RecyKOOL: Maximize Waste Diversion Through Citizen Science, Data Analytics and AI Digital Twin

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Research Timeline
Start Date: Summer 2024
End Date: TBD

Research Team

Lead Researchers

Eugene Tseng, Autonomy for Sustainability Lead
Dr. Nhut Ho, Professor in Mechanical Engineering
Dr. Bingbing Li, Professor in Manufacturing Systems Engineering

Collaborators

WM (Waste Management)
- Mike Hammer (President – Southern California Area, Waste Management of California, Inc.)
- Kimberly Ohrt (Government Affairs)
- Mark Grady (Regional Recycling Manager)
- Kevin Vaughn (Operations Manager)
- Saul Avila (Operations Manager)
- Jose Figuera (Process Engineer)
Park Parthenia Apartments
- Jose Portillo (Park Parthenia Business Manager)
LAPD Devonshire PALS: Police Activity League Supporters
- Mike Lehron (Executive Director)
- Jose Portillo (Board Member)
Los Angeles Department of Water and Power
- Maria Sison-Roces (Utility Service Manager)
EcoTelesis International
- Anthony Derderian (Research Assistant)

Student Team

Crystal Valdez, BA Psychology
Andrew Garcia Leopold, BS Comp Science
Julius Maxwell, BS Anthropology
Reza Alisamir, MS Manufacturing Systems Engineering
Cesar Aranibar David, MS Manufacturing Systems Engineering
Nik Khandandel, MS Manufacturing Systems Engineering
Arpitha Pradeep, MS Engineering Management
Monish Ramesh, MS Industrial Engineering
Shreyas William, MS Engineering Management

Funding

Funding Organization: Waste Management Inc.

Synopsis

Community Awareness

Promote awareness, education, and community action for better waste management.

Citizen Science

Implement citizen and community science projects in local communities.

Recycling Boost

Increase recycling and decrease landfill use.

Food Recovery

Reduce food waste and hunger in communities.

Methane Reduction

Decrease methane emissions from landfills.

Abstract

Limited awareness of proper waste sorting organics, recyclables and trash, particularly in apartments and urban communities, remains a major challenge. Our project aims to increase recycling participation, decrease organics in landfill waste and promote long-term sorting habits, with a focus on Los Angeles County.

Citizen science empowers community members to contribute scientific data, which directly informs AI digital twins—virtual models of systems- as they take in data. These digital twins simulate and optimize Material Recovery Facility (MRF) processes and identify areas for improvement.

By combining citizen-driven data with AI simulations, it’s feasible for residents, the MRF and the environment to benefit from. This collaborative approach aims to foster lasting sustainable practices and serve as a scalable model for other urban areas.

Motivation/Research Problem

Apartment complexes and businesses often face low participation rates due to inadequate infrastructure, lack of awareness, and behavioral barriers. Bins often fill up too quickly or contain the wrong materials, making recycling and compost separation more difficult. Another issue is signage—some bins are missing labels or have unclear instructions, leading to confusion about proper waste disposal. Keeping residents informed is an ongoing effort, as regular reminders are needed to ensure proper sorting of trash, recyclables, and compost.

Contamination is when incorrect items are placed in the wrong bin and slows down recovery efforts and reduces the amount of recyclable material that can be processed. At the Materials Recovery Facility (MRF), improper waste sorting creates AI and processing challenges, leading to downtime, maintenance repairs, and increased labor costs. By improving waste sorting, we can increase recovery rates and reduce environmental harm.

Research Objectives

Increase Participation, Recycling Literacy and Community Involvement

Increase Diversion (Recycling and Organics)

Integrated Operations and Business Data Analytics

Effectuate Long Term Behavioral Change

Increase Recovery

Decrease Contamination

Maximize Profits

Local Ambassadors

Increase Operations Uptime

Reduce Solid Waste Generation

Decrease Disposal

Increased Safety

Optimize Operations

Research Method

Step 1: Problem Identification and Awareness

Action: Identify the current challenges facing proper waste sorting—including organics, trash, and recycling—at Park Parthenia Apartments.

Objective: See what is working, what is missing and what is not working.

Step 2: Community Engagement through Citizen Science

Action: Leverage Citizen Science, where community members collaborate using scientific methods to address recycling challenges. Conduct ongoing surveys to assess current knowledge and waste-sorting practices, involving residents in the survey design to ensure relevance and effectiveness. Evaluate existing outreach and educational materials, as well as WM’s Ambassador Program, to identify areas for improvement. Additionally, explore strategies to motivate residents through positive reinforcement and incentives, refining these approaches based on community feedback and participation.

Objective: Empower residents to actively contribute to improving waste sorting practices and overall recycling outcomes that effectuate long term behavioral change.

Step 3: Process Optimization with AI Digital Twin

Action: Implement an AI digital twin—a virtual model of recycling processes.

Objective: Simulate operations, detect issues, predict machinery performance, and optimize Material Recovery Facility (MRF) workflows.

Step 4: Data Analysis and Recommendations

Action: Analyze comprehensive collected data to identify insights and challenges.

Objective: Develop recommendations to inform the development and implementation of systematic improvements aimed at optimizing waste diversion and recycling participation.