SmartBin

SmartBin - Automated Organic Waste Processor

Overview

SmartBin is an automated system that converts organic waste into compost through a controlled drying and grinding process. The system features odor control using carbon filtration, automated waste level detection, and a custom-designed ESP32-based motherboard for complete process automation.

Key Features:

• Automated waste level monitoring using ultrasonic sensors
• Odor control system with carbon filter and exhaust fan
• Controlled drying via 50W heating element
• Automated grinding using 120W AC blender motor with PWM speed control
• Custom PCB design with high/low-voltage isolation
• FreeRTOS-based task management

System Architecture

System Schematic

Process Flow:

1. Waste Collection → Ultrasonic sensor monitors fill level
2. Odor Filtration → Fan pulls air through carbon filter before exhaust
3. Drying Stage → Heating element activates at threshold level
4. Grinding Stage → AC motor pulverizes dried waste
5. Storage → Processed compost collected in separate compartment

Hardware Design

Custom Motherboard (KiCad)

Top layer of SmartBin control board

Bottom layer showing power traces and isolation

Board Specifications:

• Controller: ESP32-WROOM-32
• Power Input: 220V AC mains
• AC Loads: 120W blender motor, 50W heating element
• DC Loads: 5V fan, 3.3V control logic
• Protection: Fuse, optoisolators, flyback diodes
• PCB Layers: 2-layer, FR4
• Dimensions: [Add dimensions in mm]

Key Components:

Power Distribution:

220V AC ─┬─► AC Motor (via TRIAC dimmer)
         ├─► 50W Heater (via SSR)
         └─► AC-DC Converter ──► 12V ──► 5V ──► 3.3V ESP32
                                      ├─► Fan
                                      └─► Sensors

Hardware Setup:

1. Fabricate PCB: Upload Gerber files to JLCPCB
2. Solder Components: Follow assembly drawing
3. Connect Sensors: Wire ultrasonic sensor to designated headers
4. Power Connections: 220V AC input via terminal block
5. Motor Connection: AC motor to TRIAC output
6. Heater Connection: Heating element to SSR output

Testing & Validation

Challenges & Solutions

Future Improvements

• WiFi connectivity for remote monitoring
• Mobile app integration for status alerts
• Solar power compatibility
• Automatic compost quality sensing
• Multiple bin capacity expansion
• Machine learning for optimal cycle prediction

Author

Fatai Oyetunji
Computer Science, University of Lagos
GitHub | LinkedIn

Acknowledgments

• University of Lagos Computer Science Department
• NitHub, University of Lagos
• Project supervisor and team members
• Open-source KiCad and ESP32 communities

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