Smart Sensor Campus Waste Management System

Our design focuses on the waste collection process that a facilities team would be in charge of. Our goals will be to improve the efficiency of the overall system, and over time will be able to construct a forecasting model that works with the software to improve efficiency of the pickups when the bins are more full. In addition, the system also creates accountability for the collection company, as you can now monitor exactly when the bin was picked up, which reduces cost of unwanted pickups or if a pickup was charged for but never happened.

Our design works with the facilities team to improve the collection process, while reducing the cost of the overall system. Our current system runs with 5 different sensors placed on campus, and has already paid for itself with the reduction of pickup charges, and validation for charges of pickup. In addition to the Sensoneo system that is being tested, we wrote and tested our own code to try and replicate how the ultrasonic sensor reads levels, as well as researched the process of writing our own predictive software for schedule forecasting and trend analysis.

This project presents an effective method of monitoring waste disposal for a campus or client, and as time goes on the forecasting model is very effective at predicting future bin levels and scheduling. Researching and developing our own sensor and code alongside testing the Sensoneo product has given insight to the systems flaws, and how we can improve the design further. We also worked alongside companies such as Nordsense that gave us more insight to how the competing products are trying to come up with more effective technology, and how they have worked through different applications of the sensor for waste management (ie. different types of bins, quality of sensor, angle of placement).

We compared our sensor to other sensors available on the market, and researched the different challenges that we may face with this type of technology. Research into the different densities of waste, different angles of the sensors, protective shields, protection from environment (ie. temperature variance, placement on the bin), and financial analysis.

This type of technology is the way of the future, as verification of a bin level for a Waste Management System is a very good step in reducing wasted labor, which results in financial benefit to all parties involved, as well as a system that will learn and model future trends. We researched different case studies and projects for various companies offering similar services, and the typical decrease in cost for the waste management was 30-45%. Which also results in a reduction of carbon emissions and resources used in waste management, helping reduce the environmental footprint of the University.