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BinEnQ — for a sustainable and responsible tomorrow

How it works

One item. One decision. One learning moment.

BinEnQ turns a two-second action into a structured sequence of recognition, decision, feedback and progress.

  1. 01

    Identify

    Tap in

    Student identifies themselves using NFC or another configured identity mechanism.

  2. 02

    Present

    Show one item

    Student presents one waste item in the detection area.

  3. 03

    Analyse

    BinEnQ looks at the item

    Computer vision detects and classifies the presented item. The screen shows a scanning state, detection area and analysing status.

  4. 04

    Think

    What do you think?

    The student selects the waste category using the physical category-selection controls. The system does not reveal its own answer first.

  5. 05

    Evaluate

    Compare

    The device compares the student's decision with the system classification.

  6. 06

    Learn

    Immediate feedback

    Correct: “Great choice!” Incorrect: “Almost! Let's learn why.” A short, plain explanation follows. Students are never shamed.

  7. 07

    Dispose & Progress

    The appropriate bin opens

    The interaction record, points, participation and collective progress are updated.

Primary student interaction flow: approach the machine, identify, present a waste item, AI analyses it, category choices are shown, the student selects a category, the system evaluates it, feedback is given, the appropriate bin lid opens, the item is disposed and progress data is recorded
Primary student interaction flow — end-to-end sequence for a single item.

Real-world exception handling

Designed for how disposal actually happens.

One item

Normal interaction.

Multiple items

The student is asked to present one item at a time.

Uncertain classification

The configured fallback route is used.

Connectivity interruption

Supported edge functionality continues and synchronises later.

Single-item validation and routing: after AI detection the system checks whether one valid waste item is detected; if yes the item is classified and the student chooses a category, if no the student is asked to show one item at a time and detection restarts
Single-item validation and routing — how multiple or uncertain detections are handled.
Multiple-item detection and recovery flow: the AI layer analyses the detection area, pauses the interaction when several items are present, tells the student more than one item was detected, asks for one item at a time, restarts detection and continues once a single valid item is detected
Multiple-item detection and recovery — the interaction pauses instead of guessing.
Uncertain item flow: when classification confidence falls below the configured threshold the system explains it is unsure, routes the item to the General bin, opens that lid and records the interaction so uncertain items can improve accuracy over time
Uncertain classification — the configured fallback route, the General bin in this concept. Confidence values shown are illustrative demo data.

Hardware issue

The system should not assume an action succeeded just because a command was sent.

  1. Open command
  2. Actuator
  3. Sensor confirmation
  4. Confirmed physical state

Interactive BinEnQ Experience Demo

See the sequence in action.

A frontend simulation using predefined sample scenarios.

Interactive BinEnQ Experience Demo
Demo simulation

Ready to make an impact?

Identify yourself with your student card. The physical machine uses NFC or another configured identity mechanism.

The next generation shouldn't just know about sustainability.
They should practise it.

BinEnQ turns an everyday action into a learning opportunity — connecting Physical AI, gamification and sustainability intelligence to help build better waste-segregation habits.

Become a Pilot Partner Experience BinEnQ