Hydrothermal Liquefaction of High-Moisture Pulp and Paper Milling Residues for Renewable Biocrude Production

Canada’s $9.6B pulp industry produces an ample supply of unusable organic pulp mill residues, yet there has been relatively limited utilization of this feedstock for the production of bio-crude through hydrothermal liquefaction (HTL) – a low-temperature, high-pressure thermolytic conversion process. The proposed research project will investigate HTL catalysts that produce abundant, high-quality, and low-cost bio-crude for later transformation into renewable diesel.

Modelling Investigation of Arsenic Contamination in Drinking Water

This project supports comprehensive investigation of arsenic contamination in the aquatic ecosystems in Canada. The project aims to provide a framework/guideline to predict probability arsenic contamination in different area. This will be through appling AI and ML modelling approaches by considering geo-environment and hydrochemical predictor variables. The MITACS support will be used to hire a research assistant to support the activities envisaged in the project. The researcher will be supporting computational modelling at Memorial University (MUN).

A framework for assessing qualitative and quantitative risks of installing and operating water supply services in small, remote Indigenous communities

Water infrastructure and supply service in Canadian small, remote Indigenous (SRI) communities are facing various challenges, including long-term water quality advisories. A water infrastructure risk assessment system is much needed to help identify critical nodes that are prone to cause failure in the entire water supply service chain, ranging from pre-project planning to water service operation. In the proposed study, a comprehensive framework will be developed to assess various types of risks in water infrastructure project development and water supply service in SRI communities.

Implementation of BMP protocols to manage irrigation and controlled drainage to conserve water and nutrients within agricultural lands

Tile drainage is becoming popular as a way to control excess moisture in the field to increase productivity. Yet, the economic return on investment (ROI) on installing tile drainage is not known in Manitoba. This research will allow us to assess the impact of water management through controlled drainage on yield. Detailed water table depth at different times will help us model water flow within the rootzone and its impact on crop yield.

Deploying Phytoplankton Classification Deep Learning Models on Edge Devices

Harmful algal blooms (HABs) are causing significant damages and losses for fish farmer, and therefore must be regularly monitored. Given recent breakthroughs in deep learning, computer vision algorithms can now be created to automatically detect harmful phytoplankton in water. This research explores how to train these models in a secure manner while still meeting client data privacy requirements, as well as how to deploy these models in the field on edge devices.

Risk-time Risk Tracker through mapping HAZOP into Bayesian network using machine learning

The first step in analyzing potential risks in a process system is called hazard identification. As the name states, it identifies the potential danger in relation to system operations. In other words, this step answers what can go wrong in a system. This results in a listing of what can go wrong, its causes and consequences. There are established techniques that were developed in the past for this purpose. The industry has been using them for over half of a century. Since then, the complexity of the systems has significantly increased.

Harrow Tines: Fatigue Prediction & Real-Time Crack Detection

The agriculture sector of the Canadian prairies including the province of Saskatchewan is one of the major contributors to the Canadian economy. Farmers are the main driver of this sector, who often face delays in their harrowing process due to unexpected breakage of the harrow tines that they use. In this project, the team will develop a scientific method to predict the failure of harrow tines, as well as a live monitoring system to detect any crack in harrow tines.

Improved ductility in extruded Al-Mg-Si alloys through texture and microstructure control

The use of aluminum alloys in automotive applications is increasing since this allows the weight of the vehicle to be decreased. This is beneficial for both internal combustion and battery powered vehicles, to increase fuel economy and increase vehicle range, respectively. However, in general, aluminum alloys are more difficult to form than steel and their performance during a vehicle crash may be challenging. Thus, it is necessary to understand the linkages between the production of the components and their performance.

Multiomic characterization of stem cell derived extracellular vesicles for supporting the skin

Studies performed over the last 10 years or so have revealed that the primary power of human stem cells lies in what they produce and release or excrete, not the cells themselves; this includes growth factors, paracrine factors, peptides, extracellular vesicles (EVs), and most importantly, exosomes.

Development of Wireless sensors using microfabrication techniques

The goal of the partnership is to develop new wireless pressure and torque sensors for commercial applications, transfer knowledge to industry to commercialize research advances, and train new highly trained personnel (HQP) with skills sought after in Canada’s growing high tech sector. The industrial partner, EPIC Semiconductors, develops new innovative technologies for customers so that they can gain a competitive advantage in their industries. EPIC Semiconductors currently does not have a cleanroom facility. Through the proposed partnership between EPIC Semiconductors and Dr.