Innovative Projects Realized

Development and modification of green composites obtained from recycled polyethylene/crumb tire/ inorganic additives for exterior applications

In the current project, the use of recycled polymers such as landfilled tires and polyethylene (PE) plastic is suggested to be reused for the manufacturing of composites for exterior applications such as shingle roofing, cladding and post fencing. Even though, this material has been already used for this application, but the mechanical and thermal properties of the products need to be developed by using inorganic additives. In this project, aluminum trihydrate (ATH) and clay are suggested to improve flame retardancy and mechanical properties of the PE/crumb tire/additive. Therefore, several formulations and compounding methods will be investigated to achieve the target.

Mise en place d’un banc d’essai de phytoremédiation sur le site Blasting Cap de la propriété d’Orica Canada Inc

Afin de limiter dans le sol l’accumulation et la mobilité de contaminants pouvant entraîner un risque potentiel pour la santé humaine, animale et végétale, Orica Canada Inc., un fournisseur mondial d’explosifs commerciaux, souhaiterait décontaminer un de leur terrain hébergeant auparavant une usine de production d’explosifs. L’entreprise désire s’engager résolument dans des approches vertes et durables pour résoudre cette problématique environnementale qu’est celle des sols contaminés, et cela en faisant appel à la phytoremédiation, une technologie utilisant les végétaux, et parfois les microorganismes (bactéries et champignons) qui leur sont associés, pour éliminer, contenir ou rendre moins toxiques les contaminants présents dans le sol. Ainsi, par la mise en place de plantation à petite échelle sur une portion du site, le stagiaire Mitacs évaluera les capacités adaptatives et phytoremédiatrices de différentes espèces végétales et étudiera les interactions qu’elles établissent avec les bactéries et champignons du sol.

Testing, Integration, and Optimal Control Strategy of Residential Hybrid HVAC System

The Canadian federal government committed to encouraging low carbon alternatives and the growth of clean technology that reduces greenhouse gas (GHG) emissions. It is stated that the new target is to reduce GHG emissions by 80% by 2050, relative to 2005 GHG levels. In order to achieve this goal, one of the government’s strategic plan is to promote systems and technologies that minimize natural gas/fossil fuel usage and increase the use of clean electricity. Although various research groups studied the potentials in energy consumption reduction in residential houses, hybrid integrated energy systems are found to be effective in reducing energy consumption and its associated operating cost and GHG emissions. However, their optimal control methodology is still lacking for cost-effective large-scale deployment and adoption of such hybrid residential HVAC systems in the Canadian residential sector. Therefore, this project will examine the benefits of a state-of-the-art cloud-based Smart Dual Fuel Switching System (SDFSS) of two sets of residential hybrid HVAC system of 1) electric air source heat pump (ASHP) and natural gas furnace (NGF) and 2) ASHP, electric water heater tank, and natural gas instantaneous hot water heater, for simultaneous reduction of energy cost and GHG emission.

Human Motion Library and Predictive Capabilities for Digital Human Ergonomics Simulation Solution

Within their ergonomics process, automotive manufacturers rely heavily on computer simulation technology, specifically “Jack” (Siemens PLM, Plano, TX). Advancements to “Jack” provide users the ability to create workstations, yet much time is required to produce a single simulation. This 3-year industrial collaboration will reduce the time necessary for their completion and, improve on the accuracy of digital simulations. This will be accomplished by capturing motions of humans performing common manufacturing tasks, identified as difficult to simulate, allowing for the creation of a dynamic-motion-library that will be integrated into “Jack”.

Applying Machine Learning to Develop Meaningful Rail Condition Indices

Rail transit and freight rail properties apply rail grinding to maintain rail condition and ensure satisfactory performance of rail infrastructure systems. The proposed research investigates and applies a variety of computationally intelligent algorithms to establish useful relationships between rail corrugation, noise generation, and vibration. These relationships will support more timely and effective rail grinding interventions. The algorithms will process real-world rail corrugation, noise, and vibration data collected from three rail transit properties in North America. The long-term research goal is the development of a generic and transferrable rail corrugation index, which will help rail maintenance practitioners determine when rail corrugation is likely to generate unacceptable noise and vibration. Consequently, the research directly supports rail and vehicle asset management programs, helps reduce noise irritation for passengers and citizens in the vicinity of rail transit lines, and improves ride quality.

Semi supervised object detection

Deep learning technology is a great tool to learn complex patterns and make prediction based on this learning. In order to get the most accurate predictions, one needs to train those neural networks on vast amount of labelled data. Labelling data is a time consuming and costly task. Using semi supervised learning, it should be possible to label a fraction of the dataset and let the neural network learn by itself on the rest of the, unlabelled, data, thus greatly reducing the overhead of using deep learning technology. This project aims at identifying and implementing the best possible semi supervised strategy for object detection.

Computer Vision R&D project – Autonomous Robot Assistant

The project’s main objective is to provide a robot with the capabilities to perform diverse helping tasks in the office, such as fetching objects, greeting colleagues, clients and newcomers. This will be a stimulating project involving tangible real-world application of multiple artificial intelligence approaches to robotics, including and focused on advanced computer vision algorithms, in the objective for the robot to navigate and recognize objects using a built-in camera.
This project will provide the company with a definite increase in its expertise in the commercial application of AI to robotics. Since Menya has already shown its skills in the space industry, this project may help get into new sectors in industry, but also in societal domains such as healthcare and education.

Power network transfer capability (Phase II – data error detection)

Hydro-Québec is a public utility that generates and distributes electricity. Despite selling most of its electricity in Québec, its most lucrative sales are in the neighboring markets. To ensure the best possible quality of service, the transmission system must remain stable, but to maximize profits, the company also wants to increase its transmission capacity to maximize energy exports. The transfer limit is now conservatively estimated based on a certain combination of simulated network configurations. This project aims to more accurately estimate the transfer limits of the electric grid and the uncertainty of these estimated limits. Recent advances in machine learning, especially in deep learning, in conjunction with more traditional algorithms used in computer science, have the potential to improve these estimates and therefore augment exports for Hydro-Québec.

The Development of Anti-gingivitis Probiotics Derived From the Human Oral Microbiome

The human oral cavity contains over 700 different bacterial species. In healthy people, these bacteria are living in harmony and not likely to cause diseases. However, sometimes this bacterial balance is disturbed as the oral pathogenic bacteria start to overgrow causing many oral implications such as halitosis, sore throat, dental caries and gingivitis. A promising solution to tackle this microbial population destabilization is the use of beneficial microbes called probiotics. Previous investigations showed that the oral commensal Streptococcus salivarius is an excellent candidate for the development of new probiotic treatments. This bacterium is human friendly and is one of the first microorganisms to colonise the babies few hours after birth. S. salivarius can produce unique molecules which can be used as molecular missiles to attack pathogenic bacteria and restore the microbial balance to the oral cavity.

With the Child in Mind – Brain Development and Best Interests Decisions

Everyday across Canada, judges from both the federal and provincial courts make ‘best interest’ decisions affecting the lives of thousands of children and their families. Infants and children whose cases come before the justice sytem are more likely to have experienced abuse, neglect, exposure to family violence, parenting impaired by addiction or mental illness, and substandard or unstable home environments. Lawyers, judges and child-serving professionals need to be educated in the science of early brain development so that they can maked informed, timely decisions that will have lasting positive effects on the health and development of children. This project aims to collaborate with family court judges and lawyers to identify their information and learning needs in the area of early brain developement. These needs will be adressed by creating and piloting learning resources that will allow them to meaningfully integrate the science of early child development into their daily practice.

Monitoring of turbine runner blade strains from indirect measurements using AI

Hydro-Québec has data acquisition systems for a multitude of sensors, some of which have been installed since almost 20 years in its electrical generation equipment (turbine-generator units – TGU). The collected data is primarily used to ensure that the information is adequate in the event of an equipment breakdown or for specific behavioral studies. Data from monitoring systems are little used in routine maintenance management activities, often due to lack of time and adequate and/or effective analysis methods.
Equipment maintenance is an important part of Hydro-Québec’s equipment management activities. The creation and maintenance of a surveillance system is a major investment for the company. With the development of machine learning analysis approaches, the goal is to provide operators with a clearer view of real-time asset status and predictions about their potential for use.

Short term Electrical Load Forecasting

Load forecasting is an essential activity for a company like Hydro-Québec. It is necessary for objectives as varied as the management of production or the management and maintenance of the electricity network. Any significant forecasting error can result in reliability issues, loss of opportunity, or additional costs to the business. On the other hand, a good prediction would allow Hydro-Québec to generate additional sales in neighbouring markets. With the deployment of its Advanced Measurement Infrastructure (AMI), Hydro-Québec now has a significant amount of new consumption data. This data can be used to improve demand forecasting, increasing reliability, decreasing expenses, and potentially generating new revenue.

Macroeconomic changes such as the decline of heavy industry, the recent changes in society (teleworking, variable rates, etc.) and in the future (transport electrification, behind the meter production, storage, smart grids, active role of the consumer, etc.) are current and incoming challenges for the parametric forecasting models such as those developed and currently used by HQ, since the load is more and more difficult to modelize with no clear physical phenomena and measures to explain it.