Racing the Sun is a STEM event where regional high school teams convert gasoline-powered go-karts into solar-powered electric go-karts and then compete in an annual race event at Musselman Honda racetrack. The Racing the Sun Autonomously system aims to leverage this opportunity to showcase autonomous driving capabilities to motivate the students to pursue advanced engineering studies. Moreover, the project’s intention is to enable Racing the Sun participants to develop their own autonomous platform by releasing the software package as open-source software.

The team designed and manufactured a system that completed safe, repeatable autonomous laps at the Musselman Honda racetrack on a solar-electric kart and publish an open-source package for external use. The system uses a Raspberry Pi compute stack running Robot Operating System (ROS), a series of cameras as primary sensors, a servo motor for steering, and a touchscreen for setup and mode selection. During a run, the camera finds track edges and the software plans a smooth path with appropriate speeds. The system has three operational modes - manual, remote-controlled, and autonomous (with an emergency stop). The interface supports data logging, and easy parameter tuning. All components are packaged as ROS nodes with launch files and clear documentation, enabling teams to retrain or redevelop the vision model.

The Cat Cannon is a multi-barrel launching system designed to enhance crowd engagement through safe and exciting promotional giveaways. Unlike traditional pneumatic launchers, it employs a unique electrically powered propulsion system to fire soft foam balls, each carrying prize redemption codes. An automated loading mechanism enables rapid-fire sequences or simultaneous volleys, with capacity to launch a minimum of 50 balls per event.

Precise electronic control ensures consistent, high-arching trajectories that reach all seating levels while maintaining safety. Integrated LED displays transform the system into a moving promotional platform, synchronizing graphics and sponsor messages with launches. Proximity sensors and trajectory limits further protect bystanders, resulting in a reliable, innovative, and crowd-pleasing alternative to conventional t-shirt cannons.

Engineered to address challenges faced by elderly and disabled individuals, the final design is a robotic tug that attaches to a standard 95-gallon garbage can without drastically modifying its structure. The system integrates a Raspberry Pi 5 processor running ROS 2 to coordinate GPS, mmWave radar, and an AI camera for autonomous navigation. A linear actuator-based coupling mechanism securely attaches to the can, while a robust drive system allows stable operation on varied surfaces and inclines up to 15 degrees.

Extensive testing confirmed that the system achieves a 90% success rate in attachment, obstacle avoidance within one second, and navigation accuracy of 1.5 meters or better across 400-foot round trips. The mobile/web application enables users to set pickup schedules, define paths via waypoints and receive real-time status alerts. The design preserves manual maneuverability and standard garbage truck compatibility, ensuring seamless integration with existing collection processes. The Autonomous Garbage Can provides a reliable, cost-effective solution that enhances accessibility and convenience for homeowners.

Radio communications relay data from and to sensors and actuators in the irrigation water distribution system across the 18,000-acre BWD. The district is expanding status monitoring and distribution control automation across its geographic footprint. However, obstructions within the terrain and fields of palm trees reduce or even completely block radio signals and line of sight (LOS) between existing antennae, limiting automation expansion.

The team used the Radio Mobile software to simulate the existing radio network and identified multiple technically viable solutions. Through close coordination with the project sponsor to define stakeholder needs and priorities, the team developed comprehensive analysis and comparison criteria to determine the optimal solution.

Following the sponsor’s concurrence with the design team’s initial recommendations, the team ran additional Radio Mobile simulations to solve a secondary problem: extending connectivity to the area southwest of the office just outside of the town of Winterhaven. The team achieved this by extending the main tower to attain LOS with a greater part of the southwestern area and installing the subscriber radios on 16 ft poles mounted on concrete bases. The district can place these hardware mounts near any of the sensors or actuators in the area and relay the radio signal to other areas with obstructions. This ensures LOS and signal integrity throughout the BWD area of operation.

The Arizona Water Association (AWA) hosts a yearly student design competition that tasks students with an engineering analysis and design project related to a water treatment facility. Teams created innovative and cost-effective solutions to meet the competition’s design goals.

This year, the AWA asked teams to propose an expansion plan for the SPA 1 WRF in Surprise, Arizona. The goal was to increase the facility’s treatment capacity from 12.8 million gallons per day to 16.3 million, while maintaining operational efficiency and regulatory compliance. The team evaluated four secondary treatment alternatives for Plants 4 and 5: the modified Ludzack-Ettinger process, a membrane aerated biofilm reactor, a membrane bioreactor (MBR), and the Four-Stage Bardenpho process.

After analyzing these options, the team determined that MBR is the best solution due to its ability to provide superior effluent quality, minimize hydraulic impacts, and maximize treatment capacity within the existing footprint. The MBR system enhances operational reliability and process control while reducing the facility’s overall environmental impact. Additionally, the team’s design includes a recommended construction timeline and an assessment of operational and maintenance costs.

The pharmaceutical industry often looks to plants for effective treatments. Inulin is one of these plant-based potential therapies. It is a naturally occurring polysaccharide in plants such as dandelions, chicory root and Jerusalem artichokes. Inulin can be used as a prebiotic, a dietary fiber, an enhancement for vaccines, and in medicines targeting the colon and kidneys. Jerusalem artichokes contain a high inulin content and serve as a sustainable source for this biomaterial. However, the process for refining the inulin from Jerusalem artichokes is inefficient and expensive.

In this project, the team focused on improving this inulin refinement process. To reach pharmaceutical grade after extraction, the inulin undergoes an intensive purification process. This includes the use of reactors, desalination and moving bed chromatography units, a falling film evaporator, and a spray dryer. The team mathematically modeled each piece of equipment, comparing modeled values with prior research to determine the optimal conditions for purified inulin.

The result of this project is pieces of equipment that are optimized to improve yield and purity while still considering cost and environmental impact.

When gasoline and diesel hydrocarbon compounds are burned, naturally occurring sulfur forms compounds that damage respiratory health and contribute to toxic acid rain. For this reason, the government regulates the amount of sulfur allowed in fuel. In diesel, this limit is as low as 50 parts per million. To comply with these standards, oil refineries must remove sulfur before the diesel can be sold and used.

Hydrodesulfurization is the primary removal method. In this process, hydrocarbons from other parts in the refinery, e.g. straight-run gas oil and light cycle oil, are reacted with hydrogen in the presence of a catalyst at high temperatures and pressures to separate sulfur from the hydrocarbon chain and create hydrogen sulfide gas.

The team designed a reactor and a separation system to remove hydrogen sulfide and other gases from the diesel stream. An amine unit processes the gases to absorb hydrogen sulfide and recycle hydrogen back to the reactor. The diesel product is stabilized and separated from lighter hydrocarbons before being sold as a product.

The team simulated and optimized the hydrodesulfurization unit in Aspen HYSYS software. The goals were to reduce sulfur content in the final diesel product to a maximum of 50 parts per million to minimize energy usage, cost and the environmental impact of this process.

The energy production industry demonstrates a rising need for renewable infrastructure to replace aging equipment and increase system resilience. Microgrid technologies are an effective tool for combating these challenges. The combination of renewable resources and energy storage allows microgrids to provide energy to support demand despite the intermittency of solar and wind. However, energy storage remains a challenge. Hydrogen is an emerging energy storage solution that may offer higher efficiency and environmental sustainability than conventional batteries.

The team designed a neighborhood microgrid in Tucson that uses solar power from photovoltaic cells. To ensure reliability, the grid is backed up by hydrogen technology, which can supply further electricity when needed. Combining solar panels, an electrolyzer, hydrogen storage and a fuel cell allows the microgrid to provide energy to residents under any weather conditions. The team created a DERMS model to visualize energy flows and address challenges of widely fluctuating energy inputs and load requirements.

This design pioneers scalability and safe implementation of novel hydrogen technology. The team created tools and developed models that are designed for easy adaptation to diverse microgrid applications. Similar systems can be implemented in data centers, electric vehicle charging stations and universities.

The demand for liquid fuel has risen in the last decade while environmental standards for greenhouse gas emissions have become stricter. In response, the fuel production industry developed an innovative alternative to conventional refinery methods known as methaforming. This process converts full range naphtha – a by-product of crude oil distillation – and ethanol into gasoline, blending stock to reduce costs and a lower environmental impact.

This project aims to help meet the growing demand for fuel by developing a methaforming unit within an existing refinery in Texas. The unit utilizes full range naphtha and ethanol to create the methaformate product, and the main byproducts are hydrogen rich gas and liquefied petroleum gas. The process uses a catalytic bed reactor to increase the gasoline blending stock’s research octane number to 90 with an output rate of 5,000 barrels per standard day.

The team used modeling software to simulate the reactions that take place in a methaforming unit: dehydration of alcohol, aromatization of olefins, alkylation of aromatics, aromatization of paraffins, and isomerization of paraffins. The team iterated the unit operation conditions to meet market specifications of liquefied petroleum gas and hydrogen-rich gas and adhere to product specifications for gasoline blending stock. Introducing expander/compressor pairs – for pressure integration and maximizing the heat flux values of heat exchangers – further optimized energy efficiency.

Semiconductor manufacturing requires cleanrooms that maintain precise environmental conditions regardless of conditions outside the facility. This includes strict control over temperature, humidity and air quality. These conditions are essential for ensuring the quality and reliability of sensitive semiconductor products. HVAC systems in these facilities are primary contributors of energy consumption and thus environmental degradation. With the goal of minimizing energy consumption and environmental impact, the team set out to optimize the recirculation of air while ensuring industry standards are met.

The team designed and calculated energy consumption of the HVAC system for several different air configurations. The focus was on maximizing the use of recycled air in each system. The team used Matlab to automate hourly energy calculations over an entire year using historical weather data. They then compared each configuration and determined the most energy efficient designs that met the strict air quality needs of semiconductor cleanrooms considering outside conditions. At the end of this process, the team extrapolated climate change data to predict how the energy consumption needs and cost of HVAC systems in semiconductor fabrication facilities will change in the future.