volume and variety of information being sent out to clients, it becomes difficult to preserve the semantics of client messages across vocabulary and language variations. State of the art algorithms that solve this problem involve a translation algorithm which creates an additional overhead and makes the entire pipeline less practical to use.
This project aims at creating algorithms capable of measuring semantic similarity between unstructured enterprise contents with less overhead regardless of the natural language being used for each document. The set of similarity algorithms must scale with the size of the corpus being used.

Shipping Container Code Classification and Prediction

BlueNode is a SaaS company focused on the sanitation and analysis of marine shipping data. The research project is focused on increasing the precision and accuracy of shipped goods processed through Canadian ports. Should the research prove to be successful, the technical methods used will be directly incorporated into the BlueNode system.