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Self-Sorting Recycling Machine Self-Sorting Recycling Machine Final Technical Report The George Washington University School of Engineering and Applied Science Department of Mechanical and Aerospace Engineering Authors: Michaela Altland, Alexandra Morganti, Matthew Rosenstein, Noah Thomas May 5th, 2019 1. Table of Contents 2. Abstract ………………………………………………………………………….2 3. Team Member Roles …………………………………………………………….3 4. Introduction ……………………………………………………………………...4 5. Design Description ………………………………………………………………6 6. Evaluation & Testing ……………………………………………………………10 7. Summary & Recommendations ………………………………………………....13 8. References ……………………………………………………………………….15 9. Appendices ………………………………………………………………………18 1 2. Abstract In America, the concept of recycling as a means of waste management has gained more traction over recent time. Typically, this process involves consumers sorting their garbage into trash cans and their recyclables into a separate recycling can. When all recyclables are meant to be placed in one bin, this is referred to as “single-stream recycling.” These recycled items are taken to a facility where they are sorted into separate streams to be repurposed, with any non-recyclable items being sorted back into trash and sent to landfills. Within this system, when recycling bins contain more than 50% trash, the entire receptacle is treated as waste rather than being sorted apart. The challenge here is inherent to the method: the brunt of the responsibility of recycling falls on the consumer. The average person who is trying to dispose of their waste has to know, based on their current location, whether or not items are recyclable, or if they are too contaminated to be recycled by their nearest recycling facility. While currently the responsibility of recycling falls on the consumer, implementing green technology into this process would reduce the rates of human error and increase recycling rates. Artificial intelligence integrated anywhere into the waste stream process would likely increase recycling rates, and a local device attached directly to a trash can could increase the accuracy of the recycled items in a given can. In 2014, The George Washington University launched its Zero Waste Initiative to increase the sustainability and recycling rate around campus. GW’s Zero Waste Plan set the goal to minimize the university’s trash output and, conversely, maximize the recycling rate. New, easily readable signs were installed on the sides of the cans with the appropriate materials clearly advertised. The university also began the process of standardizing all trash/recycling cans located across campus, distributing cans in places around their Foggy Bottom campus where they had been deemed to be lacking, and ensuring that all trash cans have a recycle bin located close by [7]. While this initiative has slightly improved recycling rates and accuracy on campus, a ​ ​ self-sorting recycling bin that could sort items as either trash or recycling would remove any potential for human error in the process. On The George Washington University’s campus, an ideal self-sorting recycling machine would utilize the trash cans and recycling bins already located around campus, and would fit on top of the containers to be able to sort items into one bin or the other. Users would be able to place their items into the lid, and the Smart technology would do the rest of the work. This project aimed to create a self-contained lid for the trash receptacles located around GW’s campus to reduce the contamination rate within the recycling containers on campus. The self-sorting recycling machine uses Google’s Cloud Vision API to identify a picture of a given item and determine recyclability. The user places the item onto a platform and presses a button, which signals a Raspberry Pi to capture a picture and cross-reference its annotations with a list of recyclable and non-recyclable items. After determining recyclability, the platform rotates to drop the item into the designated can. While GW’s campus recycling currently has a contamination rate of about 30%, this machine consistently has lower than a 15% contamination rate, indicating success [Appendix 9]. 2 3. Team Member Roles Michaela Altland: Subsystem lead: PiCamera, PIR Sensor/Button Assisted in subsystems: Raspberry Pi, Servo motor, Construction Alexandra Morganti: Subsystem lead: Raspberry Pi, Servo motor Assisted in subsystems: PiCamera, Servo motor, Construction Matthew Rosenstein: Subsystem lead: Google sorting code Assisted in subsystems: Raspberry Pi, Servo motor, Construction Noah Thomas: Assisted in subsystems: Servo motor, Construction 3 4. Introduction The purpose of this project was to design an inexpensive and energy efficient trash can cover that could fit over two standard sized “slim jim” trash bins located across The George Washington University’s campus. This cover would be able to distinguish recyclable materials from non-recyclable materials and sort them into their respective trash or recycling bin. The contamination rate of recycling bins is a very important issue that this project tackles head-on as GW has high contamination rates that need to be reduced [Appendix 1 and 2]. Another important aspect was the efficiency of the project, since a sorting machine that takes too much time would not be utilized as often. The hope for this project is that a successful product can be created, refined and reworked so that many more units could be produced and placed all over GW’s campus to help reduce the contamination rate of recyclables throughout the GW community. Functional Requirements 1. Objects are placed in the device one at a time 2. Identify placed items as recyclable or non-recyclable 3. Sort these objects into two separate bins 4. Less than 30% contamination rate in the recycle bin 5. Reduce recycling contamination 6. Aimed at GW’s campus and indoor trash cans Review of technical literature The recycling of plastic specifically (or lack thereof) is becoming a massive issue. Globally, more than 80% of plastic ends up in landfills, and in the US, that number is higher than 90% [8]. A self-sorting recycling machine would rely heavily on its code to interpret images and decide their recyclability. This project uses an online API to access the images taken by the PiCamera and recognize the product to identify whether or not it is recyclable. This is being accomplished through the use of a Raspberry Pi, on which a Python code is executed, to access Google’s Cloud Vision API. Machine learning is a relatively new development, especially for mainstream use, and Google is on the forefront of these developments being intended for the public. Throughout the US, different methods have been used to assist in recycling. Currently there is no such attachable device for recycling in mass production [5]. This is due to the fact that the standards for recyclables changes throughout each state, and sometimes throughout a specific company or university. For instance, in DC paper cups are recycled and on George Washington University’s campus glass is recyclable. This project is specifically designed for GW’s campus but could be adapted for other locations if the inclusion and exclusion parameters were edited based on the area’s specific recycling requirements. API, or Application Programming Interface, technology allows a user to access certain features of a code without having to understand all of the underlying complexity [10]. Artificial Intelligence, or AI, is a computer that has the ability to practice machine learning [11]. For example, Google’s Cloud Vision API (which is being used in this sorting mechanism) allows 4 users to take a picture, access Google’s Cloud database, access their pattern recognition AI, and get back an output of multiple possible results with confidence values [1]. Machine learning is an extremely complicated process, which is the advantage of having access to an API. This allows for programmers to write codes that can locally access machine learning without having to have an internal artificially intelligent computer. Computers are trained to make predictions based on data, and then are provided with more and more knowledge to be able to improve their prediction ability [12]. In the context of Cloud Vision, every time someone completes the Google verification to ensure they are not a robot (known as ReCAPTCHA) by clicking on the images that contain a road sign, the AI is better trained to identify objects in the foreground [13]. In 2007, both Google and IBM announced plans to build data centers and allow access to students for “cloud computing” [14]. Both companies knew they had infinitely more computing power than they could utilize, so they began to make it available to more people. Without these companies making their resources available, this project would be much more difficult to complete. This project relies on Google API combined with green technology. Green technology is a field that is lacking and does not have the proper funding. It is a relatively young market with a lot of interest. Green technology comes in many forms, all with the goal of making technology that is mindful of environmentally friendly methods. With an inexpensive device such as this, fewer funds would be needed to help this community and help in the mission to become greener. The only way to maintain our reusable resources is to consciously reuse them, and this product would help in that mission. According to The Federal Ministry for the Environment, Nature Conservation and Nuclear Safety, the five methods for establishing waste management and recycling are “prevention, preparation for reuse, recycling, recovery, and disposal” [6]. With a focus on prevention and recycling, the clean technology used in this device is making significant changes to recycling rates and has the ability to make a difference across an entire community. 5 5. Design Description Image 1: Final Product The final product is an enhanced trash can cover that fits over two “slim jim” trash cans placed side by side. The main structure of the sorting machine is made of wood reinforced with screws.
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