About the Project

Smart Waste & Recycling AI is an innovative project dedicated to transforming waste management and recycling through artificial intelligence.

The project focuses on using AI-based technologies to optimize waste collection, improve sorting accuracy, reduce environmental impact, and support sustainable resource management. By combining data analytics, automation, and intelligent systems, Smart Waste & Recycling AI aims to make recycling processes more efficient and accessible for cities, industries, and communities.

At its core, the project is founded on a commitment to innovation, sustainability, and environmental responsibility, contributing to a cleaner planet and a smarter circular economy.

Our Mission

The mission of Smart Waste & Recycling AI is to harness artificial intelligence to improve waste management and recycling systems. By leveraging intelligent technologies, the project aims to reduce waste, increase recycling efficiency, optimize resource use, and minimize environmental impact.

Smart Waste & Recycling AI is committed to supporting sustainable development by providing innovative, data-driven solutions to communities, cities, and industries working toward a cleaner and more responsible future.

The vision of Smart Waste & Recycling AI

is to become a leading artificial intelligence–based solution in intelligent waste management and recycling. The project aims to shape the future of sustainable waste systems by enabling smarter cities, cleaner environments, and a truly circular economy through intelligent technology.

Through continuous innovation, Smart Waste & Recycling AI envisions a world where waste is minimized, resources are reused efficiently, and technology actively contributes to protecting the planet for future generations.

Smart Waste & Recycling AI leverages artificial intelligence and data-driven technologies to enhance waste management and recycling processes.

The system collects and analyzes data from waste streams, recycling facilities, and smart sensors. AI algorithms then identify patterns, optimize waste sorting, improve recycling efficiency, and support smarter decision-making.

By automating analysis and optimization, Smart Waste & Recycling AI helps cities and organizations reduce waste, lower costs, and minimize environmental impact, while promoting sustainable recycling practices.

💡 Imagine a city where every trash bin becomes an intelligent link in a sustainable ecosystem.

Problem: In Toulouse, waste management costs €25 million per year, with only 65% recycling rate and inefficient collection routes generating 500 tonnes of unnecessary CO₂.

Solution: Our artificial intelligence revolutionizes waste management by: 🔋 IoT Sensors: Real-time monitoring of connected trash bins. 🧠 Predictive Algorithms: Anticipation of waste peaks and dynamic route optimization (-20% kilometers driven). 📊 Citizen Blockchain: Unique barcode per household turning your sorting into tax reduction (up to €85 off property tax).

Impact: ✅ 40% reduction in landfilled waste thanks to smart sorting. ✅ +30% recycling rate in pilot districts. ✅ 15% fewer CO₂ emissions in the first year.

Our Strength: An unprecedented alliance between: 🤝 Toulouse City Council (infrastructure) 🧪 CHU and INSA (data/AI expertise) 🌱 Citizens (key players via our bonus/malus system)

Vision: Make Toulouse Europe's first zero-waste city by 2030, turning every eco-friendly gesture into tangible economic benefits.

💶 Required Investment: €1.2M to deploy 10,000 sensors and predictive AI – a guaranteed ROI in 3 years thanks to collection savings and avoided fines.

Join the green revolution that rewards! 🌍✨

"At Toulouse Smart Waste, we believe that ecology should not be a punishment but a profitable investment for all."

 Toulouse Smart Waste & Recycling AI

💡 Imagine a city where every trash bin becomes an intelligent link in a sustainable ecosystem.

**Problem: In Toulouse, waste management costs €25 million annually, with only 65% recycling rates and inefficient collection routes generating 500 tonnes of unnecessary CO₂.

Solution: Our AI revolutionizes waste management through:
🔋 IoT Sensors: Real-time monitoring of connected bins.
🧠 Predictive Algorithms: Anticipating waste peaks and optimizing routes (reducing kilometers driven by 20%).
📊 Citizen Incentives: Unique barcodes per household turn recycling into tax savings (up to €85 off property tax).

Impact:
✅ 40% reduction in landfilled waste via smart sorting.
✅ +30% recycling rates in pilot districts.
✅ 15% lower CO₂ emissions in Year 1.

Our Edge: An unprecedented alliance of:
🤝 Toulouse City Council (infrastructure)
🧪 CHU & INSA (data/AI expertise)
🌱 Citizens (driving change via bonus/malus rewards)

Vision: Make Toulouse Europe's first zero-waste city by 2030, transforming eco-actions into economic gains.

💶 Investment: €1.2M to deploy 10,000sensors and AI — with ROI guaranteed in 3 years through collection savings and avoided penalties.

Join the green revolution that pays dividends! 🌍✨

"At Toulouse Smart Waste, we believe sustainability shouldn't be a sacrifice — but a profitable investment for all."

Adam KOUADRI  is the Founder and President of Smart Waste & Recycling AI. He launched this initiative with a clear ambition: to harness artificial intelligence to transform waste management and recycling into smarter, more efficient, and more sustainable systems.

In response to today's environmental challenges, he believes that technology and innovation are key to building responsible and scalable solutions. Through Smart Waste & Recycling AI, Adam Kouadri aims to support communities, cities, and industries in better sorting waste, optimizing resources, and accelerating the transition toward a cleaner and more sustainable future.

Future Development: Advanced Technological Integration

The project will evolve into a cyber-physical platform integrating advanced technologies across the entire value chain.

Scalable IoT Architecture

Multi-Layer Sensors:
At the bin level, fill-level sensors (ultrasonic/LiDAR), composition sensors (embedded NIR spectrometers), and RFID for identification will be deployed.
At the vehicle level, real-time GPS, compaction sensors, and gas analyzers (CH₄, CO₂) will be integrated.
At the sorting center level, intelligent weighing systems and hyperspectral cameras will enable automated material analysis.

Connectivity:
LPWAN networks (LoRaWAN / NB-IoT) will support fixed sensors, while 5G will handle real-time video streams.

Embedded and Centralized Artificial Intelligence

AI-Based Waste Sorting:
Edge computing will rely on lightweight CNN models (MobileNetV3) embedded in cameras for local pre-classification.
Cloud analytics will use deep convolutional neural networks (ResNet-152) trained on more than 500,000 annotated images, with transfer learning applied to rare cases such as industrial composites.

Recycling Optimization:
A recommendation system based on reinforcement learning (Q-Learning) will dynamically adjust collection routes according to predictive fill rates using LSTM time-series models, fuel and CO₂ constraints through multi-objective linear programming, and global commodity market prices accessed via APIs.

Real-Time Analytics and Big Data

Data Pipeline:
Kafka → Spark Streaming → Feature Store (Feast) → ML Model Serving (TensorFlow Serving)

Advanced KPIs:
The Purity Index will measure contamination rates per waste stream using machine-learning-based clustering of sorting errors.
Carbon Avoidance will be assessed through an MRV (Monitoring, Reporting, Verification) model aligned with IPCC standards.

Visualization:
Power BI and ELK dashboards will provide real-time monitoring with proactive alerts, such as critical thresholds for organic waste.

Environmental Impact Reduction

Digital twins will enable Monte Carlo simulations of circular economy scenarios, for example assessing the impact of introducing bioplastics.
Blockchain technology, based on Hyperledger Fabric, will ensure traceability of recycled materials and support carbon credit mechanisms.

Industrial and Urban Scalability

Adaptive Solutions:
For cities, a fleet orchestration system will use dynamic bin-packing algorithms for vehicle routing.
For industry, AI-powered audit modules will support REACH and EPR compliance through natural language processing of regulatory texts.

Open APIs:
Interoperability will be ensured with existing systems such as SAP EWM and smart city platforms.

Technical Challenges and Key Innovations

Critical latency will be addressed through federated learning for distributed model training while preserving industrial data privacy.
Robustness will be enhanced with self-correcting AI models using GANs to generate synthetic data for rare anomalies.
Security will rely on homomorphic encryption for processing sensitive data, including medical waste.