Bearing the Load of Social Responsibility

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Cafeteria food waste at the University of the Pacific (UOP) in Stockton, CA is a problem. But this problem is hardly unique to their institution or any other university. According to the latest published US EPA estimates, food waste totaled over 66 million tons nationally in 2019 across all food retail, food service, and residential sectors. To help raise awareness of this problem, a group of UOP Mechanical Engineering students decided to devote their senior project to it. Armed with inspiration and a few load measurement devices from Tacuna Systems, they created an innovative food waste measuring device hoping to increase their fellow students’ social responsibility.

Project Technical Profile

Client / Partner: University of the Pacific (UOP) Senior Engineering Team & MESA (California’s Mathematics, Engineering and Science Achievement program) Initiative
Application Category: IoT Food Waste Measurement & Automated Logistics Tracking
The Social Challenge: Measuring food waste to highlight the national issue of over 66 million tons generated annually.
The Operational Challenge: Designing an automated system to weigh incoming cafeteria tray waste on a moving, vertical conveyor layout without disrupting student drop-off behaviors.
The Mechanical Challenge: Compensating for non-axial forces and an angular displacement of 3.87° introduced by custom wheel ramps that guide the tray structure onto the load sensors.
Hardware Enabler: Two 50 kg ANYLOAD 108BA Aluminum Single Point load cells summed via an ANYLOAD J04EA-16/J04SA-16 J-BOXER junction box running into a Raspberry Pi 4 image recognition matrix.

The MESA Initiative and the Food Waste Measurement Concept

Several decades ago, the University of California system created the MESA (Mathematics, Engineering and Science Achievement) initiative to foster better participation in STEM careers by individuals of underrepresented populations. In 2022 they held a design competition at their Sustainability Summer Camp for High Schoolers. Our previously mentioned UOP team of seniors, Fernando Montero, Pedro Madera, Ammaad Akhtar, and Robert Izquierdo (pictured here left to right), became intrigued by the competition’s winning design: a food waste measuring system using QR codes on the bottom of cafeteria trays that would text students with food waste data. This system would measure the weight of trays returned to cafeteria kitchens and remove the weight of known objects (such as trays, plates, and silverware) to determine food waste weight.

photograph of students who worked on the University of the Pacific food waste awareness project — Fernando Montero, Pedro Madera, Ammaad Akhtar, and Robert Izquierdo next to their food waste measurement test system.

Unlike the original design, the UOP group wanted to use machine learning and image recognition to automate the process of identifying the weight of known objects. To execute this redesign, they received support and sponsorship from MESA.

System Concept: Automating Weight Tracking on Cafeteria Tray Returns

The first step for the UOP team was integrating a precision weighing layout directly into the dining hall’s existing moving tray return conveyor (Figures 1a and 1b). In this standard industrial setup, vertical tray racks hang from a motorized top chain, while a set of stabilizing rear wheels glides along a mid-wall track to keep the loaded trays from scraping or tilting on their way to the kitchen.

Photo of student view of tray return area in the University fo the Pacific cafeteria — **Figure 1a: Front view of University of the Pacific cafeteria tray return system**

Photo of kitchen view of the University of the Pacific cafeteria tray return rotating system. It shows racks that support returned trays stacked horizontally, one tray wide. These racks are supported along the back by a vertical, inverted L sheet metal component that attaches to a track along the top of a support wall via pins at the top endpoint of the inverted L. Wheels aligned horizontally along the back of this sheet metal roll along a track 2/3 of the way down from the top of the inverted L. These guide the racks and prevent the sheet metal from scraping along the wall when the racks are loaded. — **Figure 1b: Rear view of University of the Pacific cafeteria tray return system**

Mechanical Design: Single-Point Load Cell Placement on Wheel Tracks

The team chose to locate its load-measuring components along this wheel track since the pressure on these stabilizing wheels is proportional to the weight of the entire tray return racks, including all place settings, plates, glasses, and food waste. With image recognition, data processing software could remove the weight of all identified place setting items. This would leave the weight of just the food waste.

Drawing of side view of load cells in the U of Pacific cafeteria tray food waste measuring system. They show how the wheels behind the rotating tray return assembly align with the load points of the load cells. — **Figure 2: Diagram of food waste measuring system load cell configuration**

Figure 2 shows a diagram of how the single-point load cells would be mounted to the support wall. This mounting includes a base that would allow the load cells to bend under the force of the support wheels. The load point of each load cell aligns with the wheel track. To guide the support wheels onto these load points, the team designed ramps from the wheel track to a separate metal plate, which was in turn attached to both load cells at their load points. Figures 3 and 4 show the installed ramps along the wheel track.

Photo of ramps along wheel track behind rotating cafeteria tray return racks that guide the rear wheels of the rotating tray system onto the load points of load cells measuring the weight of the assembly with returned trays — **Figure 3: Load cells with load points along tray return assembly wheel track**

photo of university of the pacific food tray return system with ramp along wheel track to guide loads toward the load point of load cells measuring tray system weight — **Figure 4: Detail of ramps along wheel track that guide wheels onto load cell load points**

Signal Processing: Junction Box Summing and Non-Axial Calibration

The measuring system’s physical design was just part of the challenge. The team needed to accurately collect and process the load cell’s measurements.

Because the ramps created forces that were not completely orthogonal to the wall (and therefore non-axial to the load cells), the team had to make computational adjustments to determine the tray return assembly’s actual load.

Figure 5 illustrates how the team accounted for these multi-directional forces. Because the guiding ramps cause the tray racks to pivot slightly outward from the vertical track connection point (A), the weight creates a small angular displacement ([latex]\Theta = 3.87^\circ\)). To find the true weight of the food waste (F), the team’s algorithm mapped the exact horizontal and vertical distances of the assembly’s center of gravity relative to the tracking wheels (R). By calculating this trigonometric correction, the system isolates true downward mass from non-axial structural tension.

A vector diagram showing how the ramps that guide the weight of the moving cafeteria tray return assembly onto the load points of the load cells measuring this weight change the forces on the load cell to slightly non-axial. The ramps cause the whole assembly to pivot slightly outward from vertical, at a rotation point at the very top of the whole rack assembly where the racks attach to a moving chain along a support wall. — **Figure 5: Pictorial model of non-axial load from cafeteria tray return racks caused by ramp guides**

Figure 6 shows the wiring diagram for the overall concept. Two 50 kg ANYLOAD 108BA Aluminum Single Point load cells would measure the load from the support wheels. Their signals would be combined via an ANYLOAD J04EA-16 and J04SA-16 J-BOXER junction box. The junction box’s analog output would be amplified and converted to digital via a Sparkfun HX711 ADC. A Raspberry Pi 4 Model B processor equipped with a camera module would then process the ADC output, eliminate known weights, compensate for non-normal forces, and produce the desired food waste data. The group used machine learning as part of the processor algorithm to identify objects of known weights.

wiring diagram showing two load cells connected by a junction box, signaling to a SparkFun HX711 amplifier/ADC, which in turn signals to a Rasperry Pi processor — **Figure 6: Food waste measuring system schematic**

Project Execution: Prototyping and Live Integration

Before building their concept into the cafeteria rotating rack system, the team created a test system. This test system would verify the physical design would produce appropriate aggregate weight data. Figure 7 shows the team’s mock-up.

Photo of food waste measuring system test mock-up. The mock-up is made of plywood and 2 by 4s and shows testing of the load cell mounting system and ramps that guide the wheels along the wheel track to the load cell load points — **Figure 7: Test version of UOP food waste measurement system**

Once testing proved the design’s viability, the team built their live version within the dining hall’s tray return system! Figures 8 and 9 show the front-end track assembly and back-end electronics respectively.

photo of closeup of installed load cells in food waste measurement system. it shows ramps that guide load bearing wheels onto a thin metal bar attaching two load cells at their load point. This bar completes the wheel track where the load cells are positioned. — **Figure 7: Closeup of load cells installed along wheel track**

Photo of load cell junction box connecting to the amplifier/ADC and Raspberry Pi in its live application — **Figure 8: Junction box bottom left wired to ADC and Raspberry Pi on reverse side**

The Project’s Future

The system is now live and collecting data. However, this team of seniors graduated (happily!) before they could fully complete the machine learning portion of the design. Fortunately, a new crop of students within the MESA program will be taking over the project from here.

The goal is to complete the machine learning algorithm and decide how the resulting food waste data will reach students. Several ideas are being considered. These include displaying data hourly on a cafeteria monitor or sending daily data to students via email. Because its ultimate goal is to promote social responsibility, the project would need to reach the widest possible audience. Email would have a broader reach; however, other methods may attract more attention.

In Closing…

Tacuna Systems is proud to have once again been able to be part of a university project. We are especially pleased to support one like this that has the opportunity to positively affect our environment. We wish the MESA participants well and look forward to seeing is project’s completion!