Turning sugar-mill waste into high-value advanced materials using Digital Twin + AI Optimization.
👉 Try the Live App: https://bagasse-bloom-ai.lovable.app
Experience the mill dashboard, AI optimization panel, and financial simulation in real time.
BagasseX is a mill-integrated platform that converts surplus sugarcane bagasse into graphene-oxide based advanced materials while providing a real-time AI digital twin to prove ROI, emissions impact, and operational feasibility.
Instead of burning bagasse as low-value boiler fuel, BagasseX enables sugar mills to unlock new revenue streams through a modular micro-refinery backed by predictive analytics.
- Sugar mills under-monetize bagasse as boiler fuel.
- Advanced carbon materials have significantly higher value density.
- AI-driven digital twins reduce technical and financial risk before deployment.
✔️ Modular Bagasse-to-Graphene process architecture
✔️ AI-driven operating window optimization
✔️ Digital twin for mass-energy balance simulation
✔️ Mill operator dashboard with live KPIs
✔️ Financial simulation with IRR/NPV modeling
✔️ Hybrid edge + cloud industrial architecture
- Surplus bagasse diverted from mill storage
- Carbonization converts biomass into graphitic carbon
- Controlled oxidation produces graphene oxide slurry
- Digital twin predicts yield, emissions, and profitability
- AI optimizer adjusts operating conditions
Physical Layer → IoT Sensors → Edge Gateway ↓ Digital Twin Simulation ↓ AI Optimization Engine ↓ Web Dashboard (Operators + Investors)
- 🔴 Live bagasse feed rate monitoring
- 🌡️ Reactor temperature analytics
- 💰 ROI meter & profitability insights
- 🤖 AI optimization panel
- 📈 Financial scenario simulator
| Layer | Technologies |
|---|---|
| Process Modeling | Python, NumPy, SciPy |
| AI & Optimization | Scikit-learn, TensorFlow |
| Backend | FastAPI / Flask |
| Frontend | React / Streamlit |
| IoT Integration | MQTT, OPC-UA |
| Deployment | Docker, AWS/Azure Hybrid |
| Data Storage | PostgreSQL, Time-Series DB |
BagasseX/ │ ├── digital_twin/ ├── ai_optimizer/ ├── dashboard/ ├── architecture/ ├── data/ └── README.md
git clone https://github.com/your-username/bagassex.git
cd bagassex
### 2️⃣ Install Dependencies
pip install -r requirements.txt
### 3️⃣ Run Digital Twin
python digital_twin/simulation.py
### 4️⃣ Launch Dashboard
streamlit run dashboard/app.py
📈 Use Cases
Battery & supercapacitor additives
Water treatment membranes
High-strength composite materials
🌱 Impact
Converts agricultural residue into high-value materials
Reduces CO₂ emissions vs traditional bagasse usage
Enables export-ready advanced material supply chains
⭐ Support
If you believe in sustainable industrial AI — give this repo a ⭐ and share the live demo:
👉 https://bagasse-bloom-ai.lovable.app