A high pressure humidity (HPH) system is used to humidify the process air for the application of water-based paints in the bumper paint process at Toyota Motor Manufacturing Canada (TMMC). Excess water is produced in the process, indicating that the system is not optimized. This project seeks to reduce the water consumption of the HPH system by optimizing the active number of nozzles, therefore reducing water input and increasing efficiency. We aim to contribute to the overall shopwide water reduction target at TMMC.

Partner Organization/Industry or Government Mentor: Toyota Motor Manufacturing Canada

Faculty Advisor: Professor Jason Grove

The use of gas-powered transportation methods not only contributes to climate change by emitting greenhouse gasses, but it also contributes to air pollution and smog in population centres. Replacing vehicles with battery powered scooters can reduce the emissions of these harmful gasses, while also helping to reduce traffic. This project’s main goal was to design a battery system for an electric scooter, including the battery itself, the thermal systems and a case that allows for easy removal and change-out of the battery.

Faculty Advisor: Professor Michael Fowler

Motivated in the pursuit of environmental sustainability, our vision is to develop a bioprocess, modelling various aspects in Python, that can be used to simulate the production of a food-grade, honey-like product that does not involve bees. Honey production activities hinder the maintenance of bee populations in the wild; the biosynthesis of honey without the use of bees would help advocate for environmental conservation given the alarming rate that bee populations are declining at, allowing future bee populations to flourish naturally.

Faculty Advisor: Professor Christine Moresoli

We put together a model to describe the behaviour of the fermentation process of a gluten-free beer, using an enzyme to break down the gluten. The gluten content of this beer must be under 20 ppm, and we aim to find process conditions that would result in a less expensive beer than current commercially available gluten-free beer.

Faculty Advisor: Professor Chrsitine Moresoli

Existing methods for solving fluid simulations are computationally intensive. This project aims to develop a convolutional neural network to increase the speed at which high-fidelity fluid solvers complete simulations. TensorFlow and a multiphysics finite element package are used to create a Python implementation of an accelerated fluid simulator. The neural network will be trained to predict Navier-Stokes flow across a range of conditions. We assess the feasibility of intermittent neural network injection for improved accessibility and expedited industry design cycles.

Faculty Advisor: Professor Nasser Mohieddin Abukhdeir