Projets novateurs réalisés

Investigating Innovative Approaches for Efficient Pipeline Assessment and Maintenance

KenWave Solutions has pioneered the Dynamic Response Imaging™ (DRI) technology, aimed at assisting asset managers in enhancing their operations. This groundbreaking innovation furnishes dependable insights into pipe stiffness, wall conditions, leaks, air pockets, and other anomalies, thereby optimizing utility efficiency, reducing costs, and conserving water resources. KenWave is actively collaborating with utilities and partners to pinpoint opportunities for condition assessment and leak detection, with a focus on assessing the remaining lifespan of critical assets.
DRI represents an inventive and patented dynamic response-based solution designed to gather comprehensive pipeline condition data across a broad spectrum of pipe materials and diameters. This technology operates non-destructively and non-invasively, delivering pertinent information about pipeline condition and leaks with a resolution equal to or greater than that of a pipe segment. As a result, it empowers asset owners, operators, and managers to gain a deeper understanding of the current state of critical pipeline assets, facilitating well-informed decisions in operations management, risk mitigation, and budget planning.
This project signifies a crucial step for KenWave Solutions in transitioning their DRI technology from a prototype phase to a minimally viable product, bringing it closer to its ultimate commercialization. The allocated funding will support rigorous testing of the existing system, the identification and rectification of any hardware, firmware, and software deficiencies, enable the development of automation processes, and facilitate the commercialization of the DRI technology for the benefit of utilities and partner organizations.

Développement de modèles prédictifs de la demande en aliments – moulée chez les producteurs du secteur laitiers et porcins

Dans le contexte agricole dynamique d’aujourd’hui, l’intégration de l’intelligence artificielle (IA) offre une
opportunité significative de révolutionner et d’optimiser les chaînes d’approvisionnement. L’industrie agricole est
confrontée à des défis pour maintenir des chaînes d’approvisionnement efficaces et durables, en particulier dans
le contexte de la production d’intrants agricoles tels que la moulée pour différents types de production animale.
L’objectif principal de ce projet sera de développer des méthodes de prévision avancées pour déterminer le niveau
des silos de moulée des éleveurs porcins et laitiers pour ainsi prévoir la production et la livraison de la moulée
personnalisée pour chaque exploitation. Le projet prend en compte plusieurs variables influençant le taux de
croissance des porcs et la production laitière des vaches. En termes techniques, nous sommes confrontés à une
situation de prévision dynamique multifactorielle. Notre objectif principal est de développer des méthodes robustes
pour prédire les besoins en alimentation des animaux.

Green Energy technology and C1-2 chemistry

H2O2, as a greener oxidant, has been regarded as an environmentally friendly alternative used for selective oxidations, water treatment, pulp bleaching, and topical disinfection. However, by far H2O2 is mainly produced by an indirect process involving sequential hydrogenation and oxidation of an anthraquinone. The current process requires large capital investments and is reliant on energy intensive separation processes. For economic and environmental reasons, H2O2 synthesis directly from H2 and O2 is promising to replace the indirect process because it has remarkable advantages, such as less energy consumption, and low infrastructure investment. The aim of this work is to obtain deep insight into the structure of active sites and the detailed analysis of the reaction mechanism for H2O2 synthesis and side reactions of Pd-Au catalysts supported on the chemical active support TiO2.

Physics Informed Neural Networks – Exploring the Future of Computational Electromagnetics

Computational electromagnetics (CEM) is a field of research devoted to simulating the complicated electromagnetic systems foundational in our technology, security, and exploration. Historically, problems have been solved by breaking them into many small pieces that can be solved by a computer; an approach that makes it difficult to solve very complicated problems. Today, a new approach to solving CEM problems has emerged in the form of artificial intelligence, namely, Physics Informed Neural Networks (PINNs). These techniques leverage the hardware and techniques supporting advanced machine learning to “learn” the solution to CEM problems.

PINNs are so new that their capabilities are not entirely known. We seek to learn the state-of-the-art in PINNs, and will attempt to apply them to a relatively simple problem, but important, problem of electromagnetic target dataset creation. CEMWorks, the partner organization, will benefit from creating a foundational understanding of PINNs upon which to build future innovation in their software.

Pilot Scale Optimization of Microbial Biostimulants from Organic Waste for Enhanced Crop Performance and Resistance

Fertilizers derived from organic waste are well positioned to fortify ‘sustainable’ agricultural practices in Canada and elsewhere by reducing energy-expensive fertilizer production, reducing waste and addressing the overuse of pesticides on food crops, medicinal plants and natural health products derived from plant products. Circulus Agtech Inc., with its organic waste-to-fertilizer process, produces excess microbial biomass that has potential as a biostimulant admixture for hydroponic or soil-based plant production systems in greenhouses or controlled environment agriculture. Previous research has demonstrated that bacteria isolated from this biomass are able to influence nitrogen availability in soils that can benefit the plant health. This collaborative partnership between researchers at McGill University and Circulus Agtech aims to explore pilot-scale optimization of microbial biostimulant production that will ensure superior microbial load, reliability and consistency between batches. At the same time, this interdisciplinary team of bioresource engineers, sustainable agriculture specialists and microbiologists will examine the potential benefits of these microbial biostimulant population on several hydroponically and soil-grown crops, with respect to plant growth and development, nutrient uptake, crop yield and defense against plant pests and pathogens.

Training plantar flexor flexibility to improve jumping performance

Vertical jumping is important for athletic performance (for example, basketball, volleyball), but could also involve injury risk if done incorrectly. Our purpose is to explore whether a stretching program of the calf muscles enhances vertical jump performance and decreases some of the risk factors for injury in adolescent athletes. Having more flexible calf muscles increases the depth at which a person can squat before a vertical jump, possibly increasing the jump height. Greater calf flexibility may also allow one to better absorb forces when landing from a jump; therefore, reducing injury risk. Male and female athletes (aged 12-17y) in our study will perform a comprehensive calf stretching program 5 days per week for eight weeks, with assessment of vertical jumping and landing mechanics before and after the program.

Assessing and redesigning Jackleg Hammer Drill for Mining Operation

Drilling is a major operation in the mining industry. Holes are drilled in underground mine structures to insert reinforced steel bars for support and to prevent the mine from collapsing. One of the mining operations is to break mine rocks using explosives into smaller parts that can be transported for further processing. Both cases need to drill deep holes. The jackleg hammer under study drills holes that are about 6.7cm in diameter by up to 69 cm deep. The tool operates by percussive hammering and the structure is very heavy, about 33kg. Operators are exposed to high-intensity vibration while securing the hammer in position, which puts them in danger and exposes them to injuries. This study will assess the current Jackleg Hammer and re-design it to minimize the vibration and weight for the safety of the operator and investigate the use of clean technology to operate the drill.

Self Supervised Learning of Embeddings for Semi-Supervised Track Classification

Total::Insight™ is a geospatial and time distribution Decision Support System (DSS) that includes a correlator capable of consuming many sporadic time domain signals and converting them into feature rich tracks. The problem here is how to create an AI/ML embedding that is domain relevant from the tracks.The project objective is to develop, refine and industrialize new AI/ML-enabled track analysis capabilities for the Total::Insight product. The development is challenging as, in most big data problems, the data is unstructured, sparse, unlabelled and diverse. The project scope is to develop new descriptive features, include time dependent track segmentation, develop preliminary time series foundational models, and perform at scale engineering to generate embeddings. The project outcome will enable Total::Insight™ to efficiently perform efficient classification and similarity searches of tracks with limited labeled data.
For this project, we will budget 3 IUs over 4 months, in addition to 4 Larus full-time senior and intermediate employees.

Biodegradable Physical Hydrogel with Improved Mechanical Properties

The polymer (a large chain of molecules) under study is called poly(ethylene glycol) (PEG). It has only two types of molecules (carbon and hydrogen) and is an amphiphillic copolymer, meaning it attracts both water and fat molecules. When temperature changes, this polymer can go from a liquid to a gel. This gel is unique, in that it is biodegradable and biocompatible (can be used in fields like tissue engineering as an injectable gel). The project focuses on preparing the polymer from another copolymer (poly(lactic acid) copolymer) and how the PEG polymer changes with temperature change. Another aspect of the project will be to see how the PEG polymer behaves under different loadings (stress, weight) or configurations.

A 20-Year Time-Series Analysis of Dissolved Inorganic Carbon using a Transit Time Distribution Method in the St. Lawrence Estuary and Gulf

Recently, a new approach in analyzing the transfer of carbon from land to ocean, the Land-Ocean Aquatic Continuum (LOAC), has been employed to present a more dynamic interface and reduce uncertainty in climate research. This interface is comprised of three components: 1) the classic long-range loop, 2) the upstream loop (connecting land to lakes and rivers), and 3) the downstream loop (connecting lakes and rivers to the open ocean through estuaries and wetlands). The St. Lawrence Estuary and Gulf connect the Great Lakes and St. Lawrence River to the North Atlantic Ocean and are a prime example of this downstream loop. This project was designed to quantify anthropogenic perturbation of the downstream loop of the St. Lawrence Estuary and Gulf. This objective will be met by employing a transient tracer distribution model (TTD) utilizing the mean ages of the bottom-waters calculated from tracers in conjunction with a 20-year time series of carbon data. This will allow for the isolation of the anthropogenic signal of carbon dioxide within the interior of the St. Lawrence Estuary and Gulf which will allow for a more accurate carbon budget within the system.

Cell Width Regulation

Bacteria are surrounded by envelopes that are responsible for cell shape and size. Bacterial cell shape and size are important for the organization of intracellular processes, for multicellular interactions, and for infection in case of pathogens. The cell envelope also protects bacteria from the environment. Not surprisingly, it is thus a major target for antibiotics including penicillin. Despite its importance, we lack a fundamental understanding of cell-envelope organization and shape regulation in bacteria and other organisms. Here, we aim to study specifically how cell width is controlled, through a combined approach of live-cell microscopy of cell-shape changes and through computational simulations of the peptidoglycan cell wall. Experiments and simulations will inform each other. Together, this research project will reveal potential design principles of shape maintenance. This research project will be an important learning experience for the student and it will benefit the overarching research on bacterial cell shape of the host lab.

Development of a Radioactivity Transport Code to Support the CANDU Industry

Knowledge of corrosion products and radioactivity transport in nuclear reactors is necessary to minimize radiation dose to station workers during maintenance activities. Modelling radioactivity transport can also be used predictively to assess how changes to station chemistry affect radiation fields.
The objective of this project is to develop a new code in Python, leveraging the layout and validated mechanisms of previously developed codes at the Centre for Nuclear Energy Research at the University of New Brunswick and at Canadian Nuclear Laboratories, respectively, followed by using the new platform for further development. To support continued, nuclear-based clean energy production in Canada, this code will model multiple Canadian station configurations without requiring multiple code versions.