Date of Award

Spring 2026

Document Type

Thesis

Publisher

Santa Clara : Santa Clara University, 2026

Department

Geological Engineering

First Advisor

Jes Kuczenski

Abstract

Improper waste sorting and limited visibility into bin conditions create challenges for waste management operations at Santa Clara University. This project developed a Smart Waste Management System prototype to support improved waste monitoring and provide users with real-time disposal guidance.

The system integrates ultrasonic fill-level sensing, wireless communication through an ESP32 microcontroller, a web-based dashboard, and a mobile waste classification application. Sensor data was transmitted to a dashboard for remote monitoring, while the mobile application used machine learning to classify common waste items and assist users in selecting the appropriate disposal stream.

System performance was evaluated through controlled prototype testing of sensor accuracy, communication responsiveness, battery performance, and waste classification accuracy. Results demonstrated approximately 1.5% average fill-level estimation error, near real-time dashboard updates within the target response window, approximately one week of operation under adaptive transmission conditions, and approximately 91% waste classification accuracy under controlled testing conditions.

Although full campus deployment was outside the scope of this project, results demonstrate the feasibility of integrating sensing technology and user guidance to improve waste monitoring visibility and support more informed waste management practices within a campus environment.

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